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Xu L.,Xinjiang Institute of Ecology and Geography | Xu L.,The Scientific Research Institute of the Water Conservancy of Ningxia | Zhou H.,Xinjiang Institute of Ecology and Geography | Pan F.,Urumqi Meteorological Satellite Ground Station | And 2 more authors.
Dili Xuebao/Acta Geographica Sinica | Year: 2016

Mountain-Oasis-Desert System (MODS) is the typical landscape pattern in the inland arid area of Northwest China. We set the rainfall monitoring network in the Sangong River Basin, which is located in the middle part of northern Tianshan Mountains, southern margin of the Junggar Basin, to obtain the data of May-August rainfall for 2007-2014. Then empirical orthogonal function, fractal theory and geostatistics method were used to investigate characteristics of spatial distribution pattern and spatial variability of precipitation for the Mountain-Oasis-Desert System in arid inland areas. Results indicate that: (1) The first feature vector (contribution rates of the overall changes was 82.4%) has three load sections, namely, 70-150 km, 30-70 km and 0-30 km; accordingly, and the study area is divided into mountain area, oasis area and desert area. (2) The spatial distribution of summer precipitation presents a pattern of "overall uniformly type", which means that precipitation will increase over the whole basin, and the increase range is decreasing from mountain, oasis to desert. (3) The semi variation function curve of the mountainous region fits the Gauss model, the oasis area fits the Spherical model and the variation distance is 15.3 km. The desert area in May, June and other months fits the Gauss model, the exponential model and the spherical model respectively, and the variation distance is 58.6 km, which is longer than that of the mountain and oasis. (4) In research scale, owing to the random factors arising precipitation spatial heterogeneity take up 0.01%-9.57% of all, it was mainly caused by autocorrelation. (5) The spatial variability of precipitation in the oasis region was the largest, while that of the desert region was the smallest. The spatial heterogeneity of precipitation in June was the most significant, while the minimum value was observed in August. The variation was the greatest in both the north-south (0°) and the southeast-northwest directions (135°). © 2016, Science Press. All right reserved.


Zhou M.Q.,CAS Institute of Physics | Zhou M.Q.,University of Chinese Academy of Sciences | Zhang X.Y.,National Satellite Meteorology Center | Wang P.C.,CAS Institute of Physics | And 3 more authors.
Science China Earth Sciences | Year: 2015

Based on the optimal estimation method, a satellite XCO2 retrieval algorithm was constructed by combining LBLRTM with VLIDORT. One-year GOSAT/TANSO observations over four TCCON stations were tested by our algorithm, and retrieval results were compared with GOSAT L2B products and ground-based FTS measurements. Meanwhile, the influence of CO2 line mixing effect on retrieval was estimated, and the research showed that neglecting CO2 line mixing effect could result in approximately 0.25% XCO2 underestimation. The accuracy of XCO2 retrievals was similar to GOSAT L2B products at cloud-free footprints with aerosol optical depth less than 0.3, and 1% accuracy of XCO2 retrievals can be reached based on the validation result with TCCON measurements. © 2015, Science China Press and Springer-Verlag Berlin Heidelberg.


Hu L.,Urumqi Meteorological Satellite Ground Station | Huang W.,Urumqi Meteorological Satellite Ground Station | Huang W.,Xinjiang Climate Center | Yin K.,Urumqi Meteorological Satellite Ground Station | And 2 more authors.
Shuikexue Jinzhan/Advances in Water Science | Year: 2013

In Xinjiang, snow constitutes a major water resource important to crop production, ranching, water supply, and other user needs. The snow water equivalent is estimated and its spatial-temporal distribution is analyzed using the MODIS (Moderate Resolution Imaging Spectroradiometer) EOS (Earth Observing System) remote sensing data collected during 2004-2010, and a 50-year dataset of snow depths and densities from 89 meteorological stations in Xinjiang. The result shows that the maximum value of annual snow water equivalent in Xinjiang during 2004-2010 was about 36.883 billion m3, which occurred in the winter of 2009-2010; while the minimum value was only 9.391 billion m3, which occurred in the winter of 2006-2007. The difference between maximum and minimum is about four times, and the difference in the earliest and latest dates for the peak occurrence can be 50 days long. Snow water resources in Xinjiang is mainly distributed in four regions, which are South Xinjiang, eastern Xinjiang, Yili and Bozhou, northeast of North Xinjiang. The time of peak occurrence of snow water equivalent varies from region to region around Xinjiang. The peak value of snow water equivalent is the sum of peak values in the four regions. The snow water equivalent has fluctuated dramatically in past 50 years in Xinjiang. An upward trend with a slope of 0.0832 in the snow water equivalent variation has been detected, which indicates that the snow water resource in Xinjiang has increased annually. A gradually increased fluctuation range indicates there may be years with less snow water resources.


Hao X.-H.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Wang J.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Wang J.,University of Chinese Academy of Sciences | Zhang P.,Urumqi Meteorological Satellite Ground Station | Huang C.-L.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2013

The retrieval of snow grain size is one of the important research directions for cryosphere snow remote sensing. In the present study, we designed the measurement plan of different snow grain size by different snow layer. A SVC HR-1024 ground-based spectral radiometer was used for measuring the spectral property of different snow grain size in northern Xinjiang, China. At the same time, the snow grain size and shape were measured by a hand-loupe with scale. Then the DSPP method was used to calculate the equivalent snow grain size. Finally, the asymptotic radiative transfer (ART) theory was applied to retrieve the snow grain size from measured snow spectral reflectance of different snow layer by optimizing the inversion band and the snow grain size factor "b". The retrieved snow grain size was validated by the measured snow grain size from DSPP method. The results showed that the DSPP method is an effective means of measuring the equivalent snow grain size. However, there is a large deviation of the snow grain size sample in the same snow layer. It is necessary to improve the measurement method of the single snow grain size sample; The study showed that the near-infrared bands are the most effective selection for retrieval of snow grain size. The retrieval algorithm from ART is feasible. When the snow is dry, the authors optimize the inversion band and the snow grain size factor b in the Northern Xinjiang, China. The optimal band wavelength is 1.20 μm and b is 3.62.


Dai L.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Dai L.,University of Chinese Academy of Sciences | Che T.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Wang J.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Zhang P.,Urumqi Meteorological Satellite Ground Station
Remote Sensing of Environment | Year: 2012

Static snow depth retrieval algorithms tend to underestimate the snow depth at the beginning of the snow season and overestimate the snow depth at the end of the snow season because the snow characteristics vary with the age of snow cover. A novel snow depth/water equivalent (SWE) data retrieval algorithm from passive microwave brightness temperature is proposed based on a priori snow characteristics, including the grain size, density and temperature of the layered snowpack. The layering scheme was established based on the brightness temperature difference (TBD) at two different frequencies, which indicates volume scattering, whereas the snow grain size and density of each layer were parameterized according to the age of the snow cover, and the snow temperature and temperature at the snow/soil interface were determined by the air temperature and snow depth. Furthermore, the microwave emission model of layered snowpacks (MEMLS) was used to simulate the brightness temperature at 10. GHz, 18. GHz and 36. GHz based on the a priori snow characteristics including snow grain size, density, depth and snow layering. Finally, three look-up tables (one layer, two layers and three layers) were generated for each day, which represent the relationship between the brightness temperatures at 10. GHz, 18. GHz and 36. GHz and snow depth. To avoid underestimation caused by the saturation of the microwave signal at 36. GHz, the TBD1 (the difference of brightness temperature at 18 and 36. GHz) was used to estimate the snow depth if TBD1 was less than 40. K, and TBD2 (the difference of the brightness temperature at 10 and 18. GHz) was used if TBD1 was greater than 40. K. The snow depth and SWE determined by this new algorithm were validated by snow measurements at thirteen meteorological stations in Xinjiang, China from 2003 to 2010 and compared with existing SWE products from the National Snow and Ice Data Center (NSIDC), the Environmental and Ecological Science Data Center for West China (WESTDC), the European Space Agency (ESA) and measurements with a snow course. The results showed that the root mean squared error (RMSE) and the bias from this new algorithm were greatly reduced compared to NSIDC, moderately reduced compared to ESA and slightly reduced compared to WESTDC. The understanding of a priori local snow characteristics can improve the accuracy of snow depth and snow water equivalent estimation from passive microwave remote sensing data. © 2012 Elsevier Inc.


Zhang M.,National Satellite Meteorological Center | Zhang X.-Y.,National Satellite Meteorological Center | Liu R.-X.,National Satellite Meteorological Center | Hu L.-Q.,Urumqi Meteorological Satellite Ground Station
Advances in Climate Change Research | Year: 2014

Three total column dry-air mole fractions of CO2 (XCO2) products from satellite retrievals, namely SCIAMACHY, NIES-GOSAT, and ACOS-GOSAT, in the Northern Hemisphere were validated by ground data from the Total Carbon Column Observing Network (TCCON). The results showed that the satellite data have the same seasonal fluctuations as in the TCCON data, with maximum in April or May and minimum in August or September. The three products all underestimate the XCO2. The ACOS-GOSAT and the NIES-GOSAT products are roughly equivalent, and their mean standard deviations are 2.26 × 10-6 and 2.27 × 10∼ respectively. The accuracy of the SCIMACHY product is slightly lower, with a mean standard deviation of 2.91 × 10-6. Copyright © 2014, National Climate Center (China Meteorological Administration). Production and hosting by Elsevier B.V. on behalf of KeAi.

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