Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.

Evaluation of CLDAS and GPM Precipitation Products over the Tibetan Plateau in Summer 2005–2021 Based on Hourly Rain Gauge Observations

Accurate,reliable,and high spatiotemporal resolution precipitation products are essential for precipitation research,hydrological simulation,disaster warning,and many other applications over the Tibetan Plateau(TP).The Global Precipitation Measurement(GPM) data are widely recognized as the most reliable satellite precipitation product for the TP.The China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS) precipitation fusion dataset(CLDAS-Prcp),hereafter referred to as CLDAS,is a high-resolution,self-developed precipitation product in China with regional characteristics.Focusing on the TP,this study provides a long-term evaluation of CLDAS and GPM from various aspects,including characteristics on different timescales,diurnal variation,and elevation impacts,based on hourly rain gauge data in summer from 2005 to 2021.The results show that CLDAS and GPM are highly effective alternatives to the rain gauge records over the TP.They both perform well for precipitation amount and frequency on multiple timescales.CLDAS tends to overestimate precipitation amount and underestimate precipitation frequency over the TP.However,GPM tends to overestimate both precipitation amount and frequency.The difference between them mainly lies in the trace precipitation.CLDAS and GPM effectively capture rainfall events,but their performance decreases significantly as intensity increases.They both show better accuracy in diurnal variation of precipitation amount than frequency,and their performance tends to be superior during nighttime compared to the daytime.Nevertheless,there are some differences of the two against rain gauge observations in diurnal variation,especially in the phase of the diurnal variation.The performance of CLDAS and GPM varies at different elevations.They both have the best performance over 3000–3500 m.The elevation dependence of CLDAS is relatively minor,while GPM shows a stronger elevation dependence in terms of precipitation amount.GPM tends to overestimate the precipitation amount at lower elevations and underestimate it at higher elevations.CLDAS and GPM exhibit unique strengths and weaknesses;hence,the choice should be made according to the specific situation of application.

New Version of the CMA-GFS Dynamical Core Based on the Predictor–Corrector Time Integration Scheme

The operational numerical weather prediction system established by the China Meteorological Administration(CMA),based on the Global/Regional Assimilation and Prediction System(GRAPES)model,adopts the classical semi-implicit semi-Lagrangian(SISL)time integration algorithm.This paper describes a major upgrade to the dynamical core of the CMA global forecast system(CMA-GFS),which was successfully incorporated into operation in 2020.In the upgrade,the classical SISL is further developed into a predictor–corrector scheme,a three-dimensional(3D)reference profile instead of the original isothermal reference profile is applied when implementing the semi-implicit algorithm,and a hybrid terrain-following vertical coordinate system is also applied.The new version of the dynamical core greatly improves the model performance,the time integration reaches second-order accuracy,the time step can be extended by 50%,and the efficiency is greatly improved(by approximately 30%).Atmospheric circulation simulation is systematically improved,and deviations in temperature,wind,and humidity are reduced.The new version of the dynamical core provides a solid foundation for further development of the entire operational system of the CMA.

Effect of 2-m Temperature Data Assimilation in the CMA-MESO 3DVAR System

Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study confirmed that the T_(2m)data assimilation scheme that we implemented in the kilometer-scale CMA-MESO 3DVAR system is effective.

A Recombination Clustering Technique for Forecasting of Tropical Cyclone Tracks Based on the CMA-TRAMS Ensemble Prediction System

Despite marked improvements in tropical cyclone(TC) track ensemble forecasting,forecasters still have difficulty in making quick decisions when facing multiple potential predictions,so it is demanding to develop post-processing techniques reducing the uncertainty in TC track forecasts,and one of such techniques is the cluster-based methods.To improve the effect and efficiency of the previous cluster-based methods,this study adopts recombination clustering(RC) by optimizing the use of limited TC variables and constructing better features that can accurately capture the good TC track forecasts from the ensemble prediction system(EPS) of the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS).The RC technique is further optimized by constraining the number of clusters using the absolute track bias between the ensemble mean(EM) and ensemble spread(ES).Finally,the RC-based deterministic and weighted probabilistic forecasts are compared with the TC track forecasts from traditional methods.It is found that(1) for deterministic TC track forecasts,the RC-based TC track forecasts outperform all other methods at 12–72-h lead times;compared with the skillful EM(118.6 km),the improvements introduced by the use of RC reach up to 10.8%(8.1 km),10.2%(13.7 km),and 8.7%(20.5 km) at forecast times of 24,48,and 72 h,respectively.(2) For probabilistic TC track forecasts,RC yields significantly more accurate and discriminative forecasts than traditional equal-weight track forecasts,by increasing the weight of the best cluster,with a decrease of 4.1% in brier score(BS) and an increase of 1.4% in area under the relative operating characteristic curve(AUC).(3) In particular,for cases with recurved tracks,such as typhoons Saudel(2017) and Bavi(2008),RC significantly reduces track errors relative to EM by 56.0%(125.5 km) and 77.7%(192.2 km),respectively.Our results demonstrate that the RC technique not only improves TC track forecasts but also helps to unravel skillful ensemble members,and is likely useful for feature construction in machine learning.