2021•Unpublished venueRequires access

Evaluation on Assimilation Application of Yunhai-2 Occultation Data in Regional Numerical Weather Prediction Model

Qifa Cai, Yegui Wang, Bin Zhang, Juan Li, Weiren Lan, Guangjie Wang

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Abstract

Based on the WRF (Weather Research and Forecasting) model and GSI (Gridpoint Statistical Interpolation analysis system) three-dimensional variational assimilation system, regional assimilation and prediction experiments for China’s autonomic Yunhai-2 occultation data were performed for the first time in May 2019. The results of the experiments demonstrated that after assimilating Yunhai-2 occultation data, the improvement for the wind and temperature fields is mainly reflected in the middle and later stages of the forecast, while the improvement for the humidity field is witnessed through the entire forecast period. The results also showed that the improvement degree of the wind, temperature, and humidity fields tends to be consistent with the extension of forecast time. It was found that the improvement for the wind and temperature fields is mainly reflected in the middle layer of the model, while the improvement for the water vapor mixing ratio is mainly reflected in the middle and lower layers of the model. Thus, assimilating Yunhai-2 occultation data can reasonably adjust the potential height, humidity, temperature, and wind fields in the model, thereby improving the precipitation forecast results.

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What this paper is about

Based on the WRF (Weather Research and Forecasting) model and GSI (Gridpoint Statistical Interpolation analysis system) three-dimensional variational assimilation system, regional assimilation and prediction experiments for China’s autonomic Yunhai-2 occultation data were performed for the first time in May 2019. The results of the experiments demonstrated that after assimilating Yunhai-2 occultation data, the improvement for the wind and temperature fields is mainly reflected in the middle and later stages of the forecast, while the improvement for the humidity field is witnessed through the entire forecast period. The results also showed that the improvement degree of the wind, temperature, and humidity fields tends to be consistent with the extension of forecast time. It was found that the improvement for the wind and temperature fields is mainly reflected in the middle layer of the model, while the improvement for the water vapor mixing ratio is mainly reflected in the middle and lower layers of the model. Thus, assimilating Yunhai-2 occultation data can reasonably adjust the potential height, humidity, temperature, and wind fields in the model, thereby improving the precipitation forecast results.

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Available abstract

Based on the WRF (Weather Research and Forecasting) model and GSI (Gridpoint Statistical Interpolation analysis system) three-dimensional variational assimilation system, regional assimilation and prediction experiments for China’s autonomic Yunhai-2 occultation data were performed for the first time in May 2019. The results of the experiments demonstrated that after assimilating Yunhai-2 occultation data, the improvement for the wind and temperature fields is mainly reflected in the middle and later stages of the forecast, while the improvement for the humidity field is witnessed through the entire forecast period. The results also showed that the improvement degree of the wind, temperature, and humidity fields tends to be consistent with the extension of forecast time. It was found that the improvement for the wind and temperature fields is mainly reflected in the middle layer of the model, while the improvement for the water vapor mixing ratio is mainly reflected in the middle and lower layers of the model. Thus, assimilating Yunhai-2 occultation data can reasonably adjust the potential height, humidity, temperature, and wind fields in the model, thereby improving the precipitation forecast results.

Key concepts: Data assimilation, Meteorology, Environmental science, Weather Research and Forecasting Model, Numerical weather prediction, Occultation, Humidity, Wind speed

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