基于亮温通道变率的FY-3C微波湿度计陆地云检测新方法

云检测是卫星资料同化的重要前处理步骤,无论是晴空资料同化还是有云资料同化,都需要准确地区分有云和晴空资料。由于陆地地表发射率的多变性和微波能穿透部分云类的特点,微波湿度计资料在陆地上空的云检测研究一直是难点。利用快速辐射传输模式（CRTM）分析了不同云类条件下FY-3C微波湿度计（MWHS-Ⅱ）各通道亮温的通道间变率特征,根据MWHS-Ⅱ亮温通道间变率随云高以及云中液态水含量的增大而减小的特点,提出了一个基于亮温通道变率的MWHS-Ⅱ陆地资料云检测方法。与已有的云产品比较结果表明:新的云检测算法能有效地剔除大部分受云影响的资料,剔除后的晴空资料观测和模拟偏差更好地符合高斯分布。新方法对过冷水云、冰云、重叠云的检测能力较强,正确检测率可以达到80%,对卷云以及高度较低的水云的检测能力相对较弱。新方法能有效利用MWHS-Ⅱ观测资料自身完成云检测,在MWHS-Ⅱ资料同化中有很好的应用前景。      

Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China

Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system(WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager(AHI)radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.     

MWHS/FY-3资料同化在四川盆地暴雨预报中的应用研究

为了研究同化风云三号B星(FY-3B)和C星(FY-3C)的微波湿度计(MWHS及MWHS-2)观测资料在四川暴雨数值预报中的影响,本文基于Weather Research and Forecasting Model(WRF)及其三维变分同化系统Weather Research Forecast Variatinal Data Assimilation System(WRFDA),实现了对MWHS/FY-3B和MWHS-2/FY-3C观测资料的直接同化。针对2018年7月的一次四川盆地区域性暴雨过程的同化试验结果表明:同化风云三号系列卫星的微波湿度计观测资料对试验开始时刻均有改善,对相对湿度和矢量风场等物理量场有一定的正向调整作用,尤其是同化MWHS-2/FY-3C资料对风场的调整较为明显。同化试验对龙门山北部降水有较明显的改善作用,改善了降水的分布与落区,其中同化MWHS/FY-3B对盆地中部到东北部的降水量级的预报更接近实况,雨区更为连续。同化试验证明了同化风云三号系列卫星的微波湿度计观测资料对于四川盆地暴雨数值预报有一定的业务应用价值。      

Assimilation of the FY-4A AGRI Clear-Sky Radiance Data in a Regional Numerical Model and Its Impact on the Forecast of the “21·7” Henan Extremely Persistent Heavy Rainfall

Assimilation of the Advanced Geostationary Radiance Imager(AGRI) clear-sky radiance in a regional model is performed. The forecasting effectiveness of the assimilation of two water vapor(WV) channels with conventional observations for the “21·7” Henan extremely heavy rainfall is analyzed and compared with a baseline test that assimilates only conventional observations in this study. The results show that the 24-h cumulative precipitation forecast by the assimilation experiment with the addition ...     

Assimilation of FY-3D MWTS-Ⅱ Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors. The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally, without considering the three-dimensional distribution of clouds.Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the a...     

Impact of assimilating FY-3C MWHS2 data in the RMAPS-ST forecast system on its rainfall forecasts

中层水汽初值的误差是数值预报,尤其是短期预报不确定性的重要来源.星载微波湿度计资料能很好地弥补常规资料的不足,同化微波湿度计资料对改善对流层中高层的水汽初始场精度有着重要意义.本研究基于新一代快速更新多尺度资料分析和预报系统短期数值预报系统(RMAPS-ST),选取2019年6月4-6日中国华中地区降水过程开展了 FY-3C MWHS2资料同化对于此次降水预报的影响评估.研究开展了4组试验:冷启动试验,控制试验,同化FY-3C MWHS试验,同时同化常规观测和FY-3C MWHS试验.结果表明:经质量控制及偏差订正之后FY-3C MWHS2各通道O-B明显趋于正态分布,同化FY-3C MWHS资料后,对此次降水过程降水落区和强度的预报均优于控制试验和冷启动试验,同时对于环境场的预报也有改善.     

Growing Operational Use of FY-3 Data in the ECMWF System

This paper reviews the data quality and impact of observations from the FY-3 satellite series used operationally in the ECMWF system. This includes data from the passive microwave radiometers MWHS-1, MWHS-2 and MWRI, as well as observations from the radio occultation receiver GNOS. Evaluations against background equivalents show that the quality of the observations is broadly comparable to that of similar instruments on other polar-orbiting satellites, even though biases for the passive microwave observations can be somewhat larger and more complex for some channels. An observing system experiment shows that the FY-3 instruments jointly contribute significantly to the forecast skill in the ECMWF system. Positive impact of up to 2% is seen for most variables out to the day-2 forecasts over hemispheric scales, with significant benefits for total column water vapor or for temperature and wind in the stratosphere out to day 4.     

Assessment and Assimilation of FY-3 Humidity Sounders and Imager in the UK Met Office Global Model

China's Feng Yun 3(FY-3) polar orbiting satellites are set to become an important source of observational data for numerical weather prediction(NWP),atmospheric reanalyses,and climate monitoring studies over the next two decades.As part of the Climate Science for Service Partnership China(CSSP China) program,FY-3 B Microwave Humidity Sounder 1(MWHS-1) and FY-3 C MWHS-2 observations have been thoroughly assessed and prepared for operational assimilation.This represents the first time observations from China's polar orbiting satellites have been used in the UK's global NWP model.Since 2016,continuous data quality monitoring has shown occasional bias changes found to be correlated to changes in the energy supply scheme regulating the platform heating system and other transient anomalies.Nonetheless,MWHS-1 and MWHS-2 significantly contribute to the 24-h forecast error reduction by 0.3% and 0.6%,respectively,and the combination of both instruments is shown to improve the fit to the model background of independent sounders by up to 1%.The observations from the Microwave Radiation Imager(MWRI) also are a potentially significant source of benefits for NWP models,but a solar-dependent bias observed in the instrument half-orbits has prevented their assimilation.This paper presents the bases of a correction scheme developed at the Met Office for the purpose of a future assimilation of MWRI data.     

利用卫星微波观测亮温与云辐射模拟亮温的台风定位分析北大核心

极轨气象卫星S-NPP、MetOp-A和FY-3B上搭载的微波湿度计观测资料可以反映出台风周围水汽和云雨结构。本文使用权重函数峰值在800 hPa附近的微波湿度计通道观测资料和ERA5再分析资料全天空模拟亮温,以飓风Sandy和Isaac为例,对用方位谱台风中心位置定位方法得到的观测和模拟中心位置进行了比较。利用下午星S-NPP搭载的先进技术微波探测仪(Advanced Technology Microwave Sounder,ATMS)和上午星MetOp-A搭载的微波湿度计(Microwave Humidity Sounder,MHS)观测亮温得到的飓风Sandy(Isaac)中心位置与最佳路径平均相差35.8 km(32.9 km),但用ERA5全天空模拟亮温得到的飓风Sandy(Isaac)中心位置与最佳路径平均相差73.3 km(82.1 km)。若按照热带风暴和台风等级来划分,ATMS和MHS观测和模拟亮温得到的台风中心位置与最佳路径的平均距离对热带风暴分别是36.5 km和105.9 km,对台风分别是25.8 km和56.4 km。若用FY-3B搭载的微波湿度计(以MWHS表示)替换ATMS,所得结果类似。ERA5作为全球大气再分析资料的典型代表,用方位谱台风中心位置定位方法得到的台风中心位置误差较大的原因是ERA5再分析资料全天空模拟亮温在台风中的分布结构与观测亮温相差较大,而模拟亮温与冰水路径分布结构极为相似。研究对台风数值预报中的全天空模拟亮温资料同化具有一定的参考意义。     

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.     

有云环境下MODIS亮温资料的变分同化Ⅱ——对暴雨预报的影响

卫星资料提供了大量关于云和雨的观测信息,在暴雨预报中可发挥巨大的作用,然而在数值模式资料同化中的应用水平仍然不高,特别是红外辐射资料的应用。由于有云环境下辐射传输过程的模拟难度很大,因此通常只同化晴空环境下的红外辐射资料。基于GRAPES-3DVAR(Global and Regional Assimilation and Prediction Enhanced System,全球/区域同化预报系统),根据RTTOV辐射传输模式(fast radiative transfer model for TOVS,快速辐射传输模式)的特点,增加云水含量、云冰水含量和云量作为同化系统控制变量,在改进辐射传输模式对红外资料模拟的同时,利用红外资料调整初始云参数和大气参数。针对2007年5月26日南海季风爆发后广东地区的一次暴雨过程,选取MODIS(Moderate Resolution Imaging Spectroradiometer,中分辨成像光谱仪)传感器水汽(第27)和云顶观测(第36)通道进行了同化试验,利用WRF(Weather Research and Forecasting Model,天气研究和预报模式)进行了数值模拟,结果表明同化MODIS资料,可以改进初始场水汽和温度分布,间接调整高空风场,调整趋势符合卫星观测,对短时降水预报有正面影响。     

Improving the Analyses and Forecasts of a Tropical Squall Line Using Upper Tropospheric Infrared Satellite Observations

The advent of modern geostationary satellite infrared radiance observations has noticeably improved numerical weather forecasts and analyses.However,compared to midlatitude weather systems and tropical cyclones,research into using infrared radiance observations for numerically predicting and analyzing tropical mesoscale convective systems remain mostly fallow.Since tropical mesoscale convective systems play a crucial role in regional and global weather,this deficit should be addressed.This study is the first of its kind to examine the potential impacts of assimilating all-sky upper tropospheric infrared radiance observations on the prediction of a tropical squall line.Even though these all-sky infrared radiance observations are not directly affected by lower-tropospheric winds,the high-frequency assimilation of these all-sky infrared radiance observations improved the analyses of the tropical squall line’s outflow position.Aside from that,the assimilation of all-sky infrared radiance observations improved the analyses and prediction of the squall line’s cloud field.Finally,reducing the frequency of assimilating these all-sky infrared radiance observations weakened these improvements to the analyzed outflow position,as well as the analyses and predictions of cloud fields.     

Assimilation of Feng-Yun-3B Satellite Microwave Humidity Sounder Data over Land

The ECMWF has been assimilating Feng-Yun-3B(FY-3B) satellite microwave humidity sounder(MWHS) data over ocean in an operational forecasting system since 24 September 2014. It is more difficult, however, to assimilate microwave observations over land and sea ice than over the open ocean due to higher uncertainties in land surface temperature, surface emissivity and less effective cloud screening. We compare approaches in which the emissivity is retrieved dynamically from MWHS channel 1 [150 GHz(vertical polarization)] with the use of an evolving emissivity atlas from 89 GHz observations from the MWHS onboard NOAA and EUMETSAT satellites. The assimilation of the additional data over land improves the fit of short-range forecasts to other observations, notably ATMS(Advanced Technology Microwave Sounder) humidity channels, and the forecast impacts are mainly neutral to slightly positive over the first five days. The forecast impacts are better in boreal summer and the Southern Hemisphere. These results suggest that the techniques tested allow for effective assimilation of MWHS/FY-3B data over land.     

DRP-4DVar方法同化AIRS反演资料在一次江淮流域暴雨中的应用

利用经济省时的降维投影四维变分同化方法(DRP-4DVar),在2009年7月22～23日江淮流域的一次大暴雨过程中同化晴空条件下高光谱大气红外探测仪(AIRS)反演温度、湿度廓线,改进此次强降水过程的模拟。试验结果分析显示,同化AIRS反演的温度及湿度场后,基于四维变分同化系统的模式约束,能够改进湿度场、高度场、高低层散度场。从累积降水量偏差图及同化试验增量图可以看到,正降水量偏差对应于正湿度增量、负位势高度增量及低层负散度高层正散度增量,负降水量偏差则与之相反。同化试验较参照试验可更好地模拟出暴雨的天气形势、对暴雨的落区及强度有更好的反映。此外,从单次同化与连续同化的试验对比结果看出,连续同化试验结果较单次同化结果有进一步的改进,说明不断加入新的观测资料可以更好地模拟强降水过程。     

Rainfall Algorithms Using Oceanic SatelliteObservations from MWHS-2

正1EarthEnvironmental Sciences Department, Vanderbilt University, Nashville, TN 37235, USA2Space ScienceEngineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA     

Insights into the Microwave Instruments Onboard the Fengyun 3DSatellite: Data Quality and Assimilation in theMet Office NWP System

正1Met Office, Exeter EX1 3PB, UK2National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China     

GNSS反演资料在GRAPES_Meso三维变分中的应用

为了进一步提高GRAPES_Meso的分析和预报效果,该文在GRAPES_Meso三维变分同化系统中建立了同化GNSS/RO反演的大气资料的观测算子,实现了对GNSS/RO反演的大气资料的同化应用,并通过2013年7月1个月的同化和预报试验分析了GNSS/RO反演大气资料对GRAPES_Meso模式系统分析和预报的影响。结果表明:增加了GNSS/RO反演大气资料的同化后,GRAPES_Meso位势高度场的分析误差明显减小,平均分析误差减小约8%,预报误差略有减小,平均预报误差减小约1%;湿度场的分析误差和预报误差变化不明显,常规观测资料稀少的青藏高原地区的降水预报技巧有所提高,小雨到大雨的ETS (equitable threat score) 评分提高约0.01,对全国及其他分区的降水预报技巧总体上有正效果。     

Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso-β Scale Heavy Precipitation Event During the 2002 Mei-Yu Season

Recent advances in Global Positioning System （GPS） remote sensing technology allow for a direct estimation of the precipitable water vapor （PWV） from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational （4DVAR） data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems （MCS） along the mei-yu front in China. The experiments with GPS-PWV assimilation cluster and also eliminated the erroneous rainfall successfully simulated the evolution of the observed MCS systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.     

FY-2C云迹风资料同化应用对台风预报的影响试验研究

针对0505号台风“海棠”, 采用WRF区域中尺度模式进行控制试验和两个同化试验, 利用WRF-3DVAR同化系统同化FY-2C红外和水汽两个通道云迹风反演产品, 同化分云迹风经质量控制和未经质量控制两组同化试验。通过三组试验分析云迹风资料对降水和风场等的预报结果的影响, 并进行24小时降水量分级Ts评分检验以及风场点对点检验。结果表明: 同化经质量控制云迹风资料可以提高降水落区和强度预报的准确度, 不同等级的Ts评分较其它试验都有较明显改进; 风场预报模拟也有所改善。增加两例台风, 使用与“海棠” 相似的处理方法进行模拟试验, 并对模拟结果24小时降水分析与检验, 得到与“海棠”类似结论。因此, 经过合理性选择的云迹风资料的加入, 有利于补充初始场中可能未包含的中尺度信息, 从而提高试验中对于降水、风场等的模拟效果, 提高WRF模式的模拟预报能力。     

PRELIMINARY STUDY ON THE INFLUENCE OF FY-4 LIGHTNING DATA ASSIMILATION ON PRECIPITATION PREDICTIONS

Data from the lightning mapping imager on board the Fengyun-4 meteorological satellite (FY-4) were used to study the assimilation of lightning data and its influence on precipitation predictions. We first conducted a quality control check on the events observed by the first Fengyun-4 satellite (FY-4A) lightning mapping imager, after which the noise points were removed from the lightning distribution. The subsequent distribution was more consistent with the spatial distribution and range of ground-based observations and precipitation. We selected the radar reflectivity, which was closely related to the lightning frequency, as the parameter to assimilate the lightning data and utilized a large sample of lightning frequency and radar reflectivity data from the eastern United States provided by Vaisala. Based on statistical analysis, we found the empirical relationship between the lightning frequency and radar reflectivity and established a look-up table between them. We converted the lightning event data into radar reflectivity data and found that the converted reflectivity and composite reflectivity of ground-based radar observations showed high consistency. We further assimilated the lightning data into the model, adjusted the model cloud analysis process and adjusted the model hydrometeor field by using the lightning data. A rainstorm weather process that occurred on August 8, 2017, in south China was used for the numerical forecast experiment, and three experiments were designed for comparison and analysis: a control experiment, an experiment without the assimilation of FY-4 lightning data (NoLig), and an experiment with the assimilation of FY-4 lightning data (Lig). The results show that after assimilating the FY-4A lightning data, the accuracies of the intensity, central location and range of the precipitation predicted by the Lig experiment were obviously superior to those predicted by the control and NoLig experiments, and the effect was especially obvious in the short-term (1–2 hour) forecast. The studies in this paper highlight the application value and potential of FY-4 lightning data in precipitation predictions.