Seasonal snow cover is a key global climate and hydrological system component drawing considerable attention due to glob-al warming conditions.However,the spatiotemporal snow cover patterns are challenging in western ...Seasonal snow cover is a key global climate and hydrological system component drawing considerable attention due to glob-al warming conditions.However,the spatiotemporal snow cover patterns are challenging in western Jilin,China due to natural condi-tions and sparse observation.Hence,this study investigated the spatiotemporal patterns of snow cover using fine-resolution passive mi-crowave(PMW)snow depth(SD)data from 1987 to 2018,and revealed the potential influence of climate factors on SD variations.The results indicated that the interannual range of SD was between 2.90 cm and 9.60 cm during the snowy winter seasons and the annual mean SD showed a slightly increasing trend(P>0.05)at a rate of 0.009 cm/yr.In snowmelt periods,the snow cover contributed to an increase in volumetric soil water,and the change in SD was significantly affected by air temperature.The correlation between SD and air temperature was negative,while the correlation between SD and precipitation was positive during December and March.In March,the correlation coefficient exceeded 0.5 in Zhenlai,Da’an,Qianan,and Qianguo counties.However,the SD and precipitation were neg-atively correlated over western Jilin in October,and several subregions presented a negative correlation between SD and precipitation in November and April.展开更多
The Microwave Radiation Imager (MWRI), boarded on the FY-3 series satellites: FY-3B, FY-3C, and FY-3D, is thefirst satellite-based microwave radiometer in China, commencing passive microwave brightness temperature dat...The Microwave Radiation Imager (MWRI), boarded on the FY-3 series satellites: FY-3B, FY-3C, and FY-3D, is thefirst satellite-based microwave radiometer in China, commencing passive microwave brightness temperature dataacquisition since 2010. The Advanced Microwave Scanning Radiometer 2 (AMSR2) boarded on the Global ChangeObservation Mission 1st-Water (GCOM-W1), has been operational since 2012. Despite the FY-3 series satellitesare equipped with the same MWRI and all MWRIs sharing comparable parameters and configurations as AMSR2,disparities in observation times and satellite platforms result in inconsistencies in the data obtained by differentsatellites, which further impacting the consistency of retrieved geophysical parameters. To improve the consistency of brightness temperatures from FY-3B, FY-3C, FY-3D/MWRI, and GCOM-W1/AMSR2, cross-calibrationswere conducted among brightness temperatures at ten-channel from above four platforms. The consistency ofderived snow depth from MWRIs and AMSR2 in China before and after the calibration were also analyzed. Theresults show that the correlation coefficients of brightness temperatures at all channels between sensors exceed0.98. After cross-calibration, the RMSEs and biases of brightness temperatures at all frequencies and snow depthin China derived from them reduce to varying degrees. The consistencies in both brightness temperatures andsnow depth of FY-3B/MWRI, FY-3D/MWRI, and AMSR2 are higher than those of FY-3C and others. These findingsadvocate for the utilization of cross-calibrated brightness temperatures from FY-3B/MWRI, FY-3D/MWRI, andAMSR2, which share similar satellite overpass time, to derived a long-term snow depth dataset.展开更多
The snow depth on sea ice is an extremely critical part of the cryosphere.Monitoring and understanding changes of snow depth on Antarctic sea ice is beneficial for research on sea ice and global climate change.The Mic...The snow depth on sea ice is an extremely critical part of the cryosphere.Monitoring and understanding changes of snow depth on Antarctic sea ice is beneficial for research on sea ice and global climate change.The Microwave Radiation Imager(MWRI)sensor aboard the Chinese FengYun-3D(FY-3D)satellite has great potential for obtaining information of the spatial and temporal distribution of snow depth on the sea ice.By comparing in-situ snow depth measurements during the 35th Chinese Antarctic Research Expedition(CHINARE-35),we took advantage of the combination of multiple gradient ratio(GR(36V,10V)and GR(36V,18V))derived from the measured brightness temperature of FY-3D MWRI to estimate the snow depth.This method could simultaneously introduce the advantages of high and low GR in the snow depth retrieval model and perform well in both deep and shallow snow layers.Based on this,we constructed a novel model to retrieve the FY-3D MWRI snow depth on Antarctic sea ice.The new model validated by the ship-based observational snow depth data from CHINARE-35 and the snow depth measured by snow buoys from the Alfred Wegener Institute(AWI)suggest that the model proposed in this study performs better than traditional models,with root mean square deviations(RMSDs)of 8.59 cm and 7.71 cm,respectively.A comparison with the snow depth measured from Operation IceBridge(OIB)project indicates that FY-3D MWRI snow depth was more accurate than the released snow depth product from the U.S.National Snow and Ice Data Center(NSIDC)and the National Tibetan Plateau Data Center(NTPDC).The spatial distribution of the snow depth from FY-3D MWRI agrees basically with that from ICESat-2;this demonstrates its reliability for estimating Antarctic snow depth,and thus has great potential for understanding snow depth variations on Antarctic sea ice in the context of global climate change.展开更多
Water and nitrogen （N） inputs are considered as the two main limiting factors affecting plant growth.Changes in these inputs are expected to alter the structure and composition of the plant community,thereby influen...Water and nitrogen （N） inputs are considered as the two main limiting factors affecting plant growth.Changes in these inputs are expected to alter the structure and composition of the plant community,thereby influencing biodiversity and ecosystem function.Snowfall is a form of precipitation in winter,and snow melting can recharge soil water and result in a flourish of ephemerals during springtime in the Gurbantunggut Desert,China.A bi-factor experiment was designed and deployed during the snow-covering season from 2009 to 2010.The experiment aimed to explore the effects of different snow-covering depths and N addition levels on ephemerals.Findings indicated that deeper snow cover led to the increases in water content in topsoil as well as density and coverage of ephemeral plants in the same N treatment; by contrast,N addition sharply decreased the density of ephemerals in the same snow treatment.Meanwhile,N addition exhibited a different effect on the growth of ephemeral plants：in the 50％ snow treatment,N addition limited the growth of ephemeral plants,showing that the height and the aboveground biomass of the ephemeral plants were lower than in those without N addition; while with the increases in snow depth （100％ and 150％ snow treatments）,N addition benefited the growth of the dominant individual plants.Species richness was not significantly affected by snow in the same N treatment.However,N addition significantly decreased the species richness in the same snow-covering depth.The primary productivity of ephemerals in the N addition increased with the increase of snow depth.These variations indicated that the effect of N on the growth of ephemerals was restricted by water supply.With plenty of water （100％ and 150％ snow treatments）,N addition contributed to the growth of ephemeral plants; while with less water （50％ snow treatment）,N addition restricted the growth of ephemeral plants.展开更多
The authors present evidence to suggest that variations in the snow depth over the Tibetan Plateau （TP） are connected with changes of North Atlantic Oscillation （NAO） in winter （JFM）. During the positive phase o...The authors present evidence to suggest that variations in the snow depth over the Tibetan Plateau （TP） are connected with changes of North Atlantic Oscillation （NAO） in winter （JFM）. During the positive phase of NAO, the Asian subtropical westerly jet intensifies and the India-Myanmar trough deepens. Both of these processes enhance ascending motion over the TP. The intensified upward motion, together with strengthened southerlies upstream of the India-Myanmar trough, favors stronger snowfall over the TP, which is associated with East Asian tropospheric cooling in the subsequent late spring （April-May）. Hence, the decadal increase of winter snow depth over the TP after the late 1970s is proposed to be an indicator of the connection between the enhanced winter NAO and late spring tropospheric cooling over East Asia.展开更多
Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Fle...Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Flexible Global OceanAtmosphere-Land System （FGOALS） model,Grid-point Version 2 （g2） and Spectral Version 2 （s2）,were validated against observational data.The results revealed that the spatial pattern of SD and SCF over the Northern Hemisphere （NH） are simulated well by both models,except over the Tibetan Plateau,with the average spatial correlation coefficient over all months being around 0.7 and 0.8 for SD and SCF,respectively.Although the onset of snow accumulation is captured wellby the two models in terms of the annual cycle of SD and SCF,g2 overestimates SD/SCF over most mid-and high-latitude areas of the NH.Analysis showed that g2 produces lower temperatures than s2 because it considers the indirect effects of aerosols in its atmospheric component,which is the primary driver for the SD/SCF difference between the two models.In addition,both models simulate the significant decreasing trend of SCF well over （30°-70°N） in winter during the period 1971-94.However,as g2 has a weak response to an increase in the concentration of CO2 and lower climate sensitivity,it presents weaker interannual variation compared to s2.展开更多
The interannual variability of wintertime snow depth over the Tibetan Plateau(TP) and related atmospheric circulation anomalies were investigated based on observed snow depth measurements and NCEP/NCAR reanalysis data...The interannual variability of wintertime snow depth over the Tibetan Plateau(TP) and related atmospheric circulation anomalies were investigated based on observed snow depth measurements and NCEP/NCAR reanalysis data.Empirical orthogonal function(EOF) analysis was applied to identify the spatio-temporal variability of wintertime TP snow depth.Snow depth anomalies were dominated by a monopole pattern over the TP and a dipole structure with opposite anomalies over the southeastern and northwestern TP.The atmospheric circulation conditions responsible for the interannual variability of TP snow depth were examined via regression analyses against the principal component of the most dominant EOF mode.In the upper troposphere,negative zonal wind anomalies over the TP with extensively positive anomalies to the south indicated that the southwestward shift of the westerly jet may favor the development of surface cyclones over the TP.An anomalous cyclone centered over the southeastern TP was associated with the anomalous westerly jet,which is conducive to heavier snowfall and results in positive snow depth anomalies.An anomalous cyclone was observed at 500 hPa over the TP,with an anomalous anticyclone immediately to the north,suggesting that the TP is frequently affected by surface cyclones.Regression analyses revealed that significant negative thickness anomalies exist around the TP from March to May,with a meridional dipole anomaly in March.The persistent negative anomalies due to more winter TP snow are not conducive to earlier reversal of the meridional temperature gradient,leading to a possible delay in the onset of the Asian summer monsoon.展开更多
This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A.The monthly parameters of the cross-calibration were determined and evaluated using rob...This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A.The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression.The snow depth in case of seasonal ice was calculated by using parameters of the crosscalibration of data from the MWRI Tb.The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97.The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI.Data from the MWRI Tb were cross-calibrated to the AMSR-E baseline.Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration,the standard deviations decreased significantly in the range of 1.32 K to 2.57 K,and the correlation coefficients were as high as 99%.An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated.Furthermore,the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz,23.8 GHz,and 36.5 GHz(V polarization),and at 89 GHz(H/V polarization),and were applied to the snow depths retrieval in the Arctic.The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average Tb.The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products.Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths,with the standard deviations ranging from 4.19 cm to 4.80 cm.展开更多
On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., ...On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.展开更多
Snow depth over sea ice is an essential variable for understanding the Arctic energy budget.In this study,we evaluate snow depth over Arctic sea ice during 1993-2014 simulated by 31 models from phase 6 of the Coupled ...Snow depth over sea ice is an essential variable for understanding the Arctic energy budget.In this study,we evaluate snow depth over Arctic sea ice during 1993-2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project(CMIP6)against recent satellite retrievals.The CMIP6 models capture some aspects of the observed snow depth climatology and variability.The observed variability lies in the middle of the models’simulations.All the models show negative trends of snow depth during 1993-2014.However,substantial spatiotemporal discrepancies are identified.Compared to the observation,most models have late seasonal maximum snow depth(by two months),remarkably thinner snow for the seasonal minimum,an incorrect transition from the growth to decay period,and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice.Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic.Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099.Under the SSP5-8.5 scenario,the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January.Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution,the inclusion of a hightop atmospheric model,and biogeochemistry processes are important factors for snow depth simulation.展开更多
Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect ...Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.展开更多
Studies on the impact of solar activity on climate system are very important in understanding global climate change. Previous studies in this field were mostly focus on temperature, wind and geopotential height. In th...Studies on the impact of solar activity on climate system are very important in understanding global climate change. Previous studies in this field were mostly focus on temperature, wind and geopotential height. In this paper, interdecadal correlations of solar activity with Winter Snow Depth Index （WSDI） over the Tibetan Plateau, Arctic Oscillation Index （AOI） and the East Asian Winter Monsoon Index （EAWMI） are detected respectively by using Solar Radio Flux （SRF）, Total Solar Irradiance （TSI） and Solar Sunspot Number （SSN） data and statistical methods. Arctic Oscillation and East Asian winter monsoon are typical modes of the East Asian atmospheric circulation. Research results show that on inter-decadal time scale over 11-year solar cycle, the sun modulated changes of winter snow depth over the Tibetan Plateau and East Asian atmospheric circulation. At the fourth lag year, the correlation coefficient of SRF and snow depth is 0.8013 at 0.05 significance level by Monte-Carlo test method. Our study also shows that winter snow depth over the Tibetan Plateau has significant lead and lag correlations with Arctic Oscillation and the East Asian winter monsoon on long time scale. With more snow in winter, the phase of Arctic Oscillation is positive, and East Asian winter monsoon is weak, while with less snow, the parameters are reversed. An example is the winter of 2012/2013, with decreased Tibetan Plateau snow, phase of Arctic Oscillation was negative, and East Asian winter monsoon was strong.展开更多
Satellite remote sensing is widely used to estimate snow depth and snow water equivalent(SWE)which are two key parameters in global and regional climatic and hydrological systems.Remote sensing techniques for snow dep...Satellite remote sensing is widely used to estimate snow depth and snow water equivalent(SWE)which are two key parameters in global and regional climatic and hydrological systems.Remote sensing techniques for snow depth mainly include passive microwave remote sensing,Synthetic Aperture Radar(SAR),Interferometric SAR(In SAR)and Lidar.Among them,passive microwave remote sensing is the most efficient way to estimate large scale snow depth due to its long time series data and high temporal frequency.Passive microwave remote sensing was utilized to monitor snow depth starting in 1978 when Nimbus-7 satellite with Scanning Multichannel Microwave Radiometer(SMMR)freely provided multi-frequency passive microwave data.SAR was found to have ability to detecting snow depth in 1980 s,but was not used for satellite active microwave remote sensing until 2000.Satellite Lidar was utilized to detect snow depth since the later period of 2000 s.The estimation of snow depth from space has experienced significant progress during the last 40 years.However,challenges or uncertainties still exist for snow depth estimation from space.In this study,we review the main space remote sensing techniques of snow depth retrieval.Typical algorithms and their principles are described,and problems or disadvantages of these algorithms are discussed.It was found that snow depth retrieval in mountainous area is a big challenge for satellite remote sensing due to complicated topography.With increasing number of freely available SAR data,future new methods combing passive and active microwave remote sensing are needed for improving the retrieval accuracy of snow depth in mountainous areas.展开更多
Snow cover is one of the important components of land cover,and it is necessary to accurately monitor the depth and coverage of snow cover.Using the GPS signal receiver data and the basic principle of snow depth detec...Snow cover is one of the important components of land cover,and it is necessary to accurately monitor the depth and coverage of snow cover.Using the GPS signal receiver data and the basic principle of snow depth detection based on GPS-MR technology,the snow depth of the three sites on the Greenland PBO network GLS1,GLS2,and GLS3 from 2012 to 2018 was obtained.The inversion snow depth is affected by site drift,which is a quite difference from the measured snow depth.Combined with the stable reference point,the velocity field distribution of Greenland Island and the U-direction component change value of the station can be obtained through GAMIT calculation.By analyzing the glacial flow and U-direction component,the influence of the site drift on the snow depth was deducted,and finally compared the corrected inversion snow depth and measured snow depth found that the two were better than before the correction,the results were significantly improved,and the consistency was good.The analysis of the experimental results showed that in extremely cold areas such as Greenland Island,affected by glaciers,the continuous,real-time,high-time resolution snow depth around the measured station obtained by ground-based GPS tracking stations has a large gap with the measured snow depth value,and the gap will gradually increase with time.By deducting the impact of glacier drift,the trend of the two is the same and the consistency is good.The correctness and feasibility of the application of ground-based GPS snow cover theory in the polar area further expand the application scope and practical value of ground-based GPS in snow monitoring.展开更多
Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction.Despite advances in remote sensing technology and enhanced satellite observations,the estimat...Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction.Despite advances in remote sensing technology and enhanced satellite observations,the estimation of snow depth at local scale still requires improved accuracy and flexibility.The advances in ubiquitous and wearable technology promote new prospects in tackling this challenge.In this paper,a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put forward.Significantly,the results of Random Forest classifier showed an accuracy of 94%,indicating a promising alternative in snow depth measurement compared to in situ,LiDAR,or expensive large-scale wireless sensor network,which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.展开更多
This paper obtained a set of consecutive and long-recorded observational snow depth data from 51 observation stations by choosing, removing and interpolating original observation data over the Tibetan Plateau for 1961...This paper obtained a set of consecutive and long-recorded observational snow depth data from 51 observation stations by choosing, removing and interpolating original observation data over the Tibetan Plateau for 1961-2006. We used monthly precipitation and temperature data from 160 stations in China for 1951-2006, which was collected by the National Climate Center. Through calculating and analyzing the correlation coefficient, significance test, polynomial trend fitting, composite analysis and abrupt change test, this paper studied the interdecadal change of winter snow over the Tibetan Plateau and its relationship to summer pre- cipitation and temperature in China, and to tropospheric atmospheric temperature. This paper also studied general circulation and East Asian summer monsoon under the background of global warming.展开更多
Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is con...Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager(MWRI) on board the FengYun(FY)-3 B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS(AMSR-E) Level 2 A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice(ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between-1.01 and-0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.展开更多
Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products ha...Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products have been developed since the 1970s,they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD,especially in complex mountainous regions.In this paper,we developed afine-resolution SD retrieval model(FSDM)using machine learning to improve SD estimation quality for Northeast China and produced a long-term,fine-resolution,daily SD dataset.The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products.The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation,with the root-mean-square error(RMSE),bias,and correlation coefficient(R)of 7.10 cm,-0.13 cm,and 0.60.Additionally,we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range,Lesser Khingan Mountains,and Changbai Mountain regions.The SD exhibited high-low distribution patterns with the increased latitude.The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.展开更多
Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia,and the lagging updates of datasets do not meet the operational requirements of the meteorological service department.This ...Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia,and the lagging updates of datasets do not meet the operational requirements of the meteorological service department.This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work.These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3(FY-3)satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites,which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration.Firstly,inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors.A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established,overcoming the large uncertainties induced by varying snow characteristics.This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth.The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ.Against in situ snow depth,the overall bias and root mean square error are−2.04 and 6.49 cm,respectively,facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product,which adopts the static algorithm.Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths,demonstrating a noticeable reduction since around 2000.The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.展开更多
The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temp...The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.展开更多
基金Under the auspices of the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28110502)Science and Technology Development Plan Project of Jilin Province(No.20220202035NC)+1 种基金National Natural Science Foundation of China(No.41871248)Changchun Science and Technology Development Plan Project(No.21ZY12)。
文摘Seasonal snow cover is a key global climate and hydrological system component drawing considerable attention due to glob-al warming conditions.However,the spatiotemporal snow cover patterns are challenging in western Jilin,China due to natural condi-tions and sparse observation.Hence,this study investigated the spatiotemporal patterns of snow cover using fine-resolution passive mi-crowave(PMW)snow depth(SD)data from 1987 to 2018,and revealed the potential influence of climate factors on SD variations.The results indicated that the interannual range of SD was between 2.90 cm and 9.60 cm during the snowy winter seasons and the annual mean SD showed a slightly increasing trend(P>0.05)at a rate of 0.009 cm/yr.In snowmelt periods,the snow cover contributed to an increase in volumetric soil water,and the change in SD was significantly affected by air temperature.The correlation between SD and air temperature was negative,while the correlation between SD and precipitation was positive during December and March.In March,the correlation coefficient exceeded 0.5 in Zhenlai,Da’an,Qianan,and Qianguo counties.However,the SD and precipitation were neg-atively correlated over western Jilin in October,and several subregions presented a negative correlation between SD and precipitation in November and April.
基金supported by the National Natural Science Foun-dation of China(42125604,42171143)Innovative Development Project of China Meteorological Administration(CXFZ 2022J039).
文摘The Microwave Radiation Imager (MWRI), boarded on the FY-3 series satellites: FY-3B, FY-3C, and FY-3D, is thefirst satellite-based microwave radiometer in China, commencing passive microwave brightness temperature dataacquisition since 2010. The Advanced Microwave Scanning Radiometer 2 (AMSR2) boarded on the Global ChangeObservation Mission 1st-Water (GCOM-W1), has been operational since 2012. Despite the FY-3 series satellitesare equipped with the same MWRI and all MWRIs sharing comparable parameters and configurations as AMSR2,disparities in observation times and satellite platforms result in inconsistencies in the data obtained by differentsatellites, which further impacting the consistency of retrieved geophysical parameters. To improve the consistency of brightness temperatures from FY-3B, FY-3C, FY-3D/MWRI, and GCOM-W1/AMSR2, cross-calibrationswere conducted among brightness temperatures at ten-channel from above four platforms. The consistency ofderived snow depth from MWRIs and AMSR2 in China before and after the calibration were also analyzed. Theresults show that the correlation coefficients of brightness temperatures at all channels between sensors exceed0.98. After cross-calibration, the RMSEs and biases of brightness temperatures at all frequencies and snow depthin China derived from them reduce to varying degrees. The consistencies in both brightness temperatures andsnow depth of FY-3B/MWRI, FY-3D/MWRI, and AMSR2 are higher than those of FY-3C and others. These findingsadvocate for the utilization of cross-calibrated brightness temperatures from FY-3B/MWRI, FY-3D/MWRI, andAMSR2, which share similar satellite overpass time, to derived a long-term snow depth dataset.
基金The National Natural Science Foundation of China under contract No.42076235the Fundamental Research Funds for the Central Universities under contract No.2042022kf0018.
文摘The snow depth on sea ice is an extremely critical part of the cryosphere.Monitoring and understanding changes of snow depth on Antarctic sea ice is beneficial for research on sea ice and global climate change.The Microwave Radiation Imager(MWRI)sensor aboard the Chinese FengYun-3D(FY-3D)satellite has great potential for obtaining information of the spatial and temporal distribution of snow depth on the sea ice.By comparing in-situ snow depth measurements during the 35th Chinese Antarctic Research Expedition(CHINARE-35),we took advantage of the combination of multiple gradient ratio(GR(36V,10V)and GR(36V,18V))derived from the measured brightness temperature of FY-3D MWRI to estimate the snow depth.This method could simultaneously introduce the advantages of high and low GR in the snow depth retrieval model and perform well in both deep and shallow snow layers.Based on this,we constructed a novel model to retrieve the FY-3D MWRI snow depth on Antarctic sea ice.The new model validated by the ship-based observational snow depth data from CHINARE-35 and the snow depth measured by snow buoys from the Alfred Wegener Institute(AWI)suggest that the model proposed in this study performs better than traditional models,with root mean square deviations(RMSDs)of 8.59 cm and 7.71 cm,respectively.A comparison with the snow depth measured from Operation IceBridge(OIB)project indicates that FY-3D MWRI snow depth was more accurate than the released snow depth product from the U.S.National Snow and Ice Data Center(NSIDC)and the National Tibetan Plateau Data Center(NTPDC).The spatial distribution of the snow depth from FY-3D MWRI agrees basically with that from ICESat-2;this demonstrates its reliability for estimating Antarctic snow depth,and thus has great potential for understanding snow depth variations on Antarctic sea ice in the context of global climate change.
基金funded by the National Basic Research Program of China(2009CB825102)the National Basic Research Program of China(2009CB421102E)+1 种基金the International Science & Technology Cooperation Program of China(2010DFA92720)the Natural Science Foundation of China(4117049)
文摘Water and nitrogen （N） inputs are considered as the two main limiting factors affecting plant growth.Changes in these inputs are expected to alter the structure and composition of the plant community,thereby influencing biodiversity and ecosystem function.Snowfall is a form of precipitation in winter,and snow melting can recharge soil water and result in a flourish of ephemerals during springtime in the Gurbantunggut Desert,China.A bi-factor experiment was designed and deployed during the snow-covering season from 2009 to 2010.The experiment aimed to explore the effects of different snow-covering depths and N addition levels on ephemerals.Findings indicated that deeper snow cover led to the increases in water content in topsoil as well as density and coverage of ephemeral plants in the same N treatment; by contrast,N addition sharply decreased the density of ephemerals in the same snow treatment.Meanwhile,N addition exhibited a different effect on the growth of ephemeral plants：in the 50％ snow treatment,N addition limited the growth of ephemeral plants,showing that the height and the aboveground biomass of the ephemeral plants were lower than in those without N addition; while with the increases in snow depth （100％ and 150％ snow treatments）,N addition benefited the growth of the dominant individual plants.Species richness was not significantly affected by snow in the same N treatment.However,N addition significantly decreased the species richness in the same snow-covering depth.The primary productivity of ephemerals in the N addition increased with the increase of snow depth.These variations indicated that the effect of N on the growth of ephemerals was restricted by water supply.With plenty of water （100％ and 150％ snow treatments）,N addition contributed to the growth of ephemeral plants; while with less water （50％ snow treatment）,N addition restricted the growth of ephemeral plants.
基金supported by the R&D Special Fund for Public Welfare Industry (meteorology) under Grant Nos. GYHY200706010 and GYHY200806020 the National Science Foundation of China under Grant Nos. 40625014 and 40821092 National Key Project of Scientific and Technical Supporting Programs under Grant Nos. 2007BAC03A01 and 2007BAC29B03
文摘The authors present evidence to suggest that variations in the snow depth over the Tibetan Plateau （TP） are connected with changes of North Atlantic Oscillation （NAO） in winter （JFM）. During the positive phase of NAO, the Asian subtropical westerly jet intensifies and the India-Myanmar trough deepens. Both of these processes enhance ascending motion over the TP. The intensified upward motion, together with strengthened southerlies upstream of the India-Myanmar trough, favors stronger snowfall over the TP, which is associated with East Asian tropospheric cooling in the subsequent late spring （April-May）. Hence, the decadal increase of winter snow depth over the TP after the late 1970s is proposed to be an indicator of the connection between the enhanced winter NAO and late spring tropospheric cooling over East Asia.
基金supported by the Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-Year Plan Period (Grant No. 2012BAC22B02)the National Key Basic Research Program of China (Grant No. 2013CB956603)the Ministry of Science and Technology of China (Grant No. 2013CBA01805)
文摘Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Flexible Global OceanAtmosphere-Land System （FGOALS） model,Grid-point Version 2 （g2） and Spectral Version 2 （s2）,were validated against observational data.The results revealed that the spatial pattern of SD and SCF over the Northern Hemisphere （NH） are simulated well by both models,except over the Tibetan Plateau,with the average spatial correlation coefficient over all months being around 0.7 and 0.8 for SD and SCF,respectively.Although the onset of snow accumulation is captured wellby the two models in terms of the annual cycle of SD and SCF,g2 overestimates SD/SCF over most mid-and high-latitude areas of the NH.Analysis showed that g2 produces lower temperatures than s2 because it considers the indirect effects of aerosols in its atmospheric component,which is the primary driver for the SD/SCF difference between the two models.In addition,both models simulate the significant decreasing trend of SCF well over （30°-70°N） in winter during the period 1971-94.However,as g2 has a weak response to an increase in the concentration of CO2 and lower climate sensitivity,it presents weaker interannual variation compared to s2.
基金supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW- Q11-04)the National Basic Research Program of China (Grant No. 2010CB950402)the National Natural Science Foundation of China (Grant No. 40975052)
文摘The interannual variability of wintertime snow depth over the Tibetan Plateau(TP) and related atmospheric circulation anomalies were investigated based on observed snow depth measurements and NCEP/NCAR reanalysis data.Empirical orthogonal function(EOF) analysis was applied to identify the spatio-temporal variability of wintertime TP snow depth.Snow depth anomalies were dominated by a monopole pattern over the TP and a dipole structure with opposite anomalies over the southeastern and northwestern TP.The atmospheric circulation conditions responsible for the interannual variability of TP snow depth were examined via regression analyses against the principal component of the most dominant EOF mode.In the upper troposphere,negative zonal wind anomalies over the TP with extensively positive anomalies to the south indicated that the southwestward shift of the westerly jet may favor the development of surface cyclones over the TP.An anomalous cyclone centered over the southeastern TP was associated with the anomalous westerly jet,which is conducive to heavier snowfall and results in positive snow depth anomalies.An anomalous cyclone was observed at 500 hPa over the TP,with an anomalous anticyclone immediately to the north,suggesting that the TP is frequently affected by surface cyclones.Regression analyses revealed that significant negative thickness anomalies exist around the TP from March to May,with a meridional dipole anomaly in March.The persistent negative anomalies due to more winter TP snow are not conducive to earlier reversal of the meridional temperature gradient,leading to a possible delay in the onset of the Asian summer monsoon.
基金The National Key Research and Development Program of China under contract Nos 2019YFA0607001 and2016YFC1402704the Global Change Research Program of China under contract No.2015CB9539011
文摘This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A.The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression.The snow depth in case of seasonal ice was calculated by using parameters of the crosscalibration of data from the MWRI Tb.The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97.The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI.Data from the MWRI Tb were cross-calibrated to the AMSR-E baseline.Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration,the standard deviations decreased significantly in the range of 1.32 K to 2.57 K,and the correlation coefficients were as high as 99%.An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated.Furthermore,the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz,23.8 GHz,and 36.5 GHz(V polarization),and at 89 GHz(H/V polarization),and were applied to the snow depths retrieval in the Arctic.The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average Tb.The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products.Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths,with the standard deviations ranging from 4.19 cm to 4.80 cm.
基金Under the auspices of Special Basic Research Fund for Central Public Scientific Research Institutes (No. 2007-03)
文摘On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.
基金supported by the NOAA Climate Program Office(Grant No.NA15OAR4310163)the National Key R&D Program of China(Grant Nos.2018YFA0605904 and 2018YFA0605901)the National Natural Science Foundation of China(Grant No.41676185)。
文摘Snow depth over sea ice is an essential variable for understanding the Arctic energy budget.In this study,we evaluate snow depth over Arctic sea ice during 1993-2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project(CMIP6)against recent satellite retrievals.The CMIP6 models capture some aspects of the observed snow depth climatology and variability.The observed variability lies in the middle of the models’simulations.All the models show negative trends of snow depth during 1993-2014.However,substantial spatiotemporal discrepancies are identified.Compared to the observation,most models have late seasonal maximum snow depth(by two months),remarkably thinner snow for the seasonal minimum,an incorrect transition from the growth to decay period,and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice.Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic.Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099.Under the SSP5-8.5 scenario,the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January.Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution,the inclusion of a hightop atmospheric model,and biogeochemistry processes are important factors for snow depth simulation.
基金supported by Projects of International Cooperation and Exchanges NSFC (grant: 41361140361)the Special fund project of Chinese Academy of Sciences (grant: Y371164001)the key deployment project of Chinese Academy of Sciences (Grant No. KZZD-EW-12-2, KZZD-EW12-3)
文摘Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.
基金funded by the National Science Foundation of China (No. 41575091)the National Basic Research and Development (973) Program of China (Grant No. 2012CB957803 and No. 2012CB957804)
文摘Studies on the impact of solar activity on climate system are very important in understanding global climate change. Previous studies in this field were mostly focus on temperature, wind and geopotential height. In this paper, interdecadal correlations of solar activity with Winter Snow Depth Index （WSDI） over the Tibetan Plateau, Arctic Oscillation Index （AOI） and the East Asian Winter Monsoon Index （EAWMI） are detected respectively by using Solar Radio Flux （SRF）, Total Solar Irradiance （TSI） and Solar Sunspot Number （SSN） data and statistical methods. Arctic Oscillation and East Asian winter monsoon are typical modes of the East Asian atmospheric circulation. Research results show that on inter-decadal time scale over 11-year solar cycle, the sun modulated changes of winter snow depth over the Tibetan Plateau and East Asian atmospheric circulation. At the fourth lag year, the correlation coefficient of SRF and snow depth is 0.8013 at 0.05 significance level by Monte-Carlo test method. Our study also shows that winter snow depth over the Tibetan Plateau has significant lead and lag correlations with Arctic Oscillation and the East Asian winter monsoon on long time scale. With more snow in winter, the phase of Arctic Oscillation is positive, and East Asian winter monsoon is weak, while with less snow, the parameters are reversed. An example is the winter of 2012/2013, with decreased Tibetan Plateau snow, phase of Arctic Oscillation was negative, and East Asian winter monsoon was strong.
基金supported by the National Key Research and Development Program of China(Grand No.2020YFA0608501)the National Natural Science Foundation of China(Grand No.42171143)the CAS’Light of West China’Program(E029070101)
文摘Satellite remote sensing is widely used to estimate snow depth and snow water equivalent(SWE)which are two key parameters in global and regional climatic and hydrological systems.Remote sensing techniques for snow depth mainly include passive microwave remote sensing,Synthetic Aperture Radar(SAR),Interferometric SAR(In SAR)and Lidar.Among them,passive microwave remote sensing is the most efficient way to estimate large scale snow depth due to its long time series data and high temporal frequency.Passive microwave remote sensing was utilized to monitor snow depth starting in 1978 when Nimbus-7 satellite with Scanning Multichannel Microwave Radiometer(SMMR)freely provided multi-frequency passive microwave data.SAR was found to have ability to detecting snow depth in 1980 s,but was not used for satellite active microwave remote sensing until 2000.Satellite Lidar was utilized to detect snow depth since the later period of 2000 s.The estimation of snow depth from space has experienced significant progress during the last 40 years.However,challenges or uncertainties still exist for snow depth estimation from space.In this study,we review the main space remote sensing techniques of snow depth retrieval.Typical algorithms and their principles are described,and problems or disadvantages of these algorithms are discussed.It was found that snow depth retrieval in mountainous area is a big challenge for satellite remote sensing due to complicated topography.With increasing number of freely available SAR data,future new methods combing passive and active microwave remote sensing are needed for improving the retrieval accuracy of snow depth in mountainous areas.
文摘Snow cover is one of the important components of land cover,and it is necessary to accurately monitor the depth and coverage of snow cover.Using the GPS signal receiver data and the basic principle of snow depth detection based on GPS-MR technology,the snow depth of the three sites on the Greenland PBO network GLS1,GLS2,and GLS3 from 2012 to 2018 was obtained.The inversion snow depth is affected by site drift,which is a quite difference from the measured snow depth.Combined with the stable reference point,the velocity field distribution of Greenland Island and the U-direction component change value of the station can be obtained through GAMIT calculation.By analyzing the glacial flow and U-direction component,the influence of the site drift on the snow depth was deducted,and finally compared the corrected inversion snow depth and measured snow depth found that the two were better than before the correction,the results were significantly improved,and the consistency was good.The analysis of the experimental results showed that in extremely cold areas such as Greenland Island,affected by glaciers,the continuous,real-time,high-time resolution snow depth around the measured station obtained by ground-based GPS tracking stations has a large gap with the measured snow depth value,and the gap will gradually increase with time.By deducting the impact of glacier drift,the trend of the two is the same and the consistency is good.The correctness and feasibility of the application of ground-based GPS snow cover theory in the polar area further expand the application scope and practical value of ground-based GPS in snow monitoring.
基金supported by the European Regional Funding Project IPa Wa(2019-2022)Innovative Urban Planning and Storm water Management in a Resilient and Smart Cities。
文摘Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction.Despite advances in remote sensing technology and enhanced satellite observations,the estimation of snow depth at local scale still requires improved accuracy and flexibility.The advances in ubiquitous and wearable technology promote new prospects in tackling this challenge.In this paper,a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put forward.Significantly,the results of Random Forest classifier showed an accuracy of 94%,indicating a promising alternative in snow depth measurement compared to in situ,LiDAR,or expensive large-scale wireless sensor network,which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.
基金supported by the Ministry of Science and Technology Project under No.2012CB957803 and No. 2007BAC29B02Special Fund on Climate Change of China Meteorological Administration under Grant No. CCSF2007-2C
文摘This paper obtained a set of consecutive and long-recorded observational snow depth data from 51 observation stations by choosing, removing and interpolating original observation data over the Tibetan Plateau for 1961-2006. We used monthly precipitation and temperature data from 160 stations in China for 1951-2006, which was collected by the National Climate Center. Through calculating and analyzing the correlation coefficient, significance test, polynomial trend fitting, composite analysis and abrupt change test, this paper studied the interdecadal change of winter snow over the Tibetan Plateau and its relationship to summer pre- cipitation and temperature in China, and to tropospheric atmospheric temperature. This paper also studied general circulation and East Asian summer monsoon under the background of global warming.
基金Funding for this project was provided by the National Key Research and Development Program of China (No. 2016YFC1402704)the Global Change Research Program of China (No. 2015CB953901)
文摘Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager(MWRI) on board the FengYun(FY)-3 B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS(AMSR-E) Level 2 A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice(ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between-1.01 and-0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.
基金supported by Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA28110502]National Natural Science Foundation of China[grant number 41871248]+1 种基金Changchun Science and Technology Development Plan Project[grant number 21ZY12]Innovation and Entrepreneurship Talent Project of Jilin Province[grant number 2023QN15].
文摘Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products have been developed since the 1970s,they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD,especially in complex mountainous regions.In this paper,we developed afine-resolution SD retrieval model(FSDM)using machine learning to improve SD estimation quality for Northeast China and produced a long-term,fine-resolution,daily SD dataset.The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products.The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation,with the root-mean-square error(RMSE),bias,and correlation coefficient(R)of 7.10 cm,-0.13 cm,and 0.60.Additionally,we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range,Lesser Khingan Mountains,and Changbai Mountain regions.The SD exhibited high-low distribution patterns with the increased latitude.The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.
基金funded by the National Natural Science Foundation of China(42125604 and 42171143)Innovative Development Project of China Meteorological Administration(CXFZ 2022J039)and CAS Light of West China Program.The National Oceanic and Atmospheric Administration,USA,provided in situ snow depth data in the Eurasian continent except China and passive microwave brightness temperature data on the DMSP series of satellites.China Meteorological Administration provided FengYun satellite data and in situ snow depth in China,and NASA provided AMSR2 brightness temperature and sea ice concentration data.
文摘Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia,and the lagging updates of datasets do not meet the operational requirements of the meteorological service department.This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work.These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3(FY-3)satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites,which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration.Firstly,inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors.A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established,overcoming the large uncertainties induced by varying snow characteristics.This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth.The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ.Against in situ snow depth,the overall bias and root mean square error are−2.04 and 6.49 cm,respectively,facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product,which adopts the static algorithm.Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths,demonstrating a noticeable reduction since around 2000.The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.
基金supported by the National Natural Science Foundation of China(Grant Nos.41171260&41030534)
文摘The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.
