Application of Latent Heat Nudging Method to Assimilating Surface Precipitation Observations

For the spin-up problem in initial integration of meso-scale numerical weather prediction model, especially the time lags in the prediction of rain belt, latent heat nudging method is applied to assimilate the intensified automatic weather station (AWS) precipitation observations, so that it can effectively improve the performance of model in the very short-term forecasts. Based on GRAPES_Meso model with high resolution developed by China Meteorological Administration, three groups of latent heat nudging experiments are designed for generating different initial conditions, including the control run, the traditional cold-latent heat nudging (C-LHN) assimilation and the revised warm-latent heat nudging (W-LHN) assimilation. The last one consists of W6-LHN and W12-LHN with 6 h and 12 h warm-start period before nudging, respectively.Batch tests are carried out from 0000 UTC 20 June to 0000 UTC 20 July in 2013, preliminary conclusions can be drawn as follows. Firstly, initial temperature profiles are significantly modified due to the adjustment of forecasted latent heat profiles, according to analyzed differences between observations and forecasts in the pre-forecast period. And initial distributions of specific humidity and wind vectors are modified indirectly that convergence and divergence of water vapor increase at lower and middle levels. Thus the convective instability in the heavy rain area is strengthened. Secondly, compared with the control run without any initial precipitation information, the application of latent heat nudging method in GRAPES_Meso model can reduce the spin-up time, precipitation is triggered quickly in the first 3 hours, which is important for the very short-term forecast and nowcasting in particular. Therefore, the location and intensity are much closer to observations, and enhancing forecast skills of 3 h, 6 h and 12 h accumulated precipitation such as TS, ETS and Bias scores. In addition, when comparing the warm and cold latent heat nudging methods, both of them has its advantages and disadvantages, performances differ with forecast length and precipitation magnitudes divided into light, moderate, heavy, hard and torrential rainfall, but 3 h, 6 h and 12 h light and moderate precipitations are always better predicted by W-LHN. Finally, W6-LHN experiments achieve more favorable rainfall forecasts, but W12-LHN experiments tend to overestimate the heavy and torrential rain.All in all, application of latent heat nudging method in assimilating the observed precipitation for very short-term forecast is operationally prospective, with advantages of lower cost but higher performance, thus it is easy to meet the operational demand for being available to public very soon. However, the impact on improving precipitation forecasts cannot last long because meso-and micro-scale characteristics fade away with the increasing forecast length. In the near future, it is expected that three dimensional variational analysis will be incorporated for an extended prediction.