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A study on weather radar data assimilation for numerical rainfall prediction

Author(s): J. Liu | M. Bray | D. Han

Journal: Hydrology and Earth System Sciences Discussions
ISSN 1812-2108

Volume: 9;
Issue: 9;
Start page: 10323;
Date: 2012;
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Mesoscale NWP model is gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations especially the weather radar data can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in Southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three dimensional variational (3D-Var) data assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauges, the radar data is assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types or combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation is evaluated by examining the rainfall cumulative curves and the rainfall totals which have direct impact on rainfall-runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study is discussed and suggestions are made on more efficient utilisation of radar data in NWP assimilation.
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