风云三号C星微波全球地表温度产品精度评估

地表温度是衡量地表水热平衡的关键参数,微波地表温度因其范围大、全天候等独特的优势,在气候、农业和环境等领域得到广泛应用。基于经质量控制的MODIS地表温度产品对风云三号卫星C星的微波地表温度日产品和月平均产品进行验证评估,结果显示:FY-3C卫星升轨(夜晚)和降轨(白天)微波地表温度日产品平均分别高估8. 7 K、低估13. 2 K,月平均产品平均分别高估7. 9 K、低估12. 0 K,日产品和月平均产品的反演误差都在15K以内。在全球空间分布上,升轨(夜晚)和降轨(白天)月产品误差分别呈现高估和低估,热带雨林区和沙漠、荒漠区域在夜晚分别高估5 K以内和30 K以内,白天则分别低估10 K以内和10～30 K。不同土地覆盖类型间FY-3C微波地表温度反演精度存在差异,总体上升轨(夜晚)比降轨(白天)的精度高,反演精度最高和最低的土地类型分别是常绿阔叶林和荒漠、稀疏植被,不同土地覆盖类型间的地表温度反演精度在季节上存在明显差异。根据分析结果,改进FY-3C微波地表温度的反演算法,可进一步提高微波地表温度的反演精度。     

西藏林芝地区混合像元MODIS地表温度产品验证

西藏林芝地区地形复杂、土地覆盖类型多样,MODIS地表温度 (land surface temperature,LST) 产品验证面临处理混合像元的难题,为获得与像元尺度 (1 km) 相匹配的地表温度数据,该文提出采用多点同时观测结合面积加权的方法,将该方法应用于验证林芝地区2013年6月10日夜间晴空MODIS/LST产品。结果显示:单点观测对像元的代表性不足,容易低估产品精度 (10个样本均方根误差为2.2 K),面积加权法可获得综合性更好的地面LST信息,对MODIS/LST产品的精度给出更高的评价 (30个样本均方根误差为1.40 K)。对于地表类型混杂程度高且地势较为平坦的像元,面积加权法的优势更为明显,可将卫星LST产品与地面LST之间的差异由3 K降至1 K以内。     

阿勒泰地区气温日较差的气候变化特征

利用线性趋势法对1961-2008年阿勒泰地区7个气象站点气温日较差进行趋势研究,并根据各因子趋势值,应用相关统计法分析了影响气温日较差呈减小趋势的因子。结果表明：阿勒泰地区四季日较差呈现显著减小趋势,其中冬季最显著,秋季变化最弱。各季节最低气温上升趋势最明显,而最高气温上升趋势较弱。阿勒泰地区与月平均气温日较差相关性最强的因子是日照时数,呈正相关;其次分别为总云量、降水量和水汽压,都呈负相关。年气温日较差与降水量和水汽压相关性最大。     

GMS 5反演中国几类典型下垫面晴空地表温度的日变化及季节变化

文中利用单时相双光谱分裂窗算法以GMS 5/VISSR红外资料反演地表温度,揭示了中国几类典型下垫面晴空地表温度的日变化及季节变化特征.塔里木盆地、青藏高原、浑善达克沙地、华北平原北部、华南部分地区因地表反射率、土壤含水量、受太阳辐射影响程度不同等地表温度季节变化差异很大,月平均地表温度日较差一年内基本上呈双峰双谷型.作为比较,东亚部分陆地的地表温度与台湾海峡南部、黄海的海表温度及其日变化、季节变化一并进行了分析.塔里木盆地、浑善达克沙地不仅具有强烈的日变化,而且季节变化也显著.2000年两地月平均地表温度日较差最大值超过30 K,浑善达克沙地的年较差高达58.50 K.青藏高原地表温度季节变化小于东亚部分陆地、塔里木盆地、浑善达克沙地,但该区日变化幅度在所研究几个区中最大,2000年年平均日较差达28.05 K.文中将研究时段扩充到1998～2000年后揭示了连续三年地表温度及其日变化的年际变化特征.所获得这几类地表温度的变化特征与量值对于气候与辐射收支研究以及推测地表状况会有一定参考价值.     

利用Nimbus-7行星反射率观测资料估算青藏高原地区的总辐射

本文利用1982年8月—1983年7月期间Nimbus-7行星反射率的月平均资料用“物理模式方法(Raschke and Preuss,1979)”估算了青藏高原及其邻近地区月平均地面总辐射的分布。得到的结果较好地反映了纬度、海拔与云量三个主要因子对总辐射分布的支配作用。根据高原及其邻近地区23个测站的资料,对总辐射的计算值与观测值进行了比较。统计分析表明,相关系数f=0.90,标准误差RMS=27w/m~2,平均绝对误差ABS=21w/m~2(相当于有效平均总辐射的11.7％)。文中还对误差来源和敏感性问题进行了讨论。     

Air Temperature Estimation with MODIS Data over the Northern Tibetan Plateau

Time series of MODIS land surface temperature(T_s) and normalized difference vegetation index(NDVI) products,combined with digital elevation model(DEM) and meteorological data from 2001 to 2012,were used to map the spatial distribution of monthly mean air temperature over the Northern Tibetan Plateau(NTP). A time series analysis and a regression analysis of monthly mean land surface temperature(T_s) and air temperature(T_a) were conducted using ordinary linear regression(OLR) and geographical weighted regression(GWR). The analyses showed that GWR,which considers MODIS T_s,NDVI and elevation as independent variables,yielded much better results [R_(Adj)~2 0.79; root-mean-square error(RMSE) =0.51℃–1.12℃] associated with estimating T_a compared to those from OLR(R_(Adj)~2= 0.40-0.78; RMSE = 1.60℃–4.38℃).In addition,some characteristics of the spatial distribution of monthly T_a and the difference between the surface and air temperature(T_d) are as follows. According to the analysis of the 0℃ and 10℃ isothermals,T_a values over the NTP at elevations of 4000–5000 m were greater than 10℃ in the summer(from May to October),and T_a values at an elevation of3200 m dropped below 0℃ in the winter(from November to April). T_a exhibited an increasing trend from northwest to southeast. Except in the southeastern area of the NTP,T d values in other areas were all larger than 0℃ in the winter.     

ASCAT与HY-2A风场产品在南海的精度评估

基于中国科学院南海海洋研究所提供的2012年1月1日—2013年12月31日西沙自动气象站观测资料以及同时间序列的欧洲中心ERA-interim再分析风场产品,统计了ASCAT和HY-2A散射计风场产品的误差特征,分析散射计资料在南海的适用性。分析得出:ASCAT和HY-2A的风速、风向与自动站一致性高,相关系数均大于0.85,ASCAT风速和风向均方根误差分别为1.57 m/s和15.42 °,HY-2A均方根误差略微偏大,分别为2.02 m/s和24.75 °;ASCAT和HY-2A散射计与ERA-interim风速、风向有很好的一致性,在不考虑低风速（ < 3 m/s）的条件下,风速均方根误差分别为1.40 m/s和1.56 m/s,风向均方根误差分别为15.09 °和17.07 °,与设计精度一致,表明ASCAT与HY-2A风场产品在南海是适用的。此外,散射计相对再分析风场的偏差没有明显的季节性变化      

Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia

In this study,the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland,Australia,was assessed by inputting recognized climate indices,monthly historical rainfall data,and atmospheric temperatures into a prototype stand-alone,dynamic,recurrent,time-delay,artificial neural network.Outputs,as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009,were compared with observed rainfall data using time-series plots,root mean squared error(RMSE),and Pearson correlation coefficients.A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology’s Predictive Ocean Atmosphere Model for Australia(POAMA)-1.5 general circulation model(GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared.The application of artificial neural networks to rainfall forecasting was reviewed.The prototype design is considered preliminary,with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.     

Validation of TRMM Precipitation Radar satellite data over Indonesian region

Research has been conducted to validate monthly and seasonal rain rates derived from the Tropical Rainfall Measuring Mission Precipitation Radar (PR) using rain gauge data analysis from 2004 to 2008. The study area employed 20 gauges across Indonesia to monitor three Indonesian regional rainfall types. The relationship of PR and rain gauge data statistical analysis included the linear correlation coefficient, the mean bias error (MBE), and the root mean square error (RMSE). Data validation was conducted with point-by-point analysis and spatial average analysis. The general results of point-by-point analysis indicated satellite data values of medium correlation, while values of MBE and RMSE tended to indicate underestimations with high square errors. The spatial average analysis indicated the PR data values are lower than gauge values of monsoonal and semi-monsoonal type rainfall, while anti-monsoonal type rainfall was overestimated. The validation analysis showed very good correlation with the gauge data of monsoonal type rainfall, high correlation for anti-monsoonal type rainfall, but medium correlation for semi-monsoonal type rainfall. In general, the statistical error level of monthly seasonal monsoonal type conditions is more stable compared to other rainfall types. Unstable correlations were observed in months of high rainfall for semi-monsoonal and anti-monsoonal type rainfall.     

云量的时间精细化预报研究-以榆中为例

利用2001年7月至2011年7月甘肃省榆中县地面测站的每日8次云量资料和同期NCEP每日4次等压面资料,由NCEP资料构造预报因子,以总云量和低云量为预报对象,分析预报因子和预报对象的相关性,采用逐步回归方法建立榆中县逐月8个时次的云量预报方程并进行回代;并利用2012年的资料检验预报方程的预报效果。结果表明：云量主要受整层湿度、垂直运动、不稳定能量、槽强度指数和700 hPa水汽通量散度影响,其中湿度状况和垂直运动是重要因素。建立的预报方程对总云量的预报效果比低云量好;总云量平均预报误差在2成左右,低云量平均预报误差在3成左右;预报值变化趋势可以部分地反映实际云量的变化趋势。     

利用ISCCP资料分析青藏高原云气候特征

利用ISCCP提供的1983年7月－1993年12月3h一次的月平均卫星决云量资料，将整个高原分为39个网格点，分析了高原总云量的年、季节、日变化规律及其空间分布特征，并根据高原水汽条件和地形动力影响以及环流特征作出一定的科学解释。将ISCCP总云量与地面观测总云量分布形势作了比较，证明了ISCCP－D2资料的合理性。对总云量与OLR进行相关分析，发现夏季相关好，冬季相关差。     

A high-quality monthly total cloud amount dataset for Australia

A high-quality monthly total cloud amount dataset for 165 stations has been developed for monitoring and assessing long-term trends in cloud cover over Australia. The dataset is based on visual 9 a.m. and 3 p.m. observations of total cloud amount, with most records starting around 1957. The quality control process involved examination of historical station metadata, together with an objective statistical test comparing candidate and reference cloud series. Individual cloud series were also compared against rainfall and diurnal temperature range series from the same site, and individual cloud series from neighboring sites. Adjustments for inhomogeneities caused by relocations and changes in observers were applied, as well as adjustments for biases caused by the shift to daylight saving time in the summer months. Analysis of these data reveals that the Australian mean annual total cloud amount is characterised by high year-to-year variability and shows a weak, statistically non-significant increase over the 1957–2007 period. A more pronounced, but also non-significant, decrease from 1977 to 2007 is evident. A strong positive correlation is found between all-Australian averages of cloud amount and rainfall, while a strong negative correlation is found between mean cloud amount and diurnal temperature range. Patterns of annual and seasonal trends in cloud amount are in general agreement with rainfall changes across Australia, however the high-quality cloud network is too coarse to fully capture topographic influences. Nevertheless, the broadscale consistency between patterns of cloud and rainfall variations indicates that the new total cloud amount dataset is able to adequately describe the broadscale patterns of change over Australia. Favourable simple comparisons between surface and satellite measures of cloudiness suggest that satellites may ultimately provide the means for monitoring long-term changes in cloud over Australia. However, due to the relative shortness and homogeneity problems of the satellite record, a robust network of surface cloud observations will be required for many years to come.     

西南地区云水量与可降水量比值的分布特征和变化趋势

采用1980～2009年云水量和可降水量的NCEP逐月再分析资料,通过统计分析,研究30a来西南地区（云南、贵州、讴庆、四川）云水量与可降水量比值的时空分布特征和变化趋势。结果表明：（1）西南地区年、季17孟水量与可降水比值均具有明显的地区性差异,由西北向东南递减,高值区位于川西高原;（2）云水量与可降水比值年内分布不均匀,从2月到8月逐渐减小,9月至1月逐渐增大,同时,季节差异较大,夏季最小,冬季最大;（3）30a来,整个西南地区年、夏季和秋季云水量与可降水量比值呈显著减少趋势。     

Simulation of maximum surface air temperature over India using the UK Met Office Global Seasonal (GloSea) Model

Summary A comparative study was performed to evaluate the performance of the UK Met Office’s Global Seasonal (GloSea) prediction General Circulation Model (GCM) for the forecast of maximum surface air temperature (T_max) over the Indian region using the model generated hindcast of 15-members ensemble for 16 years (1987–2002). Each hindcast starts from 1^st January and extends for a period of six months in each year. The model hindcast T_max is compared with T_max obtained from verification analysis during the hot weather season on monthly and seasonal scales from March to June. The monthly and seasonal model hindcast climatology of T_max from 240 members during March to June and the corresponding observed climatology show highly significant (above 99.9% level) correlation coefficients (CC) although the hindcast T_max is over-estimated (warm bias) over most parts of the Indian region. At the station level over New Delhi, although the forecast error (forecast-observed) at the monthly scale gradually increases from March to June, the forecast error at the seasonal scale during March to May (MAM) is found to be just 1.67 °C. The GloSea model also simulates well T_max anomalies on monthly and seasonal scales during March to June with the lower Root Mean Square Error (RMSE) of bias corrected forecast (less than 1.2 °C), which is much less than the corresponding RMSE of climatology (reference) forecast. The anomaly CCs (ACCs) over the station in New Delhi are also highly significant (above 95% level) on monthly to seasonal time scales from March to June, except for April. The skill of the GloSea model for the seasonal forecast of T_max as measured from the ACC map and the bias corrected RMSE map is reasonably good during MAM and April to June (AMJ) with higher ACC (significant at 95% level) and lower RMSE (less than 1.5 °C) found over many parts of the Indian regions. Authors’ addresses: D. R. Pattanaik, H. R. Hatwar, G. Srinivasan, Y. V. Ramarao, India Meteorological Department (IMD), New Delhi, India; U. C. Mohanty, P. Sinha, Centre for Atmospheric Sciences, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India; Anca Brookshaw, UK Met Office, UK.     

Comparison between MODIS-derived Day and Night Cloud Cover and Surface Observations over the North China Plain

Satellite and human visual observation are two of the most important observation approaches for cloud cover. In this study, the total cloud cover(TCC) observed by MODIS onboard the Terra and Aqua satellites was compared with Synop meteorological station observations over the North China Plain and its surrounding regions for 11 years during daytime and7 years during nighttime. The Synop data were recorded eight times a day at 3-h intervals. Linear interpolation was used to interpolate the Synop data to the MODIS overpass time in order to reduce the temporal deviation between the satellite and Synop observations. Results showed that MODIS-derived TCC had good consistency with the Synop observations; the correlation coefficients ranged from 0.56 in winter to 0.73 in summer for Terra MODIS, and from 0.55 in winter to 0.71 in summer for Aqua MODIS. However, they also had certain differences. On average, the MODIS-derived TCC was 15.16%higher than the Synop data, and this value was higher at nighttime(15.58%–16.64%) than daytime(12.74%–14.14%). The deviation between the MODIS and Synop TCC had large seasonal variation, being largest in winter(29.53%–31.07%) and smallest in summer(4.46%–6.07%). Analysis indicated that cloud with low cloud-top height and small cloud optical thickness was more likely to cause observation bias. Besides, an increase in the satellite view zenith angle, aerosol optical depth, or snow cover could lead to positively biased MODIS results, and this affect differed among different cloud types.     

Diurnal and Seasonal Variation of Clear-Sky Land Surface Temperature of Several Representative Land Surface Types in China Retrieved by GMS-5

The retrieved results in this paper by GMS-5/VISSR thermal infrared data with single time/dual channel Split-Window Algorithm reveal the characteristics of diurnal and seasonal variation of clear-sky land surface temperature (LST) of several representative land surface types in China,including Tarim Basin,Qinghai- Tibetan Plateau,Hunshandake Sands,North China Plain,and South China.The seasonal variation of clear-sky LST in above areas varies distinctly for the different surface albedo,soil water content,and the extent of influence by solar radiation.The monthly average diurnal ranges of LST have two peaks and two valleys in one year.The characteristics of LST in most land of East Asia and that of sea surface temperature (SST) in the south of Taiwan Strait and the Yellow Sea are also analyzed as comparison.Tarim Basin and Hunshandake Sands have not only considerable LST diurnal cycle but also remarkable seasonal variation. In 2000,the maximum monthly average diurnal ranges of LST in both areas are over 30 K,and the annual range in Hunshadake Sands reaches 58.50 K.Seasonal variation of LST in the Qinghai-Tibetan Plateau is less than those in East Asia,Tarim Basin,and Hunshandake Sands.However,the maximum diurnal range exists in this area.The yearly average diurnal range is 28.05 K in the Qinghai-Tibetan Plateau in 2000.The characteristics of diurnal,seasonal,and annual variation from 1998 to 2000 are also shown in this research. All the results will be valuable to the research of climate change,radiation balance,and estimation for the change of land surface types.     

Characteristics of sea surface temperature retrieved from MTSAT-1R and in-situ data

Multi-channel sea surface temperature (MCSST) data were retrieved from the Japanese geostationary satellite, MTSAT-1R, for East Asia in western North Pacific. The coefficients used to calculate the MCSST data were estimated by assuming a linear relationship between the brightness temperatures obtained from the satellite and the in-situ buoy SST. It is important to remove cloud contamination pixels to retrieve meaningful information from infrared data. Therefore, the cloud detection algorithm was improved by using a 10-day maximum or minimum composite map for infrared and visible channels. The RMSE of the MCSST in comparison with the two-year buoy SST was about 0.89oC. The error was the largest at mid-latitudes in summer. Additionally, the error between the two SSTs showed that diurnal variation had a positive bias during daytime and a negative bias during nighttime. Furthermore, in 2007, both SSTs showed seasonal and spatial diurnal variation. The magnitude of the daily variation in the MCSST was two times larger than that in the buoy SST, and this was attributed to diurnal heating with a weak surface wind speed.     

民航广汉机场气象能见度的周期性变化特征的初步分析

基于民航广汉机场1986~1995年每日逐时气象能见度的观测资料,利用相关性分析,功率谱分析、带通滤波,以及小波分析等方法,分析和研究了广汉机场气象能见度的周期性变化特征。研究结果表明,机场能见度的变化与机场相对湿度和低云量呈显著负相关,与机场地面温度和地面风速呈正相关特征。广汉机场气象能见度的变化明显具有10~20d的准双周振荡特征和30~50d准周期性季节内振荡特征。      

我国散射辐射的气候计算方法及其分布特征

使用全国６４个日射站的散射辐射资料，首先计算与建立了各地１月、４月、７月和１０月的月散射辐射值与总云量、日照百分率之间的相关系数与经验关系式，并对经验关系式进行了方差检验。该经验关系式为：Ｄ＝Ｑ０（ｓ１＋０．０１）（ａ＋ｂＮ）。应用该经验关系式和２００多个地面气象站的资料，计算了各地的１月、４月、７月和１０月的散射辐射值。最后对我国四季散射辐射的分布及其年变化作简要的分析。     

Assessing Drought Conditions in Cloudy Regions Using Reconstructed Land Surface Temperature

Temperature vegetation dryness index (TVDI) in a triangular or trapezoidal feature space can be calculated from the land surface temperature (LST) and normalized difference vegetation index (NDVI), and has been widely applied to regional drought monitoring. However, thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud pixels. In cloudy areas, LST data include a large number of cloudy pixels, seriously degrading the spatial and temporal continuity of drought monitoring. In this paper, the Remotely Sensed Daily Land Surface Temperature Reconstruction model (RSDAST) is combined with the LST reconstructed (RLST) by the RSDAST and applied to drought monitoring in a cloudy area. The drought monitoring capability of the reconstructed temperature vegetation drought index (RTVDI) under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature reconstruction. Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI (OTVDI) in 2011. In addition, RLST/NDVI scatter plots cover a wide range of values, with the fitted dry-wet boundaries more representative of real soil moisture conditions. Under continuously cloudy conditions, the OTVDI inverted from the original LST (OLST) loses its drought monitoring capability, whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study area. The correlation between TVDI and soil moisture is stronger for RTVDI (R = −0.45) than that for OTVDI (R = −0.33). In terms of the spatial and temporal distributions, the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI. Hence, in continuously cloudy areas, RTVDI not only expands drought monitoring capability in time and space, but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared data under extreme conditions.