Assimilation of Radial Winds Over India Using a Community GSI Analysis System
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Pure and Applied Geophysics
Assimilation of Radial Winds Over India Using a Community GSI Analysis System SUJATA PATTANAYAK1 Abstract—With the modernization of the India Meteorological Department, the observational network of the country, both in situ and remote sensing, is enhanced. The Doppler Weather Radar (DWR) network is improved, and the National Centre for Medium Range Weather Forecasting is acquiring radar data from 20 stations all over the country. The maximum utilization of DWR observations in the numerical models remains a challenging task. This study represents the first assessment of assimilation of radial wind observations from all 20 DWR stations, utilizing the resources of weather research and a forecasting model with a community gridpoint statistical interpolation system. DWR observations are an important data source for mesoscale and microscale weather analysis and forecasting because of their high temporal and spatial resolution. However, the representation of DWR radial wind and reflectivity in a desired format seems to be crucial in the modeling approach. A series of experiments are conducted to evaluate the sensitivity of the analysis to the velocity azimuth display quality control (VADQC) and without VADQC (VARQC) to understand the effect of QC on analysis. The statistical analysis of assimilation of DWR radial wind suggests that a gate distance of 250 m or its multiple is imperative for the setup of the DWR. Additionally, the density of the super-observation is amplified in the VARQC approach. The analysis procedure is implemented for the recent severe cyclone Phethai (December 2018) over the Bay of Bengal, and a few preliminary results are discussed. Keywords: Indian Doppler weather radar, WRF, comGSI, radial wind, gate distance, VADQC.
1. Introduction Numerical weather prediction (NWP) is an initial value problem and demands accuracy in the initial state of the atmosphere to predict the evolution of weather conditions. Data assimilation engenders the best estimate of the initial state of the atmosphere by combining all sources of information, including the
1 National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, A-50, Sector-62, Noida, UP 201309, India. E-mail: [email protected]
and V. S. PRASAD1 first guess from previous short-term model forecasts, observations, and associated uncertainties in each source of information. Many assimilation techniques have been developed for meteorology and oceanography. They differ in their numerical cost, their optimality, and in their suitability for real-time application (Bouttier and Courtier 1999). Real-time data assimilation (DA) procedures with very high resolution in the convective scale in the NWP models remain a crucial and challenging task for the research and operational meteorologist. Also, it may be noted that convective-scale DA is an emerging field of weather prediction due to a lack of balance constraints resulting in lower predictability (Droegmeier 1997; Sun 2005). Initialization of a dynamically
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