Verification and Diagnostics for Data Assimilation System of Global GRAPES

Numerical Weather Prediction Center of China Meteorological Administration has upgraded the global GRAPES (Global/Regional Assimilation and PrEdiction System) variation data assimilation system. The new data assimilation system employs the same coordinate, grids and atmospheric state variables as those of the GRAPES model. It can reduce analysis errors due to the interpolation and variable transformations, and also provide basics for developing GRAPES four-dimension variation assimilation system. Some key characteristics of the new global GRAPES data assimilation system are discussed, and then the performance is evaluated in detail, by comparing with observations, analysis or reanalysis data from advanced operational numerical weather prediction centers, and the medium-range forecast from background and analysis fields and different forecast models. Some guidelines for further optimizing the system is also given based on diagnosis and quantitatively estimating the impact of observations. Results show that the GRAPES data assimilation system assimilates conventional observations, satellite radiances and radio occultation observations effectively, making analyses closer to the real atmosphere and improving the forecast skill. The analysis of GRAPES are similar to those of European Centre for Medium-Range Weather Forecasts and National Center for Environmental Prediction at the large-scale circulation fields. However, some differences still remains, which actually expose issues of GRAPES. These differences are related to overlarge contributions from radiosonde, surface, ships, aircraft and radio occultation observations, and the relatively weaker influence of satellite radiance observations.There is broad consensus among the global numerical weather prediction centers that these types of observations tend to be the highest-ranked contributors to forecast skill: Microwave temperature sounder, hyper-spectral infrared sounder, radiosondes, aircraft observations, radio occultation and atmospheric motion vectors, although not necessarily uniformly in this order. However, contributions of the microwave temperature sounder and hyper-spectral infrared sounder in GRAPES are not dominant, because GRAPES still uses less radiance data, and on the other hand, the bias correction effect is not so good.Contributions of wind and humidity observation are less in GRAPES. Additionally, biases in regions of the Tibet Plateau, upper levels of the model and the tropics are relatively larger compared to observations and the reanalysis, which are related to the treatment method of topography and upper boundary of model. To gain better analysis and forecast skill, there is a requirement to place more emphasis on the above issues.