scholarly journals Mesoscale and Local Scale Evaluations of Quantitative Precipitation Estimates by Weather Radar Products during a Heavy Rainfall Event

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Basile Pauthier ◽  
Benjamin Bois ◽  
Thierry Castel ◽  
D. Thévenin ◽  
Carmela Chateau Smith ◽  
...  
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Rain Gauge ◽  
Local Scale ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Preliminary Evaluation ◽  
Rainfall Variation ◽  
Hourly Rainfall ◽  
Rain Gauge Network

A 24-hour heavy rainfall event occurred in northeastern France from November 3 to 4, 2014. The accuracy of the quantitative precipitation estimation (QPE) by PANTHERE and ANTILOPE radar-based gridded products during this particular event, is examined at both mesoscale and local scale, in comparison with two reference rain-gauge networks. Mesoscale accuracy was assessed for the total rainfall accumulated during the 24-hour event, using the Météo France operational rain-gauge network. Local scale accuracy was assessed for both total event rainfall and hourly rainfall accumulations, using the recently developed HydraVitis high-resolution rain gauge network Evaluation shows that (1) PANTHERE radar-based QPE underestimates rainfall fields at mesoscale and local scale; (2) both PANTHERE and ANTILOPE successfully reproduced the spatial variability of rainfall at local scale; (3) PANTHERE underestimates can be significantly improved at local scale by merging these data with rain gauge data interpolation (i.e., ANTILOPE). This study provides a preliminary evaluation of radar-based QPE at local scale, suggesting that merged products are invaluable for applications at very high resolution. The results obtained underline the importance of using high-density rain-gauge networks to obtain information at high spatial and temporal resolution, for better understanding of local rainfall variation, to calibrate remotely sensed rainfall products.

scholarly journals Role of land state in a high resolution mesoscale model for simulating the Uttarakhand heavy rainfall event over India

2016 ◽  
Vol 125 (3) ◽  
pp. 475-498 ◽  
Author(s):  
P V Rajesh ◽  
S Pattnaik ◽  
D Rai ◽  
K K Osuri ◽  
U C Mohanty ◽  
...  
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Mesoscale Model ◽  
Rainfall Event ◽  
Heavy Rainfall Event

scholarly journals A Comparison Between Global Satellite Mapping of Precipitation Data and High-Resolution Radar Data – A Case Study of Localized Torrential Rainfall over Japan

2021 ◽  
Vol 16 (4) ◽  
pp. 786-793
Author(s):  
Yoshiaki Hayashi ◽  
Taichi Tebakari ◽  
Akihiro Hashimoto ◽  
◽  
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Daily Rainfall ◽  
Radar Data ◽  
Rain Gauge ◽  
Hourly Rainfall ◽  
The Difference ◽  
Microwave Imager ◽  
Satellite Mapping

This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. The study also obliged us to clarify the spatial and temporal resolution of GSMaP data using high-accuracy ground-based radar, and evaluate the performance and reporting frequency of GSMaP satellites. The GSMaP_Gauge_RNL data with less than 70 mm/day of daily rainfall was similar to the data of both radars, but the GSMaP_Gauge_RNL data with over 70 mm/day of daily rainfall was not, and the calibration by rain-gauge data was poor. Furthermore, both direct/indirect observations by the Global Precipitation Measurement/Microwave Imager (GPM/GMI) and the frequency thereof (once or twice) significantly affected the difference between GPM/GMI data and C-band radar data when the daily rainfall was less than 70 mm/day and the hourly rainfall was less than 20 mm/h. Therefore, it is difficult for GSMaP_Gauge to accurately estimate localized heavy rainfall with high-density particle precipitation.

Simulation of a heavy rainfall event during southwest monsoon using high-resolution NCUM-modeling system: a case study

2018 ◽  
Vol 131 (4) ◽  
pp. 1035-1054 ◽  
Author(s):  
Devajyoti Dutta ◽  
A. Routray ◽  
D. Preveen Kumar ◽  
John P. George ◽  
Vivek Singh
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Southwest Monsoon ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
System A

Gridded Hourly Precipitation Analysis from High-Density Rain Gauge Network over the Yangtze–Huai Rivers Basin during the 2007 Mei-Yu Season and Comparison with CMORPH

2013 ◽  
Vol 14 (4) ◽  
pp. 1243-1258 ◽  
Author(s):  
Yali Luo ◽  
Weimiao Qian ◽  
Renhe Zhang ◽  
Da-Lin Zhang
Keyword(s):  
Heavy Rainfall ◽  
Rain Gauge ◽  
Rainfall Analysis ◽  
Hourly Precipitation ◽  
Spatial Consistency ◽  
Hourly Rainfall ◽  
Reflectivity Data ◽  
Climate Prediction Center ◽  
Rain Gauge Network

Abstract Heavy rainfall hit the Yangtze–Huai Rivers basin (YHRB) of east China several times during the prolonged 2007 mei-yu season, causing the worst flood since 1954. There has been an urgent need for attaining and processing high-quality, kilometer-scale, hourly rainfall data in order to understand the mei-yu precipitation processes, especially at the mesoβ and smaller scales. In this paper, the authors describe the construction of the 0.07°-resolution gridded hourly rainfall analysis over the YHRB region during the 2007 mei-yu season that is based on surface reports at 555 national and 6572 regional automated weather stations with an average resolution of about 7 km. The gridded hourly analysis is obtained using a modified Cressman-type objective analysis after applying strict quality control, including not only the commonly used internal temporal and spatial consistency and extreme value checks, but also verifications against mosaic radar reflectivity data. This analysis reveals many convectively generated finescale precipitation structures that could not be seen from the national station reports. A comprehensive quantitative assessment ensures the quality of the gridded hourly precipitation data. A comparison of this dataset with the U.S. Climate Prediction Center morphing technique (CMORPH) dataset on the same resolution suggests the dependence of the latter's performance on different rainfall intensity categories, with substantial underestimation of the magnitude and width of the mei-yu rainband as well as the nocturnal and morning peak rainfall amounts, due mainly to its underestimating the occurrences of heavy rainfall (i.e., >10 mm h−1).

scholarly journals Corrigendum #2 to “Mesoscale and Local Scale Evaluations of Quantitative Precipitation Estimates by Weather Radar Products during a Heavy Rainfall Event”

2019 ◽  
Vol 2019 ◽  
pp. 1-1
Author(s):  
Basile Pauthier ◽  
Benjamin Bois ◽  
Thierry Castel ◽  
D. Thévenin ◽  
Carmela Chateau Smith ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Weather Radar ◽  
Local Scale ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

Numerical Experiments on Spatially Averaged Precipitation in Heavy Rainfall Event Using the WRF Model

2015 ◽  
Vol 10 (3) ◽  
pp. 436-447 ◽  
Author(s):  
Yuji Sugihara ◽  
◽  
Sho Imagama ◽  
Nobuhiro Matsunaga ◽  
Yukiko Hisada ◽  
...  
Keyword(s):  
Numerical Experiments ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Analysis Data ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Heavy Rainfall Event ◽  
Spatial Averaging ◽  
Hourly Rainfall ◽  
Precipitation Analysis

It is difficult to forecast hourly rainfall locally even using the latest meteorological models, although hourly rainfall averaged spatially to some extent can be used for calculating practical rainfall. This study conducts numerical experiments with triple nesting on the 2012 heavy rainfall event in northern Kyushu using the weather research and forecasting (WRF) model and examines the features of hourly rainfall averaged spatially. The dependence of rainfall is averaged spatially on a spatial averaging scale and clarified by comparing rainfall calculated by simulation using the WRF model with radar/AMeDAS precipitation analysis data. This study’s findings indicate the effective spatial averaging scale making relative error of calculated values to the observed ones minimum.

scholarly journals Evaluation of the Polarimetric-Radar Quantitative Precipitation Estimates of an Extremely Heavy Rainfall Event and Nine Common Rainfall Events in Guangzhou

Atmosphere ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 330 ◽  
Author(s):  
Yang Zhang ◽  
Liping Liu ◽  
Hao Wen ◽  
Chong Wu ◽  
Yonghua Zhang
Keyword(s):  
Heavy Rainfall ◽  
Doppler Radar ◽  
Heavy Rain ◽  
Rainfall Event ◽  
Retrieval Algorithm ◽  
Heavy Rainfall Event ◽  
Polarimetric Radar ◽  
Rainfall Events ◽  
Factors Affecting ◽  
Hourly Rainfall

The development and application of operational polarimetric radar (PR) in China is still in its infancy. In this study, an operational PR quantitative precipitation estimation (QPE) algorithm is suggested based on data for PR hydrometeor classification and local drop size distribution (DSD). Even though this algorithm performs well for conventional rainfall events, in which hourly rainfall accumulations are less than 50 mm, the capability of a PR to estimate extremely heavy rainfall remains unclear. The proposed algorithm is used for nine different types of rainfall events that occurred in Guangzhou, China, in 2016 and for an extremely heavy rainfall event that occurred in Guangzhou on 6 May 2017. It performs well for all data of these nine rainfall events and for light-to-moderate rain (hourly accumulation <50 mm) in this extremely heavy rainfall event. However, it severely underestimated heavy rain (>50 mm) and the extremely heavy rain at stations where total rainfall exceeded 300 mm within 5 h in this extremely heavy rainfall event. To analyze the reasons for underestimation, a rain microphysics retrieval algorithm is presented to retrieve Dm and Nw from the PR measurements. The DSD characteristics and the factors affecting QPE are analyzed based on Dm and Nw. The results indicate that compared with statistical DSD data in Yangjiang (estimators are derived from these data), the average raindrop diameter during this rainfall event occurred on 6 May 2017 was much smaller and the number concentration was higher. The algorithm underestimated the precipitation with small and midsize particles, but overestimated the precipitation with midsize and large particles. Underestimations occurred when Dm and Nw are both very large, and the severe underestimations for heavy rain are mainly due to these particles. It is verified that some of these particles are associated with melting hail. Owing to the big differences in DSD characteristics, R(KDP, ZDR) underestimates most heavy rain. Therefore, R(AH), which is least sensitive to DSD variations, replaces R(KDP, ZDR) to estimate precipitation. This improved algorithm performs well even for extremely heavy rain. These results are important for evaluating S-band Doppler radar polarization updates in China.

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