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。     

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。     

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。     

CMORPH卫星降水数据在中国区域的误差特征研究

空报数据和漏报数据是高时空分辨率卫星降水产品误差的重要表现形式,研究漏报和空报数据的特征对于改进降水反演算法、提高卫星降水数据的质量具有重要意义。通过对2009年6~8月中国区域内的CMORPH卫星数据(Climate Prediction Center Morphing)与观测站点逐小时降水数据分析,发现CMOPRH数据中的漏报数据和空报数据存在以下特征:①CMORPH数据中空报数据远远高于漏报数据,导致CMORPH数据模拟的降水面积要高于实际降水面,表明空报数据对CMORPH精度的影响要大于漏报数据;②当降水量小于5 mm时,CMORPH的空报率随着降水量的升高呈现出非线性的下降趋势,经过二次项拟合之后的相关系数达到0.99以上;③CMORPH中空报降水数据的面积与总降水面积之间存在很强的正相关性,二者在6~8月的相关系数分别达到0.9133、0.9474和0.9482,因此可以通过CMORPH数据的总降水面积对空报降水面积进行估算;④从CMORPH空报率的空间分布上看,我国东南沿海以及东北地区的空报率较低,而西北、青藏高原地区的空报率最高。     

武夷山热带降雨测量卫星降水产品降尺度研究

热带降雨测量卫星(tropical rainfall measuring missionsatellite,TRMM)虽可测得大范围降水,但其空间分辨率较低,不能满足各种模型研究。以武夷山及周边地区为研究区,基于TRMM降水数据融合多源数据,对TRMM进行降尺度,从而得到高分辨率的降水产品。对2001—2010年的TRMM3B43月降水产品进行降尺度处理,将其空间分辨率由0.25°×0.25°（约28 km×28 km）提高到1 km×1 km,并利用验证站点对降尺度结果进行精度检验。结果表明,多源数据融合的降尺度方法在中国武夷山及周边地区具有较好的适用性。降尺度结果与验证站点降水量的相关系数R均在0.9以上,平均相对误差（MRE）及均方根误差（RMSE）较降尺度前都有所减小。与气象站点实测数据相比,降尺度结果能较好地模拟降水的时空分布及局地特征,且能够反映地形降水的差异性分布。     

武夷山热带降雨测量卫星降水产品降尺度研究

热带降雨测量卫星(tropical rainfall measuring missionsatellite,TRMM)虽可测得大范围降水,但其空间分辨率较低,不能满足各种模型研究。以武夷山及周边地区为研究区,基于TRMM降水数据融合多源数据,对TRMM进行降尺度,从而得到高分辨率的降水产品。对2001—2010年的TRMM3B43月降水产品进行降尺度处理,将其空间分辨率由0.25°×0.25°(约28km×28km)提高到1km×1km,并利用验证站点对降尺度结果进行精度检验。结果表明,多源数据融合的降尺度方法在中国武夷山及周边地区具有较好的适用性。降尺度结果与验证站点降水量的相关系数R均在0.9以上,平均相对误差(MRE)及均方根误差(RMSE)较降尺度前都有所减小。与气象站点实测数据相比,降尺度结果能较好地模拟降水的时空分布及局地特征,且能够反映地形降水的差异性分布。     

2个代表性的国外卫星遥感降水观测系统

应用卫星遥感数据估算降水,是获取降水信息的重要手段之一。针对天气雷达对降水量"测不准"和地面雨量计"测不到"的问题,通过卫星遥感技术手段获取降水量数据,可弥补常规手段降水量观测的不足,尤其是高原寒区、海洋等人迹罕至的地区。主要讨论国际上2种代表性的卫星遥感降水观测系统(TRMM和GPM),并对卫星遥感降水观测技术的应用作简要分析,结果表明,应用卫星遥感降水数据解决点降水观测数据区域代表性不强、无降水资料地区,以及大时空尺度降水量估计等问题方面,可以发挥重要作用。     

基于FY-3C MWHTS的台风降水反演算法研究

为估测台风带来的地表瞬时降雨率，利用FY-3C上搭载的微波湿温探测仪（Microwave Humidity and Temperature Sounder，MWHTS)的L1级在轨观测亮度温度数据与多卫星降水分析TMPA（Tropical Rainfall Measuring Mission（TRMM）Multi-Satellite Precipitation Analysis）3B42降水产品数据，通过多元线性回归和BP神经网络两种算法对台风区的降水情况进行了反演研究。结果表明，由这两种算法反演的降水分布图可以清晰地看到台风中心、云墙以及螺旋雨带等台风的位置、分布及结构信息，这与TMPA 3B42降水产品数据估测到的台风降水分布图相一致。此外，从定量的角度来看，TMPA 3B42降水数据与这两种反演算法反演的地表瞬时降水量（mm/hr）都具有较高的相关性和较小的偏差和均方根误差，反演的精度较高。故这两种算法都可以用来反演台风区的降水量，同时也表明FY-3C MWHTS微波在轨观测资料在台风区监测及降水研究中能发挥出较高的应用价值。     

基于FY-3C MWHTS的台风降水反演算法研究

为估测台风带来的地表瞬时降雨率,利用FY-3C上搭载的微波湿温探测仪(Microwave Humidity and Temperature Sounder,MWHTS)的L1级在轨观测亮度温度数据与多卫星降水分析TMPA(Tropical Rainfall Measuring Mission(TRMM)Multi-Satellite Precipitation Analysis)3B42降水产品数据,通过多元线性回归和BP神经网络两种算法对台风区的降水情况进行了反演研究。结果表明,由这两种算法反演的降水分布图可以清晰地看到台风中心、云墙以及螺旋雨带等台风的位置、分布及结构信息,这与TMPA 3B42降水产品数据估测到的台风降水分布图相一致。此外,从定量的角度来看,TMPA 3B42降水数据与这两种反演算法反演的地表瞬时降水量(mm/hr)都具有较高的相关性和较小的偏差和均方根误差,反演的精度较高。故这两种算法都可以用来反演台风区的降水量,同时也表明FY-3C MWHTS微波在轨观测资料在台风区监测及降水研究中能发挥出较高的应用价值。     

青藏高原GPM降水数据空间降尺度研究

高分辨率的降水数据对于复杂地形区的精确水文预报和气候模拟至关重要.利用青藏高原的植被、地形和地理位置特征,建立了与降水的回归模型,将全球降水测量(GPM)IMERG的年降水量从0.1°降尺度至1 km,通过分解年降水获得月降水量数据,并用气象站点的实测数据进行校准.得出以下结论:①GPM IMERG月降水量略大于地面观...     

Evaluating satellite-based precipitation products in monitoring drought events in southwest China

Southwest China (SWC) is one of the areas that has most frequently been affected by a variety of drought events in recent years. Satellite-based precipitation products with high spatial resolution, having greatly improved their accuracy and applicability, are expected to offer an alternative to improve drought monitoring. The purpose of this article is to evaluate the reliability of the Tropical Rainfall Measuring Mission (TRMM) V7 3B43 products using the observed monthly precipitation data obtained from 118 meteorological stations from 1998 to 2013 and to monitor the temporal and spatial variations of drought conditions using the Standardized Precipitation Index (SPI), which is derived by a non-parametric approach. The results showed that the TRMM 3B43 products performed well in terms of monthly precipitation, although they slightly overestimated the total precipitation amount, mainly in summer. They matched the observed data well, yielding high correlations and low biases in most parts of SWC. For drought assessment, the SPI based on monthly TRMM 3B43 data oscillated around zero and showed a consistent inter-annual variability compared with gauges. Moreover, the TRMM 3B43 showed similar temporal drought behaviour by capturing most of the drought events at various timescales, and both of them described similar spatial patterns of drought. The TRMM products precisely described the occurrence and development process of the 2009/2010 drought and revealed that compared to the 2009/2010 drought the 2011 drought was more severe and affected a larger area. In general, using the TRMM 3B43 product is suitable and credible for drought monitoring over SWC.     

Identification of dry and wet soil conditions using TRMM/TMI brightness temperatures and potential for drought monitoring

In this study we present a methodology for monitoring drought conditions directly from microwave brightness temperature observations. Tropical Rainfall Measurement Mission (TRMM)/TRMM Microwave Imager (TMI) 10.7 GHz brightness temperatures were analysed along with TRMM merged rainfall products during June–August for 4 years to depict the spatial and temporal extent of dry and wet soil conditions. Comparison of brightness temperature anomalies with rainfall anomalies clearly shows the contrasting features of drought year 2002 and normal monsoon year 2001.     

Multitemporal comparative analysis of TRMM-3B42 satellite-estimated rainfall with surface gauge data at basin scales: daily,decadal and monthly evaluations

In order to examine the reliability and applicability of Tropical Rainfall Measuring Mission (TRMM) and Other Satellites Precipitation Product (3B42) Version 6 (TRMM-3B42) at basin scales, satellite rainfall estimates were compared with geostatistically interpolated reference data from 12 rain gauge stations for three consecutive years: 2005, 2006 and 2007. Gauge–TRMM-3B42 statistical properties for daily, decadal and monthly multitemporal precipitations were compared using the following cross-validation continuous statistical measures: mean bias error (MBE), root mean square difference (RMSD), mean absolute difference (MAD) and coefficient of determination (r ²) metrics. The averaged spatial–temporal comparisons showed that the TRMM-3B42 rainfall estimates were much closer to the geostatistically interpolated gauge data, with minimal biases of??0.40 mm day^?1,??1.78 mm decad^?1 and??6.72 mm month^?1 being observed in 2006. In the same year, the gauge and TRMM-3B42 rainfall estimates marginally correlated better than in 2005 and 2007, with the daily, decadal and monthly coefficients of determination being 82.2%, 93.9% and 96.5%, respectively. The results showed that the correlations between the gauge-derived precipitation and the TRMM-3B42-derived precipitation increased with increasing temporal intervals for all three considered years. Quantitatively, the TRMM-3B42 observations slightly overestimated the precipitations during the wet seasons and underestimated the observed rainfall during the dry seasons. The results of the study show that the estimates from TRMM-3B42 precipitation retrievals can effectively be applied in the interpolation of missing gauge data, and in the verification of precipitation uncertainties at the basin scales with minor adjustments, depending on the timescales considered.     

Indonesian rainfall variability observation using TRMM multi-satellite data

It is important to understand the characteristics of Indonesian rainfall within the world’s climate system. The large rainfall in the Indonesian archipelago plays an essential role as a central atmospheric heat source of the Earth’s climate system throughout the year. Monthly rainfall satellite data, measured by the Tropical Rainfall Measuring Mission (TRMM) 3B43 over the course of 13 years, were employed to analyse monthly means, total means, maximum and minimum variability, standard deviation, and the trends analysis of Indonesian rainfall variability. The rainfall estimated from satellite data was then compared to the rain gauge data over the Indonesian region to determine the accuracy level. The results show that oceans, islands, monsoons, and topography clearly affect the spatial patterns of Indonesian rainfall. Most high-rainfall events in Indonesia peak during the December–January–February (DJF) season and the lowest rainfall events occur during the June–July–August (JJA) season. Those conditions are associated and generated with the northwest and southeast monsoon patterns. High fluctuations between maximum and minimum monthly rainfall data of over 400 mm month^?1 occur over Jawa (Java) Island, the Jawa Sea, and southern Sulawesi Island. A high annual and monthly rainfall typically occurs throughout Indonesia over island areas. The trend analysis shows an increasing trend in rainfall from 1998 to 2010 in Kalimantan, Jawa, Sumatra, and Papua. Decreasing rainfall trends occur along the west and south coast of Sumatra, eastern Jawa, southern Sulawesi, Maluku Islands, western Papua, and Bali Island.     

Evaluation and comparison of satellite-based rainfall products in Burkina Faso,West Africa

The performance of seven operational high-resolution satellite-based rainfall products – Africa Rainfall Estimate Climatology (ARC 2.0), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation (RFE 2.0), Tropical Applications of Meteorology using SATellite (TAMSAT), African Rainfall Climatology and Time-series (TARCAT), and Tropical Rainfall Measuring Mission (TRMM) daily and monthly estimates – was investigated for Burkina Faso. These were compared to ground data for 2001–2014 on a point-to-pixel basis at daily to annual time steps. Continuous statistics was used to assess their performance in estimating and reproducing rainfall amounts, and categorical statistics to evaluate rain detection capabilities. The north–south gradient of rainfall was captured by all products, which generally detected heavy rainfall events, but showed low correlation for rainfall amounts. At daily scale they performed poorly. As the time step increased, the performance improved. All (except TARCAT) provided excellent scores for Bias and Nash–Sutcliffe Efficiency coefficients, and overestimated rainfall amounts at the annual scale. RFE performed the best, whereas TARCAT was the weakest. Choice of product depends on the specific application: ARC, RFE, and TARCAT for drought monitoring, and PERSIANN, CHIRPS, and TRMM daily for flood monitoring in Burkina Faso.     

Vegetation,water and thermal stress index for study of drought in Nepal and central northeastern India

Drought is the degradation of land in arid, semi-arid and dry sub-humid regions caused primarily by human activity and climatic variations. The present study is the first attempt to identify and monitor drought using a vegetation index, a vegetation-water index and land surface temperature (LST) data for Nepal and central northeastern India. We propose a Vegetation Water Temperature Condition Index (VWTCI) for monitoring drought on a regional scale. The VWTCI includes the Normalized Difference Water Index (NDWI), which measures the water status in vegetation, the Normalized Difference Vegetation Index (NDVI) and LST data. To validate the approach, the VWTCI was compared with the Vegetation Temperature Condition Index (VTCI) and Tropical Rainfall Measuring Mission (TRMM) 3B31 Precipitation Radar (PR) data. The study revealed a gradual increase in the extent of drought in the central part of the study area from 2000 to 2004. Certain constant drought areas were also identified and the results indicate that these areas are spreading slowly towards the northeast into the central part of the study area. Comparison of the drought areas also shows a decrease in rainfall in June and July from 2000 to 2004.     

Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin

The availability of accurate rainfall data at proper temporal and spatial scales is vital for knowledge of renewable water resources and safe withdrawals for irrigation. Rain gauge networks in mountainous basins such as the Indus are sparse and insufficient to plan withdrawals and water management applications. Satellite rainfall estimates can be used as an alternative source of information but need area-specific calibration and validation due to the indirect nature of the radiation measurements. In this study, a calibration protocol is worked out for rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite because uncalibrated TRMM rainfall data are inaccurate for use in rainfall–runoff studies and in soil water balance studies. Two alternative techniques, regression analysis (RA) and geographical differential analysis (GDA), were used to calibrate TRMM rainfall data for different periods and spatial distributions. The validity of these techniques was tested using Nash–Sutcliffe efficiency and the standard error of estimate. The GDA technique proved to be better, with higher efficiency and smaller error in complex mountainous terrains. The deviation between TRMM data and rain gauge data was decreased considerably from 10.9% (pre-calibration at 625 km²) to 6.1% (post-calibration at 3125 km²) for annual time periods. For monthly periods, the deviation of 34.9% (pre-calibration at 625 km²) was decreased to 15.4% (post-calibration at 3125 km²). Calibration can be improved further if more rain gauges are available. The GDA technique can be applied to calibrate TRMM rainfall data in regions with limited rain gauge data and can provide a sufficiently accurate estimate of the key hydrological process that can be used in water management applications.     

The determination of the three-dimensional distribution of rain from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar

This paper describes a recent development in rainfall estimation using satellite-flown and ground-based radars. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), its algorithms and data processing are discussed. The ground validation algorithms and processing of the ground-based radar reflectivity data are explained. The estimates of attenuation-corrected radar reflectivity factor and rainfall rate are given at each resolution cell of the PR. The estimated near-surface rainfall rate and average rainfall rate at the altitudes of 2 km are calculated for each beam position. The TRMM PR profiling algorithm and processing of the PR reflectivity for rain distribution are explained. The TRMM rain products and their geophysical parameters are derived from the measurements from the satellite and ground-based radar. The derived geophysical parameters include vertical rain and hydrometeor profile, rain type, radar back-scatter cross-section, raindrop size distribution, rain gauge rain rates and 5-day and monthly average rain rates. For validation purposes the instantaneous and climatological comparison of the rain estimates from both the Precipitation Radar and ground-based radar at Melbourne, Florida, was carried out on the basis of rain type; i.e. convective/stratiform, vertical structure and rain maps. The error sources in rain profile retrieval from space-borne radar; i.e. the PR and ground-based radar with their algorithm limitations are discussed. A second set of data, this time for an area where no simultaneous ground data are available has also been analysed; the data were chosen for the three-dimensional rain distribution over some parts of India. The issues such as discrimination of rain from surface clutter, calibration accuracy and sensitivity of precipitation radar and discrimination of rain echo from noise are discussed.     

Estimating spatially downscaled rainfall by regression kriging using TRMM precipitation and elevation in Zhejiang Province,southeast China

Estimating regional daily rainfall accurately is of prime importance for many environmental applications, such as hydrology, meteorology, and ecology. The rainfall product from the Tropical Rainfall Monitoring Mission (TRMM) satellite is better able to estimate rainfall than rain gauge interpolation in some regions with coarse rain gauge spatial resolution. In the present article, analyses were made at 1379 rain gauge stations in Zhejiang Province, China, during January 2011 to July 2012 (536 days). A good relationship was found between the rain gauge data and the data analysis from the TRMM, especially for the precipitation that was between 2 and 10 mm day^–1. However, gaps exist between TRMM products and rain gauge records, which could be considered as uncertainty. To predict rainfall more precisely, four categories of daily rainfall and three regression kriging (RK) models were selected for analysis. TRMM and elevation data were used as auxiliary variables to construct RK₁. The auxiliary variable in RK₂ and RK₃ was TRMM and elevation data, respectively. Residuals (four rainfall categories × three RK models) of RK models showed spatial auto-correlation. Compared with RK₂, which has a 0.25° resolution, RK₁ and RK₃ are predicted at a finer 1 km spatial resolution. However, RK₁ has the best performance in rainfall prediction according to validation statistics. The root mean square error was decreased from 0.667 to 0.437 and the mean of error was improved from –0.250 to –0.007 in the prediction of mean daily rainfall. RK₁ may facilitate easy downscaling of precipitation and capture the trends in daily rainfall variability.     

Characteristics of drought indices and rainfall in Lake Chad Basin

A basin-scale analysis of the spatial and temporal distribution of drought indices and rainfall characteristics was performed in Lake Chad Basin (LCB), located at the Sahelo–Sudanian transition zone of West Africa. The research aims to improve our understanding of distribution, scenarios, and location-specific probability distribution of rainfall in the basin. Dekadal variability and trends were constructed and analysed using a geographic information system geoprocessing tool. There is a good correlation between the Tropical Rainfall Measuring Mission (TRMM 3B43) monthly rainfall and Global Precipitation Climatology Centre (GPCC) gauge with a correlation coefficient of 0.98. Climate Prediction Centre (CPC) Rainfall Estimate (RFE), Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) (eMODIS) normalized difference vegetation index images, global net primary production (NPP) anomaly, and standard precipitation index (SPI) were characterized. Results indicate an increase in NPP and SPI values from 2002 to 2011, which supports the theory of recent greening of the Sahel. Autocorrelation analysis identified a very high drought index at the northernmost part of LCB (proximal to the Sahara Desert) with the northern part of LCB characterized as low–low, suggesting more likelihood of low rainfall, and southeast and southwest portions as high–low, suggesting a decrease in likelihood of high precipitation northward. This provides vital information to farmers and relevant authorities for making educated decisions in poor rainy seasons. The statistical coefficient of variance and rainfall average also provide crucial information on region-specific rainfall needs for crop production.