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1
Draper, D. W., and D. G. Long. "Simultaneous wind and rain retrieval using SeaWinds data." IEEE Transactions on Geoscience and Remote Sensing 42, no. 7 (July 2004): 1411–23. http://dx.doi.org/10.1109/tgrs.2004.830169.
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Nie, Congling, and David G. Long. "A C-Band Scatterometer Simultaneous Wind/Rain Retrieval Method." IEEE Transactions on Geoscience and Remote Sensing 46, no. 11 (November 2008): 3618–31. http://dx.doi.org/10.1109/tgrs.2008.922146.
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Alasgah, Abdusalam, Maria Jacob, Linwood Jones, and Larry Schneider. "Validation of the Hurricane Imaging Radiometer Forward Radiative Transfer Model for a Convective Rain Event." Remote Sensing 11, no. 22 (November 13, 2019): 2650. http://dx.doi.org/10.3390/rs11222650.
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The airborne Hurricane Imaging Radiometer (HIRAD) was developed to remotely sense hurricane surface wind speed (WS) and rain rate (RR) from a high-altitude aircraft. The approach was to obtain simultaneous brightness temperature measurements over a wide frequency range to independently retrieve the WS and RR. In the absence of rain, the WS retrieval has been robust; however, for moderate to high rain rates, the joint WS/RR retrieval has not been successful. The objective of this paper was to resolve this issue by developing an improved forward radiative transfer model (RTM) for the HIRAD cross-track viewing geometry, with separated upwelling and specularly reflected downwelling atmospheric paths. Furthermore, this paper presents empirical results from an unplanned opportunity that occurred when HIRAD measured brightness temperatures over an intense tropical squall line, which was simultaneously observed by a ground based NEXRAD (Next Generation Weather Radar) radar. The independently derived NEXRAD RR created the simultaneous 3D rain field “surface truth”, which was used as an input to the RTM to generate HIRAD modeled brightness temperatures. This paper presents favorable results of comparisons of theoretical and the simultaneous, collocated HIRAD brightness temperature measurements that validate the accuracy of this new HIRAD RTM.
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Owen, Michael P., and David G. Long. "M-ary Bayes Estimator Selection for QuikSCAT Simultaneous Wind and Rain Retrieval." IEEE Transactions on Geoscience and Remote Sensing 49, no. 11 (November 2011): 4431–44. http://dx.doi.org/10.1109/tgrs.2011.2143721.
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Ghada, Wael, Joan Bech, Nicole Estrella, Andreas Hamann, and Annette Menzel. "Weather Types Affect Rain Microstructure: Implications for Estimating Rain Rate." Remote Sensing 12, no. 21 (October 31, 2020): 3572. http://dx.doi.org/10.3390/rs12213572.
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Quantitative precipitation estimation (QPE) through remote sensing has to take rain microstructure into consideration, because it influences the relationship between radar reflectivity Z and rain intensity R. For this reason, separate equations are used to estimate rain intensity of convective and stratiform rain types. Here, we investigate whether incorporating synoptic scale meteorology could yield further QPE improvements. Depending on large-scale weather types, variability in cloud condensation nuclei and the humidity content may lead to variation in rain microstructure. In a case study for Bavaria, we measured rain microstructure at ten locations with laser-based disdrometers, covering a combined 18,600 h of rain in a period of 36 months. Rain was classified on a temporal scale of one minute into convective and stratiform based on a machine learning model. Large-scale wind direction classes were on a daily scale to represent the synoptic weather types. Significant variations in rain microstructure parameters were evident not only for rain types, but also for wind direction classes. The main contrast was observed between westerly and easterly circulations, with the latter characterized by smaller average size of drops and a higher average concentration. This led to substantial variation in the parameters of the radar rain intensity retrieval equation Z–R. The effect of wind direction on Z–R parameters was more pronounced for stratiform than convective rain types. We conclude that building separate Z–R retrieval equations for regional wind direction classes should improve radar-based QPE, especially for stratiform rain events.
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Zhang Liang, Huang Si-Xun, Zhong Jian, and Du Hua-Dong. "New GMF+RAIN model based on rain rate and application in typhoon wind retrieval." Acta Physica Sinica 59, no. 10 (2010): 7478. http://dx.doi.org/10.7498/aps.59.7478.
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Sapp, Joseph, Suleiman Alsweiss, Zorana Jelenak, Paul Chang, and James Carswell. "Stepped Frequency Microwave Radiometer Wind-Speed Retrieval Improvements." Remote Sensing 11, no. 3 (January 22, 2019): 214. http://dx.doi.org/10.3390/rs11030214.
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With the operational deployment of the *SFMR, hurricane reconnaissance and research aircraft provide near real-time observations of the 10 m ocean-surface wind-speed both within and around tropical cyclones. Hurricane specialists use these data to assist in determining wind radii and maximum sustained winds—critical parameters for determining and issuing watches and warnings. These observations are also used for post-storm analysis, model validation, and ground truth for aircraft- and satellite-based wind sensors. We present observations on the current operational wind-speed and rain-rate *SFMR retrieval procedures in the tropical cyclone environment and propose suggestions to improve them based on observed wind-speed biases. Using these new models in the *SFMR retrieval process, we correct an approximate 10% low bias in the wind-speed retrievals from 15 m / s –45 m / s with respect to *GPS dropwindsondes. In doing so, we eliminate the rain-contaminated wind-speed retrievals below 45/ h at tropical storm- and hurricane-force speeds present in the current operational model. We also update the *SFMR *RTM to include recent updates to smooth-ocean emissivity and atmospheric opacity models. All corrections were designed such that no changes to the current *SFMR calibration procedures are required.
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Klotz, Bradley W., and Eric W. Uhlhorn. "Improved Stepped Frequency Microwave Radiometer Tropical Cyclone Surface Winds in Heavy Precipitation." Journal of Atmospheric and Oceanic Technology 31, no. 11 (November 2014): 2392–408. http://dx.doi.org/10.1175/jtech-d-14-00028.1.
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AbstractSurface wind speeds retrieved from airborne stepped frequency microwave radiometer (SFMR) brightness temperature measurements are important for estimating hurricane intensity. The SFMR performance is highly reliable at hurricane-force wind speeds, but accuracy is found to degrade at weaker wind speeds, particularly in heavy precipitation. Specifically, a significant overestimation of surface wind speeds is found in these conditions, suggesting inaccurate accounting for the impact of rain on the measured microwave brightness temperature. In this study, the wind speed bias is quantified over a broad range of operationally computed wind speeds and rain rates, based on a large sample of collocated SFMR wind retrievals and global positioning system dropwindsonde surface-adjusted wind speeds. The retrieval bias is addressed by developing a new SFMR C-band relationship between microwave absorption and rain rate (κ−R) from National Oceanic and Atmospheric Administration WP-3D aircraft tail Doppler radar reflectivity and in situ Droplet Measurement Technologies Precipitation Imaging Probe measurements to more accurately model precipitation impacts. Absorption is found to be a factor of 2 weaker than is estimated by the currently operational algorithm. With this new κ–R relationship, surface wind retrieval bias is significantly reduced in the presence of rain at wind speeds weaker than hurricane force. At wind speeds greater than hurricane force where little bias exists, no significant change is found. Furthermore, maximum rain rates computed using the revised algorithm are around 50% greater than operational measurements, which is more consistent with maximum reflectivity-estimated rain rates in hurricanes.
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Hristova-Veleva, S. M., P. S. Callahan, R. S. Dunbar, B. W. Stiles, S. H. Yueh, J. N. Huddleston, S. V. Hsiao, et al. "Revealing the Winds under the Rain. Part I: Passive Microwave Rain Retrievals Using a New Observation-Based Parameterization of Subsatellite Rain Variability and Intensity—Algorithm Description." Journal of Applied Meteorology and Climatology 52, no. 12 (December 2013): 2828–48. http://dx.doi.org/10.1175/jamc-d-12-0237.1.
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AbstractScatterometer ocean surface winds have been providing very valuable information to researchers and operational weather forecasters for over 10 years. However, the scatterometer wind retrievals are compromised when rain is present. Merely flagging all rain-affected areas removes the most dynamic and interesting areas from the wind analysis. Fortunately, the Advanced Earth Observing Satellite II (ADEOS-II) mission carried a radiometer [the Advanced Microwave Scanning Radiometer (AMSR)] and a scatterometer, allowing for independent, collocated retrievals of rain. The authors developed an algorithm that uses AMSR observations to estimate the rain inside the scatterometer beam. This is the first in a series of papers that describe their approach to providing rain estimation and correction to scatterometer observations. This paper describes the retrieval algorithm and evaluates it using simulated data. Part II will present its validation when applied to AMSR observations. This passive microwave rain retrieval algorithm addresses the issues of nonuniform beam filling and hydrometeor uncertainty in a novel way by 1) using a large number of soundings to develop the retrieval database, thus accounting for the geographically varying atmospheric parameters; 2) addressing the spatial inhomogeneity of rain by developing multiple retrieval databases with different built-in inhomogeneity and rain intensity, along with a “rain indicator” to select the most appropriate database for each observed scene; 3) developing a new cloud-versus-rain partitioning that allows the use of a variety of drop size distribution assumptions to account for some of the natural variability diagnosed from the soundings; and 4) retrieving atmospheric and surface parameters just outside the rainy areas, thus providing information about the environment to help decrease the uncertainty of the rain estimates.
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Zhong, Jian, Si-Xun Huang, Jian-Fang Fei, Hua-Dong Du, and Liang Zhang. "Application of Tikhonov regularization method to wind retrieval from scatterometer data II: cyclone wind retrieval with consideration of rain." Chinese Physics B 20, no. 6 (June 2011): 064301. http://dx.doi.org/10.1088/1674-1056/20/6/064301.
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Owen, Michael P., and David G. Long. "Prior Selection for QuikSCAT Ultra-High Resolution Wind and Rain Retrieval." IEEE Transactions on Geoscience and Remote Sensing 51, no. 3 (March 2013): 1555–67. http://dx.doi.org/10.1109/tgrs.2012.2207904.
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Gao, Yuan, Jian Sun, Jie Zhang, and Changlong Guan. "Extreme Wind Speeds Retrieval Using Sentinel-1 IW Mode SAR Data." Remote Sensing 13, no. 10 (May 11, 2021): 1867. http://dx.doi.org/10.3390/rs13101867.
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With the improvement in microwave radar technology, spaceborne synthetic aperture radar (SAR) is widely used to observe the tropical cyclone (TC) wind field. Based on European Space Agency Sentinel-1 Interferometric Wide swath (IW) mode imagery, this paper evaluates the correlation between vertical transmitting–horizontal receiving (VH) polarization signals and extreme ocean surface wind speeds (>40 m/s) under strong TC conditions. A geophysical model function (GMF) Sentinel-1 IW mode wind retrieval model after noise removal (S1IW.NR) was proposed, according to the SAR images of nine TCs and collocated stepped frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) radiometer wind speed measurements. Through curve fitting and regression correction, the new GMF exploits the relationships between VH-polarization normalized radar cross section, incident angle, and wind speed in each sub-swath and covers wind speeds up to 74 m/s. Based on collocated SAR and SFMR measurements of four TCs, the new GMF was validated in the wind speed range from 2 to 53 m/s. Results show that the correlation coefficient, bias, and root mean squared error were 0.89, −0.89 m/s, and 4.13 m/s, respectively, indicating that extreme winds can be retrieved accurately by the new model. In addition, we investigated the relationship between the S1IW.NR wind retrieval bias and the SFMR-measured rain rate. The S1IW.NR model tended to overestimate wind speeds under high rain rates.
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Peng, Yihuan, Xuetong Xie, Mingsen Lin, Lishan Ran, Feng Yuan, Yuan Zhou, and Ling Tang. "A Study of Sea Surface Rain Identification Based on HY-2A Scatterometer." Remote Sensing 13, no. 17 (September 1, 2021): 3475. http://dx.doi.org/10.3390/rs13173475.
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Rain affects the wind measurement accuracy of the Ku-band spaceborne scatterometer. In order to improve the quality of the retrieved wind field, it is necessary to identify and flag rain-contaminated data. In this study, an HY-2A scatterometer is used to study rain identification. In addition to the conventional parameters, such as the retrieved wind speed, the wind direction relative to the along-track direction, and the normalized beam difference, the experiment expands the mean deviation of the backscattering coefficient, the beam difference between fore and aft, and the node number of the wind vector cell (WVC) as the sensitive parameters according to the microwave scattering characteristics of rain and the actual measurement situation of the HY-2A. Furthermore, a rain identification model for HY2 (HY2RRM) with the K-Nearest Neighborhood (KNN) algorithm was built. After several tests, the accuracy of the selected HY2RRM approach is found to about 88%, and about 70% of rain-contaminated data can be accurately identified. The research results are helpful for better understanding the characteristics of microwave backscattering and provide a possible way to further improve the wind field retrieval accuracy of the HY-2A scatterometer and other Ku-band scatterometers.
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Röhner, Luisa, and Katja Träumner. "Aspects of Convective Boundary Layer Turbulence Measured by a Dual-Doppler Lidar System." Journal of Atmospheric and Oceanic Technology 30, no. 9 (September 1, 2013): 2132–42. http://dx.doi.org/10.1175/jtech-d-12-00193.1.
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Abstract Special designed dual-Doppler setups can be used to retrieve simultaneous measurements of two wind components with high temporal resolution in several heights throughout the atmospheric boundary layer. During a field campaign in summer 2011, different scan strategies were performed to demonstrate the opportunities of obtaining variance profiles of the vertical and horizontal wind components in complex terrain. A simplified error analysis reveals the effects of the error propagation of the uncorrelated noise of the single lidar systems. A comparison shows that the course of the derived horizontal wind component is in accordance to in situ measurements. The dual-Doppler vertical wind velocity reflects the up- and downdrafts in a convective boundary layer and is even able to reflect a light rain event. The normalized profiles of the vertical velocity variances reproduce the well-known decrease from about one-third of the boundary layer height to its top. The horizontal velocity variance did not reveal a systematic behavior on the considered days.
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Sharma, Sanjay, Mahen Konwar, Diganta Kumar Sarma, M. C. R. Kalapureddy, and A. R. Jain. "Characteristics of Rain Integral Parameters during Tropical Convective, Transition, and Stratiform Rain at Gadanki and Its Application in Rain Retrieval." Journal of Applied Meteorology and Climatology 48, no. 6 (June 1, 2009): 1245–66. http://dx.doi.org/10.1175/2008jamc1948.1.
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Abstract In the present study the characteristics of rain integral parameters during tropical convective (C), transition (T), and stratiform (S) types of rain are studied with the help of Joss–Waldvogel disdrometer (JWD), L-band, and very-high-frequency wind profilers at Gadanki (13.5°N, 79.20°E). The classifications of three regimes are made with the help of an L-band wind profiler. For rain rate R < 10 mm h−1 larger drops are found in S type of rain relative to C and T rain, and for R ≥ 10 mm h−1 larger drops are found in convective rain. Empirical relations are developed for Dm–R, Dm–Z, N*0–R, Z–R, and Z/Dm–R by fitting the power-law equations. Event to event, no systematic variation of the coefficients and exponents could be found for Z–R and Z/Dm–R relations during the three types of rain. Overall, the C and S events are found to be number controlled, and T events are size controlled. During C type of rain, bigger mean raindrops are found during the presence of strong updrafts. During S type of rain, bigger mean raindrops are found to be associated with the higher mean thickness of the bright band and strong velocity gradient. For each of the developed empirical relations, the correlation coefficients are found in the order of T > C > S rain. During the three types of rain, correlations are found in the order of Z/Dm–R > Z–R > Dm–Z > Dm–R. Significant improvement is observed in rain retrieval by using the Z/Dm–R relation relative to the conventional Z–R relation. By utilizing the Z/Dm–R relations, the root-mean-square error was reduced by 19%–46%.
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Nijhuis, A. C. P. Oude, C. M. H. Unal, O. A. Krasnov, H. W. J. Russchenberg, and A. G. Yarovoy. "Velocity-Based EDR Retrieval Techniques Applied to Doppler Radar Measurements from Rain: Two Case Studies." Journal of Atmospheric and Oceanic Technology 36, no. 9 (September 2019): 1693–711. http://dx.doi.org/10.1175/jtech-d-18-0084.1.
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In this article, five velocity-based energy dissipation rate (EDR) retrieval techniques are assessed. The EDR retrieval techniques are applied to Doppler measurements from Transportable Atmospheric Radar (TARA)—a precipitation profiling radar—operating in the vertically fixed-pointing mode. A generalized formula for the Kolmogorov constant is derived, which gives potential for the application of the EDR retrieval techniques to any radar line of sight (LOS). Two case studies are discussed that contain rain events of about 2 and 18 h, respectively. The EDR values retrieved from the radar are compared to in situ EDR values from collocated sonic anemometers. For the two case studies, a correlation coefficient of 0.79 was found for the wind speed variance (WSV) EDR retrieval technique, which uses 3D wind vectors as input and has a total sampling time of 10 min. From this comparison it is concluded that the radar is able to measure EDR with a reasonable accuracy. Almost no correlation was found for the vertical wind velocity variance (VWVV) EDR retrieval technique, as it was not possible to sufficiently separate the turbulence dynamics contribution to the radar Doppler mean velocities from the velocity contribution of falling raindrops. An important cause of the discrepancies between radar and in situ EDR values is thus due to insufficient accurate estimation of vertical air velocities.
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Coto, Jonathan, W. Linwood Jones, and Gerald M. Heymsfield. "Validation of the High-Altitude Wind and Rain Airborne Profiler during the Tampa Bay Rain Experiment." Climate 9, no. 6 (May 29, 2021): 89. http://dx.doi.org/10.3390/cli9060089.
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This paper deals with the validation of rain rate and wind speed measurements from the High-Altitude Wind and Rain Airborne Profiler (HIWRAP), which occurred in September 2013 when the NASA Global Hawk unmanned aerial vehicle passed over an ocean rain squall line in the Gulf of Mexico near the North Florida coast. The three-dimensional atmospheric rain distribution and the associated ocean surface wind vector field were simultaneously measured by two independent remote sensing and two in situ systems, namely the ground-based National Weather Service Next-Generation Weather Radar (NEXRAD); the European Space Agency satellite Advanced Scatterometer (ASCAT), and two instrumented weather buoys. These independent measurements provided the necessary data to calibrate the HIWRAP radar using the measured ocean radar backscatter and to validate the HIWRAP rain and wind vector retrievals against NEXRAD, ASCAT and ocean buoys observations. In addition, this paper presents data processing procedures for the HIWRAP instrument, including the development of a geometric model to collocate time-morphed rain rates from the NEXRAD radar with HIWRAP atmospheric rain profiles. Results of the rain rate intercomparison are presented, and they demonstrate excellent agreement with the NEXRAD time-interpolated rain volume scans. In our analysis, we find that HIWRAP produces wind and rain rates that are consistent with the supporting ground and satellite estimates, thereby providing validation of the geolocation, the calibration, and the geophysical retrieval algorithms for the HIWRAP instrument.
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Xie, Xuetong, Jing Wang, and Mingsen Lin. "A Neural Network-Based Rain Effect Correction Method for HY-2A Scatterometer Backscatter Measurements." Remote Sensing 12, no. 10 (May 21, 2020): 1648. http://dx.doi.org/10.3390/rs12101648.
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The backscattering coefficients measured by Ku-band scatterometers are strongly affected by rainfall, resulting in a systematic error in sea surface wind field retrieval. In rainy conditions, the radar signals are subject to absorption by the raindrops in their round-trip propagation through the atmosphere, while the backscatter of raindrops raises the echo energy. In addition, raindrops give rise to roughness by impinging the ocean surface, resulting in an increase in the echo energy measured by a scatterometer. Under moderate wind conditions, the comprehensive impact of rainfall causes the wind speeds retrieved by the scatterometer to be higher than their actual values. The HY-2A scatterometer is a Ku-band, pencil-beam, conically scanning scatterometer. To correct the systematic error of the HY-2A scatterometer measurement in rainy conditions, a neural network model is proposed according to the characteristics of the backscatter coefficients measured by the HY-2A scatterometer in the presence of rain. With the neural network, the wind fields of the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data were used as the reference to correct the deviation in backscatter coefficients measured by the HY-2A scatterometer in rainy conditions, and the accuracy in wind speeds retrieved using the corrected backscatter coefficients was significantly improved. Compared with the cases of wind retrieval without rain effect correction, the wind speeds retrieved from the corrected backscatter coefficients by the neural network show a much lower systematic deviation, which indicates that the neural network can effectively remove the systematic deviation in the backscatter coefficients and the retrieved wind speeds caused by rain.
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Wang, Jin, Jie Zhang, and Jing Wang. "Sea surface wind speed retrieval under rain with the HY-2 microwave radiometer." Acta Oceanologica Sinica 36, no. 7 (June 30, 2017): 32–38. http://dx.doi.org/10.1007/s13131-017-1080-5.
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Hilburn, K. A., F. J. Wentz, D. K. Smith, and P. D. Ashcroft. "Correcting Active Scatterometer Data for the Effects of Rain Using Passive Radiometer Data." Journal of Applied Meteorology and Climatology 45, no. 3 (March 1, 2006): 382–98. http://dx.doi.org/10.1175/jam2357.1.
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Abstract A model for the effects of rain on scatterometer data is proposed. Data from the Advanced Microwave Scanning Radiometer (AMSR) and the SeaWinds scatterometer, both on the Midori-II satellite, are used. The model includes three basic rain effects: attenuation, rain roughening of the sea surface, and volumetric backscatter. Attenuation is calculated directly from the radiometer data and beam filling is explicitly addressed. The model simultaneously solves for both the rain roughening and volumetric backscatter. Fitting the coefficients of the model requires an estimate of the radar cross section because of wind roughening, and NCEP Global Data Assimilation System (GDAS) wind vectors are used for this purpose. Both the derived rain roughening and volumetric backscatter are similar to results in published work, but the values are slightly smaller, especially for vertical polarization. Drop size distribution variability is accounted for by formulating the radar equation in terms of the parameters of the radiative transfer equation and using additional radiometric information. Explicit inclusion of vertical profile variability results in an underdetermined problem, but it is implicitly included in fitting the model to the data. The correction makes large improvements in wind speeds and modest improvements in wind directions. Wind statistics and specific examples are shown to illustrate the nature of the improvements. The correction is limited, however, by measurement mismatch issues and the nonlinear nature of the wind retrieval.
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Unal, Christine. "High-Resolution Raindrop Size Distribution Retrieval Based on the Doppler Spectrum in the Case of Slant Profiling Radar." Journal of Atmospheric and Oceanic Technology 32, no. 6 (June 2015): 1191–208. http://dx.doi.org/10.1175/jtech-d-13-00225.1.
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AbstractDoppler spectra from vertically profiling radars are usually considered to retrieve the raindrop size distribution (DSD). However, to exploit both fall velocity spectrum and polarimetric measurements, Doppler spectra acquired in slant profiling mode should be explored. Rain DSD samples are obtained from simultaneously measured vertical and slant profile Doppler spectra and evaluated. In particular, the effect of the horizontal wind and the averaging time are investigated.The Doppler spectrum provides a way to retrieve the DSD, the radial wind, and a spectral broadening factor by means of a nonlinear optimization technique. For slant profiling of light rain when the horizontal wind is strong, the DSD results can be affected. Such an effect is demonstrated on a study case of stratiform light rain. Adding a wind profiler mode to the radar simultaneously supplies the horizontal wind and Doppler spectra. Before the retrieval procedure, the Doppler spectra are shifted in velocity to remove the mean horizontal wind contribution. The DSD results are considerably improved.Generally, averaged Doppler spectra are input into this type of algorithm. Instead, high-resolution, low-averaged Doppler spectra are chosen in order to take into account the small-scale variability of the rainfall. Investigating the linear relations at fixed median volume diameter, measured reflectivity-retrieved rainfall rate, for a slant beam, the consistency of the integrated parameters is established for two averaging periods. Nevertheless, the corresponding DSD parameter distributions reveal differences attributed to the averaging of the Doppler spectra.The new aspects are to obtain the same retrieval quality as vertically profiling and highly averaged spectra in an automated way.
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Morris, Mary, and Christopher S. Ruf. "A Coupled-Pixel Model (CPM) Atmospheric Retrieval Algorithm for High-Resolution Imagers." Journal of Atmospheric and Oceanic Technology 32, no. 10 (October 2015): 1866–79. http://dx.doi.org/10.1175/jtech-d-15-0016.1.
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AbstractLow-frequency passive microwave observations allow for oceanic remote sensing of surface wind speed and rain rate from spaceborne and airborne platforms. For most instruments, the modeling of contributions of rain absorption and reemission in a particular field of view is simplified by the observing geometry. However, the simplifying assumptions that can be applied in most applications are not always valid for the scenes that the airborne Hurricane Imaging Radiometer (HIRAD) regularly observes. Collocated Stepped Frequency Microwave Radiometer (SFMR) and HIRAD observations of Hurricane Earl (2010) indicate that retrieval algorithms based on the usual simplified model, referred to here as the decoupled-pixel model (DPM), are not able to resolve two neighboring rainbands at the edge of HIRAD’s swath. The DPM does not allow for the possibility that a single column of atmosphere can affect the observations at multiple cross-track positions. This motivates the development of a coupled-pixel model (CPM) that is developed and tested in this paper. Simulated observations as well as HIRAD’s observations of Hurricane Earl (2010) are used to test the CPM algorithm. Key to the performance of the CPM algorithm is its ability to deconvolve the cross-track scene, as well as unscramble the signatures of surface wind speed and rain rate in HIRAD’s observations. While the CPM approach was developed specifically for HIRAD, other sensors could employ this method in similar complicated observing scenarios.
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Hilburn, K. A., and F. J. Wentz. "Intercalibrated Passive Microwave Rain Products from the Unified Microwave Ocean Retrieval Algorithm (UMORA)." Journal of Applied Meteorology and Climatology 47, no. 3 (March 1, 2008): 778–94. http://dx.doi.org/10.1175/2007jamc1635.1.
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Abstract The Unified Microwave Ocean Retrieval Algorithm (UMORA) simultaneously retrieves sea surface temperature, surface wind speed, columnar water vapor, columnar cloud water, and surface rain rate from a variety of passive microwave radiometers including the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The rain component of UMORA explicitly parameterizes the three physical processes governing passive microwave rain retrievals: the beamfilling effect, cloud and rainwater partitioning, and effective rain layer thickness. Rain retrievals from the previous version of UMORA disagreed among different sensors and were too high in the tropics. These issues have been fixed with more realistic rain column heights and proper modeling of saturation and footprint-resolution effects in the beamfilling correction. The purpose of this paper is to describe the rain algorithm and its recent improvements and to compare UMORA retrievals with Goddard Profiling Algorithm (GPROF) and Global Precipitation Climatology Project (GPCP) rain rates. On average, TMI retrievals from UMORA agree well with GPROF; however, large differences become apparent when the instantaneous retrievals are compared on a pixel-to-pixel basis. The differences are due to fundamental algorithm differences. For example, UMORA generally retrieves higher total liquid water, but GPROF retrieves a higher surface rain rate for a given amount of total liquid water because of differences in microphysical assumptions. Comparison of UMORA SSM/I retrievals with GPCP shows similar spatial patterns, but GPCP has higher global averages because of greater amounts of precipitation in the extratropics. UMORA and GPCP have similar linear trends over the period 1988–2005 with similar spatial patterns.
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Pang, Shixuan, and Hartmut Graßl. "High-Frequency Single-Board Doppler Minisodar for Precipitation Measurements. Part I: Rainfall and Hail." Journal of Atmospheric and Oceanic Technology 22, no. 4 (April 1, 2005): 421–32. http://dx.doi.org/10.1175/jtech1706.1.
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Abstract A high-frequency Doppler sodar for precipitation measurements has been developed. Such a Doppler sodar (6–20 kHz) can almost always measure precipitation and turbulence spectra simultaneously. Therefore, the mean vertical wind and spectral broadening effects can be directly removed. As the acoustic refractive indices for ice and liquid water are almost the same, the acoustic retrieval of precipitation can also be applied to rain with small hail (e.g., diameter D < 10 mm) or large hail, but for the latter, neglecting the effects of different orientations and shapes of hailstones. The authors’ single-board minisodar is based on the digital signal processing (DSP) technique. The first prototype has been continuously operated at a coastal weather station since 25 October 2002. For stratiform rain events, the minisodar showed good agreement with a Joss–Waldvogel disdrometer and an optical rain gauge. However, for convective heavy showers, the minisodar always observed higher rain rates. The continuous, nonattended automatic operation of the minisodar has shown its capability for all kinds of precipitation measurements. The retrieval of precipitation rates for snow and graupel will be provided in a subsequent paper.
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Oude Nijhuis, Albert C. P., Felix J. Yanovsky, Oleg Krasnov, Christine M. H. Unal, Herman W. J. Russchenberg, and Alexander Yarovoy. "Assessment of the rain drop inertia effect for radar-based turbulence intensity retrievals." International Journal of Microwave and Wireless Technologies 8, no. 6 (June 9, 2016): 835–44. http://dx.doi.org/10.1017/s1759078716000660.
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A new model is proposed on how to account for the inertia of scatterers in radar-based turbulence intensity retrieval techniques. Rain drop inertial parameters are derived from fundamental physical laws, which are gravity, the buoyancy force, and the drag force. The inertial distance is introduced, which is a typical distance at which a particle obtains the same wind velocity as its surroundings throughout its trajectory. For the measurement of turbulence intensity, either the Doppler spectral width or the variance of Doppler mean velocities is used. The relative scales of the inertial distance and the radar resolution volume determine whether the variance of velocities is increased or decreased for the same turbulence intensity. A decrease can be attributed to the effect that inertial particles are less responsive to the variations of wind velocities. An increase can be attributed to inertial particles that have wind velocities corresponding to an average of wind velocities over their backward trajectories, which extend outside the radar resolution volume. Simulations are done for the calculation of measured radar velocity variance, given a 3-D homogeneous isotropic turbulence field, which provides valuable insight in the correct tuning of parameters for the new model.
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Didlake, Anthony C., Gerald M. Heymsfield, Lin Tian, and Stephen R. Guimond. "The Coplane Analysis Technique for Three-Dimensional Wind Retrieval Using the HIWRAP Airborne Doppler Radar." Journal of Applied Meteorology and Climatology 54, no. 3 (March 2015): 605–23. http://dx.doi.org/10.1175/jamc-d-14-0203.1.
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AbstractThe coplane analysis technique for mapping the three-dimensional wind field of precipitating systems is applied to the NASA High-Altitude Wind and Rain Airborne Profiler (HIWRAP). HIWRAP is a dual-frequency Doppler radar system with two downward-pointing and conically scanning beams. The coplane technique interpolates radar measurements onto a natural coordinate frame, directly solves for two wind components, and integrates the mass continuity equation to retrieve the unobserved third wind component. This technique is tested using a model simulation of a hurricane and compared with a global optimization retrieval. The coplane method produced lower errors for the cross-track and vertical wind components, while the global optimization method produced lower errors for the along-track wind component. Cross-track and vertical wind errors were dependent upon the accuracy of the estimated boundary condition winds near the surface and at nadir, which were derived by making certain assumptions about the vertical velocity field. The coplane technique was then applied successfully to HIWRAP observations of Hurricane Ingrid (2013). Unlike the global optimization method, the coplane analysis allows for a transparent connection between the radar observations and specific analysis results. With this ability, small-scale features can be analyzed more adequately and erroneous radar measurements can be identified more easily.
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Wang, Hui, Haiyang Qiu, Pengfei Zhi, Lei Wang, Wei Chen, Rizwan Akhtar, and Muhammad Asif Zahoor Raja. "Study of Algorithms for Wind Direction Retrieval from X-Band Marine Radar Images." Electronics 8, no. 7 (July 8, 2019): 764. http://dx.doi.org/10.3390/electronics8070764.
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After decades of research, X-band marine radars have been broadly used for wind measurement. For retrieving the wind direction based on the wind-induced streaks, a lot of effort has been expended on three celebrated approaches—the local gradient method (LGM), the adaptive reduced method (ARM), and the energy spectrum method (ESM). This paper presents a scientific study of these methods. The contrast of retrieving the real measured marine radar images and vane measured results is evaluated, in perspective of the error statistics and algorithm operation efficiency. Interference factors, such as the historical information of the measured area, reference wind speed, and sea condition showing in the monitoring equipment are also concerned. The tentative results showed that LGM is robust, which can be implemented in most radar images, because it allows for a lower selection of requirements compared with the other two methods. For ARM, the better retrieval performance is a tradeoff with extra computation, which is expensive. ESM is superior to the other two algorithms in terms of accuracy and computation load; however, this algorithm is sensitive in rain-contaminated radar images, meaning it is a good choice for data post-processing in the lab.
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Hong, Sungwook, Hwa-Jeong Seo, Nari Kim, and Inchul Shin. "Physical retrieval of tropical ocean surface wind speed under rain-free conditions using spaceborne microwave radiometers." Remote Sensing Letters 6, no. 5 (April 23, 2015): 380–89. http://dx.doi.org/10.1080/2150704x.2015.1037466.
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Fangohr, Susanne, and Elizabeth C. Kent. "An Estimate of Structural Uncertainty in QuikSCAT Wind Vector Retrievals." Journal of Applied Meteorology and Climatology 51, no. 5 (May 2012): 954–61. http://dx.doi.org/10.1175/jamc-d-11-0183.1.
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AbstractDifferences between Quick Scatterometer (QuikSCAT) level-2b wind vectors from the Jet Propulsion Laboratory [JPL; the Direction Interval Retrieval with Thresholded Nudging (DIRTH) product] and from the Remote Sensing Systems Co. (RSS; smoothed versions 3.0 and 4.0) for one sample month are presented. Each dataset is derived from the same observations, but processing methods result in differences between wind vectors. These differences originate from 1) uncertainty in the geophysical model functions that relate backscatter to wind, 2) noise in the backscatter measurements, and 3) spatial filtering. Statistics of wind vector differences from RSS and JPL are used as an indication of structural uncertainty in QuikSCAT wind retrievals. When grouped by 1 m s−1 bins, systematic differences are largest beyond 20 m s−1, where wind speeds from version 3.0 (version 4.0) of RSS can be more than 15 (10) m s−1 higher (lower) than JPL wind speeds. Below 20 m s−1, systematic differences on the order of tenths of a meter per second are attributed to differences in the retrieval methods, rain and ice contamination, and cross-swath position. Even once the recommended data flags are applied, differences in individual wind speed retrievals exceed 10 m s−1 in a few cases but are much smaller in regions of the swath for which the viewing geometry allows more reliable retrievals. In all parts of the swath, the standard deviations of the differences are smaller than 1.0 m s−1. The analyses provide a measure of the structural uncertainty in QuikSCAT wind velocity that is due to the retrieval process, although such comparisons are not able to determine which dataset is closest to the actual wind.
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Henocq, Claire, Jacqueline Boutin, Gilles Reverdin, François Petitcolin, Sabine Arnault, and Philippe Lattes. "Vertical Variability of Near-Surface Salinity in the Tropics: Consequences for L-Band Radiometer Calibration and Validation." Journal of Atmospheric and Oceanic Technology 27, no. 1 (January 1, 2010): 192–209. http://dx.doi.org/10.1175/2009jtecho670.1.
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Abstract Two satellite missions are planned to be launched in the next two years; the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Aquarius missions aim at detecting sea surface salinity (SSS) using L-band radiometry (1.4 GHz). At that frequency, the skin depth is on the order of 1 cm. However, the calibration and validation of L-band-retrieved SSS will be done with in situ measurements, mainly taken at 5-m depth. To anticipate and understand vertical salinity differences in the first 10 m of the ocean surface layer, in situ vertical profiles are analyzed. The influence of rain events is studied. Tropical Atmosphere Ocean (TAO) moorings, the most comprehensive dataset, provide measurements of salinity taken simultaneously at 1, 5, and 10 m and measurements of rain rate. Then, observations of vertical salinity differences, sorted according to their vertical levels, are expanded through the tropical band (30°S–30°N) using thermosalinographs (TSG), floats, expendable conductivity–temperature–depth (XCTD), and CTD data. Vertical salinity differences higher than 0.1 pss are observed in the Pacific, Atlantic, and Indian Oceans, mainly between 0° and 15°N, which coincides with the average position of the intertropical convergence zone (ITCZ). Some differences exceed 0.5 pss locally and persist for more than 10 days. A statistical approach is developed for the detection of large vertical salinity differences, knowing the history of rain events and the simultaneous wind intensity, as estimated from satellite measurements.
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Zhao, Ke, and Chaofang Zhao. "Evaluation of HY-2A Scatterometer Ocean Surface Wind Data during 2012–2018." Remote Sensing 11, no. 24 (December 11, 2019): 2968. http://dx.doi.org/10.3390/rs11242968.
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This study focuses on the evaluation of global Haiyang-2A satellite scatterometer (HSCAT) operational wind products from 2012 to 2018. In order to evaluate HSCAT winds, HSCAT operational wind products were collocated with buoy measurements and rainfall data. Error varieties under different atmospheric stratification and rainfall conditions were taken into consideration. After data quality control, the average bias and root mean square error (RMSE) between buoys and HSCAT data were 0.1 m/s and 1.3 m/s for wind speed, and 1° and 27° for wind direction, respectively. Especially, the varieties of the wind direction difference change a lot under non-neutral atmospheric conditions. HSCAT wind speeds are overestimated with an increasing rainfall rate while wind directions tend to be perpendicular to buoys’. In brief, the HSCAT wind product qualities are not stable during 2012 to 2018, especially for the data in 2015 and 2016. Atmospheric stratification and rain effects should be considered in wind retrieval and marine application.
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Aoki, Makoto, Hironori Iwai, Katsuhiro Nakagawa, Shoken Ishii, and Kohei Mizutani. "Measurements of Rainfall Velocity and Raindrop Size Distribution Using Coherent Doppler Lidar." Journal of Atmospheric and Oceanic Technology 33, no. 9 (September 2016): 1949–66. http://dx.doi.org/10.1175/jtech-d-15-0111.1.
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AbstractRainfall velocity, raindrop size distribution (DSD), and vertical wind velocity were simultaneously observed with 2.05- and 1.54-μm coherent Doppler lidars during convective and stratiform rain events. A retrieval method is based on identifying two separate spectra from the convolution of the aerosol and precipitation Doppler lidar spectra. The vertical wind velocity was retrieved from the aerosol spectrum peak and then the terminal rainfall velocity corrected by the vertical air motion from the precipitation spectrum peak was obtained. The DSD was derived from the precipitation spectrum using the relationship between the raindrop size and the terminal rainfall velocity. A comparison of the 1-min-averaged rainfall velocity from Doppler lidar measurements at a minimum range and that from a collocated ground-based optical disdrometer revealed high correlation coefficients of over 0.89 for both convective and stratiform rain events. The 1-min-averaged DSDs retrieved from the Doppler lidar spectrum using parametric and nonparametric methods are also in good agreement with those measured with the optical disdrometer with a correlation coefficient of over 0.80 for all rain events. To retrieve the DSD, the parametric method assumes a mathematical function for the DSD and the nonparametric method computes the direct deconvolution of the measured Doppler lidar spectrum without assuming a DSD function. It is confirmed that the Doppler lidar can retrieve the rainfall velocity and DSD during relatively heavy rain, whereas the ratio of valid data significantly decreases in light rain events because it is extremely difficult to separate the overlapping rain and aerosol peaks in the Doppler spectrum.
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Guimond, Stephen R., Lin Tian, Gerald M. Heymsfield, and Stephen J. Frasier. "Wind Retrieval Algorithms for the IWRAP and HIWRAP Airborne Doppler Radars with Applications to Hurricanes." Journal of Atmospheric and Oceanic Technology 31, no. 6 (June 1, 2014): 1189–215. http://dx.doi.org/10.1175/jtech-d-13-00140.1.
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Abstract Algorithms for the retrieval of atmospheric winds in precipitating systems from downward-pointing, conically scanning airborne Doppler radars are presented. The focus is on two radars: the Imaging Wind and Rain Airborne Profiler (IWRAP) and the High-Altitude IWRAP (HIWRAP). The IWRAP is a dual-frequency (C and Ku bands), multibeam (incidence angles of 30°–50°) system that flies on the NOAA WP-3D aircraft at altitudes of 2–4 km. The HIWRAP is a dual-frequency (Ku and Ka bands), dual-beam (incidence angles of 30° and 40°) system that flies on the NASA Global Hawk aircraft at altitudes of 18–20 km. Retrievals of the three Cartesian wind components over the entire radar sampling volume are described, which can be determined using either a traditional least squares or variational solution procedure. The random errors in the retrievals due to the airborne radar geometry and noise in the Doppler velocities are evaluated using both an error propagation analysis with least squares theory and a numerical simulation of a hurricane. These analyses show that the vertical and along-track wind errors have strong across-track dependence with values ranging from 0.25 m s−1 at nadir to 2.0 and 1.0 m s−1 at the swath edges, respectively. The average across-track wind errors are ~2.5 m s−1 or 7% of the hurricane wind speed. For typical rotated figure-four flight patterns through hurricanes, the zonal and meridional wind speed errors are ~1.5–2.0 m s−1. Evaluations of both retrieval methods show that the variational procedure is generally preferable to the least squares procedure. Examples of measured data retrievals from IWRAP during an eyewall replacement cycle in Hurricane Isabel (2003) and from HIWRAP during the development of Tropical Storm Matthew (2010) are shown. Comparisons of IWRAP-measured data retrievals at nadir to flight-level data show errors of ~2.0 m s−1 for vertical winds and ~4.0 m s−1 for horizontal wind speed (~7% of the hurricane wind speed). Additional sources of error, such as hydrometeor fall speed uncertainties and a small height offset in the comparisons, are likely responsible for the larger vertical wind errors when compared to the simulated error analyses.
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Oude Nijhuis, A. C. P., L. P. Thobois, F. Barbaresco, S. De Haan, A. Dolfi-Bouteyre, D. Kovalev, O. A. Krasnov, D. Vanhoenacker-Janvier, R. Wilson, and A. G. Yarovoy. "Wind Hazard and Turbulence Monitoring at Airports with Lidar, Radar, and Mode-S Downlinks: The UFO Project." Bulletin of the American Meteorological Society 99, no. 11 (November 2018): 2275–93. http://dx.doi.org/10.1175/bams-d-15-00295.1.
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AbstractThis article presents the prospects of measurement systems for wind hazards and turbulence at airports, which have been explored in the Ultrafast Wind Sensors (UFO) project. At France’s Toulouse–Blagnac Airport, in situ, profiling, and scanning sensors have been used to collect measurements, from which wind vectors and turbulence intensities are estimated. A scanning 1.5-µm coherent Doppler lidar and a solid state X-band Doppler radar have been developed with improved update rates, spatial resolution, and coverage. In addition, Mode-S data downlinks have been collected for data analysis. Wind vector and turbulence intensity retrieval techniques are applied to demonstrate the capabilities of these measurement systems. An optimal combination of remote measurement systems is defined for all weather monitoring at airports. In this combination, lidar and radar systems are complementary for clear-air and rainy conditions, which are formulated in terms of visibility and rain rate. The added value of the measurement systems for high-resolution numerical weather prediction models is estimated by an observing system experiment, and a positive impact on the local wind forecast is demonstrated.
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Park, S.-G., and Dong-Kyou Lee. "Retrieval of High-Resolution Wind Fields over the Southern Korean Peninsula Using the Doppler Weather Radar Network." Weather and Forecasting 24, no. 1 (February 1, 2009): 87–103. http://dx.doi.org/10.1175/2008waf2007084.1.
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Abstract The performance of a radar network for retrieving high-resolution wind fields over South Korea is examined. The network consists of a total of 18 operational radars. All of the radars possess the Doppler capability and carry out plan position indicator (PPI) volume scans comprising 6–15 elevation steps at every 6 or 10 min. An examination of the coverage of the radar network reveals that the radar network allows the retrieval of three-dimensional high-resolution wind fields over the entire area of the southern Korean Peninsula as well as nearby oceans above a height of approximately 3 km. After the quality control procedures of the radar measurements, the high-resolution wind fields (a few kilometers) are extracted using multiple-Doppler wind synthesis in the Custom Editing and Display of Reduced Information in Cartesian Space (CEDRIC) package developed by NCAR. The radar-retrieved winds are evaluated using the following two rain events: 1) Typhoon Ewiniar in 2006, which resulted in strong winds and heavy rainfall over the entire southern Korean Peninsula, and 2) a well-developed hook echo with a relatively small-scale diameter of about 30 km. The wind fields retrieved from the radar network exhibit counterclockwise rotation around the typhoon center and a general structure around a hook echo such as a cyclonically rotating updraft (i.e., mesocyclone). Comparisons with the wind measurements from four UHF wind profilers for the typhoon case reveal that the u- and υ-wind components retrieved from the radar network deviate by standard deviations of 3.6 and 4.5 m s−1 over ranges from −30 to 20 m s−1 and from 0 to 40 m s−1, respectively. Therefore, it is concluded that the operational radar network has the potential to provide three-dimensional high-resolution wind fields within the mesoscale precipitation systems over almost the entire area of the southern Korean Peninsula.
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Hong, Sungwook, Hwa-Jeong Seo, and Young-Joo Kwon. "A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis." Journal of Atmospheric and Oceanic Technology 33, no. 7 (July 2016): 1363–75. http://dx.doi.org/10.1175/jtech-d-15-0128.1.
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AbstractThis study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.
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Wei, Chih-Chiang, and Chen-Chia Hsu. "Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles." Remote Sensing 12, no. 14 (July 9, 2020): 2203. http://dx.doi.org/10.3390/rs12142203.
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The purpose of this study was to develop an optimal estimation model for rainfall rate retrievals using radar reflectivity, thereby gaining an effective grasp of rainfall information for disaster prevention uses. A process was designed for evaluating the optimal retrieval models using various dataset combinations with radar reflectivity and ground meteorological attributes. Various ground meteorological attributes (such as relative humidity, wind speed, precipitation, etc.) were obtained using the land-based weather stations affiliated with Taiwan’s Central Weather Bureau (CWB). This study used nine radar reflectivity provided by the Hualien weather surveillance radar station’s Volume Cover Pattern 21 system. The developed models are built using multiple machine learning algorithms, including linear regression (REG), support vector regression (SVR), and extreme gradient boosting (XGBoost), in addition to the Marshall–Palmer formula (MP). The study examined 14 typhoons that occurred from 2008 to 2017 at Chenggong station in southeast Taiwan, and Lanyu station in the outlying islands, and the top four major rainfall events were designated as test typhoons—Nanmadol (2011), Tembin (2012), Matmo (2014), and Nepartak (2016). The results indicated that for rainfall retrievals, radar reflectivity at a scanning (elevation) angle of 6.0° combined with ground meteorological attributes were the optimal input variables for the Chenggong station, whereas radar reflectivity at an elevation angle of 4.3° combined with ground meteorological attributes were optimal for the Lanyu station. In terms of model performance, XGBoost models had the lowest error index at Chenggong and Lanyu stations compared with MP, REG, and SVR models. XGBoost models at Lanyu station had the highest efficiency coefficient (0.903), and those at Chenggong station had the second highest (0.885). As a result, pairing the combination of optimal radar reflectivity and ground meteorological attributes, as verified by the evaluation process, with a high-efficiency algorithm (XGBoost) can effectively increase the accuracy of rainfall retrieval during typhoons.
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Meissner, Thomas, Lucrezia Ricciardulli, and Frank J. Wentz. "Capability of the SMAP Mission to Measure Ocean Surface Winds in Storms." Bulletin of the American Meteorological Society 98, no. 8 (August 1, 2017): 1660–77. http://dx.doi.org/10.1175/bams-d-16-0052.1.
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Abstract The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission was launched in January 2015 and has been providing science data since April 2015. Though designed to measure soil moisture, the SMAP radiometer has an excellent capability to measure ocean winds in storms at a resolution of 40 km with a swath width of 1,000 km. SMAP radiometer channels operate at a very low microwave frequency (L band, 1.41 GHz, 21.4 cm), which has good sensitivity to ocean surface wind speed even in very high winds and with very little impact by rain. This gives SMAP a distinct advantage over many spaceborne ocean wind sensors such as C-band [Advanced Scatterometer (ASCAT)] or Ku-band [Rapid Scatterometer (RapidScat)] scatterometers and radiometers operating at higher frequencies [Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), WindSat, Advanced Microwave Scanning Radiometer (AMSR), and Global Precipitation Measurement (GPM) Microwave Imager (GMI)], which either lose sensitivity at very high winds or degrade in rainy conditions. This article discusses the major features of a new ocean wind vector retrieval algorithm designed for SMAP. We compare SMAP wind fields in recent intense tropical cyclones with wind measurements from current scatterometer missions as well as WindSat. The most important validation source in hurricanes is the airborne stepped frequency microwave radiometer (SFMR), whose wind speeds are matched with SMAP in space and time. A comparison between SMAP and SFMR winds for eight storms in 2015, including Patricia, one of the strongest hurricanes ever recorded, shows excellent agreement up to 65 m s–1 without degradation in rain.
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Manaster, Andrew, Lucrezia Ricciardulli, and Thomas Meissner. "Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model." Remote Sensing 13, no. 12 (June 16, 2021): 2347. http://dx.doi.org/10.3390/rs13122347.
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A new data set of tropical cyclone winds (‘TC-winds’) through rain as observed by the WindSat and AMSR2 microwave radiometers has been developed by making use of a linear combination of C- and X-band frequency channels. These winds, along with tropical cyclone winds from the SMAP L-band radiometer, are compared with the Hurricane Weather Research and Forecasting (HWRF) model. Due to differences in spatial scales between the satellites and the high-resolution HWRF model, resampling must be performed on the model winds before comparisons are done. Various ways of spatial resampling are discussed in detail, and an optimal method is determined. Additionally, resampled model winds must be temporally interpolated to the time of the satellite before direct comparisons are made. This interpolation can occasionally result in un-physical 2D wind fields, especially for fast-moving storms. To assist users with this problem, a methodology for handling un-physical wind features is detailed. Results of overall comparisons between the satellites and HWRF for 19 storms between 2017 and 2020 displayed consistent storm features, with overall average biases less than 1 m/s and standard deviations below 4 m/s for all tropical cyclone winds between 10 and 60 m/s. Differences were seen when the comparisons were performed separately for the Atlantic and Pacific basins, with biases and standard deviations between the satellites and HWRF showing better agreement in the Atlantic. The impact of rain on the satellite wind retrievals is discussed, and no systematic bias was seen between the three sensors, despite the fact that they use different frequency channels in their tropical cyclone winds-through-rain retrieval algorithms.
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Kanofsky, Laura, and Phillip Chilson. "An Analysis of Errors in Drop Size Distribution Retrievals and Rain Bulk Parameters with a UHF Wind Profiling Radar and a Two-Dimensional Video Disdrometer." Journal of Atmospheric and Oceanic Technology 25, no. 12 (December 1, 2008): 2282–92. http://dx.doi.org/10.1175/2008jtecha1061.1.
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Abstract Vertically pointed wind profiling radars can be used to obtain measurements of the underlying drop size distribution (DSD) for a rain event by means of the Doppler velocity spectrum. Precipitation parameters such as rainfall rate, radar reflectivity factor, liquid water content, mass-weighted mean drop diameter, and median volume drop diameter can then be calculated from the retrieved DSD. The DSD retrieval process is complicated by the presence of atmospheric turbulence, vertical ambient air motion, selection of fall speed relationships, and velocity thresholding. In this note, error analysis is presented to quantify the effect of each of those factors on rainfall rate. The error analysis results are then applied to two precipitation events to better interpret the rainfall-rate retrievals. It was found that a large source of error in rain rate is due to unaccounted-for vertical air motion. For example, in stratiform rain with a rainfall rate of R = 10 mm h−1, a mesoscale downdraft of 0.6 m s−1 can result in a 34% underestimation of the estimated value of R. The fall speed relationship selection and source of air density information both caused negligible errors. Errors due to velocity thresholding become more important in the presence of significant contamination near 0 m s−1, such as ground clutter. If particles having an equivalent volume diameter of 0.8 mm and smaller are rejected, rainfall rate errors from −4% to −10% are possible, although these estimates depend on DSD and rainfall rate.
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Biscaro, Thiago S., and Carlos A. Morales. "Continental Passive Microwave-Based Rainfall Estimation Algorithm: Application to the Amazon Basin." Journal of Applied Meteorology and Climatology 47, no. 7 (July 1, 2008): 1962–81. http://dx.doi.org/10.1175/2007jamc1744.1.
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Abstract This paper presents a new statistical algorithm to estimate rainfall over the Amazon Basin region using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm relies on empirical relationships derived for different raining-type systems between coincident measurements of surface rainfall rate and 85-GHz polarization-corrected brightness temperature as observed by the precipitation radar (PR) and TMI on board the TRMM satellite. The scheme includes rain/no-rain area delineation (screening) and system-type classification routines for rain retrieval. The algorithm is validated against independent measurements of the TRMM–PR and S-band dual-polarization Doppler radar (S-Pol) surface rainfall data for two different periods. Moreover, the performance of this rainfall estimation technique is evaluated against well-known methods, namely, the TRMM-2A12 [the Goddard profiling algorithm (GPROF)], the Goddard scattering algorithm (GSCAT), and the National Environmental Satellite, Data, and Information Service (NESDIS) algorithms. The proposed algorithm shows a normalized bias of approximately 23% for both PR and S-Pol ground truth datasets and a mean error of 0.244 mm h−1 (PR) and −0.157 mm h−1 (S-Pol). For rain volume estimates using PR as reference, a correlation coefficient of 0.939 and a normalized bias of 0.039 were found. With respect to rainfall distributions and rain area comparisons, the results showed that the formulation proposed is efficient and compatible with the physics and dynamics of the observed systems over the area of interest. The performance of the other algorithms showed that GSCAT presented low normalized bias for rain areas and rain volume [0.346 (PR) and 0.361 (S-Pol)], and GPROF showed rainfall distribution similar to that of the PR and S-Pol but with a bimodal distribution. Last, the five algorithms were evaluated during the TRMM–Large-Scale Biosphere–Atmosphere Experiment in Amazonia (LBA) 1999 field campaign to verify the precipitation characteristics observed during the easterly and westerly Amazon wind flow regimes. The proposed algorithm presented a cumulative rainfall distribution similar to the observations during the easterly regime, but it underestimated for the westerly period for rainfall rates above 5 mm h−1. NESDIS1 overestimated for both wind regimes but presented the best westerly representation. NESDIS2, GSCAT, and GPROF underestimated in both regimes, but GPROF was closer to the observations during the easterly flow.
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Schirle, Claire E., Steven J. Cooper, Mareile Astrid Wolff, Claire Pettersen, Norman B. Wood, Tristan S. L’Ecuyer, Trond Ilmo, and Knut Nygård. "Estimation of Snowfall Properties at a Mountainous Site in Norway Using Combined Radar and In Situ Microphysical Observations." Journal of Applied Meteorology and Climatology 58, no. 6 (June 2019): 1337–52. http://dx.doi.org/10.1175/jamc-d-18-0281.1.
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AbstractThe ability of in situ snowflake microphysical observations to constrain estimates of surface snowfall accumulations derived from coincident, ground-based radar observations is explored. As part of the High-Latitude Measurement of Snowfall (HiLaMS) field campaign, a Micro Rain Radar (MRR), Precipitation Imaging Package (PIP), and Multi-Angle Snow Camera (MASC) were deployed to the Haukeliseter Test Site run by the Norwegian Meteorological Institute during winter 2016/17. This measurement site lies near an elevation of 1000 m in the mountains of southern Norway and houses a double-fence automated reference (DFAR) snow gauge and a comprehensive set of meteorological observations. MASC and PIP observations provided estimates of particle size distribution (PSD), fall speed, and habit. These properties were used as input for a snowfall retrieval algorithm using coincident MRR reflectivity measurements. Retrieved surface snowfall accumulations were evaluated against DFAR observations to quantify retrieval performance as a function of meteorological conditions for the Haukeliseter site. These analyses found differences of less than 10% between DFAR- and MRR-retrieved estimates over the field season when using either PIP or MASC observations for low wind “upslope” events. Larger biases of at least 50% were found for high wind “pulsed” events likely because of sampling limitations in the in situ observations used to constrain the retrieval. However, assumptions of MRR Doppler velocity for mean particle fall speed and a temperature-based PSD parameterization reduced this difference to +16% for the pulsed events. Although promising, these results ultimately depend upon selection of a snowflake particle model that is well matched to scene environmental conditions.
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Gibert, Ferran, Jacqueline Boutin, Wolfgang Dierking, Alba Granados, Yan Li, Eduard Makhoul, Junmin Meng, et al. "Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications." Remote Sensing 13, no. 14 (July 20, 2021): 2847. http://dx.doi.org/10.3390/rs13142847.
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This paper provides an overview of the Dragon 4 project dealing with operational monitoring of sea ice and sea surface salinity (SSS) and new product developments for altimetry data. To improve sea ice thickness retrieval, a new method was developed to match the Cryosat-2 radar waveform. Additionally, an automated sea ice drift detection scheme was developed and tested on Sentinel-1 data, and the sea ice drifty capability of Gaofen-4 geostationary optical data was evaluated. A second topic included implementation and validation of a prototype of a Fully-Focussed SAR processor adapted for Sentinel-3 and Sentinel-6 altimeters and evaluation of its performance with Sentinel-3 data over the Yellow Sea; the assessment of sea surface height (SSH), significant wave height (SWH), and wind speed measurements using different altimeters and CFOSAT SWIM; and the fusion of SSH measurements in mapping sea level anomaly (SLA) data to detect mesoscale eddies. Thirdly, the investigations on the retrieval of SSS include simulations to analyse the performances of the Chinese payload configurations of the Interferometric Microwave Radiometer and the Microwave Imager Combined Active and Passive, SSS retrieval under rain conditions, and the combination of active and passive microwave to study extreme winds.
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Guan, Ji-Ping, Yan-Tong Yin, Li-Feng Zhang, Jing-Nan Wang, and Ming-Yang Zhang. "Comparison Analysis of Total Precipitable Water of Satellite-Borne Microwave Radiometer Retrievals and Island Radiosondes." Atmosphere 10, no. 7 (July 12, 2019): 390. http://dx.doi.org/10.3390/atmos10070390.
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Total precipitable water (TPW) of satellite-borne microwave radiometer retrievals is compared with the data that were collected from 49 island radiosonde stations for the period 2007–2015. Great consistency was found between TPW measurements made by radiosonde and eight satellite-borne microwave radiometers, including SSMI-F13, SSMI-F14, SSMIS-F16, SSMIS-F17, AMSR-E, AMSR-2, GMI, and WindSat. Mean values of the TPW differences for eight satellites ranged from −0.51 to 0.38mm, both root mean square errors and standard deviations were around 3mm, and all of the correlation coefficients between satellite TPW retrievals and radiosonde TPW for each satellite can reach 0.99. Subsequently, an analysis of the comparison results was conducted, which revealed three problems in the satellite TPW retrieval and two problems in radiosonde data. For TPW retrievals of satellite, when the values are above 60 mm, the precision of TPW retrieval significantly decreases with a distinct dry bias, which can reach 4 mm; additionally, abias related to wind speed and the uncertainty with the TPW retrieval in the presence of rain, which is stronger than 1mm/h, was found. The TPW measurements of radiosonde made by the type of IM-MK3 from India were quite unreliable, and almost all of the radiosonde data during the daytime were plagued by a dry bias.
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Okamoto, Kozo, and John C. Derber. "Assimilation of SSM/I Radiances in the NCEP Global Data Assimilation System." Monthly Weather Review 134, no. 9 (September 1, 2006): 2612–31. http://dx.doi.org/10.1175/mwr3205.1.
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Abstract A technique for the assimilation of Special Sensor Microwave Imager (SSM/I) data in the National Centers for Environmental Prediction (NCEP) global data assimilation and forecast system is described. Because the radiative transfer model used does not yet allow for cloud/rain effects, it is crucial to properly identify and exclude (or correct) cloud/rain-contaminated radiances using quality control (QC) and bias correction procedures. The assimilation technique is unique in that both procedures take into account the effect of the liquid cloud on the difference between observed and simulated brightness temperature for each SSM/I channel. The estimate of the total column cloud liquid water from observed radiances is used in a frequency-dependent cloud detection component of the QC and as a predictor in the bias correction algorithm. Also, a microwave emissivity Jacobian model with respect to wind speed is developed for oceanic radiances. It was found that the surface wind information in the radiance data can be extracted through the emissivity model Jacobian rather than producing and including a separate SSM/I wind speed retrieval. A two-month-long data assimilation experiment from July to August 2004 using NCEP’s Gridpoint Statistical Interpolation analysis system and the NCEP operational forecast model was performed. In general, the assimilation of SSM/I radiance has a significant positive impact on the analyses and forecasts. Moisture is added in the Northern Hemisphere and Tropics and is slightly reduced in the Southern Hemisphere. The moisture added appears to be slightly excessive in the Tropics verified against rawinsonde observations. Nevertheless, the assimilation of SSM/I radiance data reduces model spinup of precipitation and substantially improves the dynamic fields, especially in measures of the vector wind error at 200 hPa in the Tropics. In terms of hurricane tracks, SSM/I radiance assimilation produces more cases with smaller errors and reduces the average error. No disruption of the Hadley circulation is found from the introduction of the SSM/I radiance data.
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Cecil, Daniel J., and Sayak K. Biswas. "Hurricane Imaging Radiometer (HIRAD) Wind Speed Retrievals and Validation Using Dropsondes." Journal of Atmospheric and Oceanic Technology 34, no. 8 (August 2017): 1837–51. http://dx.doi.org/10.1175/jtech-d-17-0031.1.
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AbstractSurface wind speed retrievals have been generated and evaluated using Hurricane Imaging Radiometer (HIRAD) measurements from flights over Hurricane Joaquin, Hurricane Patricia, Hurricane Marty, and the remnants of Tropical Storm Erika—all in 2015. Procedures are described here for producing maps of brightness temperature, which are subsequently used for retrievals of surface wind speed and rain rate across a ~50-km-wide swath for each flight leg. An iterative retrieval approach has been developed to take advantage of HIRAD’s measurement characteristics. Validation of the wind speed retrievals has been conducted, using 636 dropsondes released from the same WB-57 high-altitude aircraft carrying HIRAD during the Tropical Cyclone Intensity (TCI) experiment. The HIRAD wind speed retrievals exhibit very small bias relative to the dropsondes, for winds of tropical storm strength (17.5 m s−1) or greater. HIRAD has reduced sensitivity to winds weaker than tropical storm strength and a small positive bias (~2 m s−1). Two flights with predominantly weak winds according to the dropsondes have abnormally large errors from HIRAD and large positive biases. From the other flights, the root-mean-square differences between HIRAD and the dropsonde winds are 4.1 m s−1 (33%) for winds below tropical storm strength, 5.6 m s−1 (25%) for tropical storm–strength winds, and 6.3 m s−1 (16%) for hurricane-strength winds. The mean absolute differences for those three categories are 3.2 m s−1 (25%), 4.3 m s−1 (19%), and 4.8 m s−1 (12%), respectively, with a bias near zero for winds of tropical storm and hurricane strength.
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Zeng, Yong, Lianmei Yang, Zuyi Zhang, Zepeng Tong, Jiangang Li, Fan Liu, Jinru Zhang, and Yufei Jiang. "Characteristics of Clouds and Raindrop Size Distribution in Xinjiang, Using Cloud Radar Datasets and a Disdrometer." Atmosphere 11, no. 12 (December 21, 2020): 1382. http://dx.doi.org/10.3390/atmos11121382.
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Observation data from March to May 2020 of the Ka-band millimeter-wave cloud radar and disdrometer, located in Xinjiang, a typical arid region of China, were used to study the diurnal variation of clouds and precipitation, raindrop size distribution (DSD), and the physical parameters of raindrops. The results showed that there are conspicuous diurnal changes in clouds and precipitation. There is a decreasing trend of the cloud base height (CBH) from 05:00 to 19:00 CST (China Standard Time, UTC +8) and a rising trend of CBHs from 20:00 to 04:00 CST. The cloud top height (CTH) and the cloud thickness show a rising trend from 03:00 to 05:00 CST, 12:00 to 14:00 CST, and 20:00 to 01:00 CST. The diurnal variation of clouds is mainly driven by wind and temperature closely related to the topography of the study area. There are three apparent precipitation periods during the day, namely, 02:00–09:00 CST, 12:00 CST, and 17:00–21:00 CST. The changes in the physical parameters of raindrops are more drastic and evident with a lower CBH, lower CTH, and higher number of cloud layers from 12:00 to 21:00 CST than other times, which are closely related to day-to-day variations of systems moving through, and incoming solar radiation and the mountain–valley wind circulation caused by the trumpet-shaped topography that opens to the west played a secondary role. The DSD is in agreement with a normalized gamma distribution, and the value of the shape factor μ is significantly different from the fixed μ value in the Weather Research and Forecasting (WRF) Model. The rain in arid Xinjiang had a higher concentration of raindrops and a smaller average raindrop diameter than the rain in other humid regions of the Central and Southeast Asian continent. In the Z−R (radar reflectivity–rain rate) relationship, Z=249R1.20 is derived for stratiform rain, and it is significantly different from humid regions. Using Z/Dm (mass–weighted mean diameter) and R, a new empirical relationship Z/Dm=214R1.20 is established, and improvement is obtained in rain retrieval by using the Z/Dm−R relation relative to the conventional Z−R relation. Additionally, the Nt−R, Dm−R, Nw−R, and Nt−Nw relationships with larger differences from humid regions are established by fitting the power-law equations. These results are useful for improving the data parameters of microphysical processes of WRF and the accuracy of quantitative precipitation estimation in arid regions.
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Tong, Mingjing, and Ming Xue. "Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability." Monthly Weather Review 136, no. 5 (May 1, 2008): 1630–48. http://dx.doi.org/10.1175/2007mwr2070.1.
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Abstract The possibility of estimating fundamental parameters common in single-moment ice microphysics schemes using radar observations is investigated for a model-simulated supercell storm by examining parameter sensitivity and identifiability. These parameters include the intercept parameters for rain, snow, and hail/graupel, and the bulk densities of snow and hail/graupel. These parameters are closely involved in the definition of drop/particle size distributions of microphysical species but often assume highly uncertain specified values. The sensitivity of model forecast within data assimilation cycles to the parameter values, and the issue of solution uniqueness of the estimation problem, are examined. The ensemble square root filter (EnSRF) is employed for model state estimation. Sensitivity experiments show that the errors in the microphysical parameters have a larger impact on model microphysical fields than on wind fields; radar reflectivity observations are therefore preferred over those of radial velocity for microphysical parameter estimation. The model response time to errors in individual parameters are also investigated. The results suggest that radar data should be used at about 5-min intervals for parameter estimation. The response functions calculated from ensemble mean forecasts for all five individual parameters show concave shapes, with unique minima occurring at or very close to the true values; therefore, true values of these parameters can be retrieved at least in those cases where only one parameter contains error. The identifiability of multiple parameters together is evaluated from their correlations with forecast reflectivity. Significant levels of correlation are found that can be interpreted physically. As the number of uncertain parameters increases, both the level and the area coverage of significant correlations decrease, implying increased difficulties with multiple-parameter estimation. The details of the estimation procedure and the results of a complete set of estimation experiments are presented in Part II of this paper.
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Janowicz, J. R., S. L. Stuefer, K. Sand, and L. Leppänen. "Measuring winter precipitation and snow on the ground in northern polar regions." Hydrology Research 48, no. 4 (April 6, 2017): 884–901. http://dx.doi.org/10.2166/nh.2017.059.
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Measuring winter precipitation in cold and windy regions is recognized as a difficult task. Nonetheless, the accurate measurement of solid precipitation provides important input data for predicting snowmelt floods and avalanche danger and for monitoring climate change. The difficulties in measuring solid precipitation are associated with environmental factors and technological issues. Environmental factors that contribute to measurement errors include wind, freezing rain, rime, and a large range of solid particle shapes and sizes. Technological issues include gauge configuration, the need for remote, low-power-consumption operation, and difficult conditions for data transmission and retrieval. The objectives of this study were to review currently used gauges for measuring solid precipitation and snow on the ground, to summarize the positive and negative characteristics of each gauge, and to provide a discussion of best practices and design and performance criteria that might be used to stimulate research on new and/or improved precipitation gauges in Northern Research Basin (NRB) countries.
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Seto, Shinta, and Toshio Iguchi. "Rainfall-Induced Changes in Actual Surface Backscattering Cross Sections and Effects on Rain-Rate Estimates by Spaceborne Precipitation Radar." Journal of Atmospheric and Oceanic Technology 24, no. 10 (October 1, 2007): 1693–709. http://dx.doi.org/10.1175/jtech2088.1.
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Abstract In this study, the authors used Tropical Rainfall Measuring Mission precipitation radar (TRMM PR) data to investigate changes in the actual (attenuation corrected) surface backscattering cross section (σ0e) due to changes in surface conditions induced by rainfall, the effects of changes in σ0e on the path integrated attenuation (PIA) estimates by surface reference techniques (SRTs), and the effects on rain-rate estimates by the TRMM PR standard rain-rate retrieval algorithm. Over land, σ0e is statistically higher under rainfall than under no rainfall conditions (soil moisture effect) unless the land surface is densely covered by vegetation. Over ocean, the dependence of σ0e on the incident angle differs under rainfall and no-rainfall conditions (wind speed effect). The alongtrack spatial reference (ATSR) method, one of the SRTs used in the standard algorithm, partially considers these effects, while the temporal reference (TR) method, another SRT, never involves these effects; its PIA estimates thus have negative biases over land. In the hybrid spatial reference (HSR) method used over ocean, different incident angles create different biases in PIA estimates. If the TR method is replaced by the ATSR method, the monthly rainfall amount in July 2001 all over the land within the TRMM coverage increases by 0.70%. The bias in the HSR method over ocean can be mitigated by fitting a σ0–θ curve separately to smaller incident angles and to larger incident angles. This improvement increases or decreases the monthly rainfall amounts in individual incident angle regions by up to 10%.