Error analysis of non-spherical raindrops on precipitation measurement

Radar cross section （RCS） of non-sphericai raindrops is calculated by using the software CST based on finite integral method and compared with RCS of spherical raindrops. The revised factor of non-spherical raindrops is obtained. The radar reflectivity with precipitation change of four distribution models of M-P, Gamma, JD and JT combining the revised factor is gotten using trapezoidal integration. When the infuence of non-spherical raindrops is considered, the accuracy of precipitation measurement of four distribution models can be separately improved 8.77%, 8.47%, 10.53% and 8.04% in the case of rain intensity is 100 mm/h.

Comprehensive applicability evaluation of four precipitation products at multiple spatiotemporal scales in Northwest China

Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.

Fusion of Ground-Based and Spaceborne Radar Precipitation Based on Spatial Domain Regularization

High-quality and accurate precipitation estimations can be obtained by integrating precipitation information measures using ground-based and spaceborne radars in the same target area.Estimating the true precipitation state is a typical inverse problem for a given set of noisy radar precipitation observations.The regularization method can appropriately constrain the inverse problem to obtain a unique and stable solution.For different types of precipitation with different prior distributions,the L_(1) and L_(2) norms were more effective in constraining stratiform and convective precipitation,respectively.As a combination of L_(1) and L_(2) norms,the Huber norm is more suitable for mixed precipitation types.This study uses different regularization norms to combine precipitation data from the C-band dual-polarization ground radar(CDP)and dual-frequency precipitation radar(DPR)on the Global Precipitation Measurement(GPM)mission core satellite.Compared to single-source radar data,the fused figures contain more information and present a comprehensive precipitation structure encompassing the reflectivity and precipitation fields.In 27 precipitation cases,the fusion results utilizing the Huber norm achieved a structural similarity index measure(SSIM)and a peak signal-to-noise ratio(PSNR)of 0.8378 and 30.9322,respectively,compared with the CDP data.The fusion results showed that the Huber norm effectively amalgamate the features of convective and stratiform precipitation,with a reduction in the mean absolute error(MAE;16.1%and 22.6%,respectively)and root-mean-square error(RMSE;11.7%and 13.6%,respectively)compared to the 1-norm and 2-norm.Moreover,in contrast to the fusion results of scale recursive estimation(SRE),the Huber norm exhibits superior capability in capturing the localized precipitation intensity and reconstructing the detailed features of precipitation.

EXPERIMENT OF QUANTITATIVE MEASUREMENTS OF PRECIPITATION BY USE OF WEATHER RADAR IN THE HUAIHE RIVER BASIN

With the scheme of the variation analysis and Kalman filter,the radar data were adjusted by the real-time rain gauge data.The accuracy of areal rainfall calculation was improved and the results can be basically used for flood forecasting.It is concluded that the scheme is suitable in the upper and middle reaches of the Huaihe River.

Evaluation of CLDAS and GPM Precipitation Products over the Tibetan Plateau in Summer 2005–2021 Based on Hourly Rain Gauge Observations

Accurate,reliable,and high spatiotemporal resolution precipitation products are essential for precipitation research,hydrological simulation,disaster warning,and many other applications over the Tibetan Plateau(TP).The Global Precipitation Measurement(GPM) data are widely recognized as the most reliable satellite precipitation product for the TP.The China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS) precipitation fusion dataset(CLDAS-Prcp),hereafter referred to as CLDAS,is a high-resolution,self-developed precipitation product in China with regional characteristics.Focusing on the TP,this study provides a long-term evaluation of CLDAS and GPM from various aspects,including characteristics on different timescales,diurnal variation,and elevation impacts,based on hourly rain gauge data in summer from 2005 to 2021.The results show that CLDAS and GPM are highly effective alternatives to the rain gauge records over the TP.They both perform well for precipitation amount and frequency on multiple timescales.CLDAS tends to overestimate precipitation amount and underestimate precipitation frequency over the TP.However,GPM tends to overestimate both precipitation amount and frequency.The difference between them mainly lies in the trace precipitation.CLDAS and GPM effectively capture rainfall events,but their performance decreases significantly as intensity increases.They both show better accuracy in diurnal variation of precipitation amount than frequency,and their performance tends to be superior during nighttime compared to the daytime.Nevertheless,there are some differences of the two against rain gauge observations in diurnal variation,especially in the phase of the diurnal variation.The performance of CLDAS and GPM varies at different elevations.They both have the best performance over 3000–3500 m.The elevation dependence of CLDAS is relatively minor,while GPM shows a stronger elevation dependence in terms of precipitation amount.GPM tends to overestimate the precipitation amount at lower elevations and underestimate it at higher elevations.CLDAS and GPM exhibit unique strengths and weaknesses;hence,the choice should be made according to the specific situation of application.

Assessment of the GPM and TRMM Precipitation Products Using the Rain Gauge Network over the Tibetan Plateau

Using high-quality hourly observations from national-level ground-based stations, the satellite-based rainfall products from both the Global Precipitation Measurement（GPM） Integrated Multisatellit E Retrievals for GPM（IMERG） and its predecessor, the Tropical Rainfall Measuring Mission（TRMM） Multi-satellite Precipitation Analysis（TMPA）, are statistically evaluated over the Tibetan Plateau（TP）, with an emphasis on the diurnal variation.The results indicate that：（1） the half-hourly IMERG rainfall product can explicitly describe the diurnal variation over the TP, but with discrepancies in the timing of the greatest precipitation intensity and an overestimation of the maximum rainfall intensity over the whole TP. In addition, the performance of IMERG on the hourly timescale, in terms of the correlation coefficient and relative bias, is different for regions with sea level height below or above 3500 m;（2） the IMERG products, having higher correlation and lower root-mean-square error, perform better than the TMPA products on the daily and monthly timescales; and（3） the detection ability of IMERG is superior to that of TMPA, as corroborated by a higher Hanssen and Kuipers score, a higher probability of detection, a lower false alarm ratio, and a lower bias. Compared to TMPA, the IMERG products ameliorate the overestimation across the TP. In conclusion,GPM IMERG is superior to TRMM TMPA over the TP on multiple timescales.

Assessing 10 Satellite Precipitation Products in Capturing the July 2021 Extreme Heavy Rain in Henan, China

On 20 July 2021,a sudden rainstorm happened in central and northern Henan Province,China,killing at least 302people.This extreme precipitation event incurred substantial socioeconomic impacts and resulted in serious losses.Accurate monitoring of such rainstorm events is crucial.In this study,qualitative and quantitative methods are used to comprehensively evaluate the abilities of 10 high-resolution satellite precipitation products[CMORPH-Raw(Climate Prediction Center morphing technique),CMORPH-RT,PERSIANN-CCS(Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks),GPM IMERG-Early(Integrated Multisatellite Retrievals for Global Precipitation Measurement),GPM IMERG-Late,GSMaP-Now(Global Satellite Mapping of Precipitation),GSMaP-NRT,FY-2F,FY-2G,and FY-2H]in capturing this extreme rainstorm event,as well as their performances in monitoring different precipitation intensities.The results show that these satellite precipitation products are able to capture the spatial distributions of the rainstorm(e.g.,its location in central and northern Henan),but all products have underestimated the amount of precipitation in the rainstorm center.With the increase in precipitation intensity,the hit rate decreases,the threat score decreases,and the false alarm rate increases.CMORPH-RT is better at capturing the rainstorm than CMORPH-Raw,and it depictes the rainstorm process well;GPM IMERG-Late is more accurate than GPM IMERG-Early;GSMaP-NRT has performed better than GSMaP-Now;and PERSIANNCCS and FY-2F perform poorly.Among the products,CMORPH-RT performs the best,which has accurately captured the center of the rainstorm,and is also the closest to the station-based observations.In general,the satellite precipitation products that integrate infrared and passive microwave data are found to be better than those that only make use of infrared data.The satellite precipitation retrieval algorithm and the amount of passive microwave data have a relatively greater impact on the accuracy of satellite precipitation products.

Variations of Precipitation Structure and Microwave Tbs During the Evolution of a Hailstorm from TRMM Observations

In this paper, a hailstorm occurring on 9 May 1999 in Huanghuai region was studied by using the combined data from the precipitation radar （PR）, microwave image （TMI）, and visible infrared scanner （VIRS） on the Tropical Rainfall Measuring Mission （TRMM） satellite. According to the 3-orbit observations of 5- h duration from the TRMM satellite, the variation characteristics of the precipitation structures as well as cloud top temperature and microwave signals of the precipitating cloud were comprehensively analyzed during the evolution of hailstorm. The results show that the precipitation is obviously converted from early hail cloud with strong convection into the later storm cloud with weak convection. For hail cloud, there exists some strong convective cells, and the heavy solid precipitation is shown at the middle-top levels so that the contribution of rainfall amount above the freezing-layer to the column precipitation amount is rather larger than that within the melting-layer. However, for storm cloud, the convective cells are surrounded by the large area of stratiform precipitation, and the precipitation thickness gradually decreases, and the rainfall above the freezing-layer obviously reduces and the contribution of rainfall amount within the melting-layer rapidly increases. Therefore, the larger ratio of rainfall amount above the freezing layer to column precipitation amount is, the more convective the cloud is; reversely, the larger proportion of rainfall below the melting layer is, the more stable the stratiform cloud is. The different changing trends of microwave signals at different precipitation stages show that it is better to consider the structures and stages of precipitating cloud to choose the optimal microwave channels to retrieve surface rainfall.

Refined Spatialization of 10-Day Precipitation in China Based on GPM IMERG Data and Terrain Decomposition Using the BEMD Algorithm

Continuous high spatial-resolution 10-day precipitation data are essential for crop growth services and phenological research.In this study,we first use the bidimensional empirical mode decomposition(BEMD)algorithm to decompose the digital elevation model(DEM)data and obtain high-frequency(OR3),intermediate-frequency(OR5),and low-frequency(OR8)margin terrains.Then,we propose a refined precipitation spatialization model,which uses ground-based meteorological observation data,integrated multi-satellite retrievals for global precipitation measurement(GPM IMERG)satellite precipitation products,DEM data,terrain decomposition data,prevailing precipitation direction(PPD)data,and other multisource data,to construct China's high-resolution 10-day precipitation data from2001 to 2018.The decomposition results show mountainous terrain from fine to coarse scales;and the influences of altitude,slope,and aspect on precipitation are better represented in the model after topography is decomposed.Moreover,terrain decomposition data can be added to the model simulation to improve the quality of the simulation product;the simulation quality of the model in summer is better than that in spring and autumn,and is relatively poor in winter;and OR5 and OR8 can be improved in the simulation,with better OR5 and OR8 dynamically selected.In addition,preprocessing the data before precipitation spatialization is particularly important.For example,adding 0.01to the 0 value of precipitation,multiplying the small value of precipitation less than 1 by 10,and performing the normal distributions transform(e.g.,Yeo–Johnson)on the data can improve the simulation quality.

Significant impacts of the TRMM satellite orbit boost on climatological records of tropical precipitation

With the unprecedented spaceborne precipitation radar(PR),the Tropical Rainfall Measuring Mission(TRMM) satellite has collected high-quality precipitation measurements for over ten years.The TRMM/PR data are nowadays extensively exploited in numerous meteorological and hydrological fields.Yet an artificial orbit boost of the TRMM satellite in August 2001 modulated the observation parameters,which inevitably affects climatological applications of the PR data and needs to be clarified.This study investigates the orbit boost effects of the TRMM satellite on the PR-derived precipitation characteristics.Both the potential impacts on precipitation frequency(PF) and precipitation intensity(PI) are carefully analyzed.The results show that the total PF decreases by 8.3% and PI increases by 4.0% over the tropics after the orbit boost.Such changes significantly exceed the natural variabilities and imply the strong effects of orbit boost on precipitation characteristics.The impacts on stratiform precipitation and convective precipitation are inconsistent,which is attributed to their distinct precipitation features.Further analysis reveal that the increased PI of stratiform precipitation is mainly due to the decreased frequencies of light precipitation,while the semi-constant PI of convective precipitation is caused by the concurrently decreased frequencies of light and heavy precipitation.A modification is applied to the post-boost PR precipitation data to retrieve the actual trends of tropical precipitation characteristics.It is found that the PI of total-precipitation approximately keeps invariable from 1998 to 2005.The total PF has no obvious trend over tropical oceans but decreases considerably over tropical lands.

2001–2020年中国广西及东盟区域1 km分辨率降尺度月度降水数据集

中国广西及东盟区域位于亚洲东南部,同属亚热带季风气候,降水充沛,洪涝灾害频发,直接影响社会经济活动,高分辨率、高精度降水资料可有效支撑区域水资源、农业、灾害及生态等管理和研究。本研究以2001–2020年全球降水观测计划降水数据(GPM IMERG)为因变量,结合中分辨率成像光谱仪(MODIS)增强植被指数(EVI)、地表蒸散发(ET)、地表温度(LST)、先进星载热发射和反射辐射仪(ASTER)海拔(ELV)等解释变量,引入考虑变量随地理环境影响变化的地理加权回归(GWR)模型,构建空间分辨率为10 km的年尺度模型,经过测试选取验证精度良好的指数核函数。通过回代,基于空间分辨率为1 km的解释变量,构建了2001–2020年中国广西及东盟区域1 km年度降水数据集,并进一步采用比例指数法获得研究区2001–2020年1 km月度降水数据集。采用2001–2020年2679个地面观测站点数据对降尺度数据集进行验证,相关系数、均方根误差和偏差分别为0.792、74.610mm、-0.122%。本数据集能够有效反映1 km分辨率下的降水时空分布及其差异性,可广泛应用于水资源、农业、生态环境、灾害模拟等研究领域。

基于分层生成对抗网络的短临降水预报方法研究

本文使用深度学习方法中的生成对抗网络(GAN)来提升短临降水预报的准确率,提出了一个基于历史雷达回波图序列预测未来雷达回波图序列的分层生成对抗网络(HGAN)方法。HGAN方法由全局生成器和局部鉴别器两部分组成,全局生成器以多子网的层次结构构建,采用上采样过程训练模型,捕捉雷达回波的演变趋势,有利于生成清晰的未来雷达回波图。局部鉴别器根据局部区域将预测的雷达回波图与观测的雷达回波图区分开,并引入缓冲区机制,保存历史预测序列,使最终预测的结果更加符合时序性。两者以对抗的方式加以训练,得到的模型能够生成足够清晰且接近真实的未来雷达回波序列,对于回波强度极值和范围的刻画更为准确。对HGAN和GAN进行测试集检验及个例分析,分析结果验证了HGAN对雷达回波预测的有效性。同时在检验反射率阈值相同的情况下,HGAN的临界成功指数命中率高于GAN,而虚警率低于GAN,且在相同预测时长下,HGAN结构相似性指数(SSIM)优于GAN。

FY-3G降水测量雷达海洋定标精度检验与评估

2023年4月发射的风云三号气象卫星G星(FY-3G)是我国首颗专用降水测量卫星,双频降水测量雷达(precipitation measurement radar,PMR)是该颗卫星上最核心的仪器。基于2023年7月数据,利用海洋定标理论模型,模拟海洋表面后向散射截面,与观测海洋表面后向散射截面进行比对,实现对PMR定标精度的初步评估。通过与国外星载双频降水测量雷达(global precipitation measurement,dual-frequency precipitation radar,GPM DPR)海洋定标检验结果比对,评估FY-3G PMR定标的准确性。海洋定标精度检验结果表明:FY-3G PMR Ku波段在入射角小于15°时观测值与模型模拟值的偏差较小,此时FY-3G PMR的偏差为1.65~2.73 dB,偏差标准差为0.74~1.82 dB。FY-3G PMR Ka波段在18°入射角时偏差小于0.27 dB,偏差的标准差为3.49 dB。FY-3G PMR与GPM DPR的定标偏差存在较为固定的偏差,差异主要源自于数据本身的后向散射统计特性,各入射角下FY-3G PMR Ku与Ka波段海洋表面后向散射数据稳定性与GPM DPR相当。

顾及时滞效应的北部湾经济区GPM数据降尺度

为提高全球降水卫星计划(Integrated Multi-satellite Retrievals for Global Precipitation Measurement,GPM IMERG)等卫星降水数据的空间分辨率,以北部湾经济区为研究区,探讨最优的降尺度方案。以2001—2020年GPM卫星降水数据为数据源,构建地理加权回归模型(geographically weighted regression,GWR),结合归一化植被指数(normalized difference vegetation index,NDVI)、增强型植被指数(enhanced vegetation index,EVI)、数字高程模型(digital elevation model,DEM)和坡度数据;分别以NDVI和EVI为解释变量对北部湾经济区GPM数据进行降尺度,并进一步探讨滞后期为0~3月的植被指数对降尺度结果的影响;然后基于研究区内24个气象站点观测值进行精度验证。结果表明:GPM数据与气象站点实测数据在时间和空间上表现出较好的一致性和适用性,各站点决定系数介于0.65~0.93。降尺度数据在保证数据精度的前提下,空间分辨率有较大提升(由0.1°提升至1 km),且EVI降尺度GPM数据精度优于NDVI降尺度结果。考虑时滞效应时,滞后期为1~2个月的EVI为解释变量时,降尺度GPM数据精度最优,且干旱年份降尺度结果优于湿润年份。在北部湾经济区,EVI比NDVI更适合GPM数据做降尺度因子,使用滞后期为1~2个月的EVI降尺度结果更精确。

基于FY-3G降水测量雷达数据的某地区降水研究

文章运用FY-3G搭载的降水测量雷达(Precipitation Measurement Radar,PMR)数据对四川地区强降水过程进行研究,初步检验该产品的四川地区强降水过程中近地表降水率数据的准确性。基于四川省2023年区域性暴雨过程的小时雨量资料,对筛选出能够捕捉到较完整的降水信息且过境时间处在强降水时段内的PMR数据进行分析。研究结果显示,PMR在各时次轨道扫描范围内的近地表降水率均能提示相应的降水覆盖范围,且与对应的小时降水实况一致性较好。基于短时强降水实况,PMR近地表降水率中提示的降水信息分布与实况分布匹配度较高,各时次的近地表降水率与实况的匹配准确率均在90%以上,总体的匹配准确率达96.62%。

GPM反演降水数据IMERG在浙江省的适用性研究

【目的】为了研究全球降水测量计划(GPM)综合多卫星检索降水产品(IMERG)在浙江省的适用性。【方法】以浙江省为研究区域,基于浙江省自动观测站降水数据,利用相关系数、均方根误差、相对偏差、分类指数统计法分别从年尺度、季尺度、月尺度、日尺度以及小时尺度分析GPM在浙江省的适用性。【结果】(1)春、秋、冬季的GPM和自动站对应效果远好于夏季,月尺度、日尺度、小时尺度下GPM和自动站降水数据对应效果较好,但整体上存在高值区略低估、低值区略高估现象;(2)年尺度与季尺度下GPM与自动站降水平均值的变化趋势一致,但GPM降水平均值较高,其中秋季表现最好;(3)年、月、日3个尺度下,尺度越精细化,相关系数越高、相对偏差越小,均方根误差越小;小时、日、月3个尺度下,降水阈值越低,产品尺度越粗糙,估测能力越高;降水阈值越低,产品尺度越精细,误报降水的比例越低;降水阈值越低,综合探测能力越高;(4)小时尺度下,在0.5~1 mm·h^(-1)区间,GPM虽然对降水事件略有高估,但拟合效果较好。【结论】综合来看,GPM日尺度降水数据产品精度在浙江省有较大应用潜力。

雷达测雨及其在水文中应用的回顾与展望

简要介绍了雷达测雨的基本原理 ,对雷达测雨误差及其产生的原因进行了详细分析 ,并探讨了减少测雨误差的可能措施。在介绍了英国、美国、长江中游雷达网业务系统和阜阳数字雷达测雨试验的基础上 ,讨论了雷达测雨在水文中应用的前景及发展方向。

Parsivel~激光降水粒子谱仪及其在气象领域的应用

降水粒子特性是大气运动和云内微物理过程的综合结果,在云降水物理及人工影响天气领域有着重要的意义。传统的测量方法不适合对大量数据分析寻找规律,德国OTT公司的Parsivel激光降水粒子谱测量系统可以较好解决自动测量难题。该仪器是以激光测量为基础的粒子测量传感器,采用平行激光束和光电管阵列结合,当有降水粒子穿越采样空间时,自动记录遮挡物的宽度,通过穿越时间计算降水粒子的尺度和速度,根据各种参数的综合信息对降水粒子进行分类,并能够以数字形式显示瞬时降水强度、降水粒子总数、累积降水量、降水时的能见度和雷达反射因子,以图形方式显示降水粒子尺度谱、速度谱、降水粒子分类且自动生成天气现象代码,实现天气现象的自动识别。激光降水粒子谱仪主要用于气象水文观测。在雷达气象学领域可用于Z/R关系的拟合修正,比传统的用雨量筒观测数据拟合效果好得多;由降水粒子谱仪测量雨滴的降落速度,可以对天气雷达垂直向上测量的粒子径向速度谱进行校正。人工影响天气的效果检验一直是一个难题,自然降水粒子谱分布形式与人工催化以后的降水粒子谱型理论上应当具有较大的差别,人工增雨作业降水滴谱变化物理响应和降水强度时间变化响应都有明显的区别。如果能够实时检测到这些差别,就能够充分说明人工催化的有效性。未来如果能够进行联网观测记录区域性降水、降雹,就有充分证据表明人影作业的有效性,在定量化作业效果评估以及灾害损失评估等方面应用潜力巨大。利用该仪器已经对一年的自然降水过程进行了连续观测,并将所获得的降水粒子谱、雨滴浓度值随时间变化状况与卫星反演的云顶有效粒子半径时间变化趋势进行了对比,发现有较好的一致性。

用变分方法校准数字化天气雷达测定区域降水量基本原理和精度

本文用变分方法校准雷达估算的降水强度和区域降水量,结果表明:经变分校准后,不但能使雷达探测到的结果与雨量计观测到的结果比较接近,而且还能保留没有雨量计站的地方雷达探测到的降水强度变化,其平均相对误差约为20％.

基于TRMM卫星雷达降雨的流域陆面水文过程

利用热带降雨观测计划（TRMM）卫星雷达降雨数据驱动分布式陆面水文模型，研究流域尺度陆面水文过程，评估该数据在水文模拟与预报等研究领域的性能。通过与实测雨量资料比较，验证TRMM卫星雷达降雨数据的质量。分别将TRMM卫星雷达降雨与观测降雨作为耦合模型的气象输人，模拟和研究淮河流域1998～2003年的陆面水文过程时空变化。结果表明，TRMM卫星雷达降雨数据能够很好地描述降雨的时空分布，利用TRMM降雨模拟的结果与利用观测降雨模拟的结果精度相当；模拟流量与实测资料基本吻合。卫星雷达降雨数据在陆面水文过程研究中具有广泛的应用前景。