乌苏里江流域TRMM降水数据精度评价与修正

将地面站点观测的降水数据作为基准降水数据,评估汛期热带降雨观测计划(TRMM)降水产品3B42研究型数据(V7)与实时降水数据(RT)的日以及月尺度数据在黑龙江省乌苏里江流域的时空精度。结果表明:在时间尺度上,TRMM 3B42V7与3B42RT的日降水数据与站点降水数据相比,均系统性偏小,且相关性不是很好;相对而言,3B42RT与站点数据的偏差程度较小,相关性较好;月尺度数据与日尺度数据相比,月数据精度均有很大提高,且2种产品数据精度相差不大;在空间尺度上,TRMM 3B42V7和3B42RT都能较好地展现乌苏里江流域降水的空间分布特征。针对评估结果,使用加法和乘法模型对TRMM 3B42V7和3B42RT的月降水数据进行修正。修正后,2种TRMM卫星降水产品的月降水数据精度均有提高,并且加法模型修正效果更好。因此,可选取加法模型修正后的3B42RT月尺度降水数据用于汛期乌苏里江流域的相关水文研究。     

基于TRMM和FY-2C的长江流域月降水量的网格化估算研究

基于TRMM和FY-2C降水产品,结合气象站观测资料和DEM数据,采用基于区域分月的逐步回归算法,建立降水估算模型。通过模型估算得到长江流域2007年1月、4月、7月、10月降水量的空间分布图,并对得到的结果进行检验和分析。结果表明:模型对2种降水产品能进行有效的订正;模型估算的TRMM降水产品1月、4月、7月、10月的平均相对误差分别是37.7%,47.3%,44.2%,41.9%,FY-2C降水产品1月、4月、7月、10月的平均相对误差是46.3%,50.9%,39.8%,48.8%;模型模拟的TRMM降水的全年相关系数是0.838, FY-2C降水的全年相关系数是0.811,通过两者对比发现,TRMM降水产品作为趋势项的精度较高。模型估算得到的降水分布趋势和原始降水产品分布趋势基本一致,并且能体现出降水的分布规律。     

Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka

Interpolating climatic variables such as rainfall is challenging due to the highly variable nature of meteorological processes, the effects of terrain and geography, and the difficulty in establishing a representative network of stations. While interpolation models are being adapted to include these effects, often the rainfall data contain significant gaps in coverage. In this paper, we evaluated rainfall data from an agro-ecological monitoring network for producing maps of total monthly rainfall in Sri Lanka. We compared four spatial interpolation techniques: inverse distance weighting, thin-plate splines, ordinary kriging, and Bayesian kriging. Error metrics were used to validate interpolations against independent data. Satellite data were used to assess the spatial pattern of rainfall. Results indicated that Bayesian kriging and splines performed best in low and high rainfall, respectively. Rainfall maps generated from the agro-ecological network were found to have accuracies consistent with previous studies in Sri Lanka.     

Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data

While existing remote sensing-based drought indices have characterized drought conditions in arid regions successfully, their use in humid regions is limited. We propose a new remote sensing-based drought index, the Scaled Drought Condition Index (SDCI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the land surface temperature (LST) data and the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite. Each variable was scaled from 0 to 1 to discriminate the effect of drought from normal conditions, and then combined with the selected weights. When tested against in-situ Palmer Drought Severity Index (PDSI), Palmer's Z-Index (Z-Index), 3-month Standardized Precipitation Index (SPI), and 6-month SPI data during a ten-year (2000-2009) period, SDCI performed better than existing indices such as NDVI and Vegetation Health Index (VHI) in the arid region of Arizona and New Mexico as well as in the humid region of North Carolina and South Carolina. The year-to-year changes and spatial distributions of SDCI over both arid and humid regions generally agreed to the changes documented by the United States Drought Monitor (USDM) maps.     

Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States

The lack of continuous soil moisture fields at large spatial scales, based on observations, has hampered hydrologists from understanding its role in weather and climate. The most readily available observations from which a surface wetness state could be derived is the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations at 10.65 GHz. This paper describes the first attempt to map daily soil moisture from space over an extended period of time. Methods to adjust for diurnal changes associated with this temporal variability and how to mosaic these orbits are presented. The algorithm for deriving soil moisture and temperature from TMI observations is based on a physical model of microwave emission from a layered soil-vegetation-atmosphere medium. An iterative, least-squares minimization method, which uses dual polarization observations at 10.65 GHz, is employed in the retrieval algorithm. Soil moisture estimates were compared with ground measurements over the U.S. Southern Great Plains (SGP) in Oklahoma and the Little River Watershed, Georgia. The soil moisture experiment in Oklahoma was conducted in July 1999 and Little River in June 2000. During both the experiments, the region was dry at the onset of the experiment, and experienced moderate rainfall during the course of the experiment. The regions experienced a quick dry-down before the end of the experiment. The estimated soil moisture compared well with the ground observations for these experiments (standard error of 2.5%). The TMI-estimated soil moisture during 6-22 July over Southern U.S. was analyzed and found to be consistent with the observed meteorological conditions.     

The Falling Lake Victoria Water Level: GRACE,TRIMM and CHAMP Satellite Analysis of the Lake Basin

In the last 5 years, Lake Victoria water level has seen a dramatic fall that has caused alarm to water resource managers. Since the lake basin contributes about 20% of the lakes water in form of discharge, with 80% coming from direct rainfall, this study undertook a satellite analysis of the entire lake basin in an attempt to establish the cause of the decline. Gravity Recovery And Climate Experiment (GRACE), Tropical Rainfall Measuring Mission (TRMM) and CHAllenging Minisatellite Payload (CHAMP) satellites were employed in the analysis. Using 45 months of data spanning a period of 4 years (2002–2006), GRACE satellite data are used to analyse the variation of the geoid (equipotential surface approximating the mean sea level) triggered by variation in the stored waters within the lake basin. TRMM Level 3 monthly data for the same period of time are used to compute mean rainfall for a spatial coverage of .25°×.25° (25×25 km) and the rainfall trend over the same period analyzed. To assess the effect of evaporation, 59 CHAMP satellite’s occultation for the period 2001 to 2006 are analyzed for tropopause warming. GRACE results indicate an annual fall in the geoid by 1.574 mm/year during the study period 2002–2006. This fall clearly demonstrates the basin losing water over these period. TRMM results on the other hand indicate the rainfall over the basin (and directly over the lake) to have been stable during this period. The CHAMP satellite results indicate the tropopause temperature to have fallen in 2002 by about 3.9 K and increased by 2.2 K in 2003 and remained above the 189.5 K value of 2002. The tropopause heights have shown a steady increase from a height of 16.72 m in 2001 and has remained above this value reaching a maximum of 17.59 km in 2005, an increase in height by 0.87 m. Though the basin discharge contributes only 20%, its decline has contributed to the fall in the lake waters. Since rainfall over the period remained stable, and temperatures did not increase drastically to cause massive evaporation, the remaining major contributor is the discharge from the expanded Owen Falls dam.     

TRMM卫星3B43降水数据在黄河流域的精度分析

利用黄河流域90个气象站点实测降水数据,分别从流域和格网两个空间尺度,运用相关分析、相对误差等统计分析方法对TRMM卫星3B43 v7降水数据在黄河流域的精度进行了评估,在此基础上分析了精度评价指标的空间分布特征,讨论高程、降水强度等因素对精度的影响。结果表明：①在流域尺度上,TRMM月降水数据与站点实测月降水数据呈高度线性相关,TRMM降水数据比站点实测降水数据略微偏高。②在格网尺度上,大部分格网的TRMM月降水数据与站点实测月降水数据的相关系数较高,偏差较小。③TRMM降水精度与降水强度、高程相关,TRMM降水量与实测降水量的平均绝对误差呈自东南向西北递减规律,与黄河流域降水分布规律相一致;相对误差、平均误差和平均绝对误差等指标随着高程的增加呈现逐渐减小的趋势。整体上,对于黄河流域,随着降水量的增多,TRMM数据倾向于低估降水量;高海拔区域,TRMM低估降水量,低海拔区域,TRMM高估降水量。通过评估TRMM卫星降水产品在黄河流域的精度,为本地区地面降水产品提供有效补充。     

淮河流域TRMM多源卫星降水产品精度评估

不同的地区卫星降水产品的预报能力差别较大,因此需对卫星降水产品进行精度评估。基于淮河流域26个气象站的降水数据,采用统计方法评估了2001~2011年热带降水观测计划(TRMM)最新一代降水产品3B42V7和3B42RTV7在中国气候过渡带淮河流域的适用性。结果表明,两种降水产品均存在高估现象,3B42V7的性能优于3B42RTV7,两种产品夏季性能明显优于冬季;3B42V7与3B42RTV7年降水量几乎均高于地面观测年降水量,3B42RTV7年降水量的估算性能比3B42V7差,两种降水产品在湿润年的性能优于干旱年;除在小降水事件(0~1mm)发生概率明显低外,3B42RTV7、3B42V7与站点降水发生频次的趋势几乎一致,且全年优于汛期。降水强度25mm/d时,两种降水产品会高估降水量,对25mm/d的降水事件会低估,两种降水产品的趋势一致且相差不大;汛期3B42V7对降水强度25mm/d的降水事件拟合较好。与3B42RTV7相比,3B42V7有稍高的确报率、临界成功率及较低的错报率;TMPA产品对小雨或弱降水的确报率较高,对强降水尤其是25mm/d以上的降水错报率高达70%以上。     

卫星雷达测雨在长江流域的精度分析

针对传统的雨量站点观测方法存在的缺陷,基于TRMM卫星降雨数据较高的时间和空间分辨率,通过TRMM数据与长江流域实测站点数据在时间和空间分布上的对比分析,研究了TRMM数据在长江流域的适用性.结果表明,TRMM数据在长江流域有较高的精度,为分布式水文模型提供了良好的数据支撑.     

TRMM卫星3B43降水数据在黄河流域的精度分析

利用黄河流域90个气象站点实测降水数据，分别从流域和格网两个空间尺度，运用相关分析、相对误差等统计分析方法对TRMM卫星3B43 v7降水数据在黄河流域的精度进行了评估，在此基础上分析了精度评价指标的空间分布特征，讨论高程、降水强度等因素对精度的影响。结果表明：①在流域尺度上，TRMM月降水数据与站点实测月降水数据呈高度线性相关，TRMM降水数据比站点实测降水数据略微偏高。②在格网尺度上，大部分格网的TRMM月降水数据与站点实测月降水数据的相关系数较高，偏差较小。③TRMM降水精度与降水强度、高程相关，TRMM降水量与实测降水量的平均绝对误差呈自东南向西北递减规律，与黄河流域降水分布规律相一致；相对误差、平均误差和平均绝对误差等指标随着高程的增加呈现逐渐减小的趋势。整体上，对于黄河流域，随着降水量的增多，TRMM数据倾向于低估降水量；高海拔区域，TRMM低估降水量，低海拔区域，TRMM高估降水量。通过评估TRMM卫星降水产品在黄河流域的精度，为本地区地面降水产品提供有效补充。