Improved Precipitation Forecast Skill from the Use of Physical Initialization
This study explores the impact of physical initialization on the numerical weather prediction of tropical rainfall An important finding of this study is related to the point correlations of the model-based rainfall at the initial time and at the end of a one day forecast which are significantly improved with the use of physical initialization within the tropics. Physical initialization refers to the use of a number of ‘reverse’ algorithms during an assimilation phase of the model forecast. The goal is to improve the definition of the initial state by the assimilation of proposed or currently available surface and satellite-based observations during a pre-integration phase of a forecast using a global spectral model.
The ‘observed’ rainrates are obtained from algorithms that translate satellite-based measurements of outgoing longwave radiation (OLR) cmd radiances for an array of microwave frequencies. In addition, the available rain gauge records over the Umd area are incorporated to define the ‘observed’rainfall over the gaussian transform grid squares of a global spectral model at a high resolution (T106). Thus the rainfall measurements are averages over roughly 100 × 100 square kilometers and 7.5 minutes (the time step of the spectral model). It is for this averaged representation that we are able to demonstrate a very marked skill in nowcasting and one day forecasting of tropical rainfall The monthly mean rainfall climatology, thus obtained, nearly replicates the rainfall analyses provided to the physical initialization.
This study also addresses a comparison of two different rainrate algorithms for the microwave measurements from a polar orbiting satellite. We note somewhat excessive rain for one algorithm as compared to the other; however, the physical initialization appears to accept both rainrates well. This calls for a more reliable measure of observed rairu
KeywordsOutgoing Longwave Radiation Numerical Weather Prediction Rain Rate Forecast Skill Point Correlation
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