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| 1 | A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature显示文摘In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter(EnKF), the land surface temperature(LST) data derived from the new generation of Chinese meteorology satellite(FY3A-VIRR) are assimilated into common land model(CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station(AWS), eddy covariance(EC) and large aperture scintillometer(LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data. | XU TongRen LIU ShaoMin XU ZiWei LIANG ShunLin XU Lu | 2015 | Science China Earth Sciences2015，58，2： | 9 |
| 2 | Regional and Global Land Data Assimilation Systems: Innovations,Challenges, and Prospects显示文摘Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed. | Youlong XIA Zengchao HAO Chunxiang SHI Yaohui LI Jesse MENG Tongren XU Xinying WU Baoqing ZHANG | 2019 | Journal of Meteorological Research2019，33，2： | 7 |
| 3 | Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012显示文摘As is well known,clouds impact the radiative budget,climate change,hydrological processes,and the global carbon,nitrogen and sulfur cycles.To understand the wide-ranging effects of clouds,it is necessary to assess changes in cloud cover at high spatial and temporal resolution.In this study,we calculate global cloud cover during the day and at night using cloud products estimated from Moderate Resolution Imaging Spectroradiometer(MODIS)data.Results indicate that the global mean cloud cover from 2003 to 2012 was 66%.Moreover,global cloud cover increased over this recent decade.Specifically,cloud cover over land areas(especially North America,Antarctica,and Europe)decreased(slope=–0.001,R^2=0.5254),whereas cloud cover over ocean areas(especially the Indian and Pacific Oceans)increased(slope=0.0011,R^2=0.4955).Cloud cover is relatively high between the latitudes of 36°S and 68°S compared to other regions,and cloud cover is lowest over Oceania and Antarctica.The highest rates of increase occurred over Southeast Asia and Oceania,whereas the highest rates of decrease occurred over Antarctica and North America.The global distribution of cloud cover regulates global temperature change,and the trends of these two variables over the 10-year period examined in this study(2003–2012)oppose one another in some regions.These findings are very important for studies of global climate change. | MAO Kebiao YUAN Zijin ZUO Zhiyuan XU Tongren SHEN Xinyi GAO Chunyu | 2019 | Chinese Geographical Science2019，29，2： | 5 |
| 4 | Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network显示文摘It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations. | MAO Kebiao ZUO Zhiyuan SHEN Xinyi XU Tongren GAO Chunyu LIU Guang | 2018 | Chinese Geographical Science2018，28，1： | 3 |
| 5 | The study of estimation method of broadband emissivity from EOS/MODIS data显示文摘The broadband emissivity is an important parameter for estimating the energy balance of the Earth.This study focuses on estimating the window(8-12 |xm) emissivity from the MODIS(moderate-resolution imaging spectroradiometer) data,and two methods are built.The regression method obtains the broadband emissivity from MOD11B1 5KM product,whose coefficient is developed by using 128 spectra,and the standard deviation of error is about 0.0118 and the mean error is about0.0084.Although the estimation accuracy is very high while the broadband emissivity is estimated from the emissivity of bands 29,31 and 32 obtained from MOD11B1 5KM product,the standard deviations of errors of single emissivity in bands 29,31,32 are about 0.009 for MOD11B1_5KM product,so the total error is about 0.02 and resolution is about 5km×5km.A combined radiative transfer model with dynamic learning neural network method is used to estimate the broadband emissivity from MODIS 1B data.The standard deviation of error is about 0.016,the mean error is about0.01,and the resolution is about 1km ×1km.The validation and application analysis indicates that the regression is simpler and more practical,and estimation accuracy of the dynamic learning neural network method is higher.Considering the needs for accuracy and practicalities in application,one of them can be chosen to estimate the broadband emissivity from MODIS data. | 毛克彪 Ma Ying Shen Xinyi Sun Zhiwen He Tianjue Xia Lang Xu Tongren | 2014 | High Technology Letters2014，20，1： | 0 |
| 6 | Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Conditions in the Heihe River Basin显示文摘Land surface hydrothermal conditions(LSHCs) reflect land surface moisture and heat conditions, and play an important role in energy and water cycles in soil-plant-atmosphere continuum. Based on comparison of four evaluation methods(namely, the classic statistical method, geostatistical method, information theory method, and fractal method), this study proposed a new scheme for evaluating the spatial heterogeneity of LSHCs. This scheme incorporates diverse remotely sensed surface parameters, e.g., leaf area index-LAI, the normalized difference vegetation index-NDVI, net radiation-Rn, and land surface temperature-LST. The LSHCs can be classified into three categories, namely homogeneous, moderately heterogeneous and highly heterogeneous based on the remotely sensed LAI data with a 30 m spatial resolution and the combination of normalized information entropy(S’) and coefficient of variation(CV). Based on the evaluation scheme, the spatial heterogeneity of land surface hydrothermal conditions at six typical flux observation stations in the Heihe River Basin during the vegetation growing season were evaluated. The evaluation results were consistent with the land surface type characteristics exhibited by Google Earth imagery and spatial heterogeneity assessed by high resolution remote sensing evapotranspiration data. Impact factors such as precipitation and irrigation events, spatial resolutions of remote sensing data, heterogeneity in the vertical direction, topography and sparse vegetation could also affect the evaluation results. For instance, short-term changes(precipitation and irrigation events) in the spatial heterogeneity of LSHCs can be diagnosed by energy factors, while long-term changes can be indicated by vegetation factors. The spatial heterogeneity of LSHCs decreases when decreasing the spatial resolution of remote sensing data. The proposed evaluation scheme would be useful for the quantification of spatial heterogeneity of LSHCs over flux observation stations toward the global scale, and also contribute to the improvement of the accuracy of estimation and validation for remotely sensed(or model simulated) evapotranspiration. | ZHANG Yuan LIU Shaomin HU Xiao WANG Jianghao LI Xiang XU Ziwei MA Yanfei LIU Rui XU Tongren YANG Xiaofan | 2020 | Chinese Geographical Science2020，30，5： | 0 |
| 7 | Assessment and improvement of Noah-MP for simulating water and heat exchange over alpine grassland in growing season显示文摘Alpine grassland is the main ecosystem of the Tibetan Plateau(TP),thus accurate simulation of water and heat exchange in the grassland will significantly enhance the understanding of the land-atmosphere interaction process on the TP.In this study,we assessed and improved the ensemble numerical simulations of the community Noah land surface model with multiparameterization options(Noah-MP)by using observations collected from four alpine grassland observation sites.The four observation sites belong to the upper Heihe River Basin Integrated Observatory Network located in the northeastern part of the TP.First,an ensemble of 1008 numerical simulation experiments,based on multiparameterization options of seven physical processes/variables in the Noah-MP,was carried out for the vegetation growing season.The Taylor skill score was then used to assess the model performance and select the optimal combination of parameterization options for a more exact simulation of the water and heat exchange in alpine grassland.The accuracy of Noah-MP simulation was further improved by introducing new parameterizations of thermal roughness length,soil hydraulic properties,and vertical root distribution.It was found that:(1)Simulation of water and heat exchange over alpine grassland in the growing season was mainly affected by the parameterizations of dynamic vegetation,canopy stomatal resistance,runoff and groundwater dynamics,and surface exchange coefficient for heat transfer.Selection of different parameterization options for these four physical processes/variables led to large differences in the simulation of water and heat fluxes.(2)The optimal combination of parameterization options selected in the current Noah-MP framework suffered from significant overestimation of sensible heat flux(H)and underestimation of soil moisture(θ)at all observation sites.(3)The overestimation of H was significantly improved by introducing a new parameterization of thermal roughness length.Furthermore,the underestimation ofθwas resolved by introducing a new parameterization of soil hydraulic properties that considered the organic matter effect and a new vertical distribution function for the vegetation root system.The results of this study provide an important reference for further improving the simulation of water and heat exchange by using the land surface model in alpine grassland. | Shuang SUN Donghai ZHENG Shaomin LIU Ziwei XU Tongren XU Hui ZHENG Xiaofan YANG | 2022 | Science China Earth Sciences2022，65，3： | 0 |
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