Utility of Optimal Reflectivity-Rain Rate (Z-R) Relationships for Improved Precipitation Estimates

2010 ◽  
Author(s):  
Chandra Pathak ◽  
Ramesh S. Teegavarapu
Keyword(s):  
Rain Rate ◽  
Precipitation Estimates

scholarly journals PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

2020 ◽  
Vol 21 (12) ◽  
pp. 2893-2906
Author(s):  
Phu Nguyen ◽  
Mohammed Ombadi ◽  
Vesta Afzali Gorooh ◽  
Eric J. Shearer ◽  
Mojtaba Sadeghi ◽  
...  
Keyword(s):  
The Netherlands ◽  
Real Time ◽  
Rain Rate ◽  
Web Based ◽  
Precipitation Patterns ◽  
Diurnal Cycles ◽  
Critical Success ◽  
Extensive Evaluation ◽  
Precipitation Estimation ◽  
Precipitation Estimates

AbstractThis study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

scholarly journals Four Decades of Intensifying Precipitation from Tropical Cyclones

2021 ◽  
Author(s):  
Eric Shearer ◽  
Phu Nguyen ◽  
Vesta Afzali Gorooh ◽  
Kuolin Hsu ◽  
Soroosh Sorooshian
Keyword(s):  
Rain Rate ◽  
Atlantic Multidecadal Oscillation ◽  
Temperature Data ◽  
Rate Model ◽  
Spatiotemporal Resolution ◽  
Hurricane Season ◽  
Precipitation Estimates ◽  
The Mean ◽  
Increasing Trends ◽  
Limited Coverage

Abstract In this study, the Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Dynamic Infrared Rain-rate model (PDIR) product, using a consistently measured 40-year archive of satellite-measured cloud-top infrared temperature data with a spatiotemporal resolution of 0.04° and 3-hourly as forcing data, is used to investigate the trends in TC precipitation from 1980 to 2019 using robust linear fitting over multi-year moving averages and accumulations of TC precipitation volume and rates. Trend analysis identifies significantly increasing trends of TC precipitation across intensity classifications. The mean and upper tail precipitation rates in hurricanes are shown to be rapidly increasing, with the greatest increases found in the most extreme precipitation rates of the strongest hurricanes. Increases in TC precipitation over land across TC intensities were observed. Increased counts of precipitation-containing pixels across of all intensities per TC are robustly shown across all but the strongest TC categories. Lastly, TC properties are significantly correlated with climate oscillation values from the Atlantic Multidecadal Oscillation (AMO), North American Oscillation (NAO), and the Oceanic Nino Index (ONI) prior to the start of the hurricane season. These results suggest that an increase in TC precipitation can be observed and that the previous lack of consensus can be attributed to data with low spatiotemporal resolution, limited temporal extents, or limited coverage i.e., exclusively over land.

scholarly journals A Comparison of Oceanic Precipitation Estimates in the Tropics and Subtropics

2009 ◽  
Vol 48 (7) ◽  
pp. 1335-1344 ◽  
Author(s):  
Kenneth P. Bowman ◽  
Cameron R. Homeyer ◽  
Dalon G. Stone
Keyword(s):  
Remote Sensing ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Geographical Patterns ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Environmental Laboratory ◽  
Remote Sensing Systems ◽  
Microwave Imager

Abstract A number of Earth remote sensing satellites are currently carrying passive microwave radiometers. A variety of different retrieval algorithms are used to estimate surface rain rates over the ocean from the microwave radiances observed by the radiometers. This study compares several different satellite algorithms with each other and with independent data from rain gauges on ocean buoys. The rain gauge data are from buoys operated by the NOAA Pacific Marine Environmental Laboratory. Potential errors and biases in the gauge data are evaluated. Satellite data are from the Tropical Rainfall Measuring Mission Microwave Imager and from the Special Sensor Microwave Imager instruments on the operational Defense Meteorological Satellite Program F13, F14, and F15 satellites. These data have been processed into rain-rate estimates by the NASA Precipitation Measurement Mission and by Remote Sensing Systems, Inc. Biases between the different datasets are estimated by computing differences between long-term time averages. Most of the satellite datasets agree with each other, and with the gauge data, to within 10% or less. The biases tend to be proportional to the mean rain rate, but the geographical patterns of bias vary depending on the choice of data source and algorithm. Some datasets, however, show biases as large as about 25%, so care should be taken when using these data for climatological studies.

scholarly journals Automated Identification of Enhanced Rainfall Rates Using the Near-Storm Environment for Radar Precipitation Estimates

2014 ◽  
Vol 15 (3) ◽  
pp. 1238-1254 ◽  
Author(s):  
Heather M. Grams ◽  
Jian Zhang ◽  
Kimberly L. Elmore
Keyword(s):  
Rain Rate ◽  
Flash Flood ◽  
Automated Identification ◽  
Freezing Level ◽  
Warm Rain ◽  
Basin Scale ◽  
Precipitation Estimates ◽  
Importance Analysis ◽  
Two Parameters ◽  
Rain Rates

Abstract Reliable and timely flash flood warnings are critically dependent on the accuracy of real-time rainfall estimates. Precipitation is not only the most vital input for basin-scale accumulation algorithms such as the Flash Flood Monitoring and Prediction (FFMP) program used operationally by the U.S. National Weather Service, but it is the primary forcing for hydrologic models at all scales. Quantitative precipitation estimates (QPE) from radar are widely used for such a purpose because of their high spatial and temporal resolution. However, converting the native radar variables into an instantaneous rain rate is fraught with challenges and uncertainties. This study addresses the challenge of identifying environments conducive for tropical rain rates, or rain rates that are enhanced by highly productive warm rain growth processes. Model analysis fields of various thermodynamic and moisture parameters were used as predictors in a decision tree–based ensemble to generate probabilities of warm rain–dominated drop growth. Variable importance analysis from the ensemble training showed that the probability accuracy was most dependent on two parameters in particular: freezing-level height and lapse rates of temperature. The probabilities were used to assign a tropical rain rate for hourly QPE and were evaluated against existing Z–R–based QPE products available to forecasters. The probability-based delineations showed improvement in QPE over the existing methods, but the two predictands tested had varying levels of performance for the storm types evaluated and require further study.

scholarly journals Error features of the hourly GSMaP multi-satellite precipitation estimates over nine major basins of China

2017 ◽  
Vol 49 (3) ◽  
pp. 761-779 ◽  
Author(s):  
Xianhui Tan ◽  
Bin Yong ◽  
Liliang Ren
Keyword(s):  
Rain Rate ◽  
Rainfall Events ◽  
Satellite Precipitation ◽  
Future Developments ◽  
Precipitation Estimates ◽  
Satellite Sensors ◽  
Almost All ◽  
Backward Propagation ◽  
Rain Rates ◽  
Hydrological Applications

Abstract As one of the current mainstream satellite precipitation estimates, the Global Satellite Mapping of Precipitation (GSMaP) system of Japan has been developed to produce high-precision and high-resolution global rainfall products by integrating almost all of the available precipitation-related satellite sensors. To quantify the error features of GSMaP estimates and understand their hydrological potentials at short temporal scale, three widely used GSMaP products (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) were comprehensively investigated at 1 hourly and 0.1° × 0.1° resolutions over nine major basins of China. Assessment results show that GSMaP_NRT apparently underestimates the rainfall amounts, while GSMaP_MVK with both forward and backward propagation processes algorithm can effectively capture the most rainfall events and has the lower error and bias. GSMaP_Gauge displays the best error stability and error structure over most basins of China and also exhibits stronger rain-rate dependencies. However, its unexpected positive biases in southeastern basins, which mainly come from the overestimation at lower rain rates, still need to improve further in future developments. We expected the results documented here can both provide the retrieval developers with some valuable references and offer hydrologic users of GSMaP data a better understanding of their error features and potential utilizations for various hydrological applications.

scholarly journals Measuring and modeling rainfall with terrestrial sensors and radar : implications for rainfall heterogeneity and discharge

2017 ◽  
Author(s):  
◽  
Micheal Joseph Simpson
Keyword(s):  
Rain Rate ◽  
Poor Performance ◽  
Hydrologic Model ◽  
Superior Performance ◽  
False Alarms ◽  
X Band ◽  
Physically Based ◽  
Precipitation Estimates ◽  
Radar Rain Rate ◽  
Quantitative Precipitation Estimates

Quantitative precipitation estimates are fundamental for hydrometeorological analyses. Radars provide a superior spatiotemporal advantage over terrestrial-based precipitation gauges to provide such measures of rainfall. However, many regions of the Continental US (CONUS) reside outside of the optimal range of weather radar surveillance (i.e., 100 km), with few studies analyzing the performance of radar rain rate estimates beyond this range. Several years' worth of radar rain rate estimates were analyzed from distant S-band Weather Surveillance Radars -- 1988 Doppler (WSR-88D) series, demonstrating poor performance in quantitative precipitation estimates (QPE's) at distances beyond 150 km. Furthermore, these S-band radar rain rates were determined to be significantly (p less than 0.10) less accurate in Quantitative Precipitation Estimates (QPE's) when compared to a locally-sited X-band dual polarimetric radar. In general, S-band radars were best utilized when implementing R(Z,ZDR) algorithms, with the bulk of QPE errors due to missed precipitation, whereas the X-band radar was superior overall with either R(Z,ZDR) or R(ZDR,KDP) with errors primarily due to false alarms. These radar QPE's were then used as input to a physically-based hydrologic model, Vflo, which showed superior performance over spatially-averaged rain gauges, which sometimes missed precipitation events. The results presented demonstrate the importance of choosing the proper radar QPE datasets for hydrometeorological studies.

scholarly journals Precipitation estimation over radar gap areas based on satellite and adjacent radar observations

2014 ◽  
Vol 7 (6) ◽  
pp. 6299-6325 ◽  
Author(s):  
Y.-R. Lee ◽  
D.-B. Shin ◽  
J.-H. Kim ◽  
H.-S. Park
Keyword(s):  
Rain Rate ◽  
A Priori ◽  
Geostationary Satellite ◽  
Observation Data ◽  
Satellite Precipitation ◽  
Brightness Temperatures ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Using Data ◽  
Radar Rain Rate

Abstract. Continuous rainfall measurements from ground-based radars are crucial for monitoring and forecasting heavy rainfall-related events such as floods and landslides. However, complete coverage by ground-based radars is often hampered by terrain blockage and beam-related errors. In this study, we presented a method to fill the radar gap using surrounding radar-estimated precipitation and observations from a geostationary satellite. The method first estimated the precipitation over radar gap areas using data from the Communication, Ocean, and Meteorological Satellite (COMS); the first geostationary satellite of Korea. The initial precipitation estimation from COMS was based on the rain rate-brightness temperature relationships of a priori databases. The databases were built with the temporally and spatially collocated brightness temperatures at four channels (3.7, 6.7, 10.8, and 12 μm) and Jindo (126.3° E, 34.5° N) radar rain rate observations. The databases were updated with collocated datasets in a timespan of approximately one hour prior to the designated retrieval. Then, bias correction based on an ensemble bias factor field (Tesfagiorgis et al., 2011b) from radar precipitation was applied to the estimated precipitation field. Over the radar gap areas, this method finally merged the bias corrected satellite precipitation with the radar precipitation obtained by interpolating the adjacent radar observation data. The merging was based on the optimal weights that were determined from the root-mean-square error (RMSE) with the reference sensor observation or equal weights in the absence of reference data. This method was tested for major precipitation events during the summer of 2011 with assumed radar gap areas. The results suggested that successful merging appears to be closely related to the quality of the satellite precipitation estimates.

scholarly journals Precipitation estimation over radar gap areas based on satellite and adjacent radar observations

2015 ◽  
Vol 8 (2) ◽  
pp. 719-728 ◽  
Author(s):  
Y.-R. Lee ◽  
D.-B. Shin ◽  
J.-H. Kim ◽  
H.-S. Park
Keyword(s):  
Rain Rate ◽  
A Priori ◽  
Geostationary Satellite ◽  
Observation Data ◽  
Satellite Precipitation ◽  
Brightness Temperatures ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Using Data ◽  
Radar Rain Rate

Abstract. Continuous rainfall measurements from ground-based radars are crucial for monitoring and forecasting heavy rainfall-related events such as floods and landslides. However, complete coverage by ground-based radars is often hampered by terrain blockage and beam-related errors. In this study, we presented a method to fill the radar gap using surrounding radar-estimated precipitation and observations from a geostationary satellite. The method first estimated the precipitation over radar gap areas using data from the Communication, Ocean, and Meteorological Satellite (COMS); the first geostationary satellite of Korea. The initial precipitation estimation from COMS was based on the rain rate-brightness temperature relationships of a priori databases. The databases were built with temporally and spatially collocated brightness temperatures at four channels (3.7, 6.7, 10.8, and 12 μm) and Jindo (126.3° E, 34.5° N) radar rain rate observations. The databases were updated with collocated data sets in a timespan of approximately one hour prior to the designated retrieval. Then, bias correction based on an ensemble bias factor field (Tesfagiorgis et al., 2011b) from radar precipitation was applied to the estimated precipitation field. Over the radar gap areas, this method finally merged the bias-corrected satellite precipitation with the radar precipitation obtained by interpolating the adjacent radar observation data. The merging was based on optimal weights determined from the root-mean-square error (RMSE) with the reference sensor observation or equal weights in the absence of reference data. This method was tested for major precipitation events during the summer of 2011 with assumed radar gap areas. The results suggested that successful merging appears to be closely related to the quality of the satellite precipitation estimates.

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