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1
Byun, Do-Seong, and Deirdre E. Hart. "Predicting Tidal Currents Using 25-h Observations through a Complete Tidal Species Modulation with Tidal Current Constant Corrections Method." Journal of Atmospheric and Oceanic Technology 35, no. 12 (December 2018): 2405–20. http://dx.doi.org/10.1175/jtech-d-18-0120.1.
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AbstractA new approach enabling the prediction of tidal currents, for observation sites with as little as ≥25 h of data, has been developed as a practical way of utilizing very short tidal current records. We name this technique the complete tidal species modulation with tidal current constant corrections (CTSM+TCCC) method. In addition to a short-term tidal current record from the “observation site,” this technique also requires (i) ideally half a year (specifically, ≥183 days from any time) and (ii) concurrent (≥25 h) sea level observation records from a nearby “reference site.” The reliability of the CTSM+TCCC method is tested for three different tidal current regimes (almost rectilinear, elliptical, and near circular) in Geyonggi Bay, South Korea, using daily slices (25 h long) of 29-day tidal current records from each observation station, plus sea level records from the nearby Incheon reference station. RMSE analysis of the resulting prediction time series demonstrates that the CTSM+TCCC method produces reasonably accurate predictions based on observation station records derived from spring, but not neap, tide periods. We conclude that our method can be successfully employed to make tidal current predictions using 25-h observation station records obtained from spring tide periods, ideally gathered during periods of relatively calm weather. As well as testing the sensitivity of CTSM+TCCC predictions to observation record timing, the effect of reference station location was investigated using records from 20 stations spread along the South Korean coasts. Results revealed that tidal records from nearby tidal current observation and sea level reference stations produce reasonable current predictions, since proximal locations share similar tidal species’ modulated tidal and tidal current behaviors.
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Lenetsky, Jed E., Bruno Tremblay, Charles Brunette, and Gianluca Meneghello. "Subseasonal Predictability of Arctic Ocean Sea Ice Conditions: Bering Strait and Ekman-Driven Ocean Heat Transport." Journal of Climate 34, no. 11 (June 2021): 4449–62. http://dx.doi.org/10.1175/jcli-d-20-0544.1.
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AbstractWe use ocean observations and reanalyses to investigate the subseasonal predictability of summer and fall sea ice area (SIA) in the western Arctic Ocean associated with lateral ocean heat transport (OHT) through Bering Strait and vertical OHT along the Alaskan coastline from Ekman divergence and upwelling. Results show predictive skill of spring Bering Strait OHT anomalies in the Chukchi Sea and eastern East Siberian Sea for June and July SIA, followed by a sharp drop in predictive skill in August, September, and October and a resurgence of the correlation in November during freeze-up. Fall upwelling of Pacific Water along the Alaskan coastline—a mechanism that was proposed as a preconditioner for lower sea ice concentration (SIC) in the Beaufort Sea the following summer—shows minimal predictive strength on both local and regional scales for any months of the melt season. A statistical hindcast based on May Bering Strait OHT anomalies explains 77% of July Chukchi Sea SIA variance. Using OHT as a predictor of SIA anomalies in the Chukchi Sea improves hindcasts from the simple linear trend by 35% and predictions from spring sea ice thickness anomalies by 24%. This work highlights the importance of ocean heat anomalies for melt season sea ice prediction and provides observational evidence of subseasonal changes in forecast skill observed in model-based forecasts of the Chukchi Sea.
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Nichols, Charles Reid, and Lynn Donelson Wright. "The Evolution and Outcomes of a Collaborative Testbed for Predicting Coastal Threats." Journal of Marine Science and Engineering 8, no. 8 (August 16, 2020): 612. http://dx.doi.org/10.3390/jmse8080612.
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Beginning in 2003, the Southeastern Universities Research Association (SURA) enabled an open-access network of distributed sensors and linked computer models through the SURA Coastal Ocean Observing and Predicting (SCOOP) program. The goal was to support collaborations among universities, government, and industry to advance integrated observation and modeling systems. SCOOP improved the path to operational real-time data-guided predictions and forecasts of coastal ocean processes. This was critical to the maritime infrastructure of the U.S. and to the well-being of coastal communities. SCOOP integrated and expanded observations from the Gulf of Mexico, the South Atlantic Bight, the Middle Atlantic Bight, and the Chesapeake Bay. From these successes, a Coastal and Ocean Modeling Testbed (COMT) evolved with National Oceanic and Atmospheric Administration (NOAA) funding via the Integrated Ocean Observing System (IOOS) to facilitate the transition of key models from research to operations. Since 2010, COMT has been a conduit between the research community and the federal government for sharing and improving models and software tools. SCOOP and COMT have been based on strong partnerships among universities and U.S. agencies that have missions in ocean and coastal environmental prediction. During SURA’s COMT project, which ended September 2018, significant progress was made in evaluating the performance of models that are progressively becoming operational. COMT successes are ongoing.
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Storto, Andrea, Paolo Oddo, Elisa Cozzani, and Emanuel Ferreira Coelho. "Introducing Along-Track Error Correlations for Altimetry Data in a Regional Ocean Prediction System." Journal of Atmospheric and Oceanic Technology 36, no. 8 (August 2019): 1657–74. http://dx.doi.org/10.1175/jtech-d-18-0213.1.
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AbstractBecause of the systematic error in the processing of altimetry data, sea level anomaly (SLA) observation errors are likely affected by nonnegligible spatial correlations. To account for these, we exploit the synergy of altimetry data with in situ profiles from gliders, piloted to follow the altimetry tracks during the Long-Term Glider Mission for Environmental Characterization 2017 (LOGMEC17) observational campaign in the Ligurian Sea. The assimilation of along-track unfiltered sea level anomalies in a regional ocean analysis and forecast system is consequently optimized by means of introducing spatial correlations for the SLA observation errors. In particular, collocated data of glider and altimetry are used to derive an along-track error covariance model for the sea level anomaly assimilation, assuming that most of the covariance behavior versus separation distance stems from altimetry. Spatial scales of the altimetry error are found to have a correlation radius of about 12 km for the dataset utilized in the Ligurian Sea, using a simple Gaussian shape for the error correlation, shorter than the correlation radius found through assimilation output diagnostics. A variational data assimilation system is modified to relax the usual assumption of uncorrelated altimetry observation errors, thus allowing for along-track error correlations. Its implementation provides promising results in the regional ocean prediction system, outperforming in most verification skill scores the use of uncorrelated observational errors without compromising the analysis scheme efficiency.
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Kang, Minhyeop, Kyungnam Ko, and Minyeong Kim. "Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model." Energies 13, no. 1 (January 3, 2020): 254. http://dx.doi.org/10.3390/en13010254.
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An atmosphere–ocean coupled model is proposed as an optimal numerical prediction method for the offshore wind resource. Meteorological prediction models are mainly used for wind speed prediction, with active studies using atmospheric models. Seawater mixing occurring at sea due to solar radiation and wind intensity can significantly change the sea surface temperature (SST), an important variable for predicting wind resources and energy production, considering its wind effect, within a short time. This study used the weather research forecasting and ocean mixed layer (WRF-OML) model, an atmosphere–ocean coupled model, to reflect time-dependent SST and sea surface fluxes. Results are compared with those of the WRF model, another atmospheric model, and verified through comparison with observation data of a meteorological mast (met-mast) at sea. At a height of 94 m, the wind speed predicted had a bias and root mean square error of 1.09 m/s and 2.88 m/s for the WRF model, and −0.07 m/s and 2.45 m/s for the WRF-OML model, respectively. Thus, the WRF-OML model has a higher reliability. In comparing to the met-mast observation data, the annual energy production (AEP) estimation based on the predicted wind speed showed an overestimation of 15.3% and underestimation of 5.9% from the WRF and WRF-OML models, respectively.
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Duerr, Alana E. S., Manhar R. Dhanak, and James H. Van Zwieten. "Utilizing the Hybrid Coordinate Ocean Model for the Assessment of Florida Current’s Hydrokinetic Renewable Energy Resource." Marine Technology Society Journal 46, no. 5 (September 1, 2012): 24–33. http://dx.doi.org/10.4031/mtsj.46.5.2.
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AbstractTraditionally, renewable energy resources have been assessed through collection and analysis of extensive in situ observations; however, in situ data collection can be cost and time prohibitive, especially for initial site selection and feasibility studies. Ocean models, such as the HYbrid Coordinate Ocean Model (HYCOM), provide corresponding data for resource assessment at a significantly lower cost, provided the models can be validated and appropriately corrected through comparison with some in situ observations. In this study, in situ velocity observations in the Florida Current are compared with the velocities predicted by the ocean model. Measured velocity profiles at the location of a moored ADCP have been compared with corresponding predictions from the HYCOM model. The data are used to evaluate the associated hydrokinetic energy and to estimate the energy resource using the HYCOM data and in situ observational data for comparison. In general, HYCOM predictions of the velocity and the corresponding energy resource in the Florida Current are lower than those suggested by the observational data. A method for addressing these apparent discrepancies is discussed and is shown to improve prediction of the resource assessment.
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Wu, Bo, Xiaolong Chen, Fengfei Song, Yong Sun, and Tianjun Zhou. "Initialized Decadal Predictions by LASG/IAP Climate System Model FGOALS-s2: Evaluations of Strengths and Weaknesses." Advances in Meteorology 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/904826.
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Decadal prediction experiments are conducted by using the coupled global climate model FGOALS-s2, following the CMIP 5 protocol. The paper documents the initialization procedures for the decadal prediction experiments and summarizes the predictive skills of the experiments, which are assessed through indicators adopted by the IPCC AR5. The observational anomalies of surface and subsurface ocean temperature and salinity are assimilated through a modified incremental analysis update (IAU) scheme. Three sets of 10-year-long hindcast and forecast runs were started every five years in the period of 1960–2005, with the initial conditions taken from the assimilation runs. The decadal prediction experiment by FGOALS-s2 shows significant high predictive skills in the Indian Ocean, tropical western Pacific, and Atlantic, similar to the results of the CMIP5 multimodel ensemble. The predictive skills in the Indian Ocean and tropical western Pacific are primarily attributed to the model response to the external radiative forcing associated with the change of atmospheric compositions. In contrast, the high skills in the Atlantic are attributed, at least partly, to the improvements in the prediction of the Atlantic multidecadal variability coming from the initialization.
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Smith, Gregory C., Yimin Liu, Mounir Benkiran, Kamel Chikhar, Dorina Surcel Colan, Audrey-Anne Gauthier, Charles-Emmanuel Testut, et al. "The Regional Ice Ocean Prediction System v2: a pan-Canadian ocean analysis system using an online tidal harmonic analysis." Geoscientific Model Development 14, no. 3 (March 15, 2021): 1445–67. http://dx.doi.org/10.5194/gmd-14-1445-2021.
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Abstract. Canada has the longest coastline in the world and includes diverse ocean environments, from the frozen waters of the Canadian Arctic Archipelago to the confluence region of Labrador and Gulf Stream waters on the east coast. There is a strong need for a pan-Canadian operational regional ocean prediction capacity covering all Canadian coastal areas in support of marine activities including emergency response, search and rescue, and safe navigation in ice-infested waters. Here we present the first pan-Canadian operational regional ocean analysis system developed as part of the Regional Ice Ocean Prediction System version 2 (RIOPSv2) running in operations at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP). The RIOPSv2 domain extends from 26∘ N in the Atlantic Ocean through the Arctic Ocean to 44∘ N in the Pacific Ocean, with a model grid resolution that varies between 3 and 8 km. RIOPSv2 includes a multivariate data assimilation system based on a reduced-order extended Kalman filter together with a 3D-Var bias correction system for water mass properties. The analysis system assimilates satellite observations of sea level anomaly and sea surface temperature, as well as in situ temperature and salinity measurements. Background model error is specified in terms of seasonally varying model anomalies from a 10-year forced model integration, allowing inhomogeneous anisotropic multivariate error covariances. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach to reduce numerical costs and allow for the time-varying harmonic constants necessary in seasonally ice-infested waters. Compared to the Global Ice Ocean Prediction System (GIOPS) running at CCMEP, RIOPSv2 also includes a spatial filtering of model fields as part of the observation operator for sea surface temperature (SST). In addition to the tidal harmonic analysis, the observation operator for sea level anomaly (SLA) is also modified to remove the inverse barometer effect due to the application of atmospheric pressure forcing fields. RIOPSv2 is compared to GIOPS and shown to provide similar innovation statistics over a 3-year evaluation period. Specific improvements are found near the Gulf Stream for all model fields due to the higher model grid resolution, with smaller root mean squared (rms) innovations for RIOPSv2 of about 5 cm for SLA and 0.5 ∘C for SST. Verification against along-track satellite observations demonstrates the improved representation of mesoscale features in RIOPSv2 compared to GIOPS, with increased correlations of SLA (0.83 compared to 0.73) and reduced rms differences (12 cm compared to 14 cm). While the RIOPSv2 grid resolution is 3 times higher than GIOPS, the power spectral density of surface kinetic energy provides an indication that the effective resolution of RIOPSv2 is roughly double that of the global system (35 km compared to 66 km). Observations made as part of the Year of Polar Prediction (2017–2019) provide a rare glimpse at errors in Arctic water mass properties and show average salinity biases over the upper 500 m of 0.3–0.4 psu in the eastern Beaufort Sea in RIOPSv2.
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Zhang, Xuefeng, Shaoqing Zhang, Zhengyu Liu, Xinrong Wu, and Guijun Han. "Parameter Optimization in an Intermediate Coupled Climate Model with Biased Physics." Journal of Climate 28, no. 3 (February 1, 2015): 1227–47. http://dx.doi.org/10.1175/jcli-d-14-00348.1.
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Abstract Imperfect physical parameterization schemes in a coupled climate model are an important source of model biases that adversely impact climate prediction. However, how observational information should be used to optimize physical parameterizations through parameter estimation has not been fully studied. Using an intermediate coupled ocean–atmosphere model, the authors investigate parameter optimization when the assimilation model contains biased physics within a biased assimilation experiment framework. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation model and the “truth” model that is used to generate simulated observations. While the stochastic physics, implemented by initially perturbing the physical parameters, can significantly enhance the ensemble spread and improve the representation of the model ensemble, the parameter estimation is able to mitigate the model biases induced by the biased physics. Furthermore, better results for climate estimation and prediction can be obtained when only the most influential physical parameters are optimized and allowed to vary geographically. In addition, the parameter optimization with the biased model physics improves the performance of the climate estimation and prediction in the deep ocean significantly, even if there is no direct observational constraint on the low-frequency component of the state variables. These results provide some insight into decadal predictions in a coupled ocean–atmosphere general circulation model that includes imperfect physical schemes that are initialized from the climate observing system.
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Vidard, A., D. L. T. Anderson, and M. Balmaseda. "Impact of Ocean Observation Systems on Ocean Analysis and Seasonal Forecasts." Monthly Weather Review 135, no. 2 (February 1, 2007): 409–29. http://dx.doi.org/10.1175/mwr3310.1.
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Abstract The relative merits of the Tropical Atmosphere–Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TAO/TRITON) and Pilot Research Moored Array in the Tropical Atlantic mooring networks, the Voluntary Observing Ship (VOS) expendable bathythermograph (XBT) network, and the Argo float network are evaluated through their impact on ocean analyses and seasonal forecast skill. An ocean analysis is performed in which all available data are assimilated. In two additional experiments the moorings and the VOS datasets are withheld from the assimilation. To estimate the impact on seasonal forecast skill, the set of ocean analyses is then used to initialize a corresponding set of coupled ocean–atmosphere model forecasts. A further set of experiments is conducted to assess the impact of the more recent Argo array. A key parameter for seasonal forecast initialization is the depth of the thermocline in the tropical Pacific. This depth is quite similar in all of the experiments that involve data assimilation, but withdrawing the TAO data has a bigger effect than withdrawing XBT data, especially in the eastern half of the basin. The forecasts mainly indicate that the TAO/TRITON in situ temperature observations are essential to obtain optimum forecast skill. They are best combined with XBT, however, because this results in better predictions for the west Pacific. Furthermore, the XBTs play an important role in the North Atlantic. The ocean data assimilation performs less well in the tropical Atlantic. This may be partly a result of not having adequate observations of salinity.
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Lu, Lv, Shaoqing Zhang, Stephen G. Yeager, Gokhan Danabasoglu, Ping Chang, Lixin Wu, Xiaopei Lin, Anthony Rosati, and Feiyu Lu. "Impact of Coherent Ocean Stratification on AMOC Reconstruction by Coupled Data Assimilation with a Biased Model." Journal of Climate 33, no. 17 (September 1, 2020): 7319–34. http://dx.doi.org/10.1175/jcli-d-19-0735.1.
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AbstractThe Atlantic meridional overturning circulation (AMOC) is of great importance in Earth’s climate system, and reconstructing its structure and variability by combining observations with a coupled model is a key step in understanding historical and future states of AMOC. However, models always have systematic errors called bias owing to imperfect numerical representation of the real world. Model bias and the sparse nature of ocean observations, particularly in deep oceans, make it difficult to generate a complete historical picture of AMOC structure and variability. Here, two coupled models that are biased with respect to each other are used to design “twin” experiments to systematically study the influence of model bias on AMOC reconstruction. One model is used to produce the “observations” that sample the “true” solution of the AMOC to be reconstructed, while the other model is used to incorporate the “observations” to reconstruct the “truth” through coupled data assimilation (CDA). The degree to which the “truth” is recovered by a CDA scheme assesses the critical role of coherent (both upper- and deep-ocean incorporate enough observations to mitigate stratification instability) ocean stratification on AMOC reconstruction. Results show that balancing restoration of climatology and assimilation of observations is vital to better reconstruct AMOC structure and variability, given that most ocean observations are only available in the upper 2000 m. The gained results serve as a guideline in ocean-state estimation with a balance of deep restoring and upper data constraint for climate prediction initialization, especially for decadal predictions.
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Davis, Richard, Adam Comeau, Sue L'Orsa, Jude Van der Meer, Brad Covey, Jonathan Pye, and Frederick Whoriskey. "Lessons Learned in Developing a Canadian Operational Glider Fleet." Marine Technology Society Journal 52, no. 3 (May 1, 2018): 13–18. http://dx.doi.org/10.4031/mtsj.52.3.20.
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AbstractCanada's expanding “Blue Economy” requires a major expansion of existing ocean monitoring if developments are to be sustainably managed. Dalhousie University, the Ocean Tracking Network, and the Marine Environmental Observation Prediction and Response Network of Centers of Excellence have operated a mixed fleet of gliders for 7 years on missions covering >50,000 km. The data from these missions are used by research programs, nongovernmental organizations, and government agencies. The gliders have proven to be reliable platforms for ocean observation, collecting data in inclement weather, and times of the year when it is difficult to get ships at sea. However, glider operations have a steep learning curve, and much of the expertise that resides within an operational glider group is gleaned through experience. Managing glider data also poses significant challenges. Planning, risk management, rapid adaptation to the unexpected, and dedicated highly qualified personnel are the keys to sustaining successful glider operations.
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Kim, Hae-Jeong, and Joong-Bae Ahn. "Improvement in Prediction of the Arctic Oscillation with a Realistic Ocean Initial Condition in a CGCM." Journal of Climate 28, no. 22 (November 15, 2015): 8951–67. http://dx.doi.org/10.1175/jcli-d-14-00457.1.
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Abstract This study verifies the impact of improved ocean initial conditions on the Arctic Oscillation (AO) forecast skill by assessing the one-month lead predictability of boreal winter AO using the Pusan National University (PNU) coupled general circulation model (CGCM). Hindcast experiments were performed on two versions of the model, one does not use assimilated ocean initial data (V1.0) and one does (V1.1), and the results were comparatively analyzed. The forecast skill of V1.1 was superior to that of V1.0 in terms of the correlation coefficient between the predicted and observed AO indices. In the regression analysis, V1.1 showed more realistic spatial similarities than V1.0 did in predicted sea surface temperature and atmospheric circulation fields. The authors suggest the relative importance of the contribution of the ocean initial condition to the AO forecast skill was because the ocean data assimilation increased the predictability of the AO, to some extent, through the improved interaction between tropical forcing induced by realistic sea surface temperature (SST) and atmospheric circulation. In V1.1, as in the observation, the cold equatorial Pacific SST anomalies generated the weakened tropical convection and Hadley circulation over the Pacific, resulting in a decelerated subtropical jet and accelerated polar front jet in the extratropics. The intensified polar front jet implies a stronger stratospheric polar vortex relevant to the positive AO phase; hence, surface manifestations of the reflected positive AO phase were then induced through the downward propagation of the stratospheric polar vortex. The results suggest that properly assimilated initial ocean conditions might contribute to improve the predictability of global oscillations, such as the AO, through large-scale tropical ocean–atmosphere interaction.
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Mahale, Vivek N., Guifu Zhang, Ming Xue, Jidong Gao, and Heather D. Reeves. "Variational Retrieval of Rain Microphysics and Related Parameters from Polarimetric Radar Data with a Parameterized Operator." Journal of Atmospheric and Oceanic Technology 36, no. 12 (December 1, 2019): 2483–500. http://dx.doi.org/10.1175/jtech-d-18-0212.1.
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Abstract A variational retrieval of rain microphysics from polarimetric radar data (PRD) has been developed through the use of S-band parameterized polarimetric observation operators. Polarimetric observations allow for the optimal retrieval of cloud and precipitation microphysics for weather quantification and data assimilation for convective-scale numerical weather prediction (NWP) by linking PRD to physical parameters. Rain polarimetric observation operators for reflectivity ZH, differential reflectivity ZDR, and specific differential phase KDP were derived for S-band PRD using T-matrix scattering amplitudes. These observation operators link the PRD to the physical parameters of water content W and mass-/volume-weighted diameter Dm for rain, which can be used to calculate other microphysical information. The S-band observation operators were tested using a 1D variational retrieval that uses the (nonlinear) Gauss–Newton method to iteratively minimize the cost function to find an optimal estimate of Dm and W separately for each azimuth of radar data, which can be applied to a plan position indicator (PPI) radar scan (i.e., a single elevation). Experiments on two-dimensional video disdrometer (2DVD) data demonstrated the advantages of including ΦDP observations and using the nonlinear solution rather than the (linear) optimal interpolation (OI) solution. PRD collected by the Norman, Oklahoma (KOUN) WSR-88D on 15 June 2011 were used to successfully test the retrieval method on radar data. The successful variational retrieval from the 2DVD and the radar data demonstrate the utility of the proposed method.
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Atashgah, MA Amiri, MR Torkamani, and Abolfazl Lavaei. "Robust Positioning, Preliminary Orbit Determination, and Trajectory Prediction of Space Debris using In-Space Iterative-Bearing-Only Observations." Journal of Navigation 70, no. 4 (February 23, 2017): 789–809. http://dx.doi.org/10.1017/s0373463317000029.
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This paper is concerned with the preliminary localisation, orbit determination and model-based path forecasting of space debris based on a robust procedure. In this work, an in-orbit observer utilises only relative bearing observations iteratively. To this end, the problem is first formulated in order to calculate the distance vector between the space debris and any orbiting observer. Afterwards, the obtained position vector is corrected through an Extended Kalman Filter (EKF) for shrinking the sensor and process errors and increasing robustness of the computations in the presence of uncertainties. After preliminary positioning, the related classical orbital elements are acquired via the predicted position and velocity vectors using a hybrid technique. Extensive simulations demonstrate the efficacy and robustness of the aforementioned method, and in particular it is verified that the proposed scheme is capable of producing a suitable solution for preliminary localisation and orbit determination of space debris based on the presented space-based observation, which is practical in phasing and chasing manoeuvres of any grabber space robot.
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Rayner, Ralph. "The U.S. Integrated Ocean Observing System in a Global Context." Marine Technology Society Journal 44, no. 6 (November 1, 2010): 26–31. http://dx.doi.org/10.4031/mtsj.44.6.1.
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AbstractThe mission of the U.S. Integrated Ocean Observing System (IOOS®) is to deliver socioeconomic benefits through provision of readily accessible, systematic, and timely data and information about the past, present, and future state of the waters of the open ocean, the U.S. Exclusive Economic Zone, the U.S. coastal waters, and the Great Lakes. The safety, economic, and environmental benefits that can be derived in whole or in part from IOOS data and information are diverse. They range from maritime and coastal zone applications that are the primary mission of the U.S. IOOS endeavor to a broad range of much wider national benefits, such as the contribution of ocean data and information to improved weather prediction and climate projection. Fully realizing these benefits depends on the U.S. IOOS being connected to other domains of earth and atmospheric observation within the United States and to international frameworks and programs. This article outlines the structure and socioeconomic goals of the U.S. IOOS and explores how fulfilling the IOOS mission contributes to and depends upon connections to the Global Ocean Observing System and the Global Earth Observation System of Systems.
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Zhang, S. "A Study of Impacts of Coupled Model Initial Shocks and State–Parameter Optimization on Climate Predictions Using a Simple Pycnocline Prediction Model." Journal of Climate 24, no. 23 (December 1, 2011): 6210–26. http://dx.doi.org/10.1175/jcli-d-10-05003.1.
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Abstract A skillful decadal prediction that foretells varying regional climate conditions over seasonal–interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate-observing system tend to drift away from observed states toward the imperfect model climate because of the model biases arising from imperfect model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters. Here, a simple coupled model that simulates the fundamental features of the real climate system and a “twin” experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as “truth” and sampled to produce “observations” that are assimilated into other simulations to produce observation-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal–interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time-scale predictions. The coupled model state–parameter optimization greatly enhances the model predictability. While valid “atmospheric” forecasts are extended 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time-scale predictions.
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Lim, Eun-Pa, Harry H. Hendon, David L. T. Anderson, Andrew Charles, and Oscar Alves. "Dynamical, Statistical–Dynamical, and Multimodel Ensemble Forecasts of Australian Spring Season Rainfall." Monthly Weather Review 139, no. 3 (March 1, 2011): 958–75. http://dx.doi.org/10.1175/2010mwr3399.1.
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Abstract The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as “bridging”). A “homogeneous” multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA’s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions. Using hindcasts for the period 1981–2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA’s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical–dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts.
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Jiao, Shengyi, Shengmao Huang, Jianfeng Wang, and Xianqing Lv. "Inversion of Initial Field Based on a Temperature Transport Adjoint." Journal of Marine Science and Engineering 9, no. 7 (July 11, 2021): 760. http://dx.doi.org/10.3390/jmse9070760.
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The setting of initial values is one of the key problems in ocean numerical prediction, with the accuracy of sea water temperature (SWT) simulation and prediction greatly affected by the initial field quality. In this paper, we describe the development of an adjoint assimilation model of temperature transport used to invert the initial temperature field by assimilating the observed data of sea surface temperature (SST) and vertical temperature. Two ideal experiments were conducted to verify the feasibility and validity of this method. By assimilating the “observed data”, the mean absolute error (MAE) between the simulated temperature data and the “observed data” decreased from 1.74 °C and 1.87 °C to 0.13 °C and 0.14 °C, respectively. The spatial distribution of SST difference and the comparison of vertical data also indicate that the regional error of vertical data assimilation is smaller. In the practical experiment, the monthly average temperature field provided by World Ocean Atlas 2018 was selected as background filed and optimized by assimilating the SST data and Argo vertical temperature observation data, to invert the temperature field at 0 a.m. on 1 December 2014 in the South China Sea. Through data assimilation, MAE was reduced from 1.29 °C to 0.65 °C. In terms of vertical observations data comparison and SST spatial distribution, the temperature field obtained by inversion is in good agreement with SST and Argo observations.
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Nobre, Paulo, Roberto A. De Almeida, Marta Malagutti, and Emanuel Giarolla. "Coupled Ocean–Atmosphere Variations over the South Atlantic Ocean." Journal of Climate 25, no. 18 (April 18, 2012): 6349–58. http://dx.doi.org/10.1175/jcli-d-11-00444.1.
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Abstract The impact of ocean–atmosphere interactions on summer rainfall over the South Atlantic Ocean is explored through the use of coupled ocean–atmosphere models. The Brazilian Center for Weather Forecast and Climate Studies (CPTEC) coupled ocean–atmosphere general circulation model (CGCM) and its atmospheric general circulation model (AGCM) are used to gauge the role of coupled modes of variability of the climate system over the South Atlantic at seasonal time scales. Twenty-six years of summer [December–February (DJF)] simulations were done with the CGCM in ensemble mode and the AGCM forced with both observed sea surface temperature (SST) and SST generated by the CGCM forecasts to investigate the dynamics/thermodynamics of the two major convergence zones in the tropical Atlantic: the intertropical convergence zone (ITCZ) and the South Atlantic convergence zone (SACZ). The results present both numerical model and observational evidence supporting the hypothesis that the ITCZ is a thermally direct, SST-driven atmospheric circulation, while the SACZ is a thermally indirect atmospheric circulation controlling SST variability underneath—a consequence of ocean–atmosphere interactions not captured by the atmospheric model forced by prescribed ocean temperatures. Six CGCM model results of the Ensemble-based Predictions of Climate Changes and their Impacts (ENSEMBLES) project, NCEP–NCAR reanalysis data, and oceanic and atmospheric data from buoys of the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) Project over the tropical Atlantic are used to validate CPTEC’s coupled and uncoupled model simulations.
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Zheng, F., and J. Zhu. "Roles of initial ocean surface and subsurface states on successfully predicting 2006–2007 El Niño." Ocean Science Discussions 11, no. 3 (June 19, 2014): 1543–60. http://dx.doi.org/10.5194/osd-11-1543-2014.
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Abstract. The 2006–2007 El Niño event, an unusually weak event, was predicted by most models only after the warming in the eastern Pacific had commenced. In this study, on the basis of an El Niño prediction system, roles of the initial ocean surface and subsurface states on predicting the 2006–2007 El Niño event are investigated to determine conditions favorable for predicting El Niño growth and are isolated in three sets of hindcast experiments. The hindcast is initialized through assimilation of only the sea surface temperature (SST) observations to optimize the initial surface condition (Assim_SST), only the sea level (SL) data to update the initial subsurface state (Assim_SL), or both the SST and SL data (Assim_SST + SL). Results highlight that the hindcasts with three different initial states all can successfully predict the 2006–2007 El Niño event one year in advance and that the Assim_SST + SL hindcast performs best. A comparison between the various sets of hindcast results further demonstrates that successful prediction is more significantly affected by the initial subsurface state than by the initial surface condition. The accurate initial surface state can easier trigger the prediction of the 2006–2007 El Niño, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.
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Zhang, S., Y. S. Chang, X. Yang, and A. Rosati. "Balanced and Coherent Climate Estimation by Combining Data with a Biased Coupled Model." Journal of Climate 27, no. 3 (January 24, 2014): 1302–14. http://dx.doi.org/10.1175/jcli-d-13-00260.1.
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Abstract Given a biased coupled model and the atmospheric and oceanic observing system, maintaining a balanced and coherent climate estimation is of critical importance for producing accurate climate analysis and prediction initialization. However, because of limitations of the observing system (e.g., most of the oceanic measurements are only available for the upper ocean), directly evaluating climate estimation with real observations is difficult. With two coupled models that are biased with respect to each other, a biased twin experiment is designed to simulate the problem. To do that, the atmospheric and oceanic observations drawn from one model based on the modern climate observing system are assimilated into the other. The model that produces observations serves as the truth and the degree by which an assimilation recovers the truth steadily and coherently is an assessment of the impact of the data constraint scheme on climate estimation. Given the assimilation model bias of warmer atmosphere and colder ocean, where the atmospheric-only (oceanic only) data constraint produces an overcooling (overwarming) ocean through the atmosphere–ocean interaction, the constraints with both atmospheric and oceanic data create a balanced and coherent ocean estimate as the observational model. Moreover, the consistent atmosphere–ocean constraint produces the most accurate estimate for North Atlantic Deep Water (NADW), whereas NADW is too strong (weak) if the system is only constrained by atmospheric (oceanic) data. These twin experiment results provide insights that consistent data constraints of multiple components are very important when a coupled model is combined with the climate observing system for climate estimation and prediction initialization.
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Yin, Yonghong, Oscar Alves, and Peter R. Oke. "An Ensemble Ocean Data Assimilation System for Seasonal Prediction." Monthly Weather Review 139, no. 3 (March 1, 2011): 786–808. http://dx.doi.org/10.1175/2010mwr3419.1.
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Abstract A new ensemble ocean data assimilation system, developed for the Predictive Ocean Atmosphere Model for Australia (POAMA), is described. The new system is called PEODAS, the POAMA Ensemble Ocean Data Assimilation System. PEODAS is an approximate form of an ensemble Kalman filter system. For a given assimilation cycle, a central forecast is integrated, along with a small ensemble of forecasts that are forced with perturbed surface fluxes. The small ensemble is augmented with multiple small ensembles from previous assimilation cycles, yielding a larger ensemble that consists of perturbed forecasts from the last month. This larger ensemble is used to represent the system’s time-dependent background error covariance. At each assimilation cycle, a central analysis is computed utilizing the ensemble-based covariance. Each of the perturbed ensemble members are nudged toward the central analysis to control the ensemble spread and mean. The ensemble-based covariances generated by PEODAS potentially yield dynamically balanced analysis increments. The time dependence of the ensemble-based covariance yields spatial structures that change for different dynamical regimes, for example during El Niño and La Niña conditions. These differences are explored in terms of the dominant dynamics and the system’s errors. The performance of PEODAS during a 27-yr reanalysis is evaluated through a series of comparisons with assimilated and independent observations. When compared to its predecessor, POAMA version 1, and a simulation with no assimilation of subsurface observations, PEODAS demonstrates a quantitative improvement in skill. PEODAS will form the basis of Australia’s next operational seasonal prediction system.
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Lewis, Huw W., Juan Manuel Castillo Sanchez, Alex Arnold, Joachim Fallmann, Andrew Saulter, Jennifer Graham, Mike Bush, et al. "The UKC3 regional coupled environmental prediction system." Geoscientific Model Development 12, no. 6 (June 18, 2019): 2357–400. http://dx.doi.org/10.5194/gmd-12-2357-2019.
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Abstract. This paper describes an updated configuration of the regional coupled research system, termed UKC3, developed and evaluated under the UK Environmental Prediction collaboration. This represents a further step towards a vision of simulating the numerous interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land using more integrated regional coupled prediction systems at kilometre-scale resolution. The UKC3 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean surface waves (WAVEWATCH III®), coupled together using OASIS3-MCT libraries. The major update introduced since the UKC2 configuration is an explicit representation of wave–ocean feedbacks through introduction of wave-to-ocean coupling. Ocean model results demonstrate that wave coupling, in particular representing the wave-modified surface drag, has a small but positive improvement on the agreement between simulated sea surface temperatures and in situ observations, relative to simulations without wave feedbacks. Other incremental developments to the coupled modelling capability introduced since the UKC2 configuration are also detailed. Coupled regional prediction systems are of interest for applications across a range of timescales, from hours to decades ahead. The first results from four simulation experiments, each of the order of 1 month in duration, are analysed and discussed in the context of characterizing the potential benefits of coupled prediction on forecast skill. Results across atmosphere, ocean and wave components are shown to be stable over time periods of weeks. The coupled approach shows notable improvements in surface temperature, wave state (in near-coastal regions) and wind speed over the sea, whereas the prediction quality of other quantities shows no significant improvement or degradation relative to the equivalent uncoupled control simulations.
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Münchow, Andreas, Humfrey Melling, and Kelly K. Falkner. "An Observational Estimate of Volume and Freshwater Flux Leaving the Arctic Ocean through Nares Strait." Journal of Physical Oceanography 36, no. 11 (November 1, 2006): 2025–41. http://dx.doi.org/10.1175/jpo2962.1.
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Abstract The Arctic Ocean is an important link in the global hydrological cycle, storing freshwater and releasing it to the North Atlantic Ocean in a variable fashion as pack ice and freshened seawater. An unknown fraction of this return flow passes through Nares Strait between northern Canada and Greenland. Surveys of ocean current and salinity in Nares Strait were completed in the summer of 2003. High-resolution data acquired by ship-based acoustic Doppler current profiler and via hydrographic casts revealed subtidal volume and freshwater fluxes of 0.8 ± 0.3 Sv and –25 ± 12 mSv (Sv = 103 mSv = 106 m3 s−1), respectively. The observations resolved the dominant spatial scale of variability, the internal Rossby radius of deformation (LD ∼9 km), and revealed a complex, yet coherent along-channel flow with a Rossby number of about 0.13, close to geostrophic balance. Approximately one-third of the total volume flux was associated with across-channel slope of the sea surface and two-thirds (68%) with across-channel slope of isopycnal surfaces. During the period of observation, sustained wind from the southwest weakened the average down-channel flow at the surface. The speed of tidal currents exceeded subtidal components by a factor of 2. Tidal signals were resolved and removed from the observations here using two independent methods resolving horizontal and vertical variability of tidal properties, respectively. Tidal current predictions from a barotropic model agreed well with depth-averaged observations in both amplitude and phase. However, because estimates of freshwater flux require accurate surface currents (and salinity), a least squares fitting procedure using velocity data was judged more reliable, since it permits quantification of vertical tidal current variations.
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Thomas, Grant, Richard Cobb, Steven Fiorino, and Michael Hawks. "Daytime Cloudless Sky Radiance Quantification with Ground-Based Aerosol and Meteorological Observations in the Shortwave Infrared." Journal of Atmospheric and Oceanic Technology 37, no. 5 (May 2020): 777–88. http://dx.doi.org/10.1175/jtech-d-19-0157.1.
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AbstractDaytime spectral sky radiance, or sky brightness, is deceptively complex to predict accurately. The Laser Environmental Effects Definition and Reference (LEEDR) first-principles atmospheric model propagates the spectral radiance of the sun to a sensor by modeling the scattering, absorption, and transmission of the radiated light through representative atmospheric layers. For this application, LEEDR was used to ingest numerical weather prediction (NWP) models, and scale the boundary layer and incorporate aerosol loading with ground-based measurements. This study compares LEEDR-derived spectral sky radiance simulations that include measured climatological, measured meteorological, and aerosol loading data to direct sky radiance measurements. Direct measurements of the daytime sky are accomplished with a 1-m-aperture telescope and simultaneous I-band and J-band camera observations (~0.8 and ~1.2 μm, respectively). LEEDR models of the daytime sky are compared to I-band and J-band radiances at multiple azimuths, elevations, and observation times. Residual error analysis is used to determine the accuracy of models including numerical weather prediction data, historical climatology, scaled aerosol loading via in situ particle count measurements, and meteorological updates. Key findings motivate the inclusion of real-time particle count measurements into future daytime sky radiance models for increased scattering accuracy via realistic atmospheric aerosol loading.
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Kostka, Philipp M., Martin Weissmann, Robert Buras, Bernhard Mayer, and Olaf Stiller. "Observation Operator for Visible and Near-Infrared Satellite Reflectances." Journal of Atmospheric and Oceanic Technology 31, no. 6 (June 1, 2014): 1216–33. http://dx.doi.org/10.1175/jtech-d-13-00116.1.
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Abstract Operational numerical weather prediction systems currently only assimilate infrared and microwave satellite observations, whereas visible and near-infrared reflectances that comprise information on atmospheric clouds are not exploited. One of the reasons for that is the absence of computationally efficient observation operators. To remedy this issue in anticipation of the future regional Kilometer-Scale Ensemble Data Assimilation (KENDA) system of Deutscher Wetterdienst, we have developed a version that is fast enough for investigating the assimilation of cloudy reflectances in a case study approach. The operator solves the radiative transfer equation to simulate visible and near-infrared channels of satellite instruments based on the one-dimensional (1D) discrete ordinate method. As input, model output of the operational limited-area Consortium for Small-Scale Modeling (COSMO) model of Deutscher Wetterdienst is used. Assumptions concerning subgrid-scale processes, calculation of in-cloud values of liquid water content, ice water content, and cloud microphysics are summarized, and the accuracy of the 1D simulation is estimated through comparison with three-dimensional (3D) Monte Carlo solver results. In addition, the effects of a parallax correction and horizontal smoothing are quantified. The relative difference between the 1D simulation in “independent column approximation” and the 3D calculation is typically less than 9% between 0600 and 1500 UTC, computed from four scenes during one day (with local noon at 1115 UTC). The parallax-corrected version reduces the deviation to less than 6% for reflectance observations with a central wavelength of 810 nm. Horizontal averaging can further reduce the error of the 1D simulation. In all cases, the bias is less than 1% for the model domain.
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Li, Yuanlong, Weiqing Han, Wanqiu Wang, and M. Ravichandran. "Intraseasonal Variability of SST and Precipitation in the Arabian Sea during the Indian Summer Monsoon: Impact of Ocean Mixed Layer Depth." Journal of Climate 29, no. 21 (October 21, 2016): 7889–910. http://dx.doi.org/10.1175/jcli-d-16-0238.1.
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Abstract This study investigates sea surface temperature (SST) and precipitation variations in the eastern Arabian Sea (EAS) induced by the northward-propagating Indian summer monsoon (ISM) intraseasonal oscillations (MISOs) through analyzing satellite observations and the Climate Forecast System Reanalysis (CFSR) and performing ocean general circulation model (OGCM) experiments. MISOs in the EAS achieve the largest intensity in the developing stage (May–June) of the ISM. The MISOs induce intraseasonal SST variability primarily through surface heat flux forcing, contributed by both shortwave radiation and turbulent heat flux, and secondarily through mixed layer entrainment. The shallow mixed layer depth (MLD < 40 m) in the developing stage and decaying stage (September–October) of the ISM significantly amplifies the heat flux forcing effect on SST and causes large intraseasonal SST variability. Meanwhile, the high SST (>29°C) in the developing stage leads to enhanced response of MISO convection to SST anomaly. It means that the ocean state of the EAS region during the developing stage favors active two-way air–sea interaction and the formation of the strong first-pulse MISO event. These results provide compelling evidence for the vital role played by the ocean in the MISO mechanisms and have implications for understanding and forecasting the ISM onset. Compared to satellite observation, MISOs in CFSR data have weaker SST variability by ~50% and biased SST–precipitation relation. Reducing these biases in CFSR, which provides initial conditions of the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2), may help improve the ISM rainfall forecast.
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Storto, Andrea, Matthew J. Martin, Bruno Deremble, and Simona Masina. "Strongly Coupled Data Assimilation Experiments with Linearized Ocean–Atmosphere Balance Relationships." Monthly Weather Review 146, no. 4 (April 2018): 1233–57. http://dx.doi.org/10.1175/mwr-d-17-0222.1.
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Coupled data assimilation is emerging as a target approach for Earth system prediction and reanalysis systems. Coupled data assimilation may be indeed able to minimize unbalanced air–sea initialization and maximize the intermedium propagation of observations. Here, we use a simplified framework where a global ocean general circulation model (NEMO) is coupled to an atmospheric boundary layer model [Cheap Atmospheric Mixed Layer (CheapAML)], which includes prognostic prediction of near-surface air temperature and moisture and allows for thermodynamic but not dynamic air–sea coupling. The control vector of an ocean variational data assimilation system is augmented to include 2-m atmospheric parameters. Cross-medium balances are formulated either through statistical cross covariances from monthly anomalies or through the application of linearized air–sea flux relationships derived from the tangent linear approximation of bulk formulas, which represents a novel solution to the coupled assimilation problem. As a proof of concept, the methodology is first applied to study the impact of in situ ocean observing networks on the near-surface atmospheric analyses and later to the complementary study of the impact of 2-m air observations on sea surface parameters, to assess benefits of strongly versus weakly coupled data assimilation. Several forecast experiments have been conducted for the period from June to December 2011. We find that especially after day 2 of the forecasts, strongly coupled data assimilation provides a beneficial impact, particularly in the tropical oceans. In most areas, the use of linearized air–sea balances outperforms the statistical relationships used, providing a motivation for implementing coupled tangent linear trajectories in four-dimensional variational data assimilation systems. Further impacts of strongly coupled data assimilation might be found by retuning the background error covariances.
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Megann, A., D. Storkey, Y. Aksenov, S. Alderson, D. Calvert, T. Graham, P. Hyder, J. Siddorn, and B. Sinha. "GO5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications." Geoscientific Model Development 7, no. 3 (June 6, 2014): 1069–92. http://dx.doi.org/10.5194/gmd-7-1069-2014.
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Abstract. We describe a new Global Ocean standard configuration (GO5.0) at eddy-permitting resolution, developed jointly between the National Oceanography Centre and the Met Office as part of the Joint Ocean Modelling Programme (JOMP), a working group of the UK's National Centre for Ocean Forecasting (NCOF) and part of the Joint Weather and Climate Research Programme (JWCRP). The configuration has been developed with the seamless approach to modelling in mind for ocean modelling across timescales and for a range of applications, from short-range ocean forecasting through seasonal forecasting to climate predictions as well as research use. The configuration has been coupled with sea ice (GSI5.0), atmosphere (GA5.0), and land-surface (GL5.0) configurations to form a standard coupled global model (GC1). The GO5.0 model will become the basis for the ocean model component of the Forecasting Ocean Assimilation Model, which provides forced short-range forecasting services. The GC1 or future releases of it will be used in coupled short-range ocean forecasting, seasonal forecasting, decadal prediction and for climate prediction as part of the UK Earth System Model. A 30-year integration of GO5.0, run with CORE2 (Common Ocean-ice Reference Experiments) surface forcing from 1976 to 2005, is described, and the performance of the model in the final 10 years of the integration is evaluated against observations and against a comparable integration of an existing standard configuration, GO1. An additional set of 10-year sensitivity studies, carried out to attribute changes in the model performance to individual changes in the model physics, is also analysed. GO5.0 is found to have substantially reduced subsurface drift above the depth of the thermocline relative to GO1, and also shows a significant improvement in the representation of the annual cycle of surface temperature and mixed layer depth.
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Megann, A., D. Storkey, Y. Aksenov, S. Alderson, D. Calvert, T. Graham, P. Hyder, J. Siddorn, and B. Sinha. "GO5.0: The joint NERC-Met Office NEMO global ocean model for use in coupled and forced applications." Geoscientific Model Development Discussions 6, no. 4 (November 26, 2013): 5747–99. http://dx.doi.org/10.5194/gmdd-6-5747-2013.
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Abstract. We describe a new Global Ocean standard configuration (GO5.0) at eddy-permitting resolution, developed jointly between the National Oceanography Centre and the Met Office as part of the Joint Ocean Modelling Programme (JOMP). This programme is a working group of the UK's National Centre for Ocean Forecasting (NCOF) and part of the Joint Weather and Climate Research Programme (JWCRP). The configuration has been developed with the seamless approach to modelling in mind for ocean modelling across timescales and for a range of applications, from short-range ocean forecasting through seasonal forecasting to climate predictions as well as research use. The GO5.0 configuration has been coupled with sea-ice (GSI5.0), atmosphere (GA5.0) and land-surface (GL5.0) configurations to form a standard coupled global model (GC1). The GO5.0 model will become the basis for the ocean model component of the Forecasting Ocean Assimilation Model, which provides forced short-range forecasting services. The global coupled model (GC1) or future releases of it will be used in coupled short-range ocean forecasting, seasonal forecasting, decadal prediction and for climate prediction as part of the UK Earth System Model. A 30 yr integration of GO5.0, run with CORE2 surface forcing from 1976 to 2005, is described, and the performance of the model in the final ten years of the integration is evaluated against observations and against a comparable integration of an earlier configuration, GO1. An additional set of 10 yr sensitivity studies, carried out to attribute changes in the model performance to individual changes in the model physics, is also analysed. GO5.0 is found to have substantially reduced subsurface drift above the depth of the thermocline relative to GO1, and also shows a significant improvement in the representation of the annual cycle of surface temperature and mixed-layer depth.
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Zheng, F., and J. Zhu. "Roles of initial ocean surface and subsurface states on successfully predicting 2006–2007 El Niño with an intermediate coupled model." Ocean Science 11, no. 1 (February 6, 2015): 187–94. http://dx.doi.org/10.5194/os-11-187-2015.
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Abstract. The 2006–2007 El Niño event, an unusually weak event, was predicted by most models only after the warming in the eastern Pacific had commenced. In this study, on the basis of an El Niño prediction system, roles of the initial ocean surface and subsurface states on predicting the 2006–2007 El Niño event are investigated to determine conditions favorable for predicting El Niño growth and are isolated in three sets of hindcast experiments. The hindcast is initialized through assimilation of only the sea surface temperature (SST) observations to optimize the initial surface condition, only the sea level (SL) data to update the initial subsurface state, or both the SST and SL data. Results highlight that the hindcasts with three different initial states can all successfully predict the 2006–2007 El Niño event 1 year in advance and that the hindcast initialized by both the SST and SL data performs best. A comparison between the various sets of hindcast results further demonstrates that successful prediction is more significantly affected by the initial subsurface state than by the initial surface condition. The accurate initial surface state can trigger the easier prediction of the 2006–2007 El Niño, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.
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Kim, Hyemi, Frédéric Vitart, and Duane E. Waliser. "Prediction of the Madden–Julian Oscillation: A Review." Journal of Climate 31, no. 23 (December 2018): 9425–43. http://dx.doi.org/10.1175/jcli-d-18-0210.1.
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There has been an accelerating interest in forecasting the weather and climate within the subseasonal time range. The Madden–Julian oscillation (MJO), an organized envelope of tropical convection, is recognized as one of the leading sources of subseasonal predictability. This review synthesizes the latest progress regarding the MJO predictability and prediction. During the past decade, the MJO prediction skill in dynamical prediction systems has exceeded the skill of empirical predictions. Such improvement has been mainly attributed to more observations and computer resources, advances in theoretical understanding, and improved numerical models aided in part by multinational efforts through field campaigns and multimodel experiments. The state-of-the-art dynamical forecasts have shown MJO prediction skill up to 5 weeks. Prediction skill can be extended by improving the ensemble generation approach tailored for MJO prediction and by averaging multiensembles or multimodels. MJO prediction skill can be influenced by the tropical mean state and low-frequency climate mode variations, as well as by the extratropical circulation. MJO prediction skill is proven to be sensitive to model physics, ocean–atmosphere coupling, and quality of initial conditions, while the impact of the model resolution seems to be marginal. Remaining challenges and recommendations on new research avenues to fully realize the predictability of the MJO are discussed.
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Shuckburgh, Emily F. "Oceanographers' contribution to climate modelling and prediction: progress to date and a future perspective." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1980 (December 13, 2012): 5656–81. http://dx.doi.org/10.1098/rsta.2012.0402.
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The ocean plays an essential role in determining aspects of the climate through its influence on coupled processes involving the atmosphere, cyrosphere and biogeochemistry, including budgets of heat and carbon dioxide and sea-level rise. Here, the key developments in ocean modelling over the past 20 years are reviewed and the prospects for the next 20 years are outlined, considering a hierarchy of idealized, conceptual and realistic modelling frameworks. It is emphasized that any long-term modelling strategy needs to be underpinned and complemented by fundamental theoretical and observational research activities. The need to be aware of the societal and technological drivers that will shape future research directions is also articulated.
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Wang, Dazhuang, Liaoying Zhao, Huaguo Zhang, Juan Wang, Xiulin Lou, Peng Chen, Kaiguo Fan, Aiqin Shi, and Dongling Li. "On Optimal Imaging Angles in Multi-Angle Ocean Sun Glitter Remote-Sensing Platforms to Observe Sea Surface Roughness." Sensors 19, no. 10 (May 16, 2019): 2268. http://dx.doi.org/10.3390/s19102268.
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Sea surface roughness (SSR) is a key physical parameter in studies of air–sea interactions and the ocean dynamics process. The SSR quantitative inversion model based on multi-angle sun glitter (SG) images has been proposed recently, which will significantly promote SSR observations through multi-angle remote-sensing platforms. However, due to the sensitivity of the sensor view angle (SVA) to SG, it is necessary to determine the optimal imaging angle and their combinations. In this study, considering the design optimization of imaging geometry for multi-angle remote-sensing platforms, we have developed an error transfer simulation model based on the multi-angle SG remote-sensing radiation transmission and SSR estimation models. We simulate SSR estimation errors at different imaging geometry combinations to evaluate the optimal observation geometry combination. The results show that increased SSR inversion accuracy can be obtained with SVA combinations of 0° and 20° for nadir- and backward-looking SVA compared with current combinations of 0° and 27.6°. We found that SSR inversion prediction error using the proposed model and actual SSR inversion error from field buoy data are correlated. These results can provide support for the design optimization of imaging geometry for multi-angle ocean remote-sensing platforms.
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Zhang, Lixia, Tianjun Zhou, Peili Wu, and Xiaolong Chen. "Potential Predictability of North China Summer Drought." Journal of Climate 32, no. 21 (September 26, 2019): 7247–64. http://dx.doi.org/10.1175/jcli-d-18-0682.1.
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Abstract Any skillful prediction is of great benefit to North China, a region that is densely populated and greatly impacted by droughts. This paper reports potential predictability of North China summer drought 1 month ahead based on hindcasts for 1961–2005 from the “ENSEMBLES” project. Correlation scores of the standardized precipitation–evapotranspiration index and standardized precipitation index reach 0.49 and 0.39, respectively. The lower-level northwestern Pacific cyclonic circulation anomaly (NWPCCA) and East Asian upper-tropospheric temperature (UTT) cooling are the crucial circulations with regard to summer drought. Two sources of predictability are identified: 1) Pacific–Japan and Silk Road teleconnections forced by well-established eastern Pacific Ocean El Niño sea surface temperature anomalies (SSTA) in summer, when the two key circulations are both well predicted because of a good prediction of enhanced equatorial central Pacific (CP) rainfall and Indian rainfall deficit, and 2) the subtropical atmosphere–ocean coupling associated with CP El Niño developing, when the skill mainly arises from the reasonable prediction of NWPCCA. In observations, the NWPCCA persists from the preceding spring to summer through a wind–evaporation–SST feedback related to the Pacific meridional mode (PMM). In predictions, the persistence of the NWPCCA is mainly forced by the enhanced convection over the subtropical central North Pacific due to the persistence of the PMM-related meridional SSTA gradient over the CP. This predicted SSTA suppresses the equatorial Pacific rainfall, contributing to low prediction skill for the East Asian UTT cooling. This study demonstrates the importance of extratropical signals from the preceding season in North China summer drought prediction.
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Radhakrishnan, Chandrasekar, and V. Chandrasekar. "CASA Prediction System over Dallas–Fort Worth Urban Network: Blending of Nowcasting and High-Resolution Numerical Weather Prediction Model." Journal of Atmospheric and Oceanic Technology 37, no. 2 (February 2020): 211–28. http://dx.doi.org/10.1175/jtech-d-18-0192.1.
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AbstractThis study targeted improving Collaborative Adaptive Sensing of the Atmosphere’s (CASA) 6-h lead time predictive ability by blending the radar-based nowcast with the NWP model over the Dallas–Fort Worth (DFW) urban radar network. This study also depicts the recent updates in CASA’s real-time reflectivity nowcast system by assessing nine precipitation cases over the DFW urban region. CASA’s nowcast framework displayed better primer outcomes than the WRF Model forecast for the lead time of 1 h and 30 min. After that time, the predictive ability of the nowcast framework began decreasing compared to the WRF Model. To broaden CASA’s predictive system lead time to 6 h, the WRF Model forecasts were blended with Dynamic and Adaptive Radar Tracking of Storms (DARTS) nowcast. The HRRR model analysis was used as initial and boundary conditions in the WRF Model. The high-resolution dual-pol radar observations were assimilated into the WRF Model through the 3DVAR data assimilation technique. Three kinds of blending strategies were used and the results were compared: 1) hyperbolic tangent curve (HTW), 2) critical success index (CSIW), and 3) salient cross dissolve (Sal CD). The sensitivity studies were conducted to decide desirable parameters in the blending techniques. The outcomes proved that blending enhanced the prediction skills. Also, the overall performance of blending relies on the accuracy of the WRF forecast. Even though blending results are mixed, the HTW-based technique performed better than the other two techniques.
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Satti, Saleh, Benjamin F. Zaitchik, Hamada S. Badr, and Tsegaye Tadesse. "Enhancing Dynamical Seasonal Predictions through Objective Regionalization." Journal of Applied Meteorology and Climatology 56, no. 5 (May 2017): 1431–42. http://dx.doi.org/10.1175/jamc-d-16-0192.1.
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AbstractImproving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large-scale atmospheric dynamics that drive interannual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision-making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically based forecast-system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability, it is shown that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the July–September (JAS) rain months, and results show an improvement in the predictions from version 4.5 of the Max Plank Institute for Meteorology’s atmosphere–ocean general circulation model (ECHAM4.5) for applicable areas of East Africa for the two test cases presented.
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Gurbuz, G., S. Jin, and C. Mekik. "EFFECTS OF OCEAN TIDE MODELS ON GNSS-ESTIMATED ZTD AND PWV IN TURKEY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 255–58. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-255-2015.
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Global Navigation Satellite System (GNSS) observations can precisely estimate the total zenith tropospheric delay (ZTD) and precipitable water vapour (PWV) for weather prediction and atmospheric research as a continuous and all-weather technique. However, apart from GNSS technique itself, estimations of ZTD and PWV are subject to effects of geophysical models with large uncertainties, particularly imprecise ocean tide models in Turkey. In this paper, GNSS data from Jan. 1<sup>st</sup> to Dec. 31<sup>st</sup> of 2014 are processed at 4 co-located GNSS stations (GISM, DIYB, GANM, and ADAN) with radiosonde from Turkish Met-Office along with several nearby IGS stations. The GAMIT/GLOBK software has been used to process GNSS data of 30-second sample using the Vienna Mapping Function and 10° elevation cut-off angle. Also tidal and non-tidal atmospheric pressure loadings (ATML) at the observation level are also applied in GAMIT/GLOBK. Several widely used ocean tide models are used to evaluate their effects on GNSS-estimated ZTD and PWV estimation, such as IERS recommended FES2004, NAO99b from a barotropic hydrodynamic model, CSR4.0 obtained from TOPEX/Poseidon altimetry with the model FES94.1 as the reference model and GOT00 which is again long wavelength adjustments of FES94.1 using TOPEX/Poseidon data at 0.5 by 0.5 degree grid. The ZTD and PWV computed from radiosonde profile observations are regarded as reference values for the comparison and validation. In the processing phase, five different strategies are taken without ocean tide model and with four aforementioned ocean tide models, respectively, which are used to evaluate ocean tide models effects on GNSS-estimated ZTD and PWV estimation through comparing with co-located Radiosonde. Results showed that ocean tide models have greatly affected the estimation of the ZTD in centimeter level and thus the precipitable water vapour in millimeter level, respectively at stations near coasts. The ocean tide model FES2004 that is the product of assimilation of the altimetric data of ERS2, TOPEX/POSEIDON and the data of a global tide gauge network, gave the most accurate results when compared to radiosonde with ±1.99 mm in PWV at stations near coastline. While other ocean tides models agree each other at millimeter level in PWV. However, at inland GNSS stations, ocean tide models have less effects on GNSS-estimated ZTD and PWV, e.g., with ±1.0 mm in ZTD and ±0.1 mm in PWV.
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Blockley, Edward W., and K. Andrew Peterson. "Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness." Cryosphere 12, no. 11 (October 30, 2018): 3419–38. http://dx.doi.org/10.5194/tc-12-3419-2018.
Full textAbstract:
Abstract. Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue. The prospect of increased human industrial activity in the region, as well as scientific interest in the predictability of sea ice, provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operational forecasting centres now routinely produce seasonal predictions of sea-ice cover using coupled atmosphere–ocean–sea-ice models. Although assimilation of sea-ice concentration into these systems is commonplace, sea-ice thickness observations, being much less mature, are typically not assimilated. However, many studies suggest that initialization of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. Here, for the first time, we directly assess the impact of winter sea-ice thickness initialization on the skill of summer seasonal predictions by assimilating CryoSat-2 thickness data into the Met Office's coupled seasonal prediction system (GloSea). We show a significant improvement in predictive skill of Arctic sea-ice extent and ice-edge location for forecasts of September Arctic sea ice made from the beginning of the melt season. The improvements in sea-ice cover lead to further improvement of near-surface air temperature and pressure fields across the region. A clear relationship between modelled winter thickness biases and summer extent errors is identified which supports the theory that Arctic winter thickness provides some predictive capability for summer ice extent, and further highlights the importance that modelled winter thickness biases can have on the evolution of forecast errors through the melt season.
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Song, Hyo-Jong, and Jong-Yeon Park. "Bottom-Up Drivers for Global Fish Catch Assessed with Reconstructed Ocean Biogeochemistry from an Earth System Model." Climate 9, no. 5 (May 14, 2021): 83. http://dx.doi.org/10.3390/cli9050083.
Full textAbstract:
Identifying bottom-up (e.g., physical and biogeochemical) drivers for fish catch is essential for sustainable fishing and successful adaptation to climate change through reliable prediction of future fisheries. Previous studies have suggested the potential linkage of fish catch to bottom-up drivers such as ocean temperature or satellite-retrieved chlorophyll concentration across different global ecosystems. Robust estimation of bottom-up effects on global fisheries is, however, still challenging due to the lack of long-term observations of fisheries-relevant biotic variables on a global scale. Here, by using novel long-term biological and biogeochemical data reconstructed from a recently developed data assimilative Earth system model, we newly identified dominant drivers for fish catch in globally distributed coastal ecosystems. A machine learning analysis with the inclusion of reconstructed zooplankton production and dissolved oxygen concentration into the fish catch predictors provides an extended view of the links between environmental forcing and fish catch. Furthermore, the relative importance of each driver and their thresholds for high and low fish catch are analyzed, providing further insight into mechanistic principles of fish catch in individual coastal ecosystems. The results presented herein suggest the potential predictive use of their relationships and the need for continuous observational effort for global ocean biogeochemistry.
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Jordan, James F. "Navigation of Spacecraft on Deep Space Missions." Journal of Navigation 40, no. 1 (January 1987): 19–29. http://dx.doi.org/10.1017/s0373463300000266.
Full textAbstract:
Spacecraft which are sent on deep space missions to the planets must be accurately navigated in order to achieve the correct flight path. Navigation analysts use precise measurements and large computational software systems to determine a spacecraft's position throughout the mission and compute the velocity corrections for its guidance through space. When the spacecraft is launched into deep space on its voyage, it is impossible to know with great precision where it is headed. Imperfections in both the launch vehicle's terminal velocity and the uncertainty in the knowledge of the parameters which will affect the spacecraft trajectory contribute to errors in the predictions of the total flight path. Continuous navigation of the spacecraft achieves an ever-evolving prediction of its orbit from the reduction of radiometric and astrometric observations of the craft. Control of the spacecraft is achieved by computing and signalling to the craft a series of propulsive, velocity correction commands, which manoeuvre the craft to its desired course.
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Tietsche, Steffen, Ed Hawkins, and Jonathan J. Day. "Atmospheric and Oceanic Contributions to Irreducible Forecast Uncertainty of Arctic Surface Climate." Journal of Climate 29, no. 1 (December 31, 2015): 331–46. http://dx.doi.org/10.1175/jcli-d-15-0421.1.
Full textAbstract:
Abstract Uncertainty of Arctic seasonal to interannual predictions arising from model errors and initial state uncertainty has been widely discussed in the literature, whereas the irreducible forecast uncertainty (IFU) arising from the chaoticity of the climate system has received less attention. However, IFU provides important insights into the mechanisms through which predictability is lost and hence can inform prioritization of model development and observations deployment. Here, the authors characterize how internal oceanic and surface atmospheric heat fluxes contribute to the IFU of Arctic sea ice and upper-ocean heat content in an Earth system model by analyzing a set of idealized ensemble prediction experiments. It is found that atmospheric and oceanic heat flux are often equally important for driving unpredictable Arctic-wide changes in sea ice and surface water temperatures and hence contribute equally to IFU. Atmospheric surface heat flux tends to dominate Arctic-wide changes for lead times of up to a year, whereas oceanic heat flux tends to dominate regionally and on interannual time scales. There is in general a strong negative covariance between surface heat flux and ocean vertical heat flux at depth, and anomalies of lateral ocean heat transport are wind driven, which suggests that the unpredictable oceanic heat flux variability is mainly forced by the atmosphere. These results are qualitatively robust across different initial states, but substantial variations in the amplitude of IFU exist. It is concluded that both atmospheric variability and the initial state of the upper ocean are key ingredients for predictions of Arctic surface climate on seasonal to interannual time scales.
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Olsen, S. M., B. Hansen, S. Østerhus, D. Quadfasel, and H. Valdimarsson. "Biased thermohaline exchanges with the Arctic across the Iceland–Faroe Ridge in ocean climate models." Ocean Science 12, no. 2 (April 13, 2016): 545–60. http://dx.doi.org/10.5194/os-12-545-2016.
Full textAbstract:
Abstract. The northern limb of the Atlantic thermohaline circulation and its transport of heat and salt towards the Arctic strongly modulate the climate of the Northern Hemisphere. The presence of warm surface waters prevents ice formation in parts of the Arctic Mediterranean, and ocean heat is directly available for sea-ice melt, while salt transport may be critical for the stability of the exchanges. Through these mechanisms, ocean heat and salt transports play a disproportionally strong role in the climate system, and realistic simulation is a requisite for reliable climate projections. Across the Greenland–Scotland Ridge (GSR) this occurs in three well-defined branches where anomalies in the warm and saline Atlantic inflow across the shallow Iceland–Faroe Ridge (IFR) have been shown to be particularly difficult to simulate in global ocean models. This branch (IF-inflow) carries about 40 % of the total ocean heat transport into the Arctic Mediterranean and is well constrained by observation during the last 2 decades but associated with significant inter-annual fluctuations. The inconsistency between model results and observational data is here explained by the inability of coarse-resolution models to simulate the overflow across the IFR (IF-overflow), which feeds back onto the simulated IF-inflow. In effect, this is reduced in the model to reflect only the net exchange across the IFR. Observational evidence is presented for a substantial and persistent IF-overflow and mechanisms that qualitatively control its intensity. Through this, we explain the main discrepancies between observed and simulated exchange. Our findings rebuild confidence in modelled net exchange across the IFR, but reveal that compensation of model deficiencies here through other exchange branches is not effective. This implies that simulated ocean heat transport to the Arctic is biased low by more than 10 % and associated with a reduced level of variability, while the quality of the simulated salt transport becomes critically dependent on the link between IF-inflow and IF-overflow. These features likely affect sensitivity and stability of climate models to climate change and limit the predictive skill.
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Lewis, Huw W., Juan Manuel Castillo Sanchez, John Siddorn, Robert R. King, Marina Tonani, Andrew Saulter, Peter Sykes, et al. "Can wave coupling improve operational regional ocean forecasts for the north-west European Shelf?" Ocean Science 15, no. 3 (June 5, 2019): 669–90. http://dx.doi.org/10.5194/os-15-669-2019.
Full textAbstract:
Abstract. Operational ocean forecasts are typically produced by modelling systems run using a forced mode approach. The evolution of the ocean state is not directly influenced by surface waves, and the ocean dynamics are driven by an external source of meteorological data which are independent of the ocean state. Model coupling provides one approach to increase the extent to which ocean forecast systems can represent the interactions and feedbacks between ocean, waves, and the atmosphere seen in nature. This paper demonstrates the impact of improving how the effect of waves on the momentum exchange across the ocean–atmosphere interface is represented through ocean–wave coupling on the performance of an operational regional ocean prediction system. This study focuses on the eddy-resolving (1.5 km resolution) Atlantic Margin Model (AMM15) ocean model configuration for the north-west European Shelf (NWS) region. A series of 2-year duration forecast trials of the Copernicus Marine Environment Monitoring Service (CMEMS) north-west European Shelf regional ocean prediction system are analysed. The impact of including ocean–wave feedbacks via dynamic coupling on the simulated ocean is discussed. The main interactions included are the modification of surface stress by wave growth and dissipation, Stokes–Coriolis forcing, and wave-height-dependent ocean surface roughness. Given the relevance to operational forecasting, trials with and without ocean data assimilation are considered. Summary forecast metrics demonstrate that the ocean–wave coupled system is a viable evolution for future operational implementation. When results are considered in more depth, wave coupling was found to result in an annual cycle of relatively warmer winter and cooler summer sea surface temperatures for seasonally stratified regions of the NWS. This is driven by enhanced mixing due to waves, and a deepening of the ocean mixed layer during summer. The impact of wave coupling is shown to be reduced within the mixed layer with assimilation of ocean observations. Evaluation of salinity and ocean currents against profile measurements in the German Bight demonstrates improved simulation with wave coupling relative to control simulations. Further, evidence is provided of improvement to simulation of extremes of sea surface height anomalies relative to coastal tide gauges.
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Wajsowicz, Roxana C. "Seasonal-to-Interannual Forecasting of Tropical Indian Ocean Sea Surface Temperature Anomalies: Potential Predictability and Barriers." Journal of Climate 20, no. 13 (July 1, 2007): 3320–43. http://dx.doi.org/10.1175/jcli4162.1.
Full textAbstract:
Abstract Whether seasonally phased-locked persistence and predictability barriers, similar to the boreal spring barriers found for El Niño–Southern Oscillation (ENSO), exist for the tropical Indian Ocean sector climate is investigated using observations and hindcasts from two coupled ocean–atmosphere dynamical ensemble forecast systems: the National Centers for Environmental Prediction (NCEP) Coupled Forecast System (CFS) for 1990–2003, and the NASA Seasonal-to-Interannual Prediction Project (NSIPP) system for 1993–2002. The potential predictability of the climate is also assessed under the “perfect model/ensemble” assumption. Lagged correlations of the indices calculated over the east and west poles of the Indian Ocean dipole mode (IDM) index show weak sea surface temperature anomaly (SSTA) persistence barriers in boreal spring at both poles, but the major decline in correlation at the east pole occurs in boreal midwinter for all start months with an almost immediate recovery, albeit negative correlations, until summer approaches. Processes responsible for the change in sign of SSTAs associated with a major IDM event effect a similar change on much weaker SSTAs. At the west pole, a major decline occurs at the end of boreal summer for fall and winter starts when the thermocline deepens with the seasonal cycle and coupling between the ocean and atmosphere is weak. A decline in skillful prediction of SSTA at the east pole over boreal winter is also found in the hindcasts, but the relatively large thermocline depth anomalies are skillfully predicted through this time and skill in SSTA prediction returns. A predictability barrier at the onset of the boreal summer monsoon is found at both IDM poles with some return of skill in late fall. Potential predictability calculations suggest that this barrier may be overcome at the west pole with improvements to the forecast systems, but not at the east pole for forecasts initiated in boreal winter unless the ocean is initialized with a memory of fall conditions.
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Lee, Jared A., Joshua P. Hacker, Luca Delle Monache, Branko Kosović, Andrew Clifton, Francois Vandenberghe, and Javier Sanz Rodrigo. "Improving Wind Predictions in the Marine Atmospheric Boundary Layer through Parameter Estimation in a Single-Column Model." Monthly Weather Review 145, no. 1 (December 14, 2016): 5–24. http://dx.doi.org/10.1175/mwr-d-16-0063.1.
Full textAbstract:
Abstract A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly because of a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this study the WRF single-column model (SCM) is coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART) to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining , the time-varying sea surface roughness length, four WRF-SCM/DART experiments are conducted during the October–December 2006 period. The two methods for determining are the default Fairall-adjusted Charnock formulation in WRF and use of the parameter estimation techniques to estimate in DART. Using DART to estimate is found to reduce 1-h forecast errors of wind speed over the Charnock–Fairall ensembles by 4%–22%. However, parameter estimation of does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.
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Gómez-Orellana, Antonio Manuel, Juan Carlos Fernández, Manuel Dorado-Moreno, Pedro Antonio Gutiérrez, and César Hervás-Martínez. "Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux." Energies 14, no. 2 (January 17, 2021): 468. http://dx.doi.org/10.3390/en14020468.
Full textAbstract:
Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always available in an appropriate format and its preparation and pre-processing implies a lot of time and effort by researchers. This paper presents a novel software tool with a user-friendly GUI to create datasets by means of management and data integration of meteorological observations from two data sources: the National Data Buoy Center and the National Centers for Environmental Prediction and for Atmospheric Research Reanalysis Project. Such datasets can be created using buoys and reanalysis data through customisable procedures, in terms of temporal resolution, predictive and objective variables, and can be used by SC and ML methodologies for prediction tasks (classification or regression). The objective is providing the research community with an automated and versatile system for the casuistry that entails well-formed and quality data integration, potentially leading to better prediction models. The software tool can be used as a supporting tool for coastal and ocean engineering applications, sustainable energy production, or environmental modelling; as well as for decision-making in the design and building of coastal protection structures, marine transport, ocean energy converters, and well-planned running of offshore and coastal engineering activities. Finally, to illustrate the applicability of the proposed tool, a case study to classify waves depending on their significant height and to predict energy flux in the Gulf of Alaska is presented.
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Onken, Reiner. "Validation of an ocean shelf model for the prediction of mixed-layer properties in the Mediterranean Sea west of Sardinia." Ocean Science 13, no. 2 (April 3, 2017): 235–57. http://dx.doi.org/10.5194/os-13-235-2017.
Full textAbstract:
Abstract. The Regional Ocean Modeling System (ROMS) has been employed to explore the sensitivity of the forecast skill of mixed-layer properties to initial conditions, boundary conditions, and vertical mixing parameterisations. The initial and lateral boundary conditions were provided by the Mediterranean Forecasting System (MFS) or by the MERCATOR global ocean circulation model via one-way nesting; the initial conditions were additionally updated through the assimilation of observations. Nowcasts and forecasts from the weather forecast models COSMO-ME and COSMO-IT, partly melded with observations, served as surface boundary conditions. The vertical mixing was parameterised by the GLS (generic length scale) scheme Umlauf and Burchard (2003) in four different set-ups. All ROMS forecasts were validated against the observations which were taken during the REP14-MED survey to the west of Sardinia. Nesting ROMS in MERCATOR and updating the initial conditions through data assimilation provided the best agreement of the predicted mixed-layer properties with the time series from a moored thermistor chain. Further improvement was obtained by the usage of COSMO-ME atmospheric forcing, which was melded with real observations, and by the application of the k-ω vertical mixing scheme with increased vertical eddy diffusivity. The predicted temporal variability of the mixed-layer temperature was reasonably well correlated with the observed variability, while the modelled variability of the mixed-layer depth exhibited only agreement with the observations near the diurnal frequency peak. For the forecasted horizontal variability, reasonable agreement was found with observations from a ScanFish section, but only for the mesoscale wave number band; the observed sub-mesoscale variability was not reproduced by ROMS.
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Olsen, S. M., B. Hansen, S. Østerhus, D. Quadfasel, and H. Valdimarsson. "Biased thermohaline exchanges with the arctic across the Iceland-Faroe Ridge in ocean climate models." Ocean Science Discussions 12, no. 4 (July 14, 2015): 1471–510. http://dx.doi.org/10.5194/osd-12-1471-2015.
Full textAbstract:
Abstract. The northern limb of the Atlantic thermohaline circulation and its transport of heat and salt towards the Arctic strongly modulates the climate of the Northern Hemisphere. Presence of warm surface waters prevents ice formation in parts of the Arctic Mediterranean and ocean heat is in critical regions directly available for sea-ice melt, while salt transport may be critical for the stability of the exchanges. Hereby, ocean heat and salt transports play a disproportionally strong role in the climate system and realistic simulation is a requisite for reliable climate projections. Across the Greenland-Scotland Ridge (GSR) this occurs in three well defined branches where anomalies in the warm and saline Atlantic inflow across the shallow Iceland-Faroe Ridge (IFR) have shown particularly difficult to simulate in global ocean models. This branch (IF-inflow) carries about 40 % of the total ocean heat transport into the Arctic Mediterranean and is well constrained by observation during the last two decades but is associated with significant inter-annual fluctuations. The inconsistency between model results and observational data is here explained by the inability of coarse resolution models to simulate the overflow across the IFR (IF-overflow), which feeds back on the simulated IF-inflow. In effect, this is reduced in the model to reflect only the net exchange across the IFR. Observational evidence is presented for a substantial and persistent IF-overflow and mechanisms that qualitatively control its intensity. Through this, we explain the main discrepancies between observed and simulated exchange. Our findings rebuild confidence in modeled net exchange across the IFR, but reveal that compensation of model deficiencies here through other exchange branches is not effective. This implies that simulated ocean heat transport to the Arctic is biased low by more than 10 % and associated with a reduced level of variability while the quality of the simulated salt transport becomes critically dependent on the link between IF-inflow and IF-overflow. These features likely affect sensitivity and stability of climate models to climate change and limit the predictive skill.