Statewide real-time quantitative precipitation estimation using weather radar and NWP model analysis: Algorithm description and product evaluation

2020 ◽  
Vol 132 ◽  
pp. 104791
Author(s):  
Bong-Chul Seo ◽  
Witold F. Krajewski
Keyword(s):  
Real Time ◽  
Weather Radar ◽  
Product Evaluation ◽  
Model Analysis ◽  
Analysis Algorithm ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Nwp Model

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 Application of Attenuation Correction to Quantitative Precipitation Estimation on C-Band Weather Radar in Bengkulu

2021 ◽  
Vol 893 (1) ◽  
pp. 012054
Author(s):  
M F Handoyo ◽  
M P Hadi ◽  
S Suprayogi
Keyword(s):  
Attenuation Correction ◽  
Estimation Error ◽  
Weather Radar ◽  
Radar Data ◽  
Signal Interference ◽  
Active System ◽  
Estimation Errors ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract A weather radar is an active system remote sensing tool that observes precipitation indirectly. Weather radar has an advantage in estimating precipitation because it has a high spatial resolution (up to 0.5 km). Reflectivity generated by weather radar still has signal interference caused by attenuation factors. Attenuation causes the Quantitative Precipitation Estimation (QPE) by the C-band weather radar to underestimate. Therefore attenuation correction on C-band weather radar is needed to eliminate precipitation estimation errors. This study aims to apply attenuation correction to determine Quantitative Precipitation Estimation (QPE) on the c-band weather radar in Bengkulu in December 2018. Gate-by-gate method attenuation correction with Kraemer approach has applied to c-band weather radar data from the Indonesian Agency for Meteorology and Geophysics (BMKG) weather radar network Bengkulu. This method uses reflectivity as the only input. Quantitative Precipitation Estimation (QPE) has obtained by comparing weather radar-based rain estimates to 10 observation rain gauges over a month with the Z-R relation equation. Root Mean Square Error (RMSE) is used to calculate the estimation error. Weather radar data are processed using Python-based libraries Wradlib and ArcGIS 10.5. As a result, the calculation between the weather radar estimate precipitation and the observed rainfall obtained equation Z=2,65R1,3. The attenuation correction process with Kreamer's approach on the c-band weather radar has reduced error in the Qualitative Precipitation Estimation (QPE). Corrected precipitation has a smaller error value (r = 0.88; RMSE = 8.38) than the uncorrected precipitation (r = 0.83; RMSE = 11.70).

An Operational Weather Radar-Based Quantitative Precipitation Estimation and its Application in Catchment Water Resources Modeling

2011 ◽  
Vol 10 (1) ◽  
pp. 8-24 ◽  
Author(s):  
Xin He ◽  
Flemming Vejen ◽  
Simon Stisen ◽  
Torben O. Sonnenborg ◽  
Karsten H. Jensen.
Keyword(s):  
Water Resources ◽  
Weather Radar ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

scholarly journals The Results of Applying Different Methodologies to 10 Years of Quantitative Precipitation Estimation in Catalonia Using Weather Radar

Geomatics ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 347-368
Author(s):  
Tomeu Rigo ◽  
Maria Carmen Llasat ◽  
Laura Esbrí
Keyword(s):  
Quality Control ◽  
Weather Radar ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Daily Data ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
Point Data ◽  
Single Polarization

The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly and daily). In addition, an automatic re-processing using automatic weather stations helps to incorporate ground-based information. The last process of the quantitative precipitation estimation (QPE) is running the end-product again eight days later, when the data have been reviewed and corrected in the case of detecting anomalies in the radar or gauge data. These corrections are applied operationally, with the fields generated and stored automatically. The QPE fields are generated in the GeoTIFF format, allowing easy use with multiple applications and simplifying processes such as quality control. In this way, the analysis of a 10 year period of GeoTIFF QPE daily data compared with ground rainfall values is introduced. The results help to understand different points regarding the functioning of the network such as the dependance on the type of precipitation and the seasonality. In addition, the description of a heavy rainfall episode (22 October 2019) shows the variations and improvements in the different products. The main conclusions refer to how using GeoTIFF combined with point data (rain gauges), it is possible to ensure simple but effective quality control of an operational radar network.

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.

scholarly journals The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China

2021 ◽  
Vol 13 (23) ◽  
pp. 4956
Author(s):  
Linye Song ◽  
Shangfeng Chen ◽  
Yun Li ◽  
Duo Qi ◽  
Jiankun Wu ◽  
...  
Keyword(s):  
South China ◽  
Weather Radar ◽  
Absolute Error ◽  
Rain Gauge ◽  
Observation Data ◽  
Matching Method ◽  
Study Region ◽  
Surface Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Weather radar provides regional rainfall information with a very high spatial and temporal resolution. Because the radar data suffer from errors from various sources, an accurate quantitative precipitation estimation (QPE) from a weather radar system is crucial for meteorological forecasts and hydrological applications. In the South China region, multiple weather radar networks are widely used, but the accuracy of radar QPE products remains to be analyzed and improved. Based on hourly radar QPE and rain gauge observation data, this study first analyzed the QPE error in South China and then applied the Quantile Matching (Q-matching) method to improve the radar QPE accuracy. The results show that the rainfall intensity of the radar QPE is generally larger than that determined from rain gauge observations but that it usually underestimates the intensity of the observed heavy rainfall. After the Q-matching method was applied to correct the QPE, the accuracy improved by a significant amount and was in good agreement with the rain gauge observations. Specifically, the Q-matching method was able to reduce the QPE error from 39–44%, demonstrating performance that is much better than that of the traditional climatological scaling method, which was shown to be able to reduce the QPE error from 3–15% in South China. Moreover, after the Q-matching correction, the QPE values were closer to the rainfall values that were observed from the automatic weather stations in terms of having a smaller mean absolute error and a higher correlation coefficient. Therefore, the Q-matching method can improve the QPE accuracy as well as estimate the surface precipitation better. This method provides a promising prospect for radar QPE in the study region.

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 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.

scholarly journals Challenges in Estimating Quantitative Precipitation Estimation (QPE) Using Weather Radar Observation Over the Mountainous Country of Nepal

2017 ◽  
Vol 21 ◽  
pp. 50-59
Author(s):  
Shakti P.C. ◽  
Masayuki Maki
Keyword(s):  
Research Work ◽  
Weather Radar ◽  
Radar Observation ◽  
Spatial And Temporal Variation ◽  
Accurate Information ◽  
Mountain Range ◽  
Mountain Areas ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Mountainous Country

South Asian country Nepal characterizes a complex mountain range in this world. The country’s population density is increasing along with rapid growth of population especially over mountainous cities, southern hills and the Terai. On the other hand, a number of fatal natural calamities, such as flash flooding and landslides raised by clutter intensive rainfall, have been increasing since the last decade. To deal with such water hydro meteorological disasters, accurate information on spatial and temporal variation of rainfall distribution is very important. In Nepal, the amount of rainfall has been obtained from limited rain gauge networks, which may leads to many errors in making a Quantitative Precipitation Estimation (QPE). Weather radar observations have recently been highlighted as an alternative option for estimating the spatial and temporal distribution of precipitation across specified time intervals. However, estimating rainfall from radar observation has its own challenges, especially over a mountainous country like Nepal.Another mountainous country Japan is well known for using weather radar observation to make QPE product. Different types of weather radar have been used to record, monitor and forecast precipitation in Japan for both operational and research purposes. A high level research work has also been done on this field. The high spatial and temporal (250-m and 1-min) QPE product obtained from the radar observation is available for the public. It shows good harmony with ground data in the flat and mountain areas of Japan. Though Nepal and Japan are located in different regions, both countries represent complex mountain regions and have been facing natural disaster caused by extreme rainfall. In Nepal, weather radar observation for estimating precipitation amounts has not started on an operational basis till date. Hence, sharing knowledge and skills from Japan’s research on weather radar observation would play a key role to achieve the radar based QPE product in Nepal. Therefore, we discuss about the challenge in obtaining QPE product, considering an example of the progress of weather radar system in Japan. It is believed that any discussion on it will be a reference for weather radar deployment and its QPE product in Nepal in coming days.  HYDRO Nepal JournalJournal of Water Energy and EnvironmentIssue: 21, July, 2017Page: 50-59Upload Date: July 18, 2017

scholarly journals Assimilation of weather radar and binary ubiquitous sensor measurements for quantitative precipitation estimation

2015 ◽  
Vol 17 (4) ◽  
pp. 598-613 ◽  
Author(s):  
David J. Hill
Keyword(s):  
Weather Radar ◽  
Simulation Studies ◽  
Heterogeneous Sensors ◽  
Intensity Measurements ◽  
Quantitative Precipitation Estimation ◽  
Markov Random ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Temporal Measurement ◽  
Quantitative Precipitation Estimates

Assimilation of data from heterogeneous sensors and sensor networks is critical for achieving accurate measurements of environmental processes at the time and space scales necessary to improve forecasting and decision-making. Owing to different measurement accuracies and types of spatial and/or temporal measurement support of the component sensors, it is often unclear how best to combine these data. This study explores the utility of ubiquitous sensors producing categorical wet/dry rainfall measurements for improving the resolution of areal quantitative precipitation estimates through fusion with weather radar observations. The model developed in this study employs a Markov random field model to compute the probability of rainfall at sub-grid pixels. These likelihoods are used to ‘unmix’ the cell-averaged rainfall rate measured by the radar. Simulation studies using synthetic and known rainfall fields reveal that the model can improve remotely sensed quantitative rainfall intensity measurements by 40% using networks of ubiquitous sensors with a density of 56 sensors per square kilometer, and for denser networks, the accuracy can increase by as much as 50%.

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