Latest Progress of the Chinese Meteorological Satellite Program and Core Data Processing Technologies

In this paper,the latest progress,major achievements and future plans of Chinese meteorological satellites and the core data processing techniques are discussed.First,the latest three FengYun(FY)meteorological satellites(FY-2H,FY-3D,and FY-4A)and their primary objectives are introduced Second,the core image navigation techniques and accuracies of the FY meteorological satellites are elaborated,including the latest geostationary(FY-2/4)and polar-orbit(FY-3)satellites.Third,the radiometric calibration techniques and accuracies of reflective solar bands,thermal infrared bands,and passive microwave bands for FY meteorological satellites are discussed.It also illustrates the latest progress of real-time calibration with the onboard calibration system and validation with different methods,including the vicarious China radiance calibration site calibration,pseudo invariant calibration site calibration,deep convective clouds calibration,and lunar calibration.Fourth,recent progress of meteorological satellite data assimilation applications and quantitative science produce are summarized at length.The main progress is in meteorological satellite data assimilation by using microwave and hyper-spectral infrared sensors in global and regional numerical weather prediction models.Lastly,the latest progress in radiative transfer,absorption and scattering calculations for satellite remote sensing is summarized,and some important research using a new radiative transfer model are illustrated.

Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity

Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.

Using SWAT Model and Field Data to Determine Potential of NASA-POWER Data for Modelling Rainfall-Runoff in Incalaue River Basin

Incalaue is a tributary of Lugenda River in NSR (Niassa Special Reserve) in North-Eastern Mozambique. NSR is a data-poor remote area and there is a need for rainfall-runoff data to inform decisions on water resources management, and scientific methods are needed for this wide expanse of land. This study assessed the potential of a combination of NASA-POWER (National Aeronautics and Space Administration and Prediction of Worldwide Energy Resources) remotely sensed rainfall data and FAO (Food and Agriculture Organization of the United Nations) soil and land use/cover data for modelling rainfall-runoff in Incalaue river basin. DEM (Digital Elevation Model) of 1:250,000 scale and a grid resolution of 30 m × 30 m downloaded from USGS (the United States Geological Survey) website;clipped river basin FAO digital soil and land use/cover maps;and field-collected data were used. SWAT (Soil and Water Assessment Tool) model was used to assess rainfall -runoff data generated using the NASA-POWER dataset and gauged rainfall and river flow data collected during fieldwork. FAO soil and land use/cover datasets which are globally available and widely used in the region were used for comparison with soil data collected during fieldwork. Field collected data showed that soil in the area is predominantly sandy loam and only sand content and bulk density were uniformly distributed across the soil samples. SWAT model showed a good rainfall-runoff relationship using NASA-POWER data for the area (R2 = 0.7749) for the studied period (2019-2021). There was an equally strong rainfall-runoff relationship for gauged data (R2 = 0.8131). There were uniform trends for the rainfall, temperature, and relative humidity in NASA-POWER meteorological data. Timing of peaks and lows in rainfall and river flow observed in the field and modelled were confirmed by residents as the trend in the area. This approach was used because there was no historical rainfall and river flow data since the river basin is ungauged for hydrologic data. The study showed that NASA-POWER data has the potential for use for modelling the rainfall-runoff in the basin. The difference in rainfall-runoff relationship with field-collected data could be because of landscape characteristics or topsoil layer not catered for in the FAO soil data.

基于不同数据源的数字孪生小流域底板模型精度检验

为解决小流域综合治理规划设计中基础地形数据获取困难等问题,基于合成孔径雷达卫星遥感影像、倾斜摄影影像、光探测测距影像等3种主流遥感数据源,分别建立栖龙湾小流域数字孪生底板模型,进而提取主沟道纵断面、2块梯田样地边界、10个林地样区的郁闭度,与人工解译和实测结果对比进行精度检验,结果表明:以光探测测距影像为数据源的提取精度最高,以倾斜摄影影像为数据源的提取精度次之,以孔径雷达卫星遥感影像为数据源的提取精度最低。鉴于3种数据源小流域底板模型各有优缺点,可根据实际工作需要进行多源数据融合,以改善数字孪生技术在小流域综合治理规划设计中的应用效果。在栖龙湾小流域内选择石坎梯田地块进行合成孔径雷达卫星遥感影像与倾斜摄影影像数据融合、选择科普展览馆进行倾斜摄影影像与光探测测距影像数据融合,均取得良好的效果。

应用自适应HP滤波的卫星遥测数据预测方法

在应用HP(Hodrick-Prescott)滤波的卫星遥测数据预测方法的基础上进一步研究,提出了基于二分法和琴生不等式判别的自适应HP滤波的遥测数据预测方法,有效解决了平滑参数的选择困难。同时,趋势项和波动项预测在应用HP滤波的方法的基础上有创新和优化,分别采用了线性预测模型和自回归单整移动平均(ARIMA)模型,有利于有效趋势特征提取和模型参数调整范围的优化。应用文章方法对卫星在轨行波管阳极电压数据进行分析,证实了方法的正确性和有效性。此方法可有效提高预测精度,实现遥测数据中长期预测,在卫星故障诊断和预警方面具有工程应用价值。

乌梁素海湖冰完整生消过程的数值模拟

为探究气候变暖背景下乌梁素海湖冰的冰厚、冰温和冰物候特征的变化,利用ERA5-Land和MERRA-2再分析数据作为大气强迫场,结合多源卫星遥感反演得到的初始模拟日期,通过一维高分辨率热力学雪冰(HIGHTSI)模型模拟了2015—2016年乌梁素海湖冰的完整生消过程。结果显示:①研究期内,最大冰厚达到41.7 cm,初冰日和终冰日分别为2015年11月21日和2016年3月25日,冰期为126 d。②对于水深较浅而冬季日照充足的乌梁素海,气温是影响冰厚的主要因素,太阳辐射次之,两者的昼夜周期循环显著影响冰层的厚度和温度;当冰面有雪覆盖时,积雪的低导热和高反照率会明显削弱气温和太阳辐射对冰层的影响。③即使缺失现场观测数据,采用气象数据和遥感反演的初始模拟日期仍能准确地表征现场真实冰雪的完整演变过程。该研究可为中纬度干旱区季节性冰封浅水湖冰的计算和湖冰生消的年际变化研究奠定基础。

飓风中卫星臭氧总柱观测与位涡相关性和在飓风中心定位中的应用

本文使用搭载于极轨气象卫星S-NPP(Suomi National Polar-orbiting Partnership)的臭氧成像廓线仪(Ozone Mapping and Profiler Suite,OMPS)观测的臭氧总柱(Total Column Ozone,TCO)资料对大西洋飓风“Maria”(2017)进行研究。针对对流区域TCO与气柱平均位涡(Mean column-integrated Potential Vorticity,MPV)的低相关性构建了分区域线性回归模型,不仅能同时提高大尺度和对流尺度上TCO和MPV的相关性,还能提高对流区域S-NPP OMPS TCO观测资料的利用率(95.5%)。TCO观测资料可以清晰地反映出飓风的顶部结构,据此可得到对流层顶附近的飓风中心位置。卫星总柱臭氧资料对飓风Maria中心定位结果与最佳路径的距离相差58.1 km,略大于使用S-NPP先进技术微波探测仪(Advanced Technology Microwave Sounder,ATMS)、MetOp-A微波湿度计(Microwave Humidity Sounder,MHS)和FY-3B微波湿度计(MWHS)亮温观测资料得到的Maria中心定位结果与最佳路径的距离(47.5 km)。臭氧定位结果与垂直风切变(Vertical Wind Shear,VWS)具有一定关系,垂直风切变越强,臭氧资料定位中心偏离最佳路径越大。本研究构建的分区域线性回归模型可作为间接同化卫星TCO资料的观测算子,也可应用到涡旋初始化中,以改进飓风路径和强度预报水平。

A Fast Insertion Tabu Search with Conflict-Avoidance Heuristic for the Multisatellite Multimode Crosslink Scheduling Problem

An agile earth-observing satellite equipped with multimode cameras capable of transmitting observation data to other satellites is developed to rapidly respond to requests with multiple observation modes.This gives rise to the Multisatellite Multimode Crosslink Scheduling(MMCS)problem,which involves allocating observation requests to agile satellites,selecting appropriate timing and observation modes for the requests,and transmitting the data to the ground station via the satellite communication system.Herein,a mixed integer programming model is introduced to include all complex time and operation constraints.To solve the MMCS problem,a two-stage heuristic method,called Fast insertion Tabu Search with Conflict-avoidance(FTS-C)heuristic,is developed.In the first stage,a conflict-avoidance insertion algorithm is designed to generate a high-quality initial solution by considering the requests transmission and download.Further,the tabu search-based second stage optimizes the initial solution.Finally,an extensive empirical study based on a real-world situation demonstrates that FTS-C can generate a solution with higher quality in less time than other state-of-the-art algorithms and the CPLEX solver.

基于地理模型和多源遥感的林草生态综合监测方法——以日照市莒县为例

随着生态系统保护工作的不断推进,林草生态系统成为人们关注的重点问题。分析林草生态监测现状可确定目前林草生态监测存在的问题。针对当前林草生态综合监测过程,由于遥感图像分析能力不足,造成监测结果精度不足等问题,提出基于地理模型和多源遥感的林草生态综合监测方法。采集遥感图像,完成3D标定及预处理工作。通过纹理特征提取与支持向量机算法,对林草生态环境遥感影像进行判读。应用多源遥感技术构建地理加权回归模型,实现林草地图斑监测。将图斑带入马尔科夫矩阵模型中,获取林草生态综合变化监测结果。至此,基于地理模型和多源遥感的林草生态综合监测方法设计完成。通过实验证实,此方法的遥感图像分类精度以及监测精度均优于当前方法。

无基点海洋重力测量数据归算补救方法

针对海洋重力测量中可能出现基点信息缺失(也就是无有效基点信息,统称无基点)的一些特殊情况,在分析评估国际上近期发布的全球重力位模型和卫星测高重力模型的可靠性及有效性基础上,提出了利用前述两类模型作为海域重力场中长波控制基准,进而开展无基点海洋重力测量数据归算的补救方法研究,给出了数据归算的流程和步骤,分析评估了补救处理方法的适用条件和精度水平,使用海上实际观测数据对推荐的补救方法进行了有效性验证,证明将两类模型应用于无基点海洋重力测量数据补救处理是有效可行的。

高分立体卫星影像建设基础三维模型研究

实景三维中国建设总体实施方案提出,截至2025年要建成覆盖地级以上城市城镇开发边界范围的基础三维模型数据。为提高实景三维中国基础三维模型数据生产效率,本文基于高分立体卫星影像精准定向精化的RPC参数构建立体模型并生成精准几何信息的DSM、DEM、DOM成果,对全立测采集、基于DOM采集等不同技术方法获取的建筑轮廓以及立测、基于DEM/DSM获取的高程高度信息等精度差异进行了分析,并以哈尔滨市为试验区,验证了基于高分立体卫星影像大规模生产基础三维模型数据的技术路线及可行性。

融合多源数据快速构建大规模实景三维建筑白模方法研究

针对传统的大规模城市级实景三维建模费用高、生产周期长、数据量大等问题,本文采用以高分辨率立体卫星影像为主,综合利用已有二维形式表达的基础地理实体、DEM、TDOM等数据构建城市级实景三维建筑白模的方法,并通过基于北京三号国际合作星立体卫星影像在哈尔滨主城区开展的建筑白模生产试验验证了该方法的有效性——可大幅度提高生产效率、降低生产成本,为支撑城市级实景三维大范围、深层次、全民化的应用赋能。

A Hybrid Coupled Model for the Pacific Ocean–Atmosphere System.Part I: Description and Basic Performance

A hybrid coupled model （HCM） is constructed for El Nifio-Southern Oscillation （ENSO）-related modeling studies over almost the entire Pacific basin.An ocean general circulation model is coupled to a statistical atmospheric model for interannual wind stress anomalies to represent their dominant coupling with sea surface temperatures.In addition,various relevant forcing and feedback processes exist in the region and can affect ENSO in a significant way; their effects are simply represented using historical data and are incorporated into the HCM,including stochastic forcing of atmospheric winds,and feedbacks associated with freshwater flux,ocean biology-induced heating （OBH）,and tropical instability waves （TIWs）.In addition to its computational efficiency,the advantages of making use of such an HCM enable these related forcing and feedback processes to be represented individually or collectively,allowing their modulating effects on ENSO to be examined in a clean and clear way.In this paper,examples are given to illustrate the ability of the HCM to depict the mean ocean state,the circulation pathways connecting the subtropics and tropics in the western Pacific,and interannual variability associated with ENSO.As satellite data are taken to parameterize processes that are not explicitly represented in the HCM,this work also demonstrates an innovative method of using remotely sensed data for climate modeling.Further model applications related with ENSO modulations by extratropical influences and by various forcings and feedbacks will be presented in Part Ⅱ of this study.

Satellite Data Assimilation of Upper-Level Sounding Channels in HWRF with Two Different Model Tops

The Advanced Microwave Sounding Unit-A（AMSU-A） onboard the NOAA satellites NOAA-18 and NOAA-19 and the European Organization for the Exploitation of Meteorological Satellites（EUMETSAT）Met Op-A, the hyperspectral Atmospheric Infrared Sounder（AIRS） onboard Aqua, the High resolution Infra Red Sounder（HIRS） onboard NOAA-19 and Met Op-A, and the Advanced Technology Microwave Sounder（ATMS） onboard Suomi National Polar-orbiting Partnership（NPP） satellite provide upper-level sounding channels in tropical cyclone environments. Assimilation of these upper-level sounding channels data in the Hurricane Weather Research and Forecasting（HWRF） system with two different model tops is investigated for the tropical storms Debby and Beryl and hurricanes Sandy and Isaac that occurred in 2012. It is shown that the HWRF system with a higher model top allows more upper-level microwave and infrared sounding channels data to be assimilated into HWRF due to a more accurate upper-level background profile. The track and intensity forecasts produced by the HWRF data assimilation and forecast system with a higher model top are more accurate than those with a lower model top.

Analyzing the Mara River Basin Behaviour through Rainfall-Runoff Modeling

Hydrological models are considered as necessary tools for water and environmental resource management. However, modelling poorly gauged watersheds has been a challenge to hydrologists and hydraulic engineers. Research done recently has shown the potential to overcome this challenge through incorporating satellite based hydrological and meteorological data in the measured data. This paper presents results for a study that used the semi-distributed conceptual HBV Light Model to model the rainfall-runoff in the Mara River Basin, Kenya. The model simulates runoff as a function of rainfall. It is built on the basis established between satellite observed and in-situ rainfall, evaporation, temperature and the measured runoff. The model’s performance and reliability were evaluated over two sub-catchments namely: Nyangores and Amala in the Mara River Basin using the Nash-Sutcliffe Efficiency which the model referred to as Reff and the coefficient of determination (R2). The Reff for Nyangores and Amala during the calibration and (validation) period were 0.65 (0.68) and 0.59 (0.62) respectively. The model showed good flow simulations particularly during the recession flows, in the Nyangores sub-catchment whereas it simulated poorly the short term fluctuations of the high-flow for Amala sub-catchment. Results from this study can be used by water resources managers to make informed decision on planning and management of water resources.

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.

Characterizing Potential Fishing Zone of Skipjack Tuna during the Southeast Monsoon in the Bone Bay-Flores Sea Using Remotely Sensed Oceanographic Data

Potential fishing zones for skipjack tuna in the Bone Bay-Flores Sea were investigated from satellite-based oceanography and catch data, using a linear model (generalized linear model) constructed from generalized additive models and geographic information systems. Monthly mean remotely sensed sea surface temperature and surface chlorophyll-a concentration during the southeast monsoon (April-August) were used for the year 2012. The best generalized additive model was selected to assess the effect of marine environment variables (sea surface temperature and chlorophyll-a concentration) on skipjack tuna abundance (catch per unit effort). Then, the appropriate linear model was constructed from the functional relationship of the generalized additive model for generating a robust predictive model. Model selection process for the generalized additive model was based on significance of model terms, decrease in residual deviance, and increase in cumulative variance explained, whereas the model selection for the linear model was based on decrease in residual deviance, reduction in Akaike’s Information Criterion, increasing cumulative variance explained and significance of model terms. The best model was selected to predict skipjack tuna abundance and their spatial distribution patterns over entire study area. A simple linear model was used to verify the predicted values. Results indicated that the distribution pattern of potential fishing zones for skipjack during the southeast monsoon were well characterized by sea surface temperatures ranging from 28.5℃ to 30.5 ℃ and chlorophyll-a ranging from 0.10 to 0.20 mg·m-3. Predicted highest catch per unit efforts were significantly consistent with the fishing data (P 2 = 0.8), suggesting that the oceanographic indicators may correspond well with the potential feeding ground for skipjack tuna. This good feeding opportunity for skipjack was driven the dynamics of upwelling operating within study area which are capable of creating a highly potential fishing zone during the southeast monsoon.

Remote Sensing Multitemporal Data for Geomorphological Analysis of the Murghab Alluvial Fan in Turkmenistan

Archaeological studies are ever more landscape-oriented, in order to study archaeological evidences in relation with their territorial contexts. In such a framework, reconstruction and understanding of ancient landscapes assume a crucial role for archaeological research. This study proposes a first morphological analysis of the whole Murghab alluvial fan in Turkmenistan, by means of the SRTM-DEM datum, and then the reconstruction of the ancient hydrography in the northeastern fringe of the fan, based on medium-high geometric resolution satellite data, and in continuity with previous studies. The importance to know historical fluvial network is due to the strict relationship between fresh water availability and human settlement distribution. SRTM-DEM, Corona, Soyouz KFA, Landsat, and Aster data were used, overlaying them in a GIS, and digitizing palaeochannels through photo-interpretation were done. Today, this is an almost desert area of the fan, and that is why it is easier to recognize buried forms by means of photo-interpretation, even though sometimes in a doubtful way. Despite the uncertainties, this model provided a useful tool for next and focused archaeological field surveys and excavations, aimed to find out human settlement evidences in correlation with ancient waterways.

THE IMPACT OF NOAA SATELLITE SOUNDING DATA ON THE SYSTEMATIC FORECAST ERROR OF B-MODEL

This paper is to examine the impact of satellite data on the systematic error of operational B-model in China.Em- phasis is put on the study of the impact of satellite sounding data on forecasts of the sea level pressure field and 500 hPa height.The major findings are as follows. (1)The B-model usually underforecasts the strength of features in the sea level pressure(SLP)field,i.e.pressures are too low near high pressure systems and too high near low pressure systems. (2)The nature of the systematic errors found in the 500 hPa height forecasts is not as clear cut as that of the SLP forecasts,but most often the same type of pattern is seen,i.e.,the heights in troughs are not low enough and those in ridges are not high enough. (3)The use of satellite data in the B-model analysis/forecast system is found to have an impact upon the model's forecast of SLP and 500 hPa height.Systematic errors in the vicinity of surface lows/500 hPa troughs over the oceans are usually found to be significantly reduced.A less conclusive mix of positive and negative impacts was found for all other types of features.

电离层探测及模化技术研究综述

电离层作为日地空间环境的重要组成部分,会对穿越其中的无线电波产生折射、反射、散射和吸收等效应,从而影响卫星导航、通信、雷达等诸多无线电信息系统的性能.利用各类技术手段来探测电离层的特征参量,揭示其内含的变化规律,并在此基础上构建出满足空间科学研究与系统应用的电离层模型,具有重要的价值.本文首先介绍了现有常用的地基和天基电离层探测手段;其次,调研总结了国内外在经验电离层模型、理论电离层模型、数据驱动电离层模型及机器学习建模方面的发展现状;再次,给出了导航系统电离层单频延迟修正、雷达系统电离层折射误差修正和通信/导航系统信号闪烁中断预警等三个典型应用场景中的电离层模型开发情况;最后,对电离层探测和模化技术的发展趋势进行了分析.