地表反照率直接影响地表辐射平衡,进而改变当地温度(2m气温,下同),然后还可能通过大气平流过程影响下游地区的温度。为揭示利用实时更新的地表反照率替换WRF(Weather Research and Forecasting)模式的静态地表反照率对中国大陆温度模拟...地表反照率直接影响地表辐射平衡,进而改变当地温度(2m气温,下同),然后还可能通过大气平流过程影响下游地区的温度。为揭示利用实时更新的地表反照率替换WRF(Weather Research and Forecasting)模式的静态地表反照率对中国大陆温度模拟结果的影响,本文进行了两组为期6年(2002-2007年)的连续积分试验:控制试验(CT试验)采用短波波段地表反照率,取自WRF模式推荐的地表参数数据集;敏感试验(MD试验)采用分波段的(可见光和近红外)地表反照率,取自MODIS BRDF/Albedo数据产品。试验结果表明,CT试验能够模拟中国温度的基本空间格局,但是模拟温度相对于观测温度有明显偏差,青藏高原南部的模拟温度偏低(负偏差),最大偏低幅度为1.03℃,出现在秋季,东部地区的模拟温度偏高(正偏差),最大偏高幅度达3.4℃,出现在春季;MD试验模拟结果的正、负偏差格局与CT试验基本相似,但是与CT试验相比,MD试验模拟的青藏高原南部温度的负偏差更大,最大为1.32℃,而模拟的东部地区温度的正偏差明显减小,最大为2.97℃,这说明MD试验比CT试验模拟的温度普遍偏低。在青藏高原,这主要归因于MD试验比CT试验的地表反照率大,使得地表净辐射少,地表感热少,致使温度偏低;在中国东部的黄淮海至江南丘陵区,这主要归因于MD试验中北方蒙古高原的地表反照率比CT试验的大,使得MD试验中该地区的地表净辐射少,地表感热少,温度低,然后通过南下冷平流过程致使位于其下游的黄淮海至江南丘陵区温度降低。展开更多
The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insuf...The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insufficient observations. Common optimization algorithms have difficulties in providing posterior distribution and thus cannot directly acquire uncertainties in inversion results, which is of no benefit to remote sensing application. In this article, ensemble Kalman filter (EnKF) has been introduced to retrieve surface geophysical parameters from remote sensing observations, which has the capability of not merely obtaining inversion results but also giving its posterior distribution. To show the advantage of EnKF, it is compared to standard MODIS AMBRALS algorithm and highly effi-cient global optimization method SCE-UA. The inversion abilities of kernel-driven BRDF models with different kernel combinations at several main cover types are emphatically discussed when observa-tions are deficient and a priori knowledge is introduced into inversion.展开更多
Kernel-driven model was chosen to calculate global albedo in the project of multiangular remote sensor MODIS. The best kernels were selected by the venerable 'least square' method. The result of this method wa...Kernel-driven model was chosen to calculate global albedo in the project of multiangular remote sensor MODIS. The best kernels were selected by the venerable 'least square' method. The result of this method was very unstable when only a small amount of angular observations is available. A new criterion has been estalished, called 'least variance' for the kernel’s selection. It takes into consideration the effects of model and measurement based on the information inverse theory. Several tests showed that 'least variance' has many advantages. First, it is less sensitive to noises. Second, it operates well in small sample size. Third, it depends less on the sampling position.展开更多
文摘地表反照率直接影响地表辐射平衡,进而改变当地温度(2m气温,下同),然后还可能通过大气平流过程影响下游地区的温度。为揭示利用实时更新的地表反照率替换WRF(Weather Research and Forecasting)模式的静态地表反照率对中国大陆温度模拟结果的影响,本文进行了两组为期6年(2002-2007年)的连续积分试验:控制试验(CT试验)采用短波波段地表反照率,取自WRF模式推荐的地表参数数据集;敏感试验(MD试验)采用分波段的(可见光和近红外)地表反照率,取自MODIS BRDF/Albedo数据产品。试验结果表明,CT试验能够模拟中国温度的基本空间格局,但是模拟温度相对于观测温度有明显偏差,青藏高原南部的模拟温度偏低(负偏差),最大偏低幅度为1.03℃,出现在秋季,东部地区的模拟温度偏高(正偏差),最大偏高幅度达3.4℃,出现在春季;MD试验模拟结果的正、负偏差格局与CT试验基本相似,但是与CT试验相比,MD试验模拟的青藏高原南部温度的负偏差更大,最大为1.32℃,而模拟的东部地区温度的正偏差明显减小,最大为2.97℃,这说明MD试验比CT试验模拟的温度普遍偏低。在青藏高原,这主要归因于MD试验比CT试验的地表反照率大,使得地表净辐射少,地表感热少,致使温度偏低;在中国东部的黄淮海至江南丘陵区,这主要归因于MD试验中北方蒙古高原的地表反照率比CT试验的大,使得MD试验中该地区的地表净辐射少,地表感热少,温度低,然后通过南下冷平流过程致使位于其下游的黄淮海至江南丘陵区温度降低。
基金Foundation： National Natural Science Foundation of China （No.41171261, 40871193） National Basic Research Program of China （973 Program） （No. 2013CB733401）志谢本文中使用的POLDER-3/ARASOLBRDF数据由法国气候与环境科学实验室（LSCE）生产,并由POSTEL服务中心提供,在此表示衷心的感谢!
基金supported by the National Natural Science Foundation of China(Grant No.40471095)the National Basic Research Program(Grant No.G2000077908)the International Cooperation Project of MOST(Grant No.2004DFA06300).
文摘The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insufficient observations. Common optimization algorithms have difficulties in providing posterior distribution and thus cannot directly acquire uncertainties in inversion results, which is of no benefit to remote sensing application. In this article, ensemble Kalman filter (EnKF) has been introduced to retrieve surface geophysical parameters from remote sensing observations, which has the capability of not merely obtaining inversion results but also giving its posterior distribution. To show the advantage of EnKF, it is compared to standard MODIS AMBRALS algorithm and highly effi-cient global optimization method SCE-UA. The inversion abilities of kernel-driven BRDF models with different kernel combinations at several main cover types are emphatically discussed when observa-tions are deficient and a priori knowledge is introduced into inversion.
文摘Kernel-driven model was chosen to calculate global albedo in the project of multiangular remote sensor MODIS. The best kernels were selected by the venerable 'least square' method. The result of this method was very unstable when only a small amount of angular observations is available. A new criterion has been estalished, called 'least variance' for the kernel’s selection. It takes into consideration the effects of model and measurement based on the information inverse theory. Several tests showed that 'least variance' has many advantages. First, it is less sensitive to noises. Second, it operates well in small sample size. Third, it depends less on the sampling position.
