作为中国三大积雪区之一，青藏高原的积雪变化在气候系统、水文地质以及生态环境方面发挥着关键作用。已有的被动微波积雪深度反演方法存在数据分辨率低、不确定性高等问题，不适用于青藏高原复杂的山区地形。因此，本文基于FY-3B被动微波数据开发了青藏高原降尺度雪深反演模型，利用机器学习算法，将筛选后的亮温差作为参数输入，同时引入了高程、经纬度、植被覆盖度、积雪覆盖度和积雪天数等特征，最终进行了500 m分辨率的青藏高原雪深制图。结果显示，极端梯度提升XGBoost算法的决定系数（R2）和均方根误差（root mean square error,RMSE）分别为0.762和5.732 cm，明显优于支持向量回归和随机森林算法。从积雪天数、积雪覆盖度和植被覆盖度三个方面探讨了模型精度的变化，结果表明，在积雪天数为30～60 d时，模型表现良好，平均相对误差（mean relative error,MRE）最低为36.79%,RMSE为2.78 cm；随着积雪覆盖度的增加，模型的RMSE逐渐增大，在积雪覆盖度为0.25～0.50时，MRE和RMSE分别达到39.97%和3.12 c...

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)-3B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) Level 2A 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.

Regular measurements of spectral Aerosol Optical Depth (AOD) at ten wavelengths, obtained from multi-wavelength radiometer (MWR) under cloudless conditions in the outskirts of the tropical urban region of Hyderabad, India for the period January 2008 to December 2009, are examined. In general, high AOD with a coarse-mode abundance is seen during the pre-monsoon (March to May) and summer monsoon (June to September) with flat AOD spectra and low angstrom ngstrom wavelength exponent (), while in post-monsoon (OctoberNovember) and winter (DecemberFebruary) seasons, fine-mode dominance and steep AOD spectra are the basic features. The aerosol columnar size distribution (CSD) retrieved from the spectral AOD using King's inversion showed bimodal size distributions for all the seasons, except for the monsoon, with an accumulation-mode radius at 0.120.25 mu m and a coarse-mode one at 0.861.30 mu m. On the other hand, the CSD during the monsoon follows the power law for fine mode and the unimodal distribution for coarse mode. The fine-mode aerosols during post-monsoon and winter appear to be associated with air masses from continental India, while the coarse-mode particles during pre-monsoon and monsoon with air masses originating from west Asia and western India. The single-scattering albedo (SSA) calculated using the OPAC model varied from 0.83 +/- 0.05 in winter to 0.91 +/- 0.01 during the monsoon, indicating significant absorption by aerosols due to larger black carbon mixing ratio in winter, whereas a significant contribution of sea-salt in the monsoon season leads to higher SSAs. Aerosol radiative forcing (ARF) calculated using SBDART shows pronounced monthly variability at the surface, top of atmosphere (TOA) and within the atmosphere due to large variations in AOD and SSA. In general, larger negative ARF values at the surface (65 to 80 W m2) and TOA (approximate to 17 W m2) are observed during the pre-monsoon and early monsoon, while the atmospheric heating is higher (approximate to 5070 W m2) during January-April resulting in heating rates of approximate to 1.62.0 K day1. Copyright (c) 2012 Royal Meteorological Society