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Journal articles on the topic 'Climate Forecasts'
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1
Shao, Yawen, Quan J. Wang, Andrew Schepen, and Dongryeol Ryu. "Going with the Trend: Forecasting Seasonal Climate Conditions under Climate Change." Monthly Weather Review 149, no. 8 (August 2021): 2513–22. http://dx.doi.org/10.1175/mwr-d-20-0318.1.
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AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision-making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast postprocessing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical postprocessing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast postprocessing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting postprocessed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast postprocessing is expected to boost user confidence in seasonal climate forecasts.
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Steinemann, Anne C. "Using Climate Forecasts for Drought Management." Journal of Applied Meteorology and Climatology 45, no. 10 (October 1, 2006): 1353–61. http://dx.doi.org/10.1175/jam2401.1.
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Abstract Drought hazards, and the ability to mitigate them with advance warning, offer potentially valuable applications of climate forecast products. Yet the value is often untapped, owing to the gap between climate science and societal decisions. This study bridged that gap; it determined forecast needs among water managers, translated forecasts to meet those needs, and shaped drought decision making to take advantage of forecasts. NOAA Climate Prediction Center (CPC) seasonal precipitation outlooks were converted into a forecast precipitation index (FPI) tailored for water managers in the southeastern United States. The FPI expresses forecasts as a departure from the climatological normal and is consistent with other drought indicators. Evaluations of CPC seasonal forecasts issued during 1995–2000 demonstrated positive skill for drought seasons in the Southeast. In addition, using evaluation criteria of water managers, 88% of forecasts for drought seasons would have appropriately prompted drought responses. Encouraged by these evaluations, and the understandability of the FPI, state water managers started using the forecasts in 2001 for deciding whether to pay farmers to suspend irrigation. Economic benefits of this forecast information were estimated at $100–$350 million in a state-declared drought year (2001, 2002) and $5–$30 million in the other years (2003, 2004). This study provides four main contributions: 1) an investigation of the needs and potential benefits of seasonal forecast information for water management, 2) a method for translating the CPC forecasts into a format needed by water managers, 3) the integration of forecast information into agency decision making, and 4) the economic valuation of that forecast information.
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Tippett, Michael K., Timothy DelSole, and Anthony G. Barnston. "Reliability of Regression-Corrected Climate Forecasts." Journal of Climate 27, no. 9 (April 23, 2014): 3393–404. http://dx.doi.org/10.1175/jcli-d-13-00565.1.
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Abstract Regression is often used to calibrate climate model forecasts with observations. Reliability is an aspect of forecast quality that refers to the degree of correspondence between forecast probabilities and observed frequencies of occurrence. While regression-corrected climate forecasts are reliable in principle, the estimated regression parameters used in practice are affected by sampling error. The low skill and small sample sizes typically encountered in climate prediction imply substantial sampling error in the estimated regression parameters. Here the reliability of regression-corrected climate forecasts is analyzed for the case of joint-Gaussian distributed ensemble forecasts and observations with regression parameters estimated by least squares. Hypothesis testing of the regression parameters provides direct information about the skill and reliability of the uncorrected ensemble-based probability forecasts. However, the regression-corrected probability forecasts with estimated parameters are systematically “overconfident” because sampling error causes a positive bias in the regression forecast signal variance, despite the fact that the estimates of the regression parameters are themselves unbiased. An analytical description of the reliability diagram of a generic regression-corrected climate forecast is derived and is shown to depend on sample size and population correlation skill, with small sample size and low skill being factors that increase overconfidence. The analytical reliability estimate is shown to capture the effect of sampling error in synthetic data experiments and in a 29-yr dataset of NOAA Climate Forecast System version 2 predictions of seasonal precipitation totals over the Americas. The impact of sampling error on the reliability of regression-corrected forecast has been previously unrecognized and affects all regression-based forecasts. The use of regression parameters estimated by shrinkage methods such as ridge regression substantially reduces overconfidence.
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Pasternack, Alexander, Jonas Bhend, Mark A. Liniger, Henning W. Rust, Wolfgang A. Müller, and Uwe Ulbrich. "Parametric decadal climate forecast recalibration (DeFoReSt 1.0)." Geoscientific Model Development 11, no. 1 (January 25, 2018): 351–68. http://dx.doi.org/10.5194/gmd-11-351-2018.
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Abstract. Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.
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Block, P. "Tailoring seasonal climate forecasts for hydropower operations." Hydrology and Earth System Sciences 15, no. 4 (April 29, 2011): 1355–68. http://dx.doi.org/10.5194/hess-15-1355-2011.
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Abstract. Integration of seasonal precipitation forecasts into water resources operations and planning is practically nonexistent, even in regions of scarcity. This is often attributable to water manager's tendency to act in a risk averse manner, preferring to avoid consequences of poor forecasts, at the expense of unrealized benefits. Convincing demonstrations of forecast value are therefore desirable to support assimilation into practice. A dynamically linked system, including forecast, rainfall-runoff, and hydropower models, is applied to the upper Blue Nile basin in Ethiopia to compare benefits and reliability generated by actual forecasts against a climatology-based approach, commonly practiced in most water resources systems. Processing one hundred decadal sequences demonstrates superior forecast-based benefits in 68 cases, a respectable advancement, however benefits in a few forecast-based sequences are noticeably low, likely to dissuade manager's adoption. A hydropower sensitivity test reveals a propensity toward poor-decision making when forecasts over-predict wet conditions. Tailoring the precipitation forecast to highlight critical dry forecasts minimizes this inclination, resulting in 97% of the sequences favoring the forecast-based approach. Considering managerial risk preferences for the system, even risk-averse actions, if coupled with forecasts, exhibit superior benefits and reliability compared with risk-taking tendencies conditioned on climatology.
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Weisheimer, A., and T. N. Palmer. "On the reliability of seasonal climate forecasts." Journal of The Royal Society Interface 11, no. 96 (July 6, 2014): 20131162. http://dx.doi.org/10.1098/rsif.2013.1162.
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Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time.
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Goddard, L., A. G. Barnston, and S. J. Mason. "Evaluation of the IRI'S “Net Assessment” Seasonal Climate Forecasts: 1997–2001." Bulletin of the American Meteorological Society 84, no. 12 (December 1, 2003): 1761–82. http://dx.doi.org/10.1175/bams-84-12-1761.
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The International Research Institute for Climate Prediction (IRI) net assessment seasonal temperature and precipitation forecasts are evaluated for the 4-yr period from October–December 1997 to October–December 2001. These probabilistic forecasts represent the human distillation of seasonal climate predictions from various sources. The ranked probability skill score (RPSS) serves as the verification measure. The evaluation is offered as time-averaged spatial maps of the RPSS as well as area-averaged time series. A key element of this evaluation is the examination of the extent to which the consolidation of several predictions, accomplished here subjectively by the forecasters, contributes to or detracts from the forecast skill possible from any individual prediction tool. Overall, the skills of the net assessment forecasts for both temperature and precipitation are positive throughout the 1997–2001 period. The skill may have been enhanced during the peak of the 1997/98 El Niño, particularly for tropical precipitation, although widespread positive skill exists even at times of weak forcing from the tropical Pacific. The temporally averaged RPSS for the net assessment temperature forecasts appears lower than that for the AGCMs. Over time, however, the IRI forecast skill is more consistently positive than that of the AGCMs. The IRI precipitation forecasts generally have lower skill than the temperature forecasts, but the forecast probabilities for precipitation are found to be appropriate to the frequency of the observed outcomes, and thus reliable. Over many regions where the precipitation variability is known to be potentially predictable, the net assessment precipitation forecasts exhibit more spatially coherent areas of positive skill than most, if not all, prediction tools. On average, the IRI net assessment forecasts appear to perform better than any of the individual objective prediction tools.
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Krakauer, Nir Y., Michael D. Grossberg, Irina Gladkova, and Hannah Aizenman. "Information Content of Seasonal Forecasts in a Changing Climate." Advances in Meteorology 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/480210.
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We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill.
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Pirret, Jennifer S. R., Joseph D. Daron, Philip E. Bett, Nicolas Fournier, and Andre Kamga Foamouhoue. "Assessing the Skill and Reliability of Seasonal Climate Forecasts in Sahelian West Africa." Weather and Forecasting 35, no. 3 (May 6, 2020): 1035–50. http://dx.doi.org/10.1175/waf-d-19-0168.1.
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Abstract Seasonal climate forecasts have the potential to support planning decisions and provide advanced warning to government, industry, and communities to help reduce the impacts of adverse climatic conditions. Assessing the reliability of seasonal forecasts, generated using different models and methods, is essential to ensure their appropriate interpretation and use. Here we assess the reliability of forecasts for seasonal total precipitation in Sahelian West Africa, a region of high year-to-year climate variability. Through digitizing forecasts issued from the regional climate outlook forum in West Africa known as Prévisions Climatiques Saisonnières en Afrique Soudano-Sahélienne (PRESASS), we assess their reliability by comparing them to the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) project observational data over the past 20 years. The PRESASS forecasts show positive skill and reliability, but a bias toward lower forecast probabilities in the below-normal precipitation category. In addition, we assess the reliability of seasonal precipitation forecasts for the same region using available global dynamical forecast models. We find all models have positive skill and reliability, but this varies geographically. On average, NCEP’s CFS and ECMWF’s SEAS5 systems show greater skill and reliability than the Met Office’s GloSea5, and in turn than Météo-France’s Sys5, but one key caveat is that model performance might depend on the meteorological situation. We discuss the potential for improving use of dynamical model forecasts in the regional climate outlook forums, to improve the reliability of seasonal forecasts in the region and the objectivity of the seasonal forecasting process used in the PRESASS regional climate outlook forum.
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Hu, Qi, Lisa M. Pytlik Zillig, Gary D. Lynne, Alan J. Tomkins, William J. Waltman, Michael J. Hayes, Kenneth G. Hubbard, Ikrom Artikov, Stacey J. Hoffman, and Donald A. Wilhite. "Understanding Farmers’ Forecast Use from Their Beliefs, Values, Social Norms, and Perceived Obstacles*." Journal of Applied Meteorology and Climatology 45, no. 9 (September 1, 2006): 1190–201. http://dx.doi.org/10.1175/jam2414.1.
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Abstract Although the accuracy of weather and climate forecasts is continuously improving and new information retrieved from climate data is adding to the understanding of climate variation, use of the forecasts and climate information by farmers in farming decisions has changed little. This lack of change may result from knowledge barriers and psychological, social, and economic factors that undermine farmer motivation to use forecasts and climate information. According to the theory of planned behavior (TPB), the motivation to use forecasts may arise from personal attitudes, social norms, and perceived control or ability to use forecasts in specific decisions. These attributes are examined using data from a survey designed around the TPB and conducted among farming communities in the region of eastern Nebraska and the western U.S. Corn Belt. There were three major findings: 1) the utility and value of the forecasts for farming decisions as perceived by farmers are, on average, around 3.0 on a 0–7 scale, indicating much room to improve attitudes toward the forecast value. 2) The use of forecasts by farmers to influence decisions is likely affected by several social groups that can provide “expert viewpoints” on forecast use. 3) A major obstacle, next to forecast accuracy, is the perceived identity and reliability of the forecast makers. Given the rapidly increasing number of forecasts in this growing service business, the ambiguous identity of forecast providers may have left farmers confused and may have prevented them from developing both trust in forecasts and skills to use them. These findings shed light on productive avenues for increasing the influence of forecasts, which may lead to greater farming productivity. In addition, this study establishes a set of reference points that can be used for comparisons with future studies to quantify changes in forecast use and influence.
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Rhodes, Lauren A., and Bruce A. McCarl. "The Value of Ocean Decadal Climate Variability Information to United States Agriculture." Atmosphere 11, no. 4 (March 25, 2020): 318. http://dx.doi.org/10.3390/atmos11040318.
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Ocean-related decadal climate variability (ODCV) has the potential to influence regional climates and, in turn, crop yields. ODCV event forecasts with associated climate and crop yield implication information can provide farmers with the opportunity to alter their crop mixes and input usage to adapt to the forecast conditions. We investigate the value of ODCV information and the nature of adaptations. This is done by estimating the changes in welfare under differing information scenarios using a nonlinear dynamic optimization model. We find evidence that both perfect forecasts and the use of forecasts permitting a conditional probability of future phase combinations can significantly increase agriculture consumer and producer welfare. This is a new result that is an estimate of the US national value of releasing ODCV forecasts and accompanying yield information.
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Pennesi, Karen. "Making Forecasts Meaningful: Explanations of Problematic Predictions in Northeast Brazil." Weather, Climate, and Society 3, no. 2 (April 1, 2011): 90–105. http://dx.doi.org/10.1175/wcas-d-10-05005.1.
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Abstract This study illustrates the need to consider the multiple interpretations and experiences that influence how climate forecasts are evaluated in local contexts when assessing how useful forecasts can be for increasing the resilience of rural communities. Video clips of predictions made by scientific and traditional forecasters were shown in interviews and focus groups to elicit explanations for why the predictions are sometimes judged to be inaccurate, not useful, or inappropriately communicated by different sectors of the rural population in Ceará, Northeast Brazil. Results indicate that climate forecasts are not simply a decision-making tool that provides information in a one-way transfer from forecaster to user. The meanings and values of predictions are jointly created by both forecasters and their audiences. Predictions and the discussions that surround them are also an important part of expressing social identities and ideas about how the world works. Ineffective predictions are explained here in terms of religious beliefs, environmental change, forecaster identity, interactional context, and cultural practices.
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Saha, Suranjana, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, et al. "The NCEP Climate Forecast System Version 2." Journal of Climate 27, no. 6 (March 13, 2014): 2185–208. http://dx.doi.org/10.1175/jcli-d-12-00823.1.
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Abstract The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.
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Keogh, D. U., I. W. Watson, K. L. Bell, D. H. Cobon, and S. C. Dutta. "Climate information needs of Gascoyne - Murchison pastoralists: a representative study of the Western Australian grazing industry." Australian Journal of Experimental Agriculture 45, no. 12 (2005): 1613. http://dx.doi.org/10.1071/ea04275.
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The Gascoyne–Murchison region of Western Australia experiences an arid to semi-arid climate with a highly variable temporal and spatial rainfall distribution. The region has around 39.2 million hectares available for pastoral lease and supports predominantly cattle and sheep grazing leases. In recent years a number of climate forecasting systems have been available offering rainfall probabilities with different lead times and forecast periods; however, the extent to which these systems are capable of fulfilling the requirements of the local pastoralists is still ambiguous. Issues can range from ensuring forecasts are issued with sufficient lead time to enable key planning or decisions to be revoked or altered, to ensuring forecast language is simple and clear, to negate possible misunderstandings in interpretation. A climate research project sought to provide an objective method to determine which available forecasting systems had the greatest forecasting skill at times of the year relevant to local property management. To aid this climate research project, the study reported here was undertaken with an overall objective of exploring local pastoralists’ climate information needs. We also explored how well they understand common climate forecast terms such as ‘mean’, ‘median’ and ‘probability’, and how they interpret and apply forecast information to decisions. A stratified, proportional random sampling was used for the purpose of deriving the representative sample based on rainfall-enterprise combinations. In order to provide more time for decision-making than existing operational forecasts that are issued with zero lead time, pastoralists requested that forecasts be issued for May–July and January–March with lead times counting down from 4 to 0 months. We found forecasts of between 20 and 50 mm break-of-season or follow-up rainfall were likely to influence decisions. Eighty percent of pastoralists demonstrated in a test question that they had a poor technical understanding of how to interpret the standard wording of a probabilistic median rainfall forecast. This is worthy of further research to investigate whether inappropriate management decisions are being made because the forecasts are being misunderstood. We found more than half the respondents regularly access and use weather and climate forecasts or outlook information from a range of sources and almost three-quarters considered climate information or tools useful, with preferred methods for accessing this information by email, faxback service, internet and the Department of Agriculture Western Australia’s Pastoral Memo. Despite differences in enterprise types and rainfall seasonality across the region we found seasonal climate forecasting needs were relatively consistent. It became clear that providing basic training and working with pastoralists to help them understand regional climatic drivers, climate terminology and jargon, and the best ways to apply the forecasts to enhance decision-making are important to improve their use of information. Consideration could also be given to engaging a range of producers to write the climate forecasts themselves in the language they use and understand, in consultation with the scientists who prepare the forecasts.
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Takle, Eugene S., Christopher J. Anderson, Jeffrey Andresen, James Angel, Roger W. Elmore, Benjamin M. Gramig, Patrick Guinan, et al. "Climate Forecasts for Corn Producer Decision Making." Earth Interactions 18, no. 5 (March 1, 2014): 1–8. http://dx.doi.org/10.1175/2013ei000541.1.
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Abstract Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.
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Li, Ming, Huidong Jin, and Jaclyn N. Brown. "Making the Output of Seasonal Climate Models More Palatable to Agriculture: A Copula-Based Postprocessing Method." Journal of Applied Meteorology and Climatology 59, no. 3 (March 2020): 497–515. http://dx.doi.org/10.1175/jamc-d-19-0093.1.
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AbstractSeasonal climate forecasts from raw climate models at coarse grids are often biased and statistically unreliable for credible crop prediction at the farm scale. We develop a copula-based postprocessing (CPP) method to overcome this mismatch problem. The CPP forecasts are ensemble based and are generated from the predictive distribution conditioned on raw climate forecasts. CPP performs univariate postprocessing procedures at each station, lead time, and variable separately and then applies the Schaake shuffle to reorder ensemble sequence for a more realistic spatial, temporal, and cross-variable dependence structure. The use of copulas makes CPP free of strong distributional assumptions and flexible enough to describe complex dependence structures. In a case study, we apply CPP to postprocess rainfall, minimum temperature, maximum temperature, and radiation forecasts at a monthly level from the Australian Community Climate and Earth-System Simulator Seasonal model (ACCESS-S) to three representative stations in Australia. We evaluate forecast skill at lead times of 0–5 months on a cross-validation theme in the context of both univariate and multivariate forecast verification. When compared with forecasts that use climatological values as the predictor, the CPP forecast has positive skills, although the skills diminish with increasing lead times and finally become comparable at long lead times. When compared with the bias-corrected forecasts and the quantile-mapped forecasts, the CPP forecast is the overall best, with the smallest bias and greatest univariate forecast skill. As a result of the skill gain from univariate forecasts and the effect of the Schaake shuffle, CPP leads to the most skillful multivariate forecast as well. Further results investigate whether using ensemble mean or additional predictors can enhance forecast skill for CPP.
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Demirel, M. C., M. J. Booij, and A. Y. Hoekstra. "The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models." Hydrology and Earth System Sciences 19, no. 1 (January 16, 2015): 275–91. http://dx.doi.org/10.5194/hess-19-275-2015.
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Abstract. This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
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Frame, D. J., N. E. Faull, M. M. Joshi, and M. R. Allen. "Probabilistic climate forecasts and inductive problems." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1857 (June 19, 2007): 1971–92. http://dx.doi.org/10.1098/rsta.2007.2069.
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The development of ensemble-based ‘probabilistic’ climate forecasts is often seen as a promising avenue for climate scientists. Ensemble-based methods allow scientists to produce more informative, nuanced forecasts of climate variables by reflecting uncertainty from various sources, such as similarity to observation and model uncertainty. However, these developments present challenges as well as opportunities, particularly surrounding issues of experimental design and interpretation of forecast results. This paper discusses different approaches and attempts to set out what climate prediction .net and other large ensemble, complex model experiments might contribute to this research programme.
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Jury, Mark R. "Evaluation of Coupled Model Forecasts of Ethiopian Highlands Summer Climate." Advances in Meteorology 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/894318.
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This study evaluates seasonal forecasts of rainfall and maximum temperature across the Ethiopian highlands from coupled ensemble models in the period 1981–2006, by comparison with gridded observational products (NMA + GPCC/CRU3). Early season forecasts from the coupled forecast system (CFS) are steadier than European community medium range forecast (ECMWF). CFS and ECMWF April forecasts of June–August (JJA) rainfall achieve significant fit (r2=0.27, 0.25, resp.), but ECMWF forecasts tend to have a narrow range with drought underpredicted. Early season forecasts of JJA maximum temperature are weak in both models; hence ability to predict water resource gains may be better than losses. One aim of seasonal climate forecasting is to ensure that crop yields keep pace with Ethiopia’s growing population. Farmers using prediction technology are better informed to avoid risk in dry years and generate surplus in wet years.
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Luo, Lifeng, and Eric F. Wood. "Use of Bayesian Merging Techniques in a Multimodel Seasonal Hydrologic Ensemble Prediction System for the Eastern United States." Journal of Hydrometeorology 9, no. 5 (October 1, 2008): 866–84. http://dx.doi.org/10.1175/2008jhm980.1.
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Abstract Skillful seasonal hydrologic predictions are useful in managing water resources, preparing for droughts and their impacts, energy planning, and many other related sectors. In this study, a seasonal hydrologic ensemble prediction system is developed and evaluated over the eastern United States, with a focus on the Ohio River basin. The system uses a hydrologic model (i.e., the Variable Infiltration Capacity model) as the central element for producing ensemble predictions of soil moisture, snow, and streamflow with lead times up to six months. One unique feature of this system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate forecasts from multiple climate models with observed climatology in a Bayesian framework, such that the uncertainties related to the atmospheric forcings can be better quantified while the signals from individual models are combined. Simultaneously, climate model forecasts are downscaled to an appropriate spatial scale for hydrologic predictions. When generating daily meteorological forcing, the system uses the rank structures of selected historical forcing records to ensure reasonable weather patterns in space and time. Seasonal hydrologic predictions are made with this system, using seasonal climate forecast from the NCEP Climate Forecast System (CFS), and from a combination of the NCEP CFS and seven climate models in the European Union’s Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (CFS+DEMETER). Forecasts of these two types are made for the summer periods (May to October) of 1981–99 and are compared to forecasts produced with the traditional Ensemble Streamflow Prediction (ESP) approach used in operational seasonal streamflow predictions. The forecasts from this system for the summer of 1988 show very promising skill in precipitation, soil moisture, and streamflow over the Ohio River basin, especially the multimodel CFS+DEMETER forecast. The evaluation with all 19 summer forecasts shows that the multimodel CFS+DEMETER forecast is significantly better than the ESP forecast during the first two months of the forecasts. The advantage is marginal to moderate when only the CFS forecast is used. This study validates the approach of using seasonal climate predictions from dynamic climate models in hydrological predictions, and it also emphasizes the need for international collaborations to develop multimodel seasonal predictions.
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Saha, S., S. Nadiga, C. Thiaw, J. Wang, W. Wang, Q. Zhang, H. M. Van den Dool, et al. "The NCEP Climate Forecast System." Journal of Climate 19, no. 15 (August 1, 2006): 3483–517. http://dx.doi.org/10.1175/jcli3812.1.
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Abstract The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC. The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS. These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes.
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Meza, Francisco J., James W. Hansen, and Daniel Osgood. "Economic Value of Seasonal Climate Forecasts for Agriculture: Review of Ex-Ante Assessments and Recommendations for Future Research." Journal of Applied Meteorology and Climatology 47, no. 5 (May 1, 2008): 1269–86. http://dx.doi.org/10.1175/2007jamc1540.1.
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Abstract Advanced information in the form of seasonal climate forecasts has the potential to improve farmers’ decision making, leading to increases in farm profits. Interdisciplinary initiatives seeking to understand and exploit the potential benefits of seasonal forecasts for agriculture have produced a number of quantitative ex-ante assessments of the economic value of seasonal climate forecasts. The realism, robustness, and credibility of such assessments become increasingly important as efforts shift from basic research toward applied research and implementation. This paper surveys published evidence about the economic value of seasonal climate forecasts for agriculture, characterizing the agricultural systems, approaches followed, and scales of analysis. The climate forecast valuation literature has contributed insights into the influence of forecast characteristics, risk attitudes, insurance, policy, and the scale of adoption on the value of forecasts. Key innovations in the more recent literature include explicit treatment of the uncertainty of forecast value estimates, incorporation of elicited management responses into bioeconomic modeling, and treatment of environmental impacts, in addition to financial outcomes of forecast response. It is argued that the picture of the value of seasonal forecasts for agriculture is still incomplete and often biased, in part because of significant gaps in published valuation research. Key gaps include sampling of a narrow range of farming systems and locations, incorporation of an overly restricted set of potential management responses, failure to consider forecast responses that could lead to “regime shifts,” and failure to incorporate state-of-the-art developments in seasonal forecasting. This paper concludes with six recommendations to enhance the realism, robustness, and credibility of ex-ante valuation of seasonal climate forecasts.
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Chowdhury, Shahadat, and Ashish Sharma. "Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach." Journal of Climate 24, no. 7 (April 1, 2011): 1869–77. http://dx.doi.org/10.1175/2010jcli3632.1.
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Abstract This paper dynamically combined three multivariate forecasts where spatially and temporally variant combination weights are estimated using a nearest-neighbor approach. The case study presented combines forecasts from three climate models for the period 1958–2001. The variables of interest here are the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, predicted 3 months in advance. The forecast from the static weight combination is used as the base case for comparison. The forecasted sea surface temperature using the dynamic combination algorithm offers consistent improvements over the static combination approach for all seasons. This improved skill is achieved over at least 93% of the global grid cells, in four 10-yr independent validation segments. Dynamically combined forecasts reduce the mean-square error of the SSTA by at least 25% for 72% of the global grid cells when compared against the best-performing single forecast among the three climate models considered.
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O’Lenic, Edward A., David A. Unger, Michael S. Halpert, and Kenneth S. Pelman. "Developments in Operational Long-Range Climate Prediction at CPC." Weather and Forecasting 23, no. 3 (June 1, 2008): 496–515. http://dx.doi.org/10.1175/2007waf2007042.1.
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Abstract The science, production methods, and format of long-range forecasts (LRFs) at the Climate Prediction Center (CPC), a part of the National Weather Service’s (NWS’s) National Centers for Environmental Prediction (NCEP), have evolved greatly since the inception of 1-month mean forecasts in 1946 and 3-month mean forecasts in 1982. Early forecasts used a subjective blending of persistence and linear regression-based forecast tools, and a categorical map format. The current forecast system uses an increasingly objective technique to combine a variety of statistical and dynamical models, which incorporate the impacts of El Niño–Southern Oscillation (ENSO) and other sources of interannual variability, and trend. CPC’s operational LRFs are produced each midmonth with a “lead” (i.e., amount of time between the release of a forecast and the start of the valid period) of ½ month for the 1-month outlook, and with leads ranging from ½ month through 12½ months for the 3-month outlook. The 1-month outlook is also updated at the end of each month with a lead of zero. Graphical renderings of the forecasts made available to users range from a simple display of the probability of the most likely tercile to a detailed portrayal of the entire probability distribution. Efforts are under way at CPC to objectively weight, bias correct, and combine the information from many different LRF prediction tools into a single tool, called the consolidation (CON). CON ½-month lead 3-month temperature (precipitation) hindcasts over 1995–2005 were 18% (195%) better, as measured by the Heidke skill score for nonequal chances forecasts, than real-time official (OFF) forecasts during that period. CON was implemented into LRF operations in 2006, and promises to transfer these improvements to the official LRF. Improvements in the science and production methods of LRFs are increasingly being driven by users, who are finding an increasing number of applications, and demanding improved access to forecast information. From the forecast-producer side, hope for improvement in this area lies in greater dialogue with users, and development of products emphasizing user access, input, and feedback, including direct access to 5 km × 5 km gridded outlook data through NWS’s new National Digital Forecast Database (NDFD).
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Barnston, Anthony G., Simon J. Mason, Lisa Goddard, David G. DeWitt, and Stephen E. Zebiak. "Multimodel Ensembling in Seasonal Climate Forecasting at IRI." Bulletin of the American Meteorological Society 84, no. 12 (December 1, 2003): 1783–96. http://dx.doi.org/10.1175/bams-84-12-1783.
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The International Research Institute (IRI) for Climate Prediction seasonal forecast system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI forecasts are examined and compared with the skills of the more subjectively derived forecasts actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation forecasts do benefit from inclusion of empirical forecast tools. Planned pattern-level model output statistics (MOS) corrections for systematic biases in the AGCM forecasts may render them more sufficient in their own right.
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Broxton, Patrick D., Xubin Zeng, and Nicholas Dawson. "The Impact of a Low Bias in Snow Water Equivalent Initialization on CFS Seasonal Forecasts." Journal of Climate 30, no. 21 (November 2017): 8657–71. http://dx.doi.org/10.1175/jcli-d-17-0072.1.
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Across much of the Northern Hemisphere, Climate Forecast System forecasts made earlier in the winter (e.g., on 1 January) are found to have more snow water equivalent (SWE) in April–June than forecasts made later (e.g., on 1 April); furthermore, later forecasts tend to predict earlier snowmelt than earlier forecasts. As a result, other forecasted model quantities (e.g., soil moisture in April–June) show systematic differences dependent on the forecast lead time. Notably, earlier forecasts predict much colder near-surface air temperatures in April–June than later forecasts. Although the later forecasts of temperature are more accurate, earlier forecasts of SWE are more realistic, suggesting that the improvement in temperature forecasts occurs for the wrong reasons. Thus, this study highlights the need to improve atmospheric processes in the model (e.g., radiative transfer, turbulence) that would cause cold biases when a more realistic amount of snow is on the ground. Furthermore, SWE differences in earlier versus later forecasts are found to much more strongly affect April–June temperature forecasts than the sea surface temperature differences over different regions, suggesting the major role of snowpack in seasonal prediction during the spring–summer transition over snowy regions.
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Løhre, Erik. "Stronger, sooner, and more certain climate change: A link between certainty and outcome strength in revised forecasts." Quarterly Journal of Experimental Psychology 71, no. 12 (January 1, 2018): 2531–47. http://dx.doi.org/10.1177/1747021817746062.
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What do lay people think about revised forecasts of future outcomes? A series of experiments show that when climate change forecasts are revised in an upward direction (e.g., a higher sea level rise is predicted in light of new information), the forecast is perceived as more certain in contrast to revisions in a downward direction. This association is bidirectional so that people also think a forecast that has become more certain (uncertain) indicates a stronger (weaker) outcome. Furthermore, when the timing of the outcome is revised, a predicted sooner occurrence (a “stronger” outcome) was judged to be more certain than a predicted delayed occurrence. Upward revisions may also lead to more positive impressions of the forecast and the forecaster, with clear implications for the communication of uncertainty, both for climate change and in other domains. Different theoretical explanations for the results are discussed.
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Klemm, Toni, and Renee A. McPherson. "Assessing Decision Timing and Seasonal Climate Forecast Needs of Winter Wheat Producers in the South-Central United States." Journal of Applied Meteorology and Climatology 57, no. 9 (September 2018): 2129–40. http://dx.doi.org/10.1175/jamc-d-17-0246.1.
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AbstractAgricultural decision-making that adapts to climate variability is essential to global food security. Crop production can be severely impacted by drought, flood, and heat, as seen in recent years in parts of the United States. Seasonal climate forecasts can help producers reduce crop losses, but many nationwide, publicly available seasonal forecasts currently lack relevance for agricultural producers, in part because they do not reflect their decision needs. This study examines the seasonal forecast needs of winter wheat producers in the southern Great Plains to understand what climate information is most useful and what lead times are most relevant for decision-making. An online survey of 119 agricultural advisers, cooperative extension agents in Oklahoma, Kansas, Texas, and Colorado, was conducted and gave insights into producers’ preferences for forecast elements, what weather and climate extremes have the most impact on decision-making, and the decision timing of major farm practices. The survey participants indicated that winter wheat growers were interested not only in directly modeled variables, such as total monthly rainfall, but also in derived elements, such as consecutive number of dry days. Moreover, these agricultural advisers perceived that winter wheat producers needed seasonal climate forecasts to have a lead time of 0–2.5 months—the planning lead time for major farm practices, like planting or harvesting. A forecast calendar and monthly rankings for forecast elements were created that can guide forecasters and advisers as they develop decision tools for winter wheat producers and that can serve as a template for other time-sensitive decision tools developed for stakeholder communities.
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Power, S. B., N. Plummer, and P. Alford. "Making climate model forecasts more useful." Australian Journal of Agricultural Research 58, no. 10 (2007): 945. http://dx.doi.org/10.1071/ar06196.
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There is considerable potential for seasonal to inter-annual climate forecasts derived from dynamic models of the earth’s climate to be used widely to help improve management of important real-world issues in a variety of different areas (e.g. disaster management, agriculture, water management, health, natural resource management, food security, and insurance). Unfortunately, several factors currently inhibit this potential, e.g. low skill, low awareness, mismatches in what model forecasts can provide and what users need, and the complexity and probabilistic nature of the information provided. Substantial effort around the world is currently directed towards reducing these impediments. For example, climate model development continues behind the scenes, and techniques such as multi-model ensemble forecasting are progressing rapidly. Communication strategies that enable probabilistic information to be communicated more effectively have been developed and exciting developments such as the emergence of the Argo float program have dramatically improved our ability to initialise forecast systems. We can also look forward to greater computing power in the future, which will allow us to increase the resolution of the models used to perform forecasts. Research on the integration of climate forecasts with risk-management tools more useful to managers is also occurring. The great potential for much wider use of climate model forecasting cannot be denied. However, it will only be realised if models continue to be developed further, if climatic variability continues to be closely monitored from the surface, the atmosphere, the ocean, and from space, and if these data are made readily available to the research community.
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Camargo, Suzana J., and Anthony G. Barnston. "Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI." Weather and Forecasting 24, no. 2 (April 1, 2009): 472–91. http://dx.doi.org/10.1175/2008waf2007099.1.
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Abstract The International Research Institute for Climate and Society (IRI) has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper the method used to obtain these forecasts is described and the forecast performance is evaluated. The forecasts are based on tropical cyclone–like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the previous month’s observations, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors recently observed SSTs. For the recent 6-yr period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued. Despite variations from one basin to another, the levels of hindcast skill for the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent at fairly modest but statistically significant levels. The dynamical forecasts require statistical postprossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Skill levels decrease only slowly with increasing lead time up to 2–3 months. During the recent period of real-time forecasts, the issued forecasts have had higher probabilistic skill than the raw model output, due to the forecasters’ subjective elimination of the “overconfidence” bias in the model’s forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered.
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Block, P. "Tailoring seasonal climate forecasts for hydropower operations in Ethiopia's upper Blue Nile basin." Hydrology and Earth System Sciences Discussions 7, no. 3 (June 24, 2010): 3765–802. http://dx.doi.org/10.5194/hessd-7-3765-2010.
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Abstract. Explicit integration of seasonal precipitation forecasts into water resources operations and planning is practically nonexistent, even in regions of scarcity. This is often attributable to water manager's tendency to act in a risk averse manner, preferring to avoid consequences of poor forecasts, at the expense of unrealized benefits. Convincing demonstrations of forecast value are therefore desirable to support assimilation into practice. A dynamic coupled system, including forecast, rainfall-runoff, and hydropower models, is applied to the upper Blue Nile basin in Ethiopia to compare benefits and reliability generated by actual forecasts against a climatology-based approach, commonly practiced in most water resources systems. Processing one hundred decadal sequences demonstrates superior forecast-based benefits in 68 cases, a respectable advancement, however benefits in a few forecast-based sequences are noticeably low, likely to dissuade manager's adoption. A hydropower sensitivity test reveals a propensity toward poor-decision making when forecasts over-predict wet conditions. Tailoring the precipitation forecast to highlight critical dry predictions minimizes this inclination, resulting in 97% of the sequences favoring the forecast-based approach. Considering managerial risk preferences for the system, even risk-averse actions, if coupled with forecasts, exhibits superior benefits and reliability compared with risk-taking tendencies relying on climatology.
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Wang, Hui, A. Sankarasubramanian, and Ranji S. Ranjithan. "Integration of Climate and Weather Information for Improving 15-Day-Ahead Accumulated Precipitation Forecasts." Journal of Hydrometeorology 14, no. 1 (February 1, 2013): 186–202. http://dx.doi.org/10.1175/jhm-d-11-0128.1.
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Abstract Skillful medium-range weather forecasts are critical for water resources planning and management. This study aims to improve 15-day-ahead accumulated precipitation forecasts by combining biweekly weather and disaggregated climate forecasts. A combination scheme is developed to combine reforecasts from a numerical weather model and disaggregated climate forecasts from ECHAM4.5 for developing 15-day-ahead precipitation forecasts. Evaluation of the skill of the weather–climate information (WCI)-based biweekly forecasts under leave-five-out cross validation shows that WCI-based forecasts perform better than reforecasts in many grid points over the continental United States. Correlation between rank probability skill score (RPSS) and disaggregated ECHAM4.5 forecast errors reveals that the lower the error in the disaggregated forecasts, the better the performance of WCI forecasts. Weights analysis from the combination scheme also shows that the biweekly WCI forecasts perform better by assigning higher weights to the better-performing candidate forecasts (reforecasts or disaggregated ECHAM4.5 forecasts). Particularly, WCI forecasts perform better during the summer months during which reforecasts have limited skill. Even though the disaggregated climate forecasts do not perform well over many grid points, the primary reason WCI-based forecasts perform better than the reforecasts is due to the reduction in the overconfidence of the reforecasts. Since the disaggregated climate forecasts are better dispersed than the reforecasts, combining them with reforecasts results in reduced uncertainty in predicting the 15-day-ahead accumulated precipitation.
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Barnston, Anthony G., Shuhua Li, Simon J. Mason, David G. DeWitt, Lisa Goddard, and Xiaofeng Gong. "Verification of the First 11 Years of IRI’s Seasonal Climate Forecasts." Journal of Applied Meteorology and Climatology 49, no. 3 (March 1, 2010): 493–520. http://dx.doi.org/10.1175/2009jamc2325.1.
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Abstract This paper examines the quality of seasonal probabilistic forecasts of near-global temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from late 1997 through 2008, using mainly a two-tiered multimodel dynamical prediction system. Skill levels, while modest when globally averaged, depend markedly on season and location and average higher in the tropics than extratropics. To first order, seasons and regions of useful skill correspond to known direct effects as well as remote teleconnections from anomalies of tropical sea surface temperature in the Pacific Ocean (e.g., ENSO related) and in other tropical basins. This result is consistent with previous skill assessments by IRI and others and suggests skill levels beneficial to informed clients making climate risk management decisions for specific applications. Skill levels for temperature are generally higher, and less seasonally and regionally dependent, than those for precipitation, partly because of correct forecasts of enhanced probabilities for above-normal temperatures associated with warming trends. However, underforecasting of above-normal temperatures suggests that the dynamical forecast system could be improved through inclusion of time-varying greenhouse gas concentrations. Skills of the objective multimodel probability forecasts, used as the primary basis for the final forecaster-modified issued forecasts, are comparable to those of the final forecasts, but their probabilistic reliability is somewhat weaker. Automated recalibration of the multimodel output should permit improvements to their reliability, allowing them to be issued as is. IRI is currently developing single-tier prediction components.
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Plotz, Roan D., Lynda E. Chambers, and Charlotte K. Finn. "The Best of Both Worlds: A Decision-Making Framework for Combining Traditional and Contemporary Forecast Systems." Journal of Applied Meteorology and Climatology 56, no. 8 (August 2017): 2377–92. http://dx.doi.org/10.1175/jamc-d-17-0012.1.
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AbstractIn most countries, national meteorological services either generate or have access to seasonal climate forecasts. However, in a number of regions, the uptake of these forecasts by local communities can be limited, with the locals instead relying on traditional knowledge to make their climate forecasts. Both approaches to seasonal climate forecasting have benefits, and the incorporation of traditional forecast methods into contemporary forecast systems can lead to forecasts that are locally relevant and better trusted by the users. This in turn could significantly improve the communication and application of climate information, especially to remote communities. A number of different methodologies have been proposed for combining these forecasts. Through considering the benefits and limitations of each approach, practical recommendations are provided on selecting a method, in the form of a decision framework, that takes into consideration both user and provider needs. The framework comprises four main decision points: 1) consideration of the level of involvement of traditional-knowledge experts or the community that is required, 2) existing levels of traditional knowledge of climate forecasting and its level of cultural sensitivity, 3) the availability of long-term data—both traditional-knowledge and contemporary-forecast components, and 4) the level of resourcing available. No one method is suitable for everyone and every situation; however, the decision framework helps to select the most appropriate method for a given situation.
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Vo Van, Hoa, Tien Du Duc, Hung Mai Khanh, Lars Robert Hole, Duc Tran Anh, Huyen Luong Thi Thanh, and Quan Dang Dinh. "Assessment of Seasonal Winter Temperature Forecast Errors in the RegCM Model over Northern Vietnam." Climate 8, no. 6 (June 14, 2020): 77. http://dx.doi.org/10.3390/cli8060077.
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This study verified the seasonal six-month forecasts for winter temperatures for northern Vietnam in 1998–2018 using a regional climate model (RegCM4) with the boundary conditions of the climate forecast system Version 2 (CFSv2) from the National Centers for Environmental Prediction (NCEP). First, different physical schemes (land-surface process, cumulus, and radiation parameterizations) in RegCM4 were applied to generate 12 single forecasts. Second, the simple ensemble forecasts were generated through the combinations of those different physical formulations. Three subclimate regions (R1, R2, R3) of northern Vietnam were separately tested with surface observations and a reanalysis dataset (Japanese 55-year reanalysis (JRA55)). The highest sensitivity to the mean monthly temperature forecasts was shown by the land-surface parameterizations (the biosphere−atmosphere transfer scheme (BATS) and community land model version 4.5 (CLM)). The BATS forecast groups tended to provide forecasts with lower temperatures than the actual observations, while the CLM forecast groups tended to overestimate the temperatures. The forecast errors from single forecasts could be clearly reduced with ensemble mean forecasts, but ensemble spreads were less than those root-mean-square errors (RMSEs). This indicated that the ensemble forecast was underdispersed and that the direct forecast from RegCM4 needed more postprocessing.
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Baker, Sarah A., Andrew W. Wood, and Balaji Rajagopalan. "Application of Postprocessing to Watershed-Scale Subseasonal Climate Forecasts over the Contiguous United States." Journal of Hydrometeorology 21, no. 5 (May 2020): 971–87. http://dx.doi.org/10.1175/jhm-d-19-0155.1.
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AbstractSubseasonal to seasonal (S2S) climate forecasting has become a central component of climate services aimed at improving water management. In some cases, operational S2S climate predictions are translated into inputs for follow-on analyses or models, whereas the S2S predictions on their own may provide for qualitative situational awareness. At the spatial scales of water management, however, S2S climate forecasts often suffer from systematic biases, and low skill and reliability. This study assesses the potential to improve S2S forecast skill and salience for watershed applications through the use of postprocessing to harness skills in large-scale fields from the global climate model forecast outputs. To this end, the components-based technique—partial least squares regression (PLSR)—is used to improve the skill of biweekly temperature and precipitation forecasts from the Climate Forecast System version 2 (CFSv2). The PLSR method forms predictor components based on a cross-validated analysis of hindcasts from CFSv2 climate and land surface fields, and the results are benchmarked against raw CFSv2 forecasts, remapped to intermediate-scale watershed areas. We find that postprocessing affords marginal to moderate gains in skill in many watersheds, raising climate forecast skill above a usability threshold over the four seasons analyzed. In other locations, however, postprocessing fails to improve skill, particularly for extreme events, and can lead to unreliably narrow forecast ranges. This work presents evidence that the statistical postprocessing of climate forecast system outputs has potential to improve forecast skill, but that more thorough study of alternative approaches and predictors may be needed to achieve comprehensively positive outcomes.
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Harrison, Matthew T., Karen M. Christie, and Richard P. Rawnsley. "Assessing the reliability of dynamical and historical climate forecasts in simulating hindcast pasture growth rates." Animal Production Science 57, no. 7 (2017): 1525. http://dx.doi.org/10.1071/an16492.
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A priori knowledge of seasonal pasture growth rates helps livestock farmers plan with pasture supply and feed budgeting. Longer forecasts may allow managers more lead time, yet inaccurate forecasts could lead to counterproductive decisions and foregone income. By using climate forecasts generated from historical archives or the global circulation model (GCM) called the Predictive Ocean Atmosphere Model for Australia (POAMA), we simulated pasture growth rates in a whole-farm model and compared growth-rate forecasts with growth-rate hindcasts (viz. retrospective forecasts). Hindcast pasture growth rates were generated using posterior weather data measured at two sites in north-western Tasmania, Australia. Forecasts were made on a monthly basis for durations of 30, 60 and 90 days. Across sites, forecasting approaches and durations, there were no significant differences between simulated growth-rate forecasts and hindcasts when our statistical inference was conducted using either the Kolmogorov–Smirnov statistic or empirical cumulative distribution functions. However, given that both of these tests were calculated by comparing growth-rate hindcasts with monthly distributions of forecasts, we also examined linear correlations between monthly hindcast values and median monthly growth-rate forecasts. Using this approach, we found a higher correlation between hindcasts and median monthly forecasts for 30 days than for 60 or 90 days, suggesting that monthly growth-rate forecasts provide more skilful predictions than forecast durations of 2 or 3 months. The range in monthly growth-rate forecasts at 30 days was less than that at 60 or 90 days, further reinfocing the aforementioned result. The strength of the correlation between growth-rate hindcasts and median monthly forecasts from the historical approach was similar to that generated using POAMA data. Overall, the present study found that (1) statistical methods of comparing forecast data with hindcast data are important, particularly if the former is a distribution whereas the latter is a single value, (2) 1-month growth-rate forecasts have less uncertainty than forecast durations of 2 or 3 months, and (3) there is little difference between pasture growth rates simulated using climate data from either historical records or from GCMs. To test the generality of these conclusions, the study should be extended to other dairy regions. Including more regions would both enable studies of sites with greater intra-seasonal climate variability, but also better highlight the impact of seasonal and regional variation in forecast skill of POAMA as applied in our forecasting methods.
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Graham, Nicholas E., and Konstantine P. Georgakakos. "Toward Understanding the Value of Climate Information for Multiobjective Reservoir Management under Present and Future Climate and Demand Scenarios." Journal of Applied Meteorology and Climatology 49, no. 4 (April 1, 2010): 557–73. http://dx.doi.org/10.1175/2009jamc2135.1.
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Abstract Numerical simulation techniques and idealized reservoir management models are used to assess the utility of climate information for the effective management of a single multiobjective reservoir. Reservoir management considers meeting release and reservoir volume targets and minimizing wasteful spillage. The influence of reservoir size and inflow variability parameters on the management benefits is examined. The effects of climate and demand (release target) change on the management policies and performance are also quantified for various change scenarios. Inflow forecasts emulate ensembles of dynamical forecasts for a hypothetical climate system with somewhat predictable low-frequency variability. The analysis considers the impacts of forecast skill. The mathematical problem is cast in a dimensionless time and volume framework to allow generalization. The present work complements existing research results for specific applications and expands earlier analytical results for simpler management situations in an effort to draw general conclusions for the present-day reservoir management problem under uncertainty. The findings support the following conclusions: (i) reliable inflow forecasts are beneficial for reservoir management under most situations if adaptive management is employed; (ii) tolerance to forecasts of lower reliability tends to be higher for larger reservoirs; (iii) reliable inflow forecasts are most useful for a midrange of reservoir capacities; (iv) demand changes are more detrimental to reservoir management performance than inflow change effects of similar magnitude; (v) adaptive management is effective for mitigating climatic change effects and may even help to mitigate demand change effects.
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Kumar, Vinay, and T. N. Krishnamurti. "Improved Seasonal Precipitation Forecasts for the Asian Monsoon Using 16 Atmosphere–Ocean Coupled Models. Part I: Climatology." Journal of Climate 25, no. 1 (January 1, 2012): 39–64. http://dx.doi.org/10.1175/2011jcli4125.1.
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Abstract The goal of this study is to utilize several recent developments on rainfall data collection, downscaling of available climate models, training and forecasts from such models within the framework of a multimodel superensemble, and first a detailed examination of the seasonal climatology. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state-of-the-art coupled climate models. A downscaling component, with respect to observed rainfall estimates, uses a very dense Asian rain gauge network. This feature enables the forecasts of each model to be bias corrected to a common 25-km resolution. The downscaling statistics for each model, at each grid location, are developed during a training phase of the model forecasts. This is done wherever the observed rainfall estimates are available. In the “forecast phase,” the forecasts from all of the member models use the downscaling coefficients of the “training phase.” The downscaling and the extraction of the superensemble weights are done during the training phase. This makes use of the cross-validation principle. This means that the season to be forecasted is left out of the entire forecast dataset. Thus all of the statistics for downscaling and the superensemble construction are done separately for the forecasts of each season for all the years. The forecast phase is the season that is being forecast, where the aforementioned statistics are deployed for constructing the final downscaled superensemble. These forecasts are next used for the construction of a multimodel superensemble. The geographical distributions of the downscaling coefficients provide a first look at the systematic errors of the member model forecasts. This combination of multimodels, the vast rain gauge dataset, the downscaling, and the superensemble provides a major improvement for the rainfall climatology and anomalies for the forecast phase. One of the main results of this paper is on the improvement of rainfall climatology of the member models. The downscaled multimodel superensemble shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. This first part of the paper provides a description of the member models, the length of the training and forecast phases, the sensitivity of results as the numbers of forecast models are increased, and the skills of the downscaled climatology forecasts.
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Yuan, Xing, Eric F. Wood, Joshua K. Roundy, and Ming Pan. "CFSv2-Based Seasonal Hydroclimatic Forecasts over the Conterminous United States." Journal of Climate 26, no. 13 (July 1, 2013): 4828–47. http://dx.doi.org/10.1175/jcli-d-12-00683.1.
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AbstractThere is a long history of debate on the usefulness of climate model–based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%–70% of probabilistic streamflow forecasts are more skillful than climatology. All three approaches have plausible predictions of soil moisture drought frequency over the central United States out to 6 months, and climate models provide better results over the central and eastern United States. The R2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R2 of drought severity accumulated over CONUS is higher during winter, and climate models present added value, especially at long leads. This study indicates that climate models can provide better seasonal hydroclimatic forecasts than ESP through appropriate downscaling procedures, but significant improvements are dependent on the variables, seasons, and regions.
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An-Vo, Duc-Anh, Kate Reardon-Smith, Shahbaz Mushtaq, David Cobon, Shreevatsa Kodur, and Roger Stone. "Value of seasonal climate forecasts in reducing economic losses for grazing enterprises: Charters Towers case study." Rangeland Journal 41, no. 3 (2019): 165. http://dx.doi.org/10.1071/rj18004.
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Seasonal climate forecasts (SCFs) have the potential to improve productivity and profitability in agricultural industries, but are often underutilised due to insufficient evidence of the economic value of forecasts and uncertainty about their reliability. In this study we developed a bio-economic model of forecast use, explicitly incorporating forecast uncertainty. Using agricultural systems (ag-systems) production simulation software calibrated with case study information, we simulated pasture growth, herd dynamics and annual economic returns under different climatic conditions. We then employed a regret and value function approach to quantify the potential economic value of using SCFs (at both current and improved accuracy levels) in decision making for a grazing enterprise in north-eastern Queensland, Australia – a region subject to significant seasonal and intra-decadal climate variability. Applying an expected utility economic modelling approach, we show that skilled SCF systems can contribute considerable value to farm level decision making. At the current SCF skill of 62% (derived by correlating the El Niño Southern Oscillation (ENSO) signal and historical climate data) at Charters Towers, an average annual forecast value of AU$4420 (4.25%) was realised for the case study average annual net profit of AU$104000, while a perfect (no regret) forecast system could result in an increased return of AU$13475 per annum (13% of the case study average annual net profit). Continued improvements in the skill and reliability of SCFs is likely to both increase the value of SCFs to agriculture and drive wider uptake of climate forecasts in on-farm decision making. We also anticipate that an integrated framework, such as that developed in this study, may provide a pathway for better communication with end users to support improved understanding and use of forecasts in agricultural decision making and enhanced sustainability of agricultural enterprises.
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Hwang, Yeonsang, and Gregory J. Carbone. "Ensemble Forecasts of Drought Indices Using a Conditional Residual Resampling Technique." Journal of Applied Meteorology and Climatology 48, no. 7 (July 1, 2009): 1289–301. http://dx.doi.org/10.1175/2009jamc2071.1.
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Abstract The historical climate record and seasonal temperature and precipitation records provide useful datasets for making short-term drought predictions. A variety of methods have exploited these resources, but few have quantitatively measured uncertainties associated with predictions of drought index values commonly used in management plans. In this paper, stochastic approaches for estimating uncertainty are applied to drought index predictions. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) seasonal forecasts and resampling of nearest-neighbor residuals are incorporated to measure uncertainty in monthly forecasts of Palmer drought severity index (PDSI) and standardized precipitation index (SPI) in central South Carolina. Kuiper skill scores of PDSI indicate good forecast performance with up to 3-month lead time and improvements for 1-month-lead SPI forecasts. NOAA CPC climate outlook improved the forecast skill by as much as 40%, and the degree of improvement varies by season and forecast lead time.
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Shukla, S., and D. P. Lettenmaier. "Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill." Hydrology and Earth System Sciences 15, no. 11 (November 22, 2011): 3529–38. http://dx.doi.org/10.5194/hess-15-3529-2011.
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Abstract. Seasonal hydrologic forecasts derive their skill from knowledge of initial hydrologic conditions and climate forecast skill associated with seasonal climate outlooks. Depending on the type of hydrological regime and the season, the relative contributions of initial hydrologic conditions and climate forecast skill to seasonal hydrologic forecast skill vary. We seek to quantify these contributions on a relative basis across the Conterminous United States. We constructed two experiments – Ensemble Streamflow Prediction and reverse-Ensemble Streamflow Prediction – to partition the contributions of the initial hydrologic conditions and climate forecast skill to overall forecast skill. In ensemble streamflow prediction (first experiment) hydrologic forecast skill is derived solely from knowledge of initial hydrologic conditions, whereas in reverse-ensemble streamflow prediction (second experiment), it is derived solely from atmospheric forcings (i.e. perfect climate forecast skill). Using the ratios of root mean square error in predicting cumulative runoff and mean monthly soil moisture of each experiment, we identify the variability of the relative contributions of the initial hydrologic conditions and climate forecast skill spatially throughout the year. We conclude that the initial hydrologic conditions generally have the strongest influence on the prediction of cumulative runoff and soil moisture at lead-1 (first month of the forecast period), beyond which climate forecast skill starts to have greater influence. Improvement in climate forecast skill alone will lead to better seasonal hydrologic forecast skill in most parts of the Northeastern and Southeastern US throughout the year and in the Western US mainly during fall and winter months; whereas improvement in knowledge of the initial hydrologic conditions can potentially improve skill most in the Western US during spring and summer months. We also observed that at a short lead time (i.e. lead-1) contribution of the initial hydrologic conditions in soil moisture forecasts is more extensive than in cumulative runoff forecasts across the Conterminous US.
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Bolinger, Rebecca A., Andrew D. Gronewold, Keith Kompoltowicz, and Lauren M. Fry. "Application of the NMME in the Development of a New Regional Seasonal Climate Forecast Tool." Bulletin of the American Meteorological Society 98, no. 3 (March 1, 2017): 555–64. http://dx.doi.org/10.1175/bams-d-15-00107.1.
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ABSTRACT The National Oceanic and Atmospheric Administration’s Climate Prediction Center (CPC) provides access to a suite of real-time monthly climate forecasts that compose the North American Multi-Model Ensemble (NMME) in an attempt to meet the increasing demands for monthly to seasonal climate prediction. While the North American and global map-based forecasts provided by CPC are informative on a broad or continental scale, operational and decision-making institutions need products with a much more specific regional focus. To address this need, we developed a Region-Specific Seasonal Climate Forecast (RSCF–NMME) tool by combining NMME forecasts with regional climatological data. The RSCF–NMME automatically downloads and archives data and is displayed via a dynamic web-based graphical user interface. The tool has been applied to the Great Lakes region and utilized as part of operational water-level forecasting procedures by the U.S. Army Corps of Engineers, Detroit District (USACE-Detroit). Evaluation of the tool, compared with seasonal climate forecasts released by CPC, shows that the tool can provide additional useful information to users and overcomes some of the limitations of the CPC forecasts. The RSCF–NMME delivers details about a specific region’s climate, verification observations, and the ability to view different model forecasts. With its successful implementation within an operational environment, the tool has proven beneficial and thus set a precedent for expansion to other regions where there is a demand for region-specific seasonal climate forecasts.
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Sansom, Philip G., Christopher A. T. Ferro, David B. Stephenson, Lisa Goddard, and Simon J. Mason. "Best Practices for Postprocessing Ensemble Climate Forecasts. Part I: Selecting Appropriate Recalibration Methods." Journal of Climate 29, no. 20 (September 23, 2016): 7247–64. http://dx.doi.org/10.1175/jcli-d-15-0868.1.
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Abstract This study describes a systematic approach to selecting optimal statistical recalibration methods and hindcast designs for producing reliable probability forecasts on seasonal-to-decadal time scales. A new recalibration method is introduced that includes adjustments for both unconditional and conditional biases in the mean and variance of the forecast distribution and linear time-dependent bias in the mean. The complexity of the recalibration can be systematically varied by restricting the parameters. Simple recalibration methods may outperform more complex ones given limited training data. A new cross-validation methodology is proposed that allows the comparison of multiple recalibration methods and varying training periods using limited data. Part I considers the effect on forecast skill of varying the recalibration complexity and training period length. The interaction between these factors is analyzed for gridbox forecasts of annual mean near-surface temperature from the CanCM4 model. Recalibration methods that include conditional adjustment of the ensemble mean outperform simple bias correction by issuing climatological forecasts where the model has limited skill. Trend-adjusted forecasts outperform forecasts without trend adjustment at almost 75% of grid boxes. The optimal training period is around 30 yr for trend-adjusted forecasts and around 15 yr otherwise. The optimal training period is strongly related to the length of the optimal climatology. Longer training periods may increase overall performance but at the expense of very poor forecasts where skill is limited.
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Schneider, J. M., J. D. Garbrecht, and D. A. Unger. "A Heuristic Method for Time Disaggregation of Seasonal Climate Forecasts." Weather and Forecasting 20, no. 2 (April 1, 2005): 212–21. http://dx.doi.org/10.1175/waf839.1.
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Abstract To be immediately useful in practical applications that employ daily weather generators, seasonal climate forecasts issued for overlapping 3-month periods need to be disaggregated into a sequence of 1-month forecasts. Direct linear algebraic approaches to disaggregation produce physically unrealistic sequences of monthly forecasts. As an alternative, a heuristic method has been developed to disaggregate the NOAA/Climate Prediction Center (CPC) probability of exceedance seasonal precipitation forecasts, and tested on observed precipitation data for 1971–2000 for the 102 forecast divisions covering the contiguous United States. This simple method produces monthly values that replicate the direction and amplitude of variations on the 3-month time scale, and approach the amplitude of variations on the 1-month scale, without any unrealistic behavior. Root-mean-square errors between the disaggregated values and the actual precipitation over the 30-yr test period and all forecast divisions averaged 0.94 in., which is 39% of the mean monthly precipitation, and 58% of the monthly standard deviation. This method performs equally well across widely different precipitation regimes and does a reasonable job reproducing the sudden onset of strong seasonal variations such as the southwest U.S. monsoon.
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Chakraborty, Arindam, and T. N. Krishnamurti. "Improving Global Model Precipitation Forecasts over India Using Downscaling and the FSU Superensemble. Part II: Seasonal Climate." Monthly Weather Review 137, no. 9 (September 1, 2009): 2736–57. http://dx.doi.org/10.1175/2009mwr2736.1.
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Abstract This study addresses seasonal forecasts of rains over India using the following components: high-resolution rain gauge–based rainfall data covering the years 1987–2001, rain-rate initialization, four global atmosphere–ocean coupled models, a regional downscaling of the multimodel forecasts, and a multimodel superensemble that includes a training and a forecast phase at the high resolution over the internal India domain. The results of monthly and seasonal forecasts of rains for the member models and for the superensemble are presented here. The main findings, assessed via the use of RMS error, anomaly correlation, equitable threat score, and ranked probability skill score, are (i) high forecast skills for the downscaled superensemble-based seasonal forecasts compared to the forecasts from the direct use of large-scale model forecasts were possible; (ii) very high scores for rainfall forecasts have been noted separately for dry and wet years, for different regions over India and especially for heavier rains in excess of 15 mm day−1; and (iii) the superensemble forecast skills exceed that of the benchmark observed climatology. The availability of reliable measures of high-resolution rain gauge–based rainfall was central for this study. Overall, the proposed algorithms, added together, show very promising results for the prediction of monsoon rains on the seasonal time scale.
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Phan-Van, Tan, Thanh Nguyen-Xuan, Hiep Van Nguyen, Patrick Laux, Ha Pham-Thanh, and Thanh Ngo-Duc. "Evaluation of the NCEP Climate Forecast System and Its Downscaling for Seasonal Rainfall Prediction over Vietnam." Weather and Forecasting 33, no. 3 (April 25, 2018): 615–40. http://dx.doi.org/10.1175/waf-d-17-0098.1.
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Abstract This study investigates the ability to apply National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) products and their downscaling by using the Regional Climate Model version 4.2 (RegCM4.2) on seasonal rainfall forecasts over Vietnam. First, the CFS hindcasts (CFS_Rfc) from 1982 to 2009 are used to assess the ability of the CFS to predict the overall circulation and precipitation patterns at forecast lead times of up to 6 months. Second, the operational CFS forecasts (CFS_Ope) and its RegCM4.2 downscaling (RegCM_CFS) for the period 2012–14 are used to derive seasonal rainfall forecasts over Vietnam. The CFS_Rfc and CFS_Ope are validated against the ECMWF interim reanalysis, the Global Precipitation Climatology Centre (GPCC) analyzed rainfall, and observations from 150 meteorological stations across Vietnam. The results show that the CFS_Rfc can capture the seasonal variability of the Asian monsoon circulation and rainfall distribution. The higher-resolution RegCM_CFS product is advantageous over the raw CFS in specific climatic subregions during the transitional, dry, and rainy seasons, particularly in the northern part of Vietnam in January and in the country’s central highlands during July.
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Demirel, M. C., M. J. Booij, and A. Y. Hoekstra. "The skill of seasonal ensemble low flow forecasts for four different hydrological models." Hydrology and Earth System Sciences Discussions 11, no. 5 (May 23, 2014): 5377–420. http://dx.doi.org/10.5194/hessd-11-5377-2014.
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Abstract. This paper investigates the skill of 90 day low flow forecasts using two conceptual hydrological models and two data-driven models based on Artificial Neural Networks (ANNs) for the Moselle River. One data-driven model, ANN-Indicator (ANN-I), requires historical inputs on precipitation (P), potential evapotranspiration (PET), groundwater (G) and observed discharge (Q), whereas the other data-driven model, ANN-Ensemble (ANN-E), and the two conceptual models, HBV and GR4J, use forecasted meteorological inputs (P and PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low flow forecasts without any meteorological forecasts as input (ANN-I) and five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the other three models (GR4J, HBV and ANN-E). The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the four models are compared based on their skill of low flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict low flows using ensemble seasonal meteorological forcing. The largest range for 90 day low flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90 day ahead low flows in the very dry year 2003 without precipitation data, whereas ANN-I predicted the magnitude of the low flows better than the other three models. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Furthermore, the hit rate of ANN-E is higher than the two conceptual models for most lead times. However, ANN-I is not successful in distinguishing between low flow events and non-low flow events. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
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Tian, Fuqiang, Yilu Li, Tongtiegang Zhao, Hongchang Hu, Florian Pappenberger, Yunzhong Jiang, and Hui Lu. "Evaluation of the ECMWF System 4 climate forecasts for streamflow forecasting in the Upper Hanjiang River Basin." Hydrology Research 49, no. 6 (April 17, 2018): 1864–79. http://dx.doi.org/10.2166/nh.2018.176.
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Abstract This paper assesses the potential of the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 forecasts and investigates the post-processing precipitation to enhance the skill of streamflow forecasts. The investigation is based on hydrological modelling and is conducted through the case study of the Upper Hanjiang River Basin (UHRB). A semi-distributed hydrological model, TsingHua Representative Elementary Watershed (THREW), is implemented to simulate the rainfall–runoff processes, with the help of hydrological ensemble prediction system (HEPS) approach. A post-processing method, quantile mapping method, is applied to bias correct the raw precipitation forecasts. Then we evaluate the performance of raw and post-processed streamflow forecasts for the four hydrological stations along the mainstream of Hanjiang River from 2001 to 2008. The results show that the performance of the streamflow forecasts is greatly enhanced with post-processing precipitation forecasts, especially in pre-dry season (November and December), thus providing useful information for water supply management of the central route of South to North Water Diversion Project (SNWDP). The raw streamflow forecasts tend to overpredict and present similarly to forecast accuracy with the extended streamflow prediction (ESP) approach. Streamflow forecast skill is considerably improved when applying post-processing method to bias correct the ECMWF System 4 precipitation forecasts.