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 NUMERICAL SENSITIVITY EXPERIMENTS ON PRECIPITATION IN A HEAVY RAINFALL EVENT SIMULATED BY USING THE WRF MODEL

2014 ◽  
Vol 70 (4) ◽  
pp. I_541-I_546 ◽  
Author(s):  
Yuji SUGIHARA ◽  
Sho IMAGAMA ◽  
Yohei OHKUMA ◽  
Nobuhiro MATSUNAGA ◽  
Yukiko HISADA ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Wrf Model ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Sensitivity Experiments

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 Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall

2019 ◽  
Vol 11 (8) ◽  
pp. 973 ◽  
Author(s):  
Yuanbing Wang ◽  
Yaodeng Chen ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Eastern China ◽  
Wrf Model ◽  
Analysis Data ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Precipitation Analysis ◽  
The Impact

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.

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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