Evaluation of Heavy Precipitation Simulated by the WRF Model Using 4D-Var Data Assimilation with TRMM 3B42 and GPM IMERG over the Huaihe River Basin, China

Long, Yi; Zhang, Wanchang; Wang, Kai

doi:10.3390/rs10040646

Cited by 35 publications

(24 citation statements)

References 69 publications

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“…The GPM IMERG was selected as the observation operator because of its recent release, wide coverage, free download and high accuracy [70]. Moreover, the feasibility of assimilating the GPM IMERG into the WRF model has been demonstrated in Yi et al [18]. The GPM IMERG was accumulated into 6 h values and assimilated into the WRF 4D-Var system to improve the initial condition at every day 00 UTC.…”

Section: Nwp-predicted Precipitationmentioning

confidence: 99%

Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

Long

Zhang

2018

Remote Sensing

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To compare the effectivenesses of different precipitation datasets on hydrological modelling, five precipitation datasets derived from various approaches were used to simulate a two-week runoff process after a heavy rainfall event in the Wangjiaba (WJB) watershed, which covers an area of 30,000 km2 in eastern China. The five precipitation datasets contained one traditional in situ observation, two satellite products, and two predictions obtained from the Numerical Weather Prediction (NWP) models. They were the station observations collected from the China Meteorological Administration (CMA), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG), the merged data of the Climate Prediction Center Morphing (merged CMORPH), and the outputs of the Weather Research and Forecasting (WRF) model and the WRF four-dimensional variational (4D-Var) data assimilation system, respectively. Apart from the outlet discharge, the simulated soil moisture was also assessed via the Soil Moisture Active Passive (SMAP) product. These investigations suggested that (1) all the five precipitation datasets could yield reasonable simulations of the studied rainfall-runoff process. The Nash-Sutcliffe coefficients reached the highest value (0.658) with the in situ CMA precipitation and the lowest value (0.464) with the WRF-predicted precipitation. (2) The traditional in situ observation were still the most reliable precipitation data to simulate the study case, whereas the two NWP-predicted precipitation datasets performed the worst. Nevertheless, the NWP-predicted precipitation is irreplaceable in hydrological modelling because of its fine spatiotemporal resolutions and ability to forecast precipitation in the future. (3) Gauge correction and 4D-Var data assimilation had positive impacts on improving the accuracies of the merged CMORPH and the WRF 4D-Var prediction, respectively, but the effectiveness of the latter on the rainfall-runoff simulation was mainly weakened by the poor quality of the GPM IMERG used in the study case. This study provides a reference for the applications of different precipitation datasets, including in situ observations, remote sensing estimations and NWP simulations, in hydrological modelling.

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Section: Nwp-predicted Precipitationmentioning

confidence: 99%

Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

Long

Zhang

2018

Remote Sensing

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“…However, gauges in mountainous regions are often sparse, uneven, and sometimes not available [2,6,7]. Thus, in the applications that need high spatiotemporal resolution precipitation data, such as flood disaster forecasts, gauge data are often not sufficient [8,9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of GPM IMERG V05B and TRMM 3B42V7 Precipitation Products over High Mountainous Tributaries in Lhasa with Dense Rain Gauges

et al. 2019

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In most Asian high mountain areas, ground-based observations of precipitation are sparse. It is urgent to assess and apply satellite precipitation products (SPPs). In recent years, relatively dense rain gauges have been established in five tributaries in Lhasa. Therefore, based on high-density rain gauges, two SPPs (GPM IMERG V05B, TRMM 3B42V7) were evaluated at the grid, region, and time scales with different statistical indices in the five tributaries. Besides, the dependence of SPPs performances on the precipitation intensities, elevation, and slope was investigated. The results indicate that: (1) both 3B42V7 and IMERG showed similarly low correlation with rain gauges at daily scale and high correlation at monthly scale, but 3B42V7 tended to suffer from systematic overestimation of monthly precipitation; (2) IMERG product outperformed 3B42V7 except for obvious overestimation of trace precipitation (0.1~1 mm day−1) and underestimation of torrential precipitation (>50 mm day−1); (3) the precipitation over the five tributaries showed significant spatial variability with difference of characteristic values (e.g., average daily precipitation) more than 20% in some IMERG grids and most 3B42V7 grids; (4) elevation had an obvious effect on the accuracy of 3B42V7 and IMERG, and the accuracy of the two SPPs decreased significantly with the increase of elevation.

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“…DA-IMERG experiment was directly assimilated IMERG precipitation products utilizing 4D-Var. The IMERG precipitation product was applied as observation operators, which was collected and accumulated every 6h for assimilating into the DA-IMERG experiment [16]. For comparison purposes, The DA-CONV was based on the assimilation of conventional observations (surface observational weather data (ds461.0) and upper air observational weather data (ds351.0)) from the Research Data Archive at the National Center for Atmospheric Research (https://rda.ucar.edu/datasets/).…”

Section: Experiments Designmentioning

confidence: 99%

“…However, the accuracy of precipitation simulated by WRF with 4D-Var depends on the assimilation data. Yi et al [16] reported that WRF precipitation assimilated with GPM IMERG outperformed that with TRMM-3B42.…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation of High-Resolution Satellite Rainfall Product Improves Rainfall Simulation Associated with Landfalling Tropical Cyclones in the Yangtze River Delta

Wang

Yang

et al. 2020

Remote Sensing

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Floods caused by heavy rainfall events associated with landfalling tropical cyclones (TCs) represent a major risk for the Yangtze River Delta (YRD) region of China. Accurate extreme precipitation forecasting, at long lead times, is crucial for the improvement of flood prevention and warning. However, accurate prediction of timing, location, and intensity of the heavy rainfall events is a major challenge for the Numerical Weather Prediction (NWP). In this study, high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) are evaluated at the hourly timescale, and the optimal Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product is selected and applied to directly assimilate into the Weather Research and Forecasting (WRF) model via the four-dimensional variational (4D-Var) method. The TC Jondari and Rumbia events of August 2018 are evaluated to analyze the performance of the WRF model with the 4D-Var method assimilated IMERG precipitation product (DA-IMERG) and the conventional observation (DA-CONV) for real-time heavy rainfall forecasting. The results indicate that (1) IMERG precipitation products were larger and wetter than the observed precipitation values over YRD. By comparison, the performance of “late” run precipitation product (IMERG-L) was the closest to the observation data with lower deviation and higher detection capability; (2) DA-IMERG experiment substantially affected the magnitude of the WRF model primary variables, which changed the precipitation pattern of the TC heavy rain. (3) DA-IMERG experiment further improved the forecast of heavy rainbands and relatively reduced erroneous detection rate than CTL and DA-CONV experiments at the grid scale. Meanwhile, the DA-IMERG experiment has a better fractions skill score (FSS) value (especially in the threshold of 10 mm/h) than DA-CONV for TC Jondari and Rumbia at the spatial scale, while it shows a lower performance than CTL and DA-CONV experiments when the threshold is lower than the 5 mm/h for the TC Rumbia.

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Evaluation of Heavy Precipitation Simulated by the WRF Model Using 4D-Var Data Assimilation with TRMM 3B42 and GPM IMERG over the Huaihe River Basin, China

Cited by 35 publications

References 69 publications

Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

Evaluation of GPM IMERG V05B and TRMM 3B42V7 Precipitation Products over High Mountainous Tributaries in Lhasa with Dense Rain Gauges

Data Assimilation of High-Resolution Satellite Rainfall Product Improves Rainfall Simulation Associated with Landfalling Tropical Cyclones in the Yangtze River Delta

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