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Relevant bibliographies by topics / Simultanous Wind/Rain Retrieval

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Academic literature on the topic 'Simultanous Wind/Rain Retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Simultanous Wind/Rain Retrieval"

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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Dissertations / Theses on the topic "Simultanous Wind/Rain Retrieval"

1

Allen, Jeffrey R. "An Analysis of SeaWinds Simultaneous Wind/Rain Retrieval in Severe Weather Events." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd704.pdf.

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Congling, Nie. "Wind/rain backscatter modeling and wind/rain retrieval for scatterometer and synthetic aperture radar /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2288.pdf.

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Nie, Congling. "Wind/Rain Backscatter Modeling and Wind/Rain Retrieval for Scatterometer and Synthetic Aperture Radar." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1632.

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Using co-located space-borne satellite (TRMM PR, ESCAT on ERS 1/2) measurements, and numerical predicted wind fields (ECMWF), the sensitivity of C-band backscatter measurement to rain is evaluated. It is demonstrated that C-band radar backscatter can be significantly altered by rain surface perturbation, an effect that has been previously neglected. A low-order wind/rain backscatter model is developed that has inputs of surface rain rate, incidence angle, wind speed, wind direction, and azimuth angle. The wind/rain backscatter model is accurate enough for describing the total backscatter in raining areas with relatively low variance. Rain has a more significant impact on measurements at high incidence angles than at low incidence angles. Using three distinct regimes, the conditions for which wind, rain, and both wind and rain can be retrieved from scatterometer backscatter measurements are determined. The effects of rain on ESCAT wind-only retrieval are evaluated. The additional scattering from rain causes estimated wind speeds to be biased high and estimated wind directions to be biased toward the along-track direction in heavy rains. To compensate for rain-induced backscatter, we develop a simultaneous wind/rain retrieval method (SWRR), which simultaneously estimates wind and rain from ESCAT backscatter measurements with an incidence angle of over 40 degrees. The performance of SWRR under typical wind/rain conditions is evaluated through simulation and validation with collocated TRMM PR and ECMWF data sets. SWRR is shown to significantly improve wind velocity estimates and the SWRR-estimated rain rate has relatively high accuracy in moderate to heavy rain cases. RADARSAT-1 ScanSAR SWA images of Hurricane Katrina are used to retrieve surface wind vectors over the ocean. Collocated H*wind wind directions are used as the wind direction estimate and the wind speed is derived from SAR backscatter measurements by inversion of a C-band HH-polarization Geophysical Model Function (GMF) that is derived from the VV-polarization GMF, CMOD5, using a polarization ratio model. Because existing polarization models do not fit the ScanSAR SWA data well, a recalibration model is proposed to recalibrate the ScanSAR SWA images. Validated with collocated H*wind wind speed estimates, the mean difference between SAR-retrieved and H*wind speed is small and the root mean square (RMS) error is below 4 m/s. Rain effects on the ScanSAR measurements are analyzed for three different incidence angle ranges using collocated ground-based Doppler weather radar (NEXRAD) rain measurements. Compared with the scatterometer-derived model, the rain-induced backscatter observed by the ScanSAR at incidence angles 44 to 45.7 degrees is consistent with the scatterometer-derived model when the polarization difference between HH and VV polarizations is considered.

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Amarin, Ruba. "HURRICANE WIND SPEED AND RAIN RATE RETRIEVAL ALGORITHM FOR THE STEPPED FREQUENCY MICROWAVE RADIOMETER." Master's thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3218.

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This thesis presents the development and validation of the Hurricane Imaging Retrieval Algorithm (HIRA) for the measurement of oceanic surface wind speed and rain rate in hurricanes. The HIRA is designed to process airborne microwave brightness temperatures from the NOAA, Stepped Frequency Microwave Radiometer (SFMR), which routinely collects data during NOAA hurricane hunter aircraft flights. SFMR measures wind speeds and rain rates at nadir only, but HIRA will soon be integrated with an improved surface wind speed model for expanded utilization with next generation microwave hurricane imagers, such as the Hurricane Imaging Radiometer (HIRad). HIRad will expand the nadir only measurements of SFMR to allow the measurement of hurricane surface winds and rain over a wide swath Results for the validation of HIRA retrievals are presented using SFMR brightness temperature data for 22 aircraft flights in 5 hurricanes during 2003-2005. Direct comparisons with the standard NOAA SFMR empirical algorithm provided excellent results for wind speeds up to 70 m/s. and rain rates up to 50 mm/hr.
M.S.E.E.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering

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Draper, David W. "Wind Scatterometry with Improved Ambiguity Selection and Rain Modeling." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/117.

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Although generally accurate, the quality of SeaWinds on QuikSCAT scatterometer ocean vector winds is compromised by certain natural phenomena and retrieval algorithm limitations. This dissertation addresses three main contributers to scatterometer estimate error: poor ambiguity selection, estimate uncertainty at low wind speeds, and rain corruption. A quality assurance (QA) analysis performed on SeaWinds data suggests that about 5% of SeaWinds data contain ambiguity selection errors and that scatterometer estimation error is correlated with low wind speeds and rain events. Ambiguity selection errors are partly due to the "nudging" step (initialization from outside data). A sophisticated new non-nudging ambiguity selection approach produces generally more consistent wind than the nudging method in moderate wind conditions. The non-nudging method selects 93% of the same ambiguities as the nudged data, validating both techniques, and indicating that ambiguity selection can be accomplished without nudging. Variability at low wind speeds is analyzed using tower-mounted scatterometer data. According to theory, below a threshold wind speed, the wind fails to generate the surface roughness necessary for wind measurement. A simple analysis suggests the existence of the threshold in much of the tower-mounted scatterometer data. However, the backscatter does not "go to zero" beneath the threshold in an uncontrolled environment as theory suggests, but rather has a mean drop and higher variability below the threshold. Rain is the largest weather-related contributer to scatterometer error, affecting approximately 4% to 10% of SeaWinds data. A simple model formed via comparison of co-located TRMM PR and SeaWinds measurements characterizes the average effect of rain on SeaWinds backscatter. The model is generally accurate to within 3 dB over the tropics. The rain/wind backscatter model is used to simultaneously retrieve wind and rain from SeaWinds measurements. The simultaneous wind/rain (SWR) estimation procedure can improve wind estimates during rain, while providing a scatterometer-based rain rate estimate. SWR also affords improved rain flagging for low to moderate rain rates. QuikSCAT-retrieved rain rates correlate well with TRMM PR instantaneous measurements and TMI monthly rain averages. SeaWinds rain measurements can be used to supplement data from other rain-measuring instruments, filling spatial and temporal gaps in coverage.

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Conference papers on the topic "Simultanous Wind/Rain Retrieval"

1

Congling Nie and David G. Long. "Simultaneous wind and rain retrieval for ERS scatterometer measurements." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423844.

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Alasgah, Abdusalam, Maria Jacob, and Linwood Jones. "Hurricane imaging radiometer (HIRAD) wind speed retrieval using radar rain rate." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127411.

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Yali Wang and Weimin Huang. "Wind direction retrieval from rain-contaminated X-band nautical radar images." In OCEANS 2015 - MTS/IEEE Washington. IEEE, 2015. http://dx.doi.org/10.23919/oceans.2015.7401900.

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Amarin, Ruba A., Linwood Jones, James Johnson, Chris Ruf, Tim Miller, and Shuyi Chen. "Hurricane imaging radiometer wind speed and rain rate retrieval: Part-2. Analysis of retrieval accuracy." In 2010 11th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2010). IEEE, 2010. http://dx.doi.org/10.1109/microrad.2010.5559560.

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Nie, Congling, and David G. Long. "RADARSAT ScanSAR Wind Retrieval and Rain Effects on ScanSAR Measurements Under Hurricane Conditions." In 2008 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779036.

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Ying Liu, Weimin Huang, and Eric W. Gill. "Analysis of the effects of rain on surface wind retrieval from X-band marine radar images." In OCEANS 2014. IEEE, 2014. http://dx.doi.org/10.1109/oceans.2014.7003161.

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7

El-Nimri, Salem, Linwood Jones, Eric Uhlhorn, Chris Ruf, and Peter Black. "Hurricane Imaging Radiometer wind speed and rain rate retrieval: [Part-1] development of an improved ocean emissivity model." In 2010 11th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2010). IEEE, 2010. http://dx.doi.org/10.1109/microrad.2010.5559579.

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