Evaluation of snow products over the Tibetan Plateau

Four satellite‐based snow products are evaluated over the Tibetan Plateau for the 2007–2010 snow seasons. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover daily L3 Global 500‐m grid products (MOD10A1 and MYD10A1), the National Oceanic and Atmospheric Administration Interactive Multisensor Snow and Ice Mapping System (IMS) daily Northern Hemisphere snow cover product and the Advanced Microwave Scanning Radiometer – Earth Observing System Daily Snow Water Equivalent were validated against Thematic Mapper (TM) snow cover maps of Landsat‐5 and meteorological station snow depth observations. The overall accuracy of MOD10A1, MYD10A1 and IMS is higher than 91% against stations observations and than 79% against Landsat TM images. In general, the daily MODIS snow cover products show better performance than the multisensor IMS product. However, the IMS snow cover product is suitable for larger scale (~4km) analysis and applications, with the advantage over MODIS to allow for mitigation for cloud cover. The accuracy of the three products decreases with decreasing snow depth. Overestimation errors are most common over forested regions; the IMS and Advanced Microwave Scanning Radiometer – Earth Observing System Snow Water Equivalent products also show poorer performance that the MODIS products over grassland. By identifying weaknesses in the satellite products, this study provides a focus for the improvement of snow products over the Tibetan plateau. The quantitative evaluation of the products proposed here can also be used to assess their relative weight in data assimilation, against other data sources, such as modelling and in situ measurement networks. Copyright © 2015 John Wiley & Sons, Ltd.     

云雨条件下AMSR2微波成像资料同化试验及其在台风预报中的应用

采用WRF-ARW和WRFDA的3.8版本进行云雨条件下AMSR2(Advanced Microwave Scanning Radiometer 2)卫星微波成像资料的同化试验,并选择2014年7月第9号台风"威马逊"为研究对象,检验同化云雨条件下的AMSR2资料对台风预报结果的影响。结果表明:(1)云雨条件下的质量控制方案所保留的像元明显多于晴空条件下质量控制方案,且亮温模拟结果更加接近观测;(2)云雨影响的加入使得风场、高度场和海平面气压场的模拟结果好于晴空条件下模拟,且对台风中心有增温、增湿的作用。(3)云雨条件下模拟对台风的路径和强度预报均有改善。     

基于南太平洋的AMSR2L1R亮温数据质量评估

本文基于环境场较为稳定的南太平洋目标海区,以海洋大气微波辐射传输模型(Radiative Transfer Model,RTM)模拟亮温作为参考值,对2015年1月1日—2017年12月31日的高级扫描微波辐射计(Advanced Microwave Scanning Radiometer 2,AMSR2) L1R亮温数据产品进行了质量评估。结果表明AMSR2 L1R所有通道亮温数据总偏差和标准偏差的变化范围分别为1.466～6.352 K、0.270～1.693 K,其中标准偏差在水平极化通道较大的同时随着频率的增大而增大。相比同类遥感器如全球降水测量微波成像仪(Global Precipitation Measurement Microwave Imager,GMI)等的质量分析结果,AMSR2亮温数据的标准偏差较小,这表明AMSR2亮温数据精度较高。对AMSR2 L1R亮温数据3年长时间序列的变化趋势分析表明所有通道亮温偏差均在±0.5 K范围内波动但是存在微弱的季节性变化,标准偏差随时间的变化较小,这表明AMSR2 L1R亮温数据质量较为稳定。     

Features of ocean microwave emission changed by wind at 6 GHz

Ocean microwave emissions changed by the ocean wind at 6 GHz were investigated by combining data of the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds, both aboard the Advanced Earth Observation Satellite-II (ADEOS-II). This study was undertaken to improve the accuracy of the sea surface temperature (SST) retrieved from the AMSR 6 GHz data. Two quantities, 6V*(H*), were defined by the brightness temperature of the AMSR at 6 GHz with two polarizations (V-pol and H-pol), adjusted for atmospheric effects and with a calm ocean surface emission removed. These quantities represent a microwave emission change due to the ocean wind at 6 GHz. 6V* does not change in a region where 6H* is less than around 4 K (referred to as z0). Both 6V* and 6H* increase above z0. The 6V* to 6H* ratio, sp, varies with the relative wind directions. Furthermore, the sp values vary with the SST, between the northern and southern hemisphere, and seasonally. By specifying appropriate values for z0 and sp, the SST error between AMSR and buoy measurement became flat against 6H*, which is related to the ocean wind. Two extreme cases were observed: the Arabian Sea in summer and the Northwestern Atlantic Ocean in winter. The air-sea temperature difference in the former case was largely positive, while it was largely negative in the latter. The 6V* and 6H* relations differed from global conditions in both cases, which resulted in incorrect SSTs in both areas when global coefficients were applied.     

Evaluation of marine surface winds observed by SeaWinds and AMSR on ADEOS-II

Marine surface winds observed by two microwave sensors, SeaWinds and Advanced Microwave Scanning Radiometer (AMSR), on the Advanced Earth Observing Satellite-II (ADEOS-II) are evaluated by comparison with off-shore moored buoy observations. The wind speed and direction observed by SeaWinds are in good agreement with buoy data with root-mean-squared (rms) differences of approximately 1 m s⁻¹ and 20°, respectively. No systematic biases depending on wind speed or cross-track wind vector cell location are discernible. The effects of oceanographic and atmospheric environments on the scatterometry are negligible. Though the wind speed observed by AMSR also showed agreement with buoy observations with rms difference of 1.27 m s⁻¹, the AMSR wind speed is systematically lower than the buoy data for wind speeds lower than 5 m s⁻¹. The AMSR wind seems to have a discontinuous trend relative to the buoy data at wind speeds of 5–6 m s⁻¹. Similar results have been obtained in an intercomparison of wind speeds globally observed by SeaWinds and AMSR on the same orbits. A global wind speed histogram of the AMSR wind shows skewed features in comparison with those of SeaWinds and European Centre for Medium-range Weather Forecasts (ECMWF) analyses.     

光学与微波遥感的新疆积雪覆盖变化分析

利用2002-2013年冬季的MODIS光学遥感数据,以及AMSR-E、AMSR2与MWRI被动微波遥感数据,建立了新疆地区冬季每日积雪分布遥感反演模型。首先,将Terra与Aqua双星MODIS的积雪产品融合,初步去云并最大化积雪信息;然后,利用AMSR-E/AMSR2和MWRI被动微波数据进行每日雪盖提取;最后,利用被动微波遥感数据反演得到的每日雪盖结果对双星融合后依然有云的像元进行替换,得到每日积雪分布情况。据此模型提取了11年间冬季的积雪天数信息,结合气象台站观测数据,分析了新疆冬季积雪的年内和年际变化规律。结果表明,新疆地区积雪主要分布在北部新疆,积雪天数与地形关系密切,山区积雪天数较多,盆地及城市区积雪天数较少;积雪天数年内变化是从11月到次年1月随温度降低逐渐增加,从1月到3月积雪天数则逐渐减少。新疆地区积雪天数在这11年中存在一定的波动,积雪天数与该年的平均气温,以及月低于0℃的天数存在显著相关性,与降雪量关系不明显。新疆地区近年来积雪天数重心有向西向南移动的趋势,这可能与全球气候变暖导致多年积雪融化有关。     

基于随机森林算法的青藏高原AMSR2被动微波雪深反演

青藏高原因其复杂的地形地势和和积雪分布使得多种雪深算法未达到理想的精度。基于新一代被动微波数据AMSR2（Advanced Microwave Scanning Radiometer 2）, 应用随机森林算法（Random Forest, RF）将亮温（Brightness Temperature, BT）和亮温差（Brightness Temperature Difference, BTD）作为参数输入, 并将高程和纬度参数引入雪深反演模型中, 经过模拟退火算法进行有效反演因子筛选, 构建了基于随机森林算法的青藏高原雪深反演模型。结果表明： 与AMSR2全球雪深产品相比, 随机森林算法的拟合优度（R²）由0.41提升至0.60, 均方根误差（Root Mean Square Error, RMSE）由7.36 cm降至4.88 cm, 偏差（BIAS）由3.24 cm减小至-0.16 cm, 随机森林雪深反演模型在青藏高原的精度更高; 青藏高原平均海拔超过4 000 m, 当海拔大于青藏高原平均海拔时, 随机森林算法的反演效果最差, 但RMSE仅为3.78 cm, BIAS仅为-0.09 cm; 高原南部（25° ~ 30° N）因其复杂的地势和相对较少的气象站点使得反演效果较差, RMSE为5.94 cm, BIAS为-0.39 cm; 青藏高原的主要土地覆盖类型为草地, 随机森林算法在草地的RMSE约为3 cm, BIAS接近0 cm。     

Intercomparison of snow water equivalent observations in the Northern Great Plains

In the Northern Great Plains, melting snow is a primary driver of spring flooding, but limited knowledge of the magnitude and spatial distribution of snow water equivalent (SWE) hampers flood forecasting. Passive microwave remote sensing has the potential to enhance operational river flow forecasting but is not routinely incorporated in operational flood forecasting. We compare satellite passive microwave estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) to the National Oceanic and Atmospheric Administration Office of Water Prediction (OWP) airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the Northern Great Plains from 2002 to 2011. AMSR‐E SWE estimates compare favourably with USACE SWE measurements in the low relief, low vegetation study area (mean difference = ?3.8 mm, root mean squared difference [RMSD] = 34.7 mm), but less so with OWP airborne gamma SWE estimates (mean difference = ?9.5 mm, RMSD = 42.7 mm). An error simulation suggests that up to half of the error in the former comparison is potentially due to subpixel scale SWE variability, limiting the maximum achievable RMSD between ground and satellite SWE to approximately 26–33 mm in the Northern Great Plains. The OWP gamma versus AMSR‐E SWE comparison yields larger error than the point‐scale USACE versus AMSR‐E comparison, despite a larger measurement footprint (5–7 km² vs. a few square centimetres, respectively), suggesting that there are unshared errors between the USACE and OWP gamma SWE data.     

Combining MODIS and AMSR‐E observations to improve MCD43A3 short‐time snow‐covered Albedo estimation

Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.     

基于亚马逊热带雨林的微波辐射计定标基准分析

亚马逊热带雨林作为稳定地物目标,适合进行星载微波辐射计的外定标。但近些年亚马逊热带雨林受人为破坏严重,植被覆盖面积减少,植被覆盖率降低,适合进行外定标的区域较往年发生了变化。文中依据亚马逊热带雨林近些年的归一化植被指数(NDVI)的变化情况,发现纬度位于3°S^2°N,经度位于74°W^69°W范围内的区域植被覆盖率高,适合进行星载微波辐射计外定标。文中以AMSR2 L1R亮温数据为基准,对比分析了该区域在2015-2017年3 a的亮温变化趋势,并以此作为该区域的定标基准。分析发现,该区域在非厄尔尼诺事件期间的亮温变化趋势呈现出特定的季节变化规律:在每年的6-7月,亮温值达到最低;在11-12月,亮温值达到最高,7-11月波动上升,12-6月波动下降。在厄尔尼诺事件发生期间会出现亮温值异常现象。