Aerosol Data Assimilation Using Data from Fengyun-3A and MODIS:Application to a Dust Storm over East Asia in 2011

Aerosol optical depth （AOD） is the most basic paxalneter that describes the optical properties of atmospheric aerosols, and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3A （FY-3A） and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a 24-h Weather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3A/MERSI （Medium-resolution Spectral Imager） AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3A satellite aerosol products can be effectively applied to numerical models and dust weather analysis.

Algorithm for the Sea Surface Wind Imaging Products of Fengyun-3C Meteorological Satellite MWRI

This paper analyzes the sea surface backward thermal radiation data in the China Sea observed by the mmwave channel of FY3 MWRI, explains the reason for which the analysis method combined with multiple mmwave channels is conducive to wind inversion, uses the complex model of the two-scale randomly rough surface with foam scattering layer to calculate the backward heat emission, analyzes the different response characteristics of the thermal radiation characteristics of each channel with the change of the sea surface wind speed, and establishes the wind speed inversion model applying to the microwave radiometer, achieving better results than in previous studies. The sea surface medium-low wind speed precision standard deviation of new model reaches 1.2 m/s (0 - 15 m/s);the inversion strong wind data are consistent with the island fixed buoys data, and the global sea surface wind speed image schematic diagram is given.

Random Forest-Based Snow Cover Mapping in China Using Fengyun-3B VIRR Data

Currently,there is variability in the spectral band thresholds for snow cover recognition using remote sensing in different regions and for complex terrains.Using Fengyun-3B Visible and Infra-Red Radiometer(FY-3B VIRR)satellite data,we applied random forest(RF)methodology and selected 13 feature variables to obtain snow cover.A training set was generated,containing approximately 1 million snow and nonsnow samples obtained in China from the snow monitoring reports issued by the National Satellite Meteorological Centre and four snow cover products from the Interactive Multi-sensor Snow and Ice Mapping System(IMS),the FY-3B Multi-Sensor Synergy(MULSS),the Moderate Resolution Imaging Spectroradiometer(MODIS)snow cover product(MYD10A1),and the National Cryosphere Desert Data Center(NCDC).This training set contained many different samples of cloud types and snow under forest cover to help effectively distinguish snow and clouds and improve the recognition rate of snow under forest cover.Then,two RF snow cover recognition models were constructed for the snow and nonsnow seasons and they were used to conduct daily snow cover recognition in China from 2011 to 2020.The results show that the RF models constructed based on FY-3B VIRR data have good recognition performance for shallow snow,understory snow,and snow on the Qinghai–Tibetan Plateau.The recognition accuracy against weather stations and the spatial consistency with the IMS product are better than the MULSS,MYD10A1,and NCDC products.The overall accuracy of the RF product is 90.6%,and the recall rate is 93.8%.The omission and commission errors are 6.2%and11.1%,respectively.Unlike other existing snow cover algorithms,the established RF model skips the complicated atmospheric correction and cloud identification processes and does not involve external auxiliary data;thus,it is more easily popularized and operationally applicable to generating long-time series snow cover products.

Influences of Earth Incidence Angle on FY-3/MWRI SST Retrieval and Evaluation of Reprocessed SST

Sea surface temperature(SST)is a crucial physical parameter in meteorology and oceanography.This study demonstrates that the influence of earth incidence angle(EIA)on the SST retrieved from the microwave radiation imager(MWRI)onboard FengYun-3(FY-3)meteorological satellites should not be ignored.Compared with algorithms that do not consider the influence of EIA in the regression,those that integrate the EIA into the regression can enhance the accuracy of SST retrievals.Subsequently,based on the recalibrated Level 1B data from the FY-3/MWRI,a long-term SST dataset was reprocessed by employing the algorithm that integrates the EIA into the regression.The reprocessed SST data,including FY-3B/MWRI SST during 2010-2019,FY-3C/MWRI SST during 2013-2019,and FY-3D/MWRI SST during 2018-2020,were compared with the in-situ SST and the SST dataset from the Operational Sea Surface Temperature and Ice Analysis(OSTIA).The results show that the FY-3/MWRI SST data were consistent with both the in-situ SST and the OSTIA SST dataset.Compared with the Copernicus Climate Change Service V2.0 SST,the absolute deviation of the reprocessed SST,with a quality flag of 50,was less than 1.5℃.The root mean square errors of the FY-3/MWRI orbital,daily,and monthly SSTs,with a quality flag of 50,were approximately 0.82℃,0.69℃,and 0.37℃,respectively.The primary discrepancies between the FY-3/MWRI SST and the OSTIA SST were found mainly in the regions of the western boundary current and the Antarctic Circumpolar Current.Overall,this reprocessed SST product is recommended for El Niño and La Niña events monitoring.

Capability of Fengyun-3D Satellite in Earth System Observation

From the viewpoint of earth system science,this paper discusses the observation capability of the second-generation of Chinese polar-orbiting,sun-synchronous operational meteorological satellite observation systems,Fengyun-3(FY-3),based on the function and performance test results from the FY-3 D satellite observation system in orbit.The FY-3 series of satellites have numerous remote sensing instruments and a wide range of imaging and sounding electromagnetic spectrometers onboard.These instruments can obtain reflectivity data for land surface,soil,vegetation,water body,snow cover,ocean color,and sea ice on earth’s surface over a wide spectral range,as well as information on the absorption and scattering radiative transfer of molecules and particles(clouds and aerosols)in earth’s atmosphere.All of these data can be used to retrieve physical and chemical information about the land,ocean,and atmosphere of the earth system.Comprehensive observation of the earth system by the FY-3 meteorological satellites is preliminarily realized.

Liquid Water Path Retrieval Using the Lowest Frequency Channels of Fengyun-3C Microwave Radiation Imager(MWRI)

The Microwave Radiation Imager （MWRI） on board Chinese Fengyun-3 （FY-3） satellites provides measurements at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with both horizontal and vertical polarization channels. Brightness temperature measurements of those channels with their central frequencies higher than 19 GHz from satellite-based microwave imager radiometers had traditionally been used to retrieve cloud liquid water path （LWP） over ocean. The results show that the lowest frequency channels are the most appropriate for retrieving LWP when its values are large. Therefore, a modified LWP retrieval algorithm is developed for retrieving LWP of different magnitudes involving not only the high frequency channels but also the lowest frequency channels of FY-3 MWRI. The theoretical estimates of the LWP retrieval errors are between 0.11 and 0.06 mm for 10.65- and 18.7-GHz channels and between 0.02 and 0.04 mm for 36.5- and 89.0-GHz channels. It is also shown that the brightness temperature observations at 10.65 GHz can be utilized to better retrieve the LWP greater than 3 mm in the eyewall region of Super Typhoon Neoguri （2014）. The spiral structure of clouds within and around Typhoon Neoguri can be well captured by combining the LWP retrievals from different frequency channels.

Assimilation of Chinese Fengyun-3B Microwave Temperature Sounder radiances into the Global GRAPES system with an improved cloud detection threshold

Fengyun-3B （FY-3B） is the second polar- orbiting satellite in the new Fengyun-three series. This paper describes the assimilation of the FY-3B Microwave Temperature Sounder （MWTS） radiances in the Chinese Numerical Weather prediction system - the Global and Regional Assimilation and PrEdiction System （GRAPES）. A quality control procedure for the assimilation of the FY- 3B MWTS radiance was proposed. Extensive monitoring before assimilation shows that the observations of channel 4 are notably contaminated. Channels 2 and 3 are used in this research. A cloud detection algorithm with an improved cloud-detection threshold is determined and incorporated into the impact experiments. The clear field- of-view （FOV） percentage increased from 42% to 57% with the new threshold. In addition, the newly added FOVs are located in the clear region, as demonstrated by the cloud liquid water path data from NOAA-18. The impact of the MWTS radiances on the prediction of GRAPES was researched. The observation biases ofFY-3B MWTS O-B （differences between satellite observations and model simulations） significantly decreased after an empirical bias correction procedure. After assimilation, the residual biases are small. The assimilation of the FY-3B MWTS radiances shows a positive impact in the Northern Hemisphere and a neutral impact in the Southern Hemisphere.

Fast CO_(2) Retrieval Using a Semi-Physical Statistical Model for the High-Resolution Spectrometer on the Fengyun-3D Satellite

China’s Fengyun-3D meteorological satellite launched in December 2016 carries the high-resolution greenhouse-gases absorption spectrometer(GAS)aimed at providing global observations of carbon dioxide(CO_(2)).To date,GAS is one of the few instruments measuring CO_(2) from the near-infrared spectrum.On orbit,the oxygen(O_(2))A band suffers a disturbance,and the signal-to-noise ratio(SNR)is significantly lower than the nominal specification.This leads to difficulties in the retrieval of surface pressure and hence a degradation of the retrieval of the column-averaged CO_(2) dry air mole fraction(XCO_(2))if a full physics retrieval algorithm is used.Thus,a fast CO_(2) inverse method,named semi-physical statistical algorithm,was developed to overcome this deficiency.The instrument characteristics,the semi-physical statistical algorithm,and the results of comparison with ground-based measurements over land were introduced in this paper.XCO_(2) can be obtained from three bands,namely,the O_(2) A,weak CO_(2),and strong CO_(2) bands,with compensation from the Medium Resolution Spectral Imager-2(MERSI-2)products,ECMWF Reanaly-sis v5(ERA-5)data,and Total Carbon Column Observing Network(TCCON)data.The eigenvectors of covariance matrices and the least square fits were used to derive retrieval coefficients and yield cloud-free solutions.In addition to the GAS radiance,some key factors necessary for the accurate estimations of XCO_(2) were also taken as input information(e.g.,air mass,surface pressure,and a priori XCO_(2)).The global GAS XCO_(2) restricted over land was compared against the simultaneously collocated observations from TCCON.The retrieval algorithm can mitigate the issue caused by the low SNR of the O_(2) A band to a certain extent.Overall,through site-by-site comparisons,GAS XCO_(2) agreed well with the average precision(1σ)of 1.52 ppm and bias of−0.007 ppm.The seasonal variation trends of GAS XCO_(2) can be clearly seen at TCCON sites on the 1-yr timescale.

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun satellites.Moreover,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been clarified.This study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification product.Then,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP product.The FY-3D NPP is also validated with existing research results and historical field-measured NPP data.In addition,the effects of land cover types and the NDVI on NPP estimation are analyzed.The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas.These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI.Compared with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel scale.The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,respectively.Additionally,the total deviation resulting from the two factors reached 29.5%.These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.

Retrieval of Oceanic Total Precipitable Water Vapor and Cloud Liquid Water from Fengyun-3D Microwave Sounding Instruments

Fengyun-3 D(FY-3 D) satellite is the latest polar-orbiting meteorological satellite launched by China and carries 10 instruments onboard. Its microwave temperature sounder(MWTS) and microwave humidity sounder(MWHS) can acquire a total of 28 channels of brightness temperatures, providing rich information for profiling atmospheric temperature and moisture. However, due to a lack of two important frequencies at 23.8 and 31.4 GHz, it is difficult to retrieve the total precipitable water vapor(TPW) and cloud liquid water path(CLW) from FY-3 D microwave sounder data as commonly done for other microwave sounding instruments. Using the channel similarity between Suomi National Polar-orbiting Partnership(NPP) advanced technology microwave sounder(ATMS) and FY-3 D microwave sounding instruments, a machine learning(ML) technique is used to generate the two missing low-frequency channels of MWTS and MWHS. Then, a new dataset named as combined microwave sounder(CMWS) is obtained,which has the same channel setting as ATMS but the spatial resolution is consistent with MWTS. A statistical inversion method is adopted to retrieve TPW and CLW over oceans from the FY-3 D CMWS. The intercomparison between different satellites shows that the inversion products of FY-3 D CMWS and Suomi NPP ATMS have good consistency in magnitude and distribution. The correlation coefficients of retrieved TPW and CLW between CMWS and ATMS can reach 0.95 and 0.85, respectively.

Extracting Soil Moisture from Fengyun-3D Medium Resolution Spectral Imager-Ⅱ Imagery by Using a Deep Belief Network

Obtaining continuous and high-quality soil moisture(SM) data is important in scientific research and applications,especially for agriculture, meteorology, and environmental monitoring. With the continuously increasing number of artificial satellites in China, the acquisition of SM data from remote sensing images has received increasing attention.In this study, we constructed an SM inversion model by using a deep belief network(DBN) to extract SM data from Fengyun-3 D(FY-3 D) Medium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) imagery;we named this model SM-DBN.The SM-DBN consists of two subnetworks: one for temperature and the other for SM. In the temperature subnetwork, bands 1, 2, 3, 4, 24, and 25 of the FY-3 D MERSI-Ⅱ imagery, which are relevant to temperature, were used as inputs while land surface temperatures(LST) obtained from ground stations were used as the expected output value when training the model. In the SM subnetwork, the input data included LSTs generated from the temperature subnetwork, normalized difference vegetation index(NDVI), and enhanced vegetation index(EVI);and the SM data obtained from ground stations were used as the expected outputs. We selected the Ningxia Hui Autonomous Region of China as the study area and used selected MERSI-Ⅱ images and in-situ observation station data from 2018 to 2019 to develop our dataset. The results of the SM-DBN were validated by using in-situ SM data as a reference, and its performance was also compared with those of the linear regression(LR) and back propagation(BP) neural network models. The overall accuracy of these models was measured by using the root mean square error(RMSE) of the differences between the model results and in-situ SM observation data. The RMSE of the LR, BP neural network, and SM-DBN models were 0.101, 0.083, and 0.032, respectively. These results suggest that the SM-DBN model significantly outperformed the other two models.

Evaluation of the Madden–Julian Oscillation in Fengyun-3B Polar-Orbiting Satellite Reprocessed OLR Data

The present study compares the spatial and temporal characteristics of the Madden-Julian Oscillation(MJO)in Fengyun-3B(FY-3B)polar-orbiting satellite reprocessed outgoing longwave radiation(OLR)data and NOAA OLR data during 2011-2020.The spatial distributions of climatological mean and intraseasonal standard deviation of FY-3B OLR during boreal winter(November-April)and boreal summer(May-October)are highly consistent with those of NOAA OLR.The FY-3B and NOAA OLRs display highly consistent features in the wavenumber-frequency spectra,the occurrence frequency of MJO active days,the eastward propagation of MJO along the equator,and the interannual variability of MJO according to diagnoses using the all-season multivariate EOF analysis.These results indicate that the FY-3B OLR produced by the polar-orbiting satellites is of high quality and worthy of global application.

Reprocessing 12-yr Microwave Humidity Sounder Historical Data of Fengyun-3 Satellites

Atmospheric water vapor is an essential climate variable(ECV)with extensive spatial and temporal variations.Microwave humidity observations from meteorological satellites provide important information for climate system variables,including atmospheric water vapor and precipitable water,and assimilation in numerical weather prediction(NWP)and reanalysis.As one of the payloads onboard China’s second-generation polar-orbiting operational meteorological Fengyun-3(FY-3)satellites,the Microwave Humidity Sounder(MWHS)has been continuously observing the global humidity since 2008.The reprocessing of historical FY-3 MWHS data is documented in detail in this study.After calibrating and correcting the data,the quality of the reprocessed dataset is evaluated and the improvement is shown in this study.The results suggest that MWHS observations bias is reduced to approximately 0.8 K,compared with METOP-A Microwave Humidity Sounder(MHS).The temporal variability of MWHS is highly correlated with the instrument temperature.After reprocessing,the scene temperature dependency is mitigated for all 183 GHz channels,and the consistency and stability between FY-3A/B/C are also improved.

In-Orbit Calibration Uncertainty of the Microwave Radiation Imager on board Fengyun-3C

This study evaluates the in-orbit calibration uncertainty(CU)for the microwave radiation imager(MWRI)on board the Chinese polar-orbiting meteorological satellite Fengyun-3 C(FY-3 C).Uncertainty analysis of the MWRI provides a direct link to the calibration system of the sensor and quantifies the calibration confidence based on the prelaunch and postlaunch measurements.The unique design of the sensor makes the uncertainty in the calibration of the sensor highly correlate to the uncertainty in the brightness temperature(TB)measured at the hot view,while the cold view has negligible impacts on the calibration confidence.Lack of knowledge on the emission of the hot-load reflector hampers the MWRI calibration accuracy significantly in the descending passes of the orbits when the hotload reflector is heated nonuniformly by the solar illumination.Radiance contamination originating from the satellite and in-orbit environments could enter the primary reflector via the hot view and further impinge on the CU,especially at the 10.65-GHz channels where the main-beam width is much broader than that of higher-frequency channels.The monthly-mean CU is lower than 2 K at all channels,depending on the observed earth scenes and in-orbit environments,and the month-to-month variation of CU is also noticed for all channels.Due to the uncertainty in the emissive hot-load reflector,CU in the descending passes is generally larger than that in the ascending orbits.Moreover,up to 1-K CU difference between the ocean and land scenes is found for the 10.65-GHz channels,while this difference is less than 0.1 K at the 89-GHz channels.

Evaluation of Reprocessed Fengyun-3B Global Outgoing Longwave Radiation Data:Comparison with CERES OLR

Outgoing longwave radiation(OLR)at the top of the atmosphere(TOA)is a key parameter for understanding and interpreting the relationship between clouds,radiation,and climate interactions.It has been one of the operational products of the Fengyun(FY)meteorological satellites.OLR accuracy has gradually improved with advancements in satellite payload performance and the OLR retrieval algorithm.Supported by the National Key R&D Program Retrospective Calibration of Historical Chinese Earth Observation Satellite data(Richceos)project,a long-term OLR climate data record(CDR)was reprocessed based on the recalibrated Level 1 data of FY series satellites using the latest OLR retrieval algorithm.In this study,Fengyun-3B(FY-3B)’s reprocessed global OLR data from 2010 to 2018 were evaluated by using the Clouds and the Earth’s Radiant Energy System(CERES)global daily OLR data.The results showed that there was a high consistency between the FY-3B instantaneous OLR and CERES Single Scanner Footprint(SSF)OLR.Globally,between the two CDR datasets,the correlation coefficient reached 0.98,and the rootmean-square error(RMSE)was approximately 8-9 W m^(−2).The bias mainly came from the edge regions of the satellite orbit,which may be related to the satellite zenith angle and cloud cover distribution.It was shown that the longterm FY-3B OLR had temporal stability compared to CERES OLR long-term data.In terms of spatial distribution,the mean deviations showed zonal and seasonal characteristics,although seasonal fluctuations were observed in the differences between the two datasets.Effects of FY-3B OLR application to the South China Sea monsoon region and ENSO were demonstrated and analyzed,and the results showed that the seasonal deviation of FY-3B’s OLR comes mainly from the retrieval algorithm.However,it has little effect on the analysis of climate events.

基于FY-3 MERSI多光谱通道的霾判识研究

受高人为排放和不利天气条件共同作用,中国秋、冬季霾事件频发,具有影响范围大、程度重、持续时间长的特点。为对离散的地面站观测形成有效补充,本研究充分利用卫星大范围监测优势,依据云、冰雪、霾、亮/暗地表散射及发射特性不同波长的依赖性,建立了基于多通道信息组合的霾区快速判识方法。通过引入空气分子散射订正,有效消除此前霾判识工作中大角度条件下的误判,极大缩小了阈值的变化范围,为霾区的自动识别奠定了基础。将该判识方法应用于中国风云三号D星(FY-3D)搭载的中分辨率光谱成像仪(MEdium-Resolution Spectral Imager,MERSI),实现了霾区自动判识。通过与2020年10月—2021年2月秋、冬季地面气象站天气现象记录为霾以及地面环境监测站点中PM_(2.5)小时平均浓度大于75μg/m^(3)的数据进行对比,结果表明:FY-3D的霾判识结果与天气现象记录结果的一致率为91.1%,与PM_(2.5)表征结果的一致率为85.6%。气象观测中霾识别依靠的是大气消光能力,卫星霾判识也是通过气溶胶的消光特性来实现,因此两者特征更为一致。

基于敦煌场地定标的FY-3MERSI反射太阳波段在轨响应变化分析

鉴于中分辨率光谱成像仪不能实现反射太阳波段的星上绝对辐射定标,提出了基于地表方向模型、矢量辐射传输模型6SV并联合MODTRAN吸收透过率校正的敦煌场替代定标新方法,4年的同步定标结果表明,除了水汽吸收中心波段之外,定标不确定度小于5%,而多数波段优于3%。以Aqua MODIS为辐射基准的大气顶辐射计算试验表明,正演与卫星观测间的平均偏差在波长<1μm的窗区波段小于3%,波长>1μm的小于5%(除了2.1μm波段);此外,经场地定标的MERSI表观反射率与MODIS具有很好的一致性。基于多年的场地定标结果发现:可采用二次多项式拟合定标系数的时间变化,进而实现逐天的定标更新;波长<0.6μm的波段衰变较大,波段8(0.41μm)入轨第一年的衰变率约为14%;在轨初期衰变最大,一年后趋缓,两年后部分波长>0.6μm的波段出现响应增加现象。

FY-3A/MERSI热红外通道在轨辐射定标精度评估

卫星资料辐射定标精度是其定量应用的关键因素。以METOP-A/IASI的高光谱探测资料为传递基准,利用同时星下点观测的交叉定标方法,对FY-3A/MERSI热红外通道的在轨辐射定标精度进行了客观评估,并给出了亮温系统偏差的订正因子。从观测时间差异、卫星观测天顶角和方位角差异、以及目标均匀性四个方面,分析了交叉定标中所用主要匹配近似因子的不确定性。分析结果表明,目标均匀性是匹配误差的主要来源,偏差不确定性小于2%(当亮温偏差约为1K时,不确定性<0.02K),其他因素的影响可以忽略。一年多的样本统计及偏差分析结果显示,MERSI的观测亮温明显高于IASI,年平均亮温偏差约(3.18±0.34)K,月平均亮温偏差呈现季节波动特征,波动幅度约0.8K。与相近时期敦煌场和青海湖地同步观测评价结果有非常好的一致性。初步原因分析推断,造成MERSI亮温偏高的原因主要有两个,一是星上黑体发射率被高估,二是光谱响应函数向大气窗区漂移,后者可能为主导因素。

风云3号气象卫星资料在暴雨预报中的应用

介绍了风云3号(FY-3A)气象卫星资料应用示范项目《卫星资料在暴雨监测预报中的应用》的研究成果。讨论了以FY-3A资料为主,结合风云气象卫星系列其他资料和数值预报产品等,在云团识别的基础上计算云团特征量作为预报因子建立预报方程并计算云团最大降水量的暴雨临近预报方法,以及使用FY-3A资料作为"配料法"配料之一的暴雨6 h落区预报方法。结果表明,该方法对于暴雨的临近和短时预报有一定的指导作用。

Precise Orbit Determination for the FY-3C Satellite Using Onboard BDS and GPS Observations from 2013, 2015, and 2017

Using the FengYun-3C(FY-3C)onboard BeiDou Navigation Satellite System(BDS)and Global Positioning System(GPS)data from 2013 to 2017,this study investigates the performance and contribution of BDS to precise orbit determination(POD)for a low-Earth orbit(LEO).The overlap comparison result indicates that code bias correction of BDS can improve the POD accuracy by 12.4%.The multi-year averaged one-dimensional(1D)root mean square(RMS)of the overlapping orbit differences(OODs)for the GPS-only solution is 2.0,1.7,and 1.5 cm,respectively,during the 2013,2015,and 2017 periods.The 1D RMS for the BDS-only solution is 150.9,115.0,and 47.4 cm,respectively,during the 2013,2015,and 2017 periods,which is much worse than the GPS-only solution due to the regional system of BDS and the few BDS channels of the FY-3C receiver.For the BDS and GPS combined solution(also known as the GC combined solution),the averaged 1D RMS is 2.5,2.3,and 1.6 cm,respectively,in 2013,2015,and 2017,while the GC combined POD presents a significant accuracy improvement after the exclusion of geostationary Earth orbit(GEO)satellites.The main reason for the improvement seen after this exclusion is the unfavorable satellite tracking geometry and poor orbit accuracy of GEO satellites.The accuracy of BDS-only and GC combined solutions have gradually improved from 2013 to 2017,thanks to improvements in the accuracy of International GNSS Service(IGS)orbit and clock products in recent years,especially the availability of a high-frequency satellite clock product(30 s sampling interval)since 2015.Moreover,the GC POD(without GEO)was able to achieve slightly better accuracy than the GPS-only POD in 2017,indicating that the fusion of BDS and GPS observations can improve the accuracy of LEO POD.GC combined POD can significantly improve the reliability of LEO POD,simply due to system redundancy.An increased contribution of BDS to LEO POD can be expected with the launch of more BDS satellites and with further improvements in the accuracy of BDS satellite products in the near future.