The CMA-ChemRA (China Regional Weakly Coupled Chemical-Weather Reanalysis System) was developped using China's first-generation global atmospheric reanalysis product (CRA-40) as initial fields and boundary conditions, coupled with the WRF-Chem atmospheric chemical model and the WRFDA/3DVar assimilation system. By constructing a joint background error covariance matrix, CMA-ChemRA achieves weak coupling between atmospheric chemistry and meteorological variables, enabling simultaneous assimilation of diverse data sources, including hourly observations from ground stations, wind profilers, upper-air soundings, aircraft reports, and atmospheric composition measurements. To extend the dataset to periods before 2013 when China lacked PM2.5 observations, the system incorporates a reconstructed PM2.5 dataset derived by AI from visibility inversion alongside various emission inventories. The CMA-ChemRA system produces a reanalysis product from 2007 to the present, with a spatial resolution of 15 km and an hourly temporal resolution. It includes three-dimensional isobaric and near-surface layers for 6 key elements PM2.5, PM10, O3, SO2, NO2, and CO, as well as meteorological variables. This product is updated in near real-time, with a 50-min lag for forecast updates. Evaluation of the system shows substantial improvements in accuracy, with significant reductions in root mean square error (RMSE) for the six elements in the near-surface atmospheric layer post-assimilation. The model's depiction of ground-level PM2.5 concentrations aligns well with independent observational data across five urban regions, showing a narrow RMSE range of 15.5 to 32.8 μg/m3. Additionally, CMA-ChemRA demonstrates strong performance in capturing the evolution of dust storms and pollution events, particularly in accurately modeling PM2.5 concentrations during severe pollution episodes. Our innovative approach in constructing a joint background error covariance matrix and the resulting high-resolution, real-time updating CMA-ChemRA product. This represents significant advancement in the field of atmospheric and chemical weather reanalysis. The product serves as an crucial tool for environmental monitoring and forecasting in China.

Atmospheric reanalysis reproduces the past atmospheric conditions through assimilation of historical meteorological observations with fixed version of a numerical weather prediction (NWP) model and data assimilation (DA) system. It is widely used in weather, climate, and even business-related research and applications. This paper reports the development of CMA’s first-generation global atmospheric reanalysis (RA) covering 1979–2018 (CRA-40; CRA refers to CMA-RA). CRA-40 is produced by using the Global Spectral Model (GSM)/Gridpoint Statistical Interpolation (GSI) at a 6-h time interval and a TL574 spectral (34-km) resolution with the model top at 0.27 hPa. A large number of reprocessed satellite data and widely collected conventional observations were assimilated during the reanalyzing process, including the reprocessed atmospheric motion vector (AMV) products from FY-2C/D/E/G satellites, dense conventional observations (at about 120 radiosonde and 2400 synoptic stations) over China, as well as MWHS-2 and GNSS-RO observations from FY-3C. The reanalysis fitting to observations is improved over time, especially for surface pressure with root-mean-square error reduced from 1.05 hPa in 1979 to 0.8 hPa, and for upper air temperature from 1.65 K in 1979 to 1.35 K, in 2018. The patterns of global analysis increments for temperature, specific humidity, and zonal wind are consistent with the changes in the observing system. Near surface temperature from the model’s 6-h forecast reflects the global warming trend reasonably. The CRA-40 precipitation pattern matches well with those of GPCP and other reanalyses. CRA-40 also successfully captures the QBO and its vertical and temporal development, hemispherical atmospheric circulation change, and moisture transport by the East Asian summer monsoon. CRA is now operationally running in near real time as a climate data assimilation system in CMA.

For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average Dm < 1.4 mm, and 4 < average log10Nw < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line.

多普勒天气雷达VAD(Velocity Azimuth Display)风廓线资料可以提供高时间分辨率的高空风场信息.本文针对中国雷达VAD风廓线资料,设计发展了面向资料同化应用的NMIC(国家气象信息中心)质量控制方案,该方案改进了NCEP(国家环境预报中心)方案中存在的问题.利用2020 年2-8 月的L波段秒级探空风场资料,分别对比质量控制前、经过NCEP质量控制方案、以及经过NMIC质量控制方案的VAD风廓线资料,统计分析结果表明经过NMIC质量控制方案的VAD风廓线资料最接近观测,准确度最高,并且在各个高度上其偏差和均方根误差最小,充分说明了NMIC质量控制方案的有效性.相对背景场偏差分析表明,经过NMIC质量控制方案的VAD资料偏差和均方根误差最小,与背景场的偏差更接近高斯分布,更能满足资料同化的要求.本文的研究有助于推进VAD风廓线资料在数值预报科研和业务中的应用.

面向中国第一代全球大气∕陆面再分析产品(CRA)的应用需求,针对中国风廓线雷达小时产品资料特点,在美国NCEP风廓线综合质量控制方法的基础上,提出一套适用于中国风廓线雷达逐小时水平风产品的质量控制方法.通过对比质量控制前后风廓线雷达资料与探空资料的相关系数、平均偏差及均方根误差,证明了质量控制方案的有效性.以ERA-Interim资料作为间接参考场,通过比较探空资料与不同型号、不同探测高度范围、不同观测时段、不同垂直层次风廓线雷达资料相对ERA-Interim再分析资料的偏差,分析了质量控制前后中国风廓线雷达资料的整体质量.结果表明,经该算法质量控制后,风廓线雷达与探空风场表现出了更好的一致性.不同雷达型号、不同探测高度资料的相关系数从0.17～0.82上升至0.79～0.98.在相对ERA-Interim与探空资料的偏差方面,质量控制后,除边界层风廓线雷达的u风分量在300 hPa以上仍有5 m?s-1左右的偏差外,其他型号雷达的u、v风分量在各垂直层的平均偏差均在3m?s-1以内,证明质量控制算法具有识别高层粗大误差数据的能力,能够使最大探测高度以上的数据得到有效利用.

This paper presents a detailed description of integration, quality assurance procedure, and usage of global aircraft observations for China’s first generation global atmospheric reanalysis (CRA) product (1979–2018). An integration method named “classified integration” is developed. Aircraft observations from nine different sources are integrated into the Integrated Global Meteorological Observation Archive from Aircraft (IGMOAA), a new dataset from the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA). IGMOAA consists of global aircraft temperature, wind, and humidity data from the surface to 100 hPa, extending from 1973 to the present. Compared with observations assimilated in the Climate Forecast System Reanalysis (CFSR) of NCEP, the observation number of IGMOAA increased by 12.9% between 2010 and 2014, mainly as a result of adding more Chinese Aircraft Meteorological Data Relay (AMDAR) data. Complex quality control procedures for aircraft observations of NCEP are applied to detect data errors. Observations are compared with ERA-Interim reanalysis from 1979 to 2018 to investigate data quality of different types and aircraft, and subsequently to develop the blacklists for CRA. IGMOAA data have been assimilated in CRA in 2018 and are real-time updated at the CMA Data-as-a-Service (CMADaaS) platform. For CRA, the fits to observations improve over time. From 1994 to 2018, root-mean-square error (RMSE) of observations relative to CRA background decreases from 1.8 to 1.0°C for temperature above 300 hPa, and from 4.5 to 3 m s −1 for zonal wind. The RMSE for humidity appears to exhibit an apparent seasonal variation with larger errors in summer and smaller ones in winter.

用于全球大气再分析的常规气象观测资料由表面(地面站、船舶和浮标等)和高空(无线电探空、测风气球、下投式探空、飞机和风廓线雷达等)观测资料组成.基于已有工作进展,总结了我国全球大气再分析(CRA-40,1979—2018年)常规观测资料预处理与同化应用研究进展.在CRA-40研发阶段,完成了15个地面、高空、飞机和海洋观测数据源的资料的收集整理、分类整合和质量评估.相对NCEP CFSR同化的常规观测资料,CRA-40同化了更多中国地区的地面站、常规探空观测资料,增加了美国地区的TAMDAR和ACARS飞机报湿度数据.CRA-40采用RAOBCORE 1.4数据集提供的订正量完成了全球探空温度的偏差订正,采用NCEP辐射订正算法减小太阳辐射引起的热敏电阻季节性观测误差.对再分析同化结果带来负面效果的观测(如高海拔地面站观测、模式地形层以下高空观测以及300 hPa以上探空湿度等)在观测资料预处理阶段被剔除.CRA-40采用的黑名单提供了不同时期观测资料存在问题的地面站、高空站、飞机、船舶和浮标等信息.这些信息来自观测资料和ERA-Interim再分析的偏差统计结果,以及NCEP CFSR、ECMWF提供的历史黑名单,后者在使用前进行了严格的诊断评估和确认.经过预处理和质量预评估的全球历史常规资料已应用于CRA-40的10年试验产品研制.