ISSN 1006-9895

CN 11-1768/O4

Using Adjoint-Based Forecast Sensitivity Method to Evaluate Observations of WPRD&MWR Impacts on Model Forecast

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    A large number of observations assimilated can effectively improve the results of model forecast. However, there are significant differences in the effects of different observations on the forecast. It is one of the most challenging diagnostics in numerical models to reasonably evaluate the contribution of observations to the forecast. In this paper, the WRFDA-FSO system is constructed by the method of adjoint-based forecast sensitivity to observation(FSO). Based on the wind profile radar detection(WPRD) and ground-based microwave radiometer(MWR) data obtained by the mega city project in Beijing in September 2019, the experiments on the impact of observations on the 12h forecast of WRF model are carried out by using WRFDA-FSO system, and the contribution of wind, temperature and humidity observations to the forecast is analyzed. The results show that: (1) In general, the observations(MWR, WPRD, Sound, Synop and Geoamv) assimilated all reduce the 12h forecast error of WRF model, and make positive contribution to the forecast. Among them, MWR observations have the greatest impact on the forecast, and the improvement of WPRD observations on forecast is better than that of wind field observations of Sound. (2) Among the U and V observations of WPRD and temperature and Specific humidity observations of MWR, the positive contribution value of V observations and temperature observations to the forecast is higher, and the effect of improving the forecast is better. (3)The observations of WPRD and MWR at most levels reduce the forecast error and are positive contribution to forecast, and the positive contribution of temperature observations is mainly below 800 hPa near the ground.

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  • Received:November 02,2020
  • Revised:July 02,2021
  • Adopted:September 02,2021
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