Evaluating the performance of remote sensing precipitation products CMORPH,PERSIANN, and TMPA,in the arid region of northwest China

The arid region of northwest China is a large area with complex topography. Hydrological research is limited by scarcity and uneven distribution of rain gauges. Satellite precipitation products provide wide coverage and high spatial–temporal resolutions, but the accuracy needs to be evaluated before application. In this paper, the reliability of four satellite precipitation products (CMORPH [Climate Prediction Center’s morphing technique], PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks], TRMM [Tropical Rainfall Measuring Mission] 3B42, and TRMM 3B43) were evaluated through comparison with ground data or reported values on daily, monthly, and annual scales from 2003 to 2010. Indices including frequency bias index, probability of detection, and false alarm ratio were used to evaluate recorded precipitation occurrences; relative mean bias, the correlation coefficient, and the Nash coefficient were used to assess precipitation amount. Satellite precipitation products were more accurate in the warm than in the cold season, and performed better in northern Xinjiang than in other regions during the cold season. CMORPH and PERSIANN tended to overestimate precipitation. TRMM 3B42 and TRMM 3B43 performed best because the former most accurately detected precipitation occurrences on a daily scale, and both produced accurate space–time distribution of precipitation and the best consistency with rain gauge observations. Only a few monthly precipitation values for TRMM 3B42 and TRMM 3B43, and annual precipitation values for TRMM 3B42 were with satisfactory precision. TRMM3B42 and TRMM 3B43 are therefore recommended, but correction will be needed before application. Factors including elevation, relative relief, longitude, and latitude had significant effects on the performance of satellite precipitation products, and these factors may be helpful in correcting satellite precipitation.     

中国中东部区域TRMM降水产品降尺度研究及其时空特征分析

该研究以中国中东部区域(17°～50°N,98°～135°E)为研究范围,在前人研究基础上,根据水汽与降水之间的关系,基于MOD05水汽产品,采用偏最小二乘法,对中国中东部区域2001—2010年10 a平均TRMM3B43_V 7月降水产品进行降尺度,旨在得到空间分辨率为1 km×1 km的月降水空间分布。通过比较分析,发现该降尺度模型能大幅提高TRMM产品空间分辨率,估算结果平均相对误差为15.35%,与地面观测较接近,能体现中国中东部区域降水宏观分布趋势,且估算结果精度高于前人基于归一化植被指数(NDVI)的降尺度模型,能满足降水产品的精细化需求。     

中国区域逐日融合降水数据集与国际降水产品的对比评估

中国国家气象信息中心基于2400多个国家级台站观测日降水量和CMORPH卫星反演降水产品,采用概率密度匹配和最优插值相结合的两步数据融合方法,研制了中国区域1998年以来的0.25°×0.25°分辨率的逐日融合降水产品(CMPA_Daily)。通过该数据集与广泛应用于中国天气气候领域的两种国际上降水融合产品TRMM 3B42(Tropical Rainfall Measuring Mission, 3B42)和GPCP(Global Precipitation Climatology Project, 1 degree daily)的对比评估,考察CMPA_Daily产品的质量,评价其能否合理体现中国降水的天气气候特征。首先利用2008—2010年5—9月独立检验数据定量对比了CMPA_Daily、TRMM 3B42和GPCP 三种降水产品的误差,结果表明,在误差的时间变化和空间分布上,CMPA_Daily均具有最高的相关系数和最小的平均偏差及均方根误差,TRMM 3B42其次,GPCP的误差相对较大。CMPA_Daily只低估了大暴雨,TRMM 3B42低估了大雨以上量级的降水,而GPCP低估了除小雨以外的所有降水。CMPA_Daily产品因融入了更多的站点观测信息,不论在中国东部沿海,还是中西部地形复杂区,其精度均优于TRMM 3B42和GPCP产品,即使在站点稀疏的青藏高原地区,CMPA_Daily降水量也更加接近站点观测,呈现明显的高相关。CMPA_Daily与独立检验数据的高相关在地形起伏时效果也较稳定,TRMM和GPCP的相关系数则随着地形变化幅度陡变而非常明显地降低。进一步通过对比分析各降水产品1998—2012年的气候平均降水特征表明,3种资料对中国区域气候平均降水量、降水强度、频率分布以及年际变化的总体描述基本一致,因有效融入了更多的中国站点观测信息,不论降水空间分布还是降水量,CMPA_Daily与地面观测均最为接近,在中国的中东部大部分地区对降水的估计精度明显更高,而在站点分布较稀疏的青藏高原地区,CMPA_Daily的降水分布型与TRMM、GPCP卫星融合资料类似,较地面站点插值产品更能体现出合理的降水分布。对中国强降水事件监测对比表明,CMPA_Daily产品可以更加准确地描述降水的强度变化,细致刻画降水空间分布,在把握降水小尺度特征上具有明显的优势,体现出高分辨率、高精度降水产品的特点。     

Validation of satellite rainfall products for operational flood forecasting: the case of the Evros catchment

An extensive validation of two of the most popular and recently upgraded satellite rainfall products, 3B42 and 3B42RT, was performed over the Evros catchment in southeastern Europe using data recorded from January 2000 to April 2009. For conducting this validation study, the Climate Prediction Center's (CPC) ground data were used. The satellite data products were aggregated to daily time series, remapped to spatial resolution of 0.5°, validated against CPC, and intercompared using a variety of statistical indices and coefficients. After the validation process, all three data sets (CPC, 3B42, and 3B42RT) were separately fed in a statistical rainfall?Crunoff model, in order to predict the five major recorded flood events which occurred in the Evros catchment during the last decade. It has been found that post-calibration with ground data, which is present only in 3B42 product, is a necessity for operational flood forecasting and similar studies conducted in areas at mid-latitudes. Knowledge of rainfall events with small intensities is crucial for estimating the total rainfall height and drastically improves the skill of the satellite product.     

基于TRMM卫星的近10a甘肃临夏降水变化特征

基于2001~2010年TRMM 3B43降水资料和数字高程模型（DEM）数据,采用回归模型＋残差的方法,对甘肃临夏回族自治州近10 a的TRMM 3B43降水数据进行降尺度运算,并结合研究区6个雨量站的观测值,对TRMM 3B43降尺度结果进行精度检验,在此基础上定量研究了临夏回族自治州近10 a的降水量时空变化特征。结果表明：TRMM 3B43降尺度降水量数据整体上具有一定的可信度,但比地面台站的观测值偏小;甘肃临夏州年降水量呈现出由西南向东北递减的趋势,且降水量随着海拔高度的升高而逐渐增加,两者相关系数为0.82;年内降水主要集中在5~9月,基本占全年降水量的70%以上,其中6月降水量最大,12月降水量最小。     

星载降水测量雷达降水产品研究进展

利用星载技术获得的降水信息与其他常规观测手段得到的信息相比,具有更广的空间覆盖性。星载产品的研究对降水微观信息的认识、数值预报的改进和水文农业的发展都有十分重要的意义。本文对美国宇航局NASA和日本宇航局JAXA联合开发的气象卫星降水产品,即第1部TRMM(Tropical Rainfall Measuring Mission)卫星的降水雷达(PR))和GPM(Global Precipitation Measurement)卫星的双频降水雷达(DPR)的降水产品,从评估验证、统计分析和个例分析的角度分类阐述了目前对TRMM和GPM降水产品的相关研究工作,最后提出了目前星载降水产品存在的一些问题、星载仪器的局限性以及对未来星载降水产品发展的展望。     

Validation of Daily Precipitation from Two High-Resolution Satellite Precipitation Datasets over the Tibetan Plateau and the Regions to Its East

Daily precipitation amounts and frequencies from the CMORPH (Climate Prediction Center Morphing Technique) and TRMM (Tropical Rainfall Measuring Mission) 3B42 precipitation products are validated against warm season in-situ precipitation observations from 2003 to 2008 over the Tibetan Plateau and the regions to its east. The results indicate that these two satellite datasets can better detect daily precipitation frequency than daily precipitation amount. The ability of CMORPH and TRMM 3B42 to accurately detect daily precipitation amount is dependent on the underlying terrain. Both datasets are more reliable over the relatively flat terrain of the northeastern Tibetan Plateau, the Sichuan basin, and the mid-lower reaches of the Yangtze River than over the complex terrain of the Tibetan Plateau. Both satellite products are able to detect the occurrence of daily rainfall events; however, their performance is worse in regions of complex topography, such as the Tibetan Plateau. Regional distributions of precipitation amount by precipitation intensity based on TRMM 3B42 are close to those based on rain gauge data. By contrast, similar distributions based on CMORPH differ substantially. CMORPH overestimates the amount of rain associated with the most intense precipitation events over the mid-lower reaches of the Yangtze River while underestimating the amount of rain associated with lighter precipitation events. CMORPH underestimates the amount of intense precipitation and overestimates the amount of lighter precipitation over the other analyzed regions. TRMM 3B42 underestimates the frequency of light precipitation over the Sichuan basin and the mid-lower reaches of the Yangtze River. CMORPH overestimates the frequencies of weak and intense precipitation over the mid-lower reaches of the Yangtze River, and underestimates the frequencies of moderate and heavy precipitation. CMORPH also overestimates the frequency of light precipitation and underestimates the frequency of intense precipitation over the other three regions. The TRMM 3B42 product provides better characterizations of the regional gamma distributions of daily precipitation amount than the CMORPH product, for which the cumulative distribution functions are biased toward lighter precipitation events.     

The frequency, intensity, and diurnal cycle of precipitation in surface and satellite observations over low- and mid-latitudes

Global precipitation data sets with high spatial and temporal resolution are needed for many applications, but they were unavailable before the recent creation of several such satellite products. Here, we evaluate four different satellite data sets of hourly or 3-hourly precipitation (namely CMORPH, PERSIANN, TRMM 3B42 and a microwave-only product referred to as MI) by comparing the spatial patterns in seasonal mean precipitation amount, daily precipitation frequency and intensity, and the diurnal and semidiurnal cycles among them and with surface synoptic weather reports. We found that these high-resolution products show spatial patterns in seasonal mean precipitation amount comparable to other monthly products for the low- and mid-latitudes, and the mean daily precipitation frequency and intensity maps are similar among these pure satellite-based precipitation data sets and consistent with the frequency derived using weather reports over land. The satellite data show that spatial variations in mean precipitation amount come largely from precipitation frequency rather than intensity, and that the use of satellite infrared (IR) observations to improve sampling does not change the mean frequency, intensity and the diurnal cycle significantly. Consistent with previous studies, the satellite data show that sub-daily variations in precipitation are dominated by the 24-h cycle, which has an afternoon–evening maximum and mean-to-peak amplitude of 30–100% of the daily mean in precipitation amount over most land areas during summer. Over most oceans, the 24-h harmonic has a peak from midnight to early morning with an amplitude of 10–30% during both winter and summer. These diurnal results are broadly consistent with those based on the weather reports, although the time of maximum in the satellite precipitation is a few hours later (especially for TRMM and PERSIANN) than that in the surface observations over most land and ocean, and it is closer to the phase of showery precipitation from the weather reports. The TRMM and PERSIANN precipitation shows a spatially coherent time of maximum around 0300–0600 local solar time (LST) for a weak (amplitude <20%) semi-diurnal (12-h) cycle over most mid- to high-latitudes, comparable to 0400–0600 LST in the surface data. The satellite data also confirm the notion that the diurnal cycle of precipitation amount comes mostly from its frequency rather than its intensity over most low and mid-latitudes, with the intensity has only about half of the strength of the diurnal cycle in the frequency and amount. The results suggest that these relatively new precipitation products can be useful for many applications.     

Comparison of TRMM and Water District Rain Rates over New Mexico

This paper compares monthly and seasonal rain rates derived from the Version 5 (V5) and Version 6 (V6) TRMM Precipitation Radar (TPR, TSDIS reference 2A25), TRMM Microwave Imager (TMI, 2A12), TRMM Combined Instrument (TCI, 2B31), TRMM calibrated IR rain estimates (3B42) and TRMM merged gauge and satellite analysis (3B43) algorithms over New Mexico (NM) with rain gauge analyses provided by the New Mexico water districts (WD). The average rain rates over the NM region for 1998–2002 are 0.91mmd?1 for WD and 0.75, 1.38, 1.49, 1.27, and 1.07mmd?1 for V5 3B43, 3B42, TMI, PR and TCA; and 0.74, 1.38, 0.87 and 0.97 mm d?1 for V6 3B43, TMI, TPR and TCA, respectively. Comparison of V5 3B43 with WD rain rates and the daily TRMM mission index (TPR and TMI) suggests that the low bias of V5 3B43 for the wet months (summer to early fall) may be due to the non-inclusion of some rain events in the operational gauge analyses that are used in the production of V5 3B43. Correlation analyses show that the WD rain rates vary in phase, with higher correlation between neighboring WDs. High temporal correlations (>0.8) exist between WD and the combined algorithms (3B42, 3B43 and TCA for both V5 and V6) while satellite instrument algorithms (PR, TMI and TCI) are correlated best among themselves at the monthly scale. Paired t-tests of the monthly time series show that V5 3B42 and TMI are statistically different from the WD rain rates while no significant difference exists between WD and the other products. The agreements between the TRMM satellite and WD gauge estimates are best for the spring and fall and worst for winter and summer. The reduction in V6 TMI (?7.4%) and TPR (?31%) rain rates (compared to V5) results in better agreement between WD estimates and TMI in winter and TPR during summer.     

新型卫星降水产品在黄河源区的适用性分析——以SWAT模型为例

以黄河源区水文循环过程为主要研究对象,应用TRMM卫星和GPM卫星的日降水产品(TMPA3B42和IMERG-Final)驱动流域分布式水文模型SWAT,将结果进行比较分析,评估了新型卫星降水在黄河源区的适用性及其模拟潜力。研究表明:(1)对于大尺度流域而言,同时对多个子流域进行参数的敏感性分析及率定的结果不适用于每一个站点。因此,本文采用对每个水文站点所对应的子流域依次进行敏感性分析与验证的方式进行订正,最终得到验证期内3个站点径流模拟结果的纳什效率系数均在0.50以上,决定系数都在0.60以上。(2)从模拟结果来看,IMERG-Final产品的模拟结果要优于TMPA3B42产品。两种卫星降水产品均能模拟出黄河源区月径流变化的主要趋势,但均表现为对于径流峰值的模拟偏高。新型卫星产品(GPM)较前任TRMM卫星产品的精度确实有提高,且具备一定的模拟潜力,但对于高海拔地区的模拟能力有待提高,需要进一步订正。     

Are satellite products good proxies for gauge precipitation over Singapore?

The uncertainties in two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations over Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of seasonal, monthly and daily variations, the diurnal cycle, and extreme precipitation over a 10-year period (2000–2010). Results indicate that the uncertainties in extreme precipitation is higher in GSMaP than in TRMM, possibly due to the issues such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and the swath time of satellite. Such discrepancies between satellite-borne and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results. Overall, both satellite products are unable to capture the observed extremes and provide a good agreement with observations only at coarse time scales. Also, the satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the Intertropical Convergence Zone (ITCZ) is located over Singapore. However, they do not reproduce the gauge-observed diurnal cycle in summer. The disagreement in summer could be attributed to the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. From the analyses of extreme precipitation indices, it is inferred that both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spells. This study on quantification of their uncertainty is useful in many aspects especially that these satellite products stand scrutiny over places where there are no good ground data to be compared against. This has serious implications on climate studies as in model evaluations and in particular, climate model simulated future projections, when information on precipitation extremes need to be reliable as they are highly crucial for adaptation and mitigation.     

华南地区几种降水产品的对比分析

对华南地区3种比较常用的降水产品（CMAP、GPCP、TRMM）进行了比较和分析。结果表明,在华南地区,这3种降水产品测出的降雨量在空间上具有一致的大尺度分布特征,在时间演变上具有一致的演变趋势,都能很好地反映雨带的季节性迁移特征和华南汛期的降雨特征;但是在不同的具体区域,三者的细节特征差异还是比较显著的,相比较而言,GPCP和TRMM具有更为一致的空间分布特征;在地形复杂的区域,TRMM具有更良好的表现能力。     

Simulations of precipitation using NRCM and comparisons with satellite observations and CAM: annual cycle

The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36?km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000?C2005) from within the Tropics (30°S?C30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36?km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations.     

Validating a rapid-update satellite precipitation analysis across telescoping space and time scales

In order to properly utilize remotely sensed precipitation estimates in hydrometeorological applications, knowledge of the accuracy of the estimates are needed. However, relatively few ground validation networks operate with the necessary spatial density and time-resolution required for validation of high-resolution precipitation products (HRPP) generated at fine space and time scales (e.g., hourly accumulations produced on a 0.25° spatial scale). In this article, we examine over-land validation statistics for an operationally designed, meteorological satellite-based global rainfall analysis that blends intermittent passive microwave-derived rainfall estimates aboard a variety of low Earth-orbiting satellite platforms with sub-hourly time sampling capabilities of visible and infrared imagers aboard operational geostationary platforms. The validation dataset is comprised of raingauge data collected from the dense, nearly homogeneous, 1-min reporting Automated Weather Station (network of the Korean Meteorological Administration during the June to August 2000 summer monsoon season. The space-time RMS error, mean bias, and correlation matrices were computed using various time windows for the gauge averaging, centered about the satellite observation time. For ±10 min time window, a correlation of 0.6 was achieved at 0.1° spatial scale by averaging more than 3 days; coarsening the spatial scale to 1.8° produced the same correlation by averaging over 1 h. Finer than approximately 24-h and 1° time and space scales, respectively, a rapid decay of the error statistics was obtained by trading-off either spatial or time resolution. Beyond a daily time scale, the blended estimates were nearly unbiased and with an RMS error of no worse than 1 mm day^?1.     

Accuracy of TRMM precipitation data in the southwest monsoon region of China

Accurate, high-resolution precipitation data is important for hydrological applications and water resource management, particularly within mountainous areas about which data is presently scarce. The goal of the this study was to assess the accuracy of TRMM 3B43 precipitation data from the southwest monsoon region of China between 1998 and 2011 based on the correlation coefficients, regression, and geostatistical methods. We found a strong correlation between TRMM 3B43 data and observational data obtained from meteorological stations, but the TRMM 3B43 precipitation data was consistently lower than that obtained from the weather stations. The TRMM 3B43 data was significantly different from the data obtained by weather stations located in the northwest and northeast regions of the Hengduan Mountains. The spatial distribution of precipitation obtained from TRMM 3B43 was also different from meteorological data, but the deviation was predominantly distributed along the northern longitude and southern latitude. In addition, the TRMM data more accurately reflected the regional precipitation patterns. Our results indicate that the TRMM 3B43 data should be used for hydrological applications and water resource management at meteorological stations that have a sparse and uneven distribution of observation stations in the southwest monsoon region.

     

Validation of high-resolution TRMM-3B43 precipitation product using rain gauge measurements over Kyrgyzstan

This paper presents the validation of monthly precipitation using Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA)—3B43 product with conventional rain gauge observations for the period 1998–2007 over Kyrgyzstan. This study is carried out to quantify the accuracy of TMPA-3B43 product over the high latitude and complex orographic region. The present work is quite important because it is highly desirable to compare the TMPA precipitation product with the ground truth data at a regional scale, so that the satellite product can be fine-tuned at that scale. For the validation, four different types of spatial collocation have been used: station wise, climatic zone wise, topographically and seasonal. The analysis has been done at the same spatial and temporal scales in order to eliminate the sampling biases in the comparisons. The results show that TMPA-3B43 product has statistically significant correlation (r?=?0.36–0.88) with rain gauge data over the most parts of the country. The minimum linear correlation is observed around the large continental water bodies (e.g., Issyk-Kul lake; r?=?0.17–0.19). The overall result suggests that the precipitation estimated using TMPA-3B43 product performs reasonably well over the plain regions and even over the orographic regions except near the big lake regions. Also, the negative bias suggests the systematic underestimation of high precipitation by TMPA-3B43 product. The analyses suggest the need of a better algorithm for precipitation estimation over this region separately to capture the different types of rain events more reliably.     

Evaluating spatial scales of climate variability in sub-Saharan Africa

Summary Spatial scales of variability in seasonal rainfall over Africa are investigated by means of statistical and numerical techniques. In the statistical analysis spatial structure is studied using gridded 0.5° resolution monthly data in the period 1948–1998. The de-seasonalized time series are subjected to successive principal component (PC) analysis, allowing the number of modes to vary from 10 to 24, producing cells of varying dimension. Then the original rainfall data within each cell are cross-correlated (internal), then averaged and compared with the adjacent cells (external) for each PC solution. By considering the ratio of internal to external correlation, the spatial scales of rainfall variability are evaluated and an optimum solution is found whose cell dimensions are approximately 10⁶ km². The aspect of scale is further studied for southern Africa by consideration of numerical model ensemble simulations over the period 1985–1999 forced with observed sea surface temperatures (SSTs). The hindcast products are compared with observed January to March (JFM) rainfall, based on a station-satellite merged analysis of precipitation (CMAP) data at 2.5° resolution. Validations for different sized areas indicate that cumulative standardized errors are greatest at the scale of a single grid cell (10⁴ km²) and decrease 20–30% by averaging over successively larger areas (10⁶ km²).     

Association between cloudiness and rainfall over Fars province in Iran

Relationship between precipitation sum and cloud properties over Fars province in Iran was analyzed for the cases of light (4 mm), moderate (17 mm), and heavy (62 mm) precipitation. The cloud properties (temperature and pressure at the top, cloud optical thickness and cloud water path) were obtained from satellite data of spectoradiometer MODIS (MODO6). The spatial distribution of rainfall was obtained from the 3-hourly data of TRMM (3B42). The multivariate regression model was developed to predict the spatial distribution of rainfall. A strong significant positive association between the spatial distribution of cloud characteristics and heavy precipitation was found, while no clear correlation was revealed between light precipitation and cloud properties. The developed regression model comprised 64, 47, and 24% of spatial variance of heavy, moderate, and light rainfall, respectively. The influence of cloud water path on the spatial distribution of rainfall dominates.     

Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data

In the present study, an attempt has been made to estimate and validate the daily and monthly rainfall during the Indian summer monsoon seasons of 2008 and 2009 using INSAT (Indian National Satellite System) Multispectral Rainfall Algorithm (IMSRA) technique utilizing Kalpana-1 very high resolution radiometer (VHRR) measurements. In contrary to infrared (IR), microwave (MW) rain rates are based on measurements that sense precipitation in clouds and do not rely merely on cloud top temperature. Geostationary satellites provide broad coverage and frequent refresh measurements but microwave measurements are accurate but sparse. IMSRA technique is the combination of the infrared and microwave measurements which make use of the best features of both IR- and MW-based rainfall estimates. The development of this algorithm included two major steps: (a) classification of rain-bearing clouds using proper cloud classification scheme utilizing Kalpana-1 IR and water vapor (WV) brightness temperatures (Tb) and (b) collocation of Kalpana-1 IR brightness temperature with Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) surface rain rate and establishment of a regression relation between them. In this paper, the capability of IMSRA as an operational algorithm has been tested for the two monsoon seasons 2008 and 2009. For this, IMSRA has been used to estimate daily and monthly rainfall and has been intercompared on daily and monthly scales with TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 V6 product and Global Precipitation Climatology Project (GPCP) rain product during these two monsoon years. The daily and monthly IMSRA rainfall has also been validated against ground-based observations from Automatic Weather Station (AWS) Rain Gauge and Buoy data. The algorithm proved to be in good correlation with AWS data over land up to 0.70 for daily rain estimates except orographic regions like North-East and South-West India and 0.72 for monthly rain estimates. The validation with Buoys gives the reasonable correlation of 0.49 for daily rain estimates and 0.66 for monthly rain estimates over Tropical Indian Ocean.     

GRAPES三维云初始场形成及在短临预报中的应用

围绕GRAPES_Meso的云初始场形成,以ARPS模式云分析方案为基础,优化诊断后应用我国风云二号静止气象卫星云产品、多普勒天气雷达三维组网拼图产品等观测资料结合模式背景信息,根据云热力-动力学原理及观测试验经验关系等,对云初始场的信息进行分析并通过松弛逼近同化方法实现对云内微物理信息同化应用。GRAPES_Meso中采用优化后的云初始场方案,水平分辨率为0.03°×0.03°和0.1°×0.1°的1个月（2014年7月15日-8月14日）连续试验和个例分析结果显示:云初始场形成方案能够分析出飑线等天气系统的云系和云内微物理变量特征。从模拟云图看,包含云初始场信息的GRAPES_Meso的云系的形态特征和分布范围短时临近预报结果更为准确。云初始场信息同化应用后,在1 h的时间尺度上,即可预报出与实况更为接近的降水;0~12 h时间范围内对降水均有积极的影响,可满足短时临近预报的需求,降水量级略偏大。批量连续试验（水平分辨率为0.03°×0.03°和0.1°×0.1°）的各个量级降水ETS（equitable threat score）评分都显著提高。