Near-Real-Time Applications of CloudSat Data

2008 ◽  
Vol 47 (7) ◽  
pp. 1982-1994 ◽  
Author(s):  
Cristian Mitrescu ◽  
Steven Miller ◽  
Jeffrey Hawkins ◽  
Tristan L’Ecuyer ◽  
Joseph Turk ◽  
...  
Keyword(s):  
Real Time ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Research Field ◽  
Naval Research ◽  
Earth Observing System ◽  
Quantitative Precipitation Estimation ◽  
The Earth ◽  
Precipitation Estimation ◽  
Support Of Research

Abstract Within 2 months of its launch in April 2006 as part of the Earth Observing System A-Train satellite constellation, the National Aeronautics and Space Administration Earth System Science Pathfinder (ESSP) CloudSat mission began making significant contributions toward broadening the understanding of detailed cloud vertical structures around the earth. Realizing the potential benefit of CloudSat to both the research objectives and operational requirements of the U.S. Navy, the Naval Research Laboratory coordinated early on with the CloudSat Data Processing Center to receive and process first-look 94-GHz Cloud Profiling Radar datasets in near–real time (4–8 h latency), thereby making the observations more relevant to the operational community. Applications leveraging these unique data, described herein, include 1) analysis/validation of cloud structure and properties derived from conventional passive radiometers, 2) tropical cyclone vertical structure analysis, 3) support of research field programs, 4) validation of numerical weather prediction model cloud fields, and 5) quantitative precipitation estimation in light rainfall regimes.

scholarly journals Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

2016 ◽  
Vol 97 (4) ◽  
pp. 621-638 ◽  
Author(s):  
Jian Zhang ◽  
Kenneth Howard ◽  
Carrie Langston ◽  
Brian Kaney ◽  
Youcun Qi ◽  
...  
Keyword(s):  
Weather Prediction ◽  
High Volume ◽  
Radar Data ◽  
Rain Gauge ◽  
Numerical Weather Prediction Model ◽  
Weather Radars ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Environmental Prediction ◽  
Very High

Abstract Rapid advancements of computer technologies in recent years made the real-time transferring and integration of high-volume, multisource data at a centralized location a possibility. The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction demonstrates such capabilities by integrating about 180 operational weather radars from the conterminous United States and Canada into a seamless national 3D radar mosaic with very high spatial (1 km) and temporal (2 min) resolution. The radar data can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. This paper provides an overview of the initial operating capabilities of MRMS QPE products.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

2020 ◽  
Vol 101 (3) ◽  
pp. E286-E302 ◽  
Author(s):  
Phu Nguyen ◽  
Eric J. Shearer ◽  
Mohammed Ombadi ◽  
Vesta Afzali Gorooh ◽  
Kuolin Hsu ◽  
...  
Keyword(s):  
Real Time ◽  
Rain Rate ◽  
Extreme Rainfall ◽  
Rate Model ◽  
Spatiotemporal Resolution ◽  
Atmospheric Rivers ◽  
Estimation Algorithms ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Products Analysis

Abstract Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (Tb) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) Tb–rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

Predicting Typhoon Morakot’s Catastrophic Rainfall with a Convection-Permitting Mesoscale Ensemble System

2010 ◽  
Vol 25 (6) ◽  
pp. 1816-1825 ◽  
Author(s):  
Fuqing Zhang ◽  
Yonghui Weng ◽  
Ying-Hwa Kuo ◽  
Jeffery S. Whitaker ◽  
Baoguo Xie
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Decision Makers ◽  
Extreme Weather Events ◽  
Management Decision ◽  
Typhoon Morakot ◽  
Weather Events ◽  
Flooding Event

Abstract This study examines the prediction and predictability of the recent catastrophic rainfall and flooding event over Taiwan induced by Typhoon Morakot (2009) with a state-of-the-art numerical weather prediction model. A high-resolution convection-permitting mesoscale ensemble, initialized with analysis and flow-dependent perturbations obtained from a real-time global ensemble data assimilation system, is found to be able to predict this record-breaking rainfall event, producing probability forecasts potentially valuable to the emergency management decision makers and the general public. Since all the advanced modeling and data assimilation techniques used here are readily available for real-time operational implementation provided sufficient computing resources are made available, this study demonstrates the potential and need of using ensemble-based analysis and forecasting, along with enhanced computing, in predicting extreme weather events like Typhoon Morakot at operational centers.

scholarly journals Understanding Winter Precipitation Impacts on Automated Gauge Observations within a Real-Time System

2015 ◽  
Vol 16 (6) ◽  
pp. 2345-2363 ◽  
Author(s):  
Steven M. Martinaitis ◽  
Stephen B. Cocks ◽  
Youcun Qi ◽  
Brian T. Kaney ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Bias Correction ◽  
Ground Truth ◽  
Winter Precipitation ◽  
Correction Factors ◽  
Time System ◽  
Real Time System ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Precipitation Gauge

Abstract Precipitation gauge observations are routinely classified as ground truth and are utilized in the verification and calibration of radar-derived quantitative precipitation estimation (QPE). This study quantifies the challenges of utilizing automated hourly gauge networks to measure winter precipitation within the real-time Multi-Radar Multi-Sensor (MRMS) system from 1 October 2013 to 1 April 2014. Gauge observations were compared against gridded radar-derived QPE over the entire MRMS domain. Gauges that reported no precipitation were classified as potentially stuck in the MRMS system if collocated hourly QPE values indicated nonzero precipitation. The average number of potentially stuck gauge observations per hour doubled in environments defined by below-freezing surface wet-bulb temperatures, while the average number of observations when both the gauge and QPE reported precipitation decreased by 77%. Periods of significant winter precipitation impacts resulted in over a thousand stuck gauge observations, or over 10%–18% of all gauge observations across the MRMS domain, per hour. Partial winter impacts were observed prior to the gauges becoming stuck. Simultaneous postevent thaw and precipitation resulted in unreliable gauge values, which can introduce inaccurate bias correction factors when calibrating radar-derived QPE. The authors then describe a methodology to quality control (QC) gauge observations compromised by winter precipitation based on these results. A comparison of two gauge instrumentation types within the National Weather Service (NWS) Automated Surface Observing System (ASOS) network highlights the need for improved gauge instrumentation for more accurate liquid-equivalent values of winter precipitation.

Time-Expanded Sampling for Ensemble-Based Data Assimilation Applied to Conventional and Satellite Observations

2015 ◽  
Vol 30 (4) ◽  
pp. 855-872 ◽  
Author(s):  
Qingyun Zhao ◽  
Qin Xu ◽  
Yi Jin ◽  
Justin McLay ◽  
Carolyn Reynolds
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Naval Research ◽  
Ensemble Size ◽  
Nested Grids ◽  
Effectiveness And Efficiency ◽  
Time Critical ◽  
Computational Resources ◽  
Operational Constraints

Abstract The time-expanded sampling (TES) method, designed to improve the effectiveness and efficiency of ensemble-based data assimilation and subsequent forecast with reduced ensemble size, is tested with conventional and satellite data for operational applications constrained by computational resources. The test uses the recently developed ensemble Kalman filter (EnKF) at the Naval Research Laboratory (NRL) for mesoscale data assimilation with the U.S. Navy’s mesoscale numerical weather prediction model. Experiments are performed for a period of 6 days with a continuous update cycle of 12 h. Results from the experiments show remarkable improvements in both the ensemble analyses and forecasts with TES compared to those without. The improvements in the EnKF analyses by TES are very similar across the model’s three nested grids of 45-, 15-, and 5-km grid spacing, respectively. This study demonstrates the usefulness of the TES method for ensemble-based data assimilation when the ensemble size cannot be sufficiently large because of operational constraints in situations where a time-critical environment assessment is needed or the computational resources are limited.

scholarly journals Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

2020 ◽  
Vol 12 (14) ◽  
pp. 2337
Author(s):  
Wonsook S. Ha ◽  
George R. Diak ◽  
Witold F. Krajewski
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Numerical Weather Prediction Model ◽  
Sensing Data ◽  
Numerical Weather ◽  
Spatio Temporal

This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation ( R n ) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived R n (GOES- R n ) data were incorporated into each dataset for comparison with the RAP and HRRR R n -based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr−1) of 0.08, and a mean bias (mm hr−1) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.

scholarly journals A Physically Based Multisensor Quantitative Precipitation Estimation Approach for Gap-Filling Radar Coverage

2020 ◽  
Vol 21 (7) ◽  
pp. 1485-1511
Author(s):  
Steven M. Martinaitis ◽  
Andrew P. Osborne ◽  
Micheal J. Simpson ◽  
Jian Zhang ◽  
Kenneth W. Howard ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Monthly Precipitation ◽  
Atmospheric Conditions ◽  
Spatiotemporal Resolution ◽  
Mountainous Regions ◽  
Quantitative Precipitation Estimation ◽  
Statistical Measures ◽  
Precipitation Regimes ◽  
Physically Based ◽  
Precipitation Estimation

AbstractWeather radars and gauge observations are the primary observations to determine the coverage and magnitude of precipitation; however, radar and gauge networks have significant coverage gaps, which can underrepresent or even miss the occurrence of precipitation. This is especially noticeable in mountainous regions and in shallow precipitation regimes. The following study presents a methodology to improve spatial representations of precipitation by seamlessly blending multiple precipitation sources within the Multi-Radar Multi-Sensor (MRMS) system. A high spatiotemporal resolution multisensor merged quantitative precipitation estimation (QPE) product (MSQPE) is generated by using gauge-corrected radar QPE as a primary precipitation source with a combination of hourly gauge observations, monthly precipitation climatologies, numerical weather prediction short-term precipitation forecasts, and satellite observations to use in areas of insufficient radar coverage. The merging of the precipitation sources is dependent upon radar coverage based on an updated MRMS radar quality index, surface and atmospheric conditions, topography, gauge locations, and precipitation values. Evaluations of the MSQPE product over the western United States resulted in improved statistical measures over its individual input precipitation sources, particularly the locally gauge-corrected radar QPE. The MSQPE scheme demonstrated its ability to sufficiently fill in areas where radar alone failed to detect precipitation due to significant beam blockage or poor coverage while minimizing the generation of false precipitation and underestimation biases that resulted from radar overshooting precipitation.

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