Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

Yajie Qi,Jiajia Mao,Bai Li,Dawei Lin,Shuiyong Fan

doi:10.3390/atmos13010074

Yajie Qi, Jiajia Mao + Show 3 more

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https://doi.org/10.3390/atmos13010074

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Journal: Atmosphere	Publication Date: Dec 31, 2021
Citations: 8	License type: CC BY 4.0

Affiliation: China Meteorological Administration

Abstract

Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing.

Highlights

The temporal variation and spatial distribution of meteorological elements represent the state of the atmosphere in the troposphere, and the vertical distribution and variation of meteorological elements are very important for simulating and predicting atmospheric movement in numerical weather prediction models, as the World Meteorological Organization guidance for numerical weather prediction applications has highlighted
Weather Research and Forecasting (WRF) model was used to assimilate MWR temperature and humidity profiles for simulating a rainstorm event that occurred in Beijing, China, and the results showed that the assimilation of MWR data had a positive impact on the distribution and intensity of rainfall [16]
We found that the assimilation of ground-based microwave radiometers increased the scope of heavy rainfall in MWRPS, which better agreed with the observations in spatial distribution patterns, as compared to the control experiment

Summary

Introduction

The temporal variation and spatial distribution of meteorological elements represent the state of the atmosphere in the troposphere, and the vertical distribution and variation of meteorological elements are very important for simulating and predicting atmospheric movement in numerical weather prediction models, as the World Meteorological Organization guidance for numerical weather prediction applications has highlighted. Radiosondes have a better vertical resolution on atmospheric profiles [2,3]. They cannot provide continuous monitoring data since their data are usually available at an interval of 12 h [4]. Its secondary products can detect temperature profile, humidity profile, and other elements [5,6,7,8], and can conduct continuous observation of vertical changes of meteorological elements within a certain precision range

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