Abstract
Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.
Highlights
Heavy rainfall events represent a significant area of concern among the scientific community due to their dramatic social, economic, and ecological impacts
Local heavy rainfall forecasting is greatly dependent on the accuracy of the initial conditions that are considered in numerical weather prediction (NWP) models [2]
We focused on the influence of assimilating radiances data from the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounding (MHS) on refresh multi-scale analysis and prediction system (RMAPS) heavy rainfall forecasting
Summary
Heavy rainfall events represent a significant area of concern among the scientific community due to their dramatic social, economic, and ecological impacts. Local heavy rainfall is generally a short-range mesoscale weather process, which makes it difficult to accurately predict the evolution and development of these mesoscale weather systems that lead to heavy rainfall events. Over the last several decades significant improvements have been made in short-range forecasting, the accurate prediction and quantification of heavy rainfall events still remains a challenge [1]. Local heavy rainfall forecasting is greatly dependent on the accuracy of the initial conditions that are considered in numerical weather prediction (NWP) models [2]. With the development of remote sensing technologies, satellite observations have played a significant role in the improvement of numerical forecasting techniques by providing a more accurate estimation of the initial conditions. The assimilation of satellite observations into the operational NWP system has emerged as an important method for improving quantitative precipitation forecasting
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