中国风云三号(FY-3)A星于2008年5月27日成功发射,其上装载的微波温度计(Microwave Temperature Sounder,MWTS)有4个通道,频率分别为50.3,53.596,54.94和57.29GHz,可提供地面至平流层下部的大气温度廓线信息。介绍了MWTS的仪器特征和定...中国风云三号(FY-3)A星于2008年5月27日成功发射,其上装载的微波温度计(Microwave Temperature Sounder,MWTS)有4个通道,频率分别为50.3,53.596,54.94和57.29GHz,可提供地面至平流层下部的大气温度廓线信息。介绍了MWTS的仪器特征和定标方法,对FY-3A发射后MWTS的仪器性能相关参数的长序列特征进行了监测分析,这些参数主要包括灵敏度、冷空和内部暖黑体的计数值、仪器温度和通道增益。通过再分析资料和微波辐射传输模拟,比较了FY-3A卫星MWTS与NOAA-18卫星先进微波温度探测器(Advanced Microwave Sounder Unit-A,AMSU-A)的观测和模拟的亮温偏差。结果表明,MWTS所有通道的灵敏度值优于其指标;通道1和3的亮温偏差与AMSU-A接近;通道2和4的偏差比AMSU-A要大一些。展开更多
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...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.展开更多
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) ca...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.展开更多
With the launch of the first civilian early-morning orbit satellite Fengyun-3E(FY-3E),higher demands are placed on the accuracy of radiative transfer simulations for hyperspectral infrared data.Therefore,several key i...With the launch of the first civilian early-morning orbit satellite Fengyun-3E(FY-3E),higher demands are placed on the accuracy of radiative transfer simulations for hyperspectral infrared data.Therefore,several key issues are investigated in the paper.First,the accuracy of the fast atmospheric transmittance model implemented in the Advanced Research and Modeling System(ARMS)has been evaluated with both the line-by-line radiative transfer model(LBLRTM)and the actual satellite observations.The results indicate that the biases are generally less than 0.25 K when compared to the LBLRTM,while below 1.0 K for the majority of the channels when compared to the observations.However,during both comparisons,significant biases are observed in certain channels.The accuracy of Hyperspectral Infrared Atmospheric Sounder-II(HIRAS-II)onboard FY-3E is comparable to,and even superior to that of the Cross-track Infrared Sounder(CrIS)onboard NOAA-20.Furthermore,apodization is a crucial step in the processing of hyperspectral data in that the apodization function is utilized as the instrument channel spectral response function to produce the satellite channel-averaged transmittance.To further explore the difference between the apodized and unapodized simulations,Sinc function is adopted in the fast transmittance model.It is found that the use of Sinc function can make the simulations fit the original satellite observations better.When simulating with apodized observations,the use of Sinc function exhibits larger deviations compared to the Hamming function.Moreover,a correction module is applied to minimize the impact of Non-Local Thermodynamic Equilibrium(NLTE)in the shortwave infrared band.It is verified that the implementation of the NLTE correction model leads to a significant reduction in the bias between the simulation and observation for this band.展开更多
继新一代气象极轨卫星上午窗口星风云三号A星于2008年5月27日成功发射以后,下午窗口星风云三号B星在2010年11月5日成功发射。搭载于风云三号卫星上的微波温度探测仪(MWTS,MicroWave Temperature Sounder)有4个通道,分别与先进微波探测仪...继新一代气象极轨卫星上午窗口星风云三号A星于2008年5月27日成功发射以后,下午窗口星风云三号B星在2010年11月5日成功发射。搭载于风云三号卫星上的微波温度探测仪(MWTS,MicroWave Temperature Sounder)有4个通道,分别与先进微波探测仪(AMSU-A,Advanced Microwave Sounding Unit-A)的3,5,7和9通道相对应。通过与模式模拟亮温以及NOAA-18 AMSU-A同频率观测亮温的对比,评估了风云三号B星MWTS观测资料的质量。研究发现,MWTS通道3和AMSU-A通道7的观测偏差有较强的纬度依赖性;MWTS通道4的观测资料在约30°—40°N的狭窄纬度带上受到了严重污染;并且,通道4的扫描偏差不对称,第4个像元的偏差全球系统性地高于其他临近像元。造成这些异常偏差的可能原因包括卫星天线旁瓣污染和未知来源的信号干扰。为了将MWTS观测应用于数值天气预报,提出了一个质量控制算法剔除通道4的异常观测资料。展开更多
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 operatio...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.展开更多
基金Supported by the Science Foundation of Shandong(ZR2017MD018)Key Research and Development Program of Ningxia(2019BEH03008)+3 种基金Open Research Project of the Key Laboratory for Meteorological Disaster MonitoringEarly Warning and Risk Management of Characteristic Agriculture in Arid Regions(CAMF-201701 and CAMF-201803)Arid Meteorological Science Research Fund Project by the Key Open Laboratory of Arid Climate Change and Disaster Reduction of China Metrological Administration(IAM201801)Science Foundation of Ningxia(NZ12278)。
文摘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.
基金the National Key Research and Development Program of China (2018YFC1506500)National Natural Science Foundation of China (41675030 and 41675027)National Satellite Meteorological Center [FY3 (02P)-MAS-1803]。
文摘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.
基金Supported by the Startup Project of Donghai Laboratory(DH-2023QD0002)National Key Research and Development Program of China(2021YFB3900400)Hunan Provincial Natural Science Foundation of China(2021JC0009)。
文摘With the launch of the first civilian early-morning orbit satellite Fengyun-3E(FY-3E),higher demands are placed on the accuracy of radiative transfer simulations for hyperspectral infrared data.Therefore,several key issues are investigated in the paper.First,the accuracy of the fast atmospheric transmittance model implemented in the Advanced Research and Modeling System(ARMS)has been evaluated with both the line-by-line radiative transfer model(LBLRTM)and the actual satellite observations.The results indicate that the biases are generally less than 0.25 K when compared to the LBLRTM,while below 1.0 K for the majority of the channels when compared to the observations.However,during both comparisons,significant biases are observed in certain channels.The accuracy of Hyperspectral Infrared Atmospheric Sounder-II(HIRAS-II)onboard FY-3E is comparable to,and even superior to that of the Cross-track Infrared Sounder(CrIS)onboard NOAA-20.Furthermore,apodization is a crucial step in the processing of hyperspectral data in that the apodization function is utilized as the instrument channel spectral response function to produce the satellite channel-averaged transmittance.To further explore the difference between the apodized and unapodized simulations,Sinc function is adopted in the fast transmittance model.It is found that the use of Sinc function can make the simulations fit the original satellite observations better.When simulating with apodized observations,the use of Sinc function exhibits larger deviations compared to the Hamming function.Moreover,a correction module is applied to minimize the impact of Non-Local Thermodynamic Equilibrium(NLTE)in the shortwave infrared band.It is verified that the implementation of the NLTE correction model leads to a significant reduction in the bias between the simulation and observation for this band.
基金Supported by the National Key Research and Development Program of China(2018YFB0504900 and 2018YFB0504905)National Natural Science Foundation of China(41801278).
文摘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.
