Performance Evaluation of Cloud Analysis in GRAPES 3km Model over Northwest China

Cloud analysis module (GCAS) in GRAPES model can combine radar reflectivity data, satellite data and surface observations to provide there-dimensional cloud information. To show application effect of GCAS on 3-km resolution forecasts in arid and semi-arid areas of Northwest China, three sets of forecast experiments were conducted with GRAPES_Meso model, which includes control experiment (Con_exp), gcas experiment (Gcas_exp) and hot-start experiment (Hot_exp). The impact of cloud analysis on the prediction effect was investigated using 13 heavy rainfall cases and one month continuous experiments.

These experimental results show the use of cloud analysis can significantly improve forecasting skills of precipitation. Compared with hourly precipitation observations, Gcas_exp performed better than Con_exp and Hot_exp, which gets a higher threat scores of precipitation both for 13 cases and for one-month continuous experiments. Hot_exp presented an positive effect only in the first few hours. Oftentimes, Hot_exp got a worse forecast than Con_exp after the first several hours. In addition, gcas_exp has a positive effect on the prediction of 2m temperature, 10m wind and other variables, but forecasted composite reflectivity was stronger than its observations. Hot_exp can reduce this strength bias to some extent.