MOTOR-DA stands out for its native multi-grid MPI parallelism, allowing for efficient and scalable processing. It also boasts the capability to interface seamlessly with various forecasting systems, enhancing its versatility and compatibility.
The initial version of MOTOR-DA has already been released, incorporating several notable features. Alongside the multi-grid variational assimilation feature, we have implemented a novel data thinning scheme and an optimized covariance matrix scheme. These advancements contribute to improved accuracy in assimilation analysis. Moreover, we have developed modules that enable the assimilation of radar radial wind data and FY4 satellite data. These additions expand the range of observations that can be incorporated into the assimilation process, further enhancing the system’s capabilities.
Currently, MOTOR-DA has been integrated with the CMA-GD (China Meteorological Administration-Guangdong regional model) forecast model and adopted CMA Global Forecast System (CMA-GFS) as background and boundary conditions, is already operational in the South China region. Through extensive batch testing and operational evaluation, significant positive effects on precipitation and typhoon track forecasting have been observed, and the forecasts driven by MOTOR-DA is comparable with those driven by ECMWF IFS analysis field in skill scores.
Moving forward, we aim to continuously enhance MOTOR-DA by incorporating cutting-edge techniques, such as artificial intelligence and strong physical constraints. These ongoing developments will ensure that MOTOR-DA remains at the forefront of data assimilation systems, providing valuable insights and accurate forecasts for meteorological applications.
*.Guangdong-Hong Kong-Macao Greater Bay Area Weather Research Center for Monitoring Warning and Forecasting (Shenzhen Institute of Meteorological Innovation)
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