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 MultisatellitE 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 the performance of satellite-based precipitation products and its dependence on topography over Poyang Lake basin

Satellite-based precipitation products (SPPs) have greatly improved their applicability and are expected to offer an alternative to ground-based precipitation estimates in the present and the foreseeable future. There is a strong need for a quantitative evaluation of the usefulness and limitations of SPPs in operational meteorology and hydrology. This study compared two widely used high-resolution SPPs, the Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) in Poyang Lake basin which is located in the middle reach of the Yangtze River in China. The bias of rainfall amount and occurrence frequency under different rainfall intensities and the dependence of SPPs performance on elevation and slope were investigated using different statistical indices. The results revealed that (1) TRMM 3B42 usually underestimates the rainy days and overestimates the average rainfall as well as annual rainfall, while the PERSIANN data were markedly lower than rain gauge data; (2) the rainfall contribution rates were underestimated by TRMM 3B42 in the middle rainfall class but overestimated in the heavy rainfall class, while the opposite trend was observed for PERSIANN; (3) although the temporal distribution characteristics of monthly rainfall were correctly described by both SPPs, PERSIANN tended to suffer a systematic underestimation of rainfall in every month; and (4) the performances of both SPPs had clear dependence on elevation and slope, and their relationships can be fitted using quadratic equations.     

Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA

This study focuses on the evaluation of 3-hourly 0.25° × 0.25° satellite-based rainfall estimates produced by the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). The evaluation is performed during six heavy rainfall events that were generated by tropical storms passing over Louisiana, United States. Two surface-based rainfall datasets from gauge and radar observations are used as a ground reference for evaluating the real-time (RT) version of the TMPA product and the post-real-time bias adjusted research version. The evaluation analysis is performed at the native temporal and spatial scales of the TMPA products, 3-hourly and 0.25° × 0.25°. Several graphical and statistical techniques are applied to characterize the deviation of the TMPA estimates from the reference datasets. Both versions of the TMPA products track reasonably well the temporal evolution and fluctuations of surface rainfall during the analyzed storms with moderate to high correlation values of 0.5–0.8. The TMPA estimates reported reasonable levels of rainfall detection especially when light rainfall rates are excluded. On a storm scale, the TMPA products are characterized by varying degrees of bias which was mostly within ± 25% and ± 50% for the research and RT products, respectively. Analysis of the error distribution indicated that, on average, the TMPA products tend to overestimate small rain rates and underestimate large rain rates. Compared to the real-time estimates, the research product shows significant improvement in the overall and conditional bias, and in the correlation coefficients, with slight deterioration in the probability of detecting rainfall occurrences. A fair agreement in terms of reproducing the tail of the distribution of rain rates (i.e., probability of surface rainfall exceeding certain thresholds) was observed especially for the RT estimates. Despite the apparent differences with surface rainfall estimates, the results reported in this study highlight the TMPA potential as a valuable resource of high-resolution rainfall information over many areas in the world that lack capabilities for monitoring landfalling tropical storms.     

Accuracy validation of TRMM Multisatellite Precipitation Analysis daily precipitation products in the Lancang River Basin of China

Using statistical methods and contingency table method, this paper evaluates the accuracy of 12 years (1998–2009) Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) daily-accumulated precipitation products within a year, the dry season, and rain season for each of the five subbasins and for each grid point (0.25?×?0.25°) in the Lancang River basin by comparing the results with data from the 35 rain gauges. The results indicate that TMPA daily precipitation estimates tend to show an underestimation comparing to the rain gauge daily precipitations under any scenarios, especially for the middle stream in the dry season. The accuracy of TMPA-averaged precipitation deteriorates with the increase of elevation at both basin and grid scale, with upstream and downstream having the worst and best accuracy, respectively. A fair capability was shown when using daily TMPA accumulations to detect rain events at drizzle rain and this capability improves with the increase of elevation. However, the capability deteriorates when it is used to detect moderate rain and heavy rain events. The accuracy of TMPA precipitation estimate products is better in the rain season than in the dry season at all scenarios. Time difference and elevation are the main factors that have impact on the accuracy of TMPA daily-accumulated precipitation products.     

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 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.     

Validation of TRMM-3B42 precipitation product over the tropical Indian Ocean using rain gauge data from the RAMA buoy array

In the present study, an attempt has been made to validate the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-3B42 recently released version 7 product over the tropical Indian Ocean using surface rain gauges from the National Oceanic and Atmospheric Administration/Pacific Marine Environmental Laboratory Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction buoy array available since late 2004. The validation exercise is carried out at daily scale for an 8-year period of 2004–2011. Results show statistically significant linear correlation between these two precipitation estimates ranging from 0.40 to 0.89 and the root-mean-square error varies from about 1 to 22 mm day^?1. Although systematic overestimation of precipitation by the TMPA product is evident over most of the buoy locations, the TMPA noticeably underestimates higher (more than 100 mm day^?1) and light (less than 0.5 mm day^?1) precipitation events. The highest correlation is observed during the southwest monsoon season (June–September) even though bias is the maximum possibly due to relatively lower fraction of stratiform precipitation during the monsoon season than other seasons. Furthermore, the TMPA estimates slightly underestimate or misses intermittent warm precipitation events as compared to the precipitation radar derived precipitation rates.     

A strategy for merging objective estimates of global daily precipitation from gauge observations,satellite estimates,and numerical predictions

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates(SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps.First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions(MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation(OI)–based merging scheme(referred as the HL-OI scheme)is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge—satellite–model merged daily precipitation analysis, called BMEP-d(Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period(2011–14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality.Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD(GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.     

Evaluation of TRMM multi-satellite precipitation analysis (TMPA) against terrestrial measurement over a humid sub-tropical basin,India

To support the GPM mission which is homologous to its predecessor, the Tropical Rainfall Measuring Mission (TRMM), this study has been undertaken to evaluate the accuracy of Tropical Rainfall Measuring Mission multi-satellite precipitation analysis (TMPA) daily-accumulated precipitation products for 5 years (2008–2012) using the statistical methods and contingency table method. The analysis was performed on daily, monthly, seasonal and yearly basis. The TMPA precipitation estimates were also evaluated for each grid point i.e. 0.25° × 0.25° and for 18 rain gauge stations of the Betwa River basin, India. Results indicated that TMPA precipitation overestimates the daily and monthly precipitation in general, particularly for the middle sub-basin in the non-monsoon season. Furthermore, precision of TMPA precipitation estimates declines with the decrease of altitude at both grid and sub-basin scale. The study also revealed that TMPA precipitation estimates provide better accuracy in the upstream of the basin compared to downstream basin. Nevertheless, the detection capability of daily TMPA precipitation improves with increase in altitude for drizzle rain events. However, the detection capability decreases during non-monsoon and monsoon seasons when capturing moderate and heavy rain events, respectively. The veracity of TMPA precipitation estimates was improved during the rainy season than during the dry season at all scenarios investigated. The analyses suggest that there is a need for better precipitation estimation algorithm and extensive accuracy verification against terrestrial precipitation measurement to capture the different types of rain events more reliably over the sub-humid tropical regions of India.     

GPM卫星和地面雷达对江苏盐城龙卷风强降水估测的对比

为综合评估卫星和天气雷达在2016年6月23日盐城龙卷风期间的强降水过程的降水估测精度,以国家级雨量站观测数据为基准,结合相关系数(CC)、相对误差(RB)、均方根误差(RMSE)以及分级评分指标,利用S波段的天气雷达定量降雨估测产品(RQPE)和全球降水观测计划多卫星融合产品(IMERG_FRCal,IMERG_FRUncal,IMERG_ERCal)进行比较。结果表明,雷达和卫星的累积降水量与雨量站的空间相关性很强(相关系数大于0.9),基本上能捕捉到整个降水过程的空间分布。降水主要分布在江苏省北部,但卫星高估了江苏省东北部强降水中心的降水量;对于小时时序区域平均降水,卫星高估了降水,而雷达低估了累积降水量。综合降水中心区域分析,IMERG的强降水区域降水量与雨量站的时间序列的偏差显著;RQPE在降水峰值达到之前及峰值之后与地面雨量站的变化趋势基本一致,但对降雨量峰值有明显的偏低。RQPE能较为准确地在时间上捕捉到降雨强度的变化趋势,但对于大雨及暴雨的估测能力不佳;RQPE的POD、SCI值都远远高于IMERG, FAR也较小。IMERG几乎未能监测到强降水的发生。总体上,RQPE对此次龙卷风强降水量的估测表现优于3种IMERG产品,特别是在捕捉强降水区域的空间分布方面,但对于强降水的估测能力仍需进一步改善。     

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.     

贵州地区CMORPH卫星降水产品的误差订正

CMORPH卫星反演降水产品具有全天候、全球覆盖的特点,其时空分布相对均匀、独立,但是CMORPH本质上是通过间接手段反演得到,其降水精度无法与地面观测降水精度相比,并且存在一定的系统误差。结合地面自动站降水资料采用概率密度匹配法对贵州地区CMORPH卫星反演降水产品进行系统误差订正,该方法将每个格点的卫星降水累积概率分布曲线和地面降水概率密度分布曲线匹配,获取降水误差订正值;其中误差订正效果受降水累积概率分布拟合曲线的影响,而考虑到降水累积概率分布是非正态分布,因此选用Gamma分布拟合降水累积概率分布曲线。通过对2018年5月三次降水过程的订正结果分析得到如下结论:(1) 逐时的CMORPH卫星反演降水产品存在明显的非独立系统误差,误差范围随降水量级的变化而变化,存在低值高估的特点;(2) 在小时尺度下地面降水的累积概率密度呈指数衰减分布,而CMORPH的降水累积概率密度分布更加复杂,其在中雨、大雨区间内的降水概率较高;(3) 通过概率密度匹配法订正后的CMORPH与订正前相比降水空间结构更加贴近地面降水,强降水中心的量级和范围明显减小,平均绝对误差和均方根误差均减小,其中偏差订正值在0.114~0.468 mm/h,均方根误差订正在0.24~1.49 mm/h之间。经概率密度匹配法订正后的CMORPH卫星反演降水产品精度明显提升,更加接近于实际降水。      

Towards combining GPM and MFG observations to monitor near real time heavy precipitation at fine scale over India and nearby oceanic regions

This is the first attempt to merge highly accurate precipitation estimates from Global Precipitation Measurement (GPM) with gap free satellite observations from Meteosat to develop a regional rainfall monitoring algorithm to estimate heavy rainfall over India and nearby oceanic regions. Rainfall signature is derived from Meteosat observations and is co-located against rainfall from GPM to establish a relationship between rainfall and signature for various rainy seasons. This relationship can be used to monitor rainfall over India and nearby oceanic regions. Performance of this technique was tested by applying it to monitor heavy precipitation over India. It is reported that our algorithm is able to detect heavy rainfall. It is also reported that present algorithm overestimates rainfall areal spread as compared to rain gauge based rainfall product. This deficiency may arise from various factors including uncertainty caused by use of different sensors from different platforms (difference in viewing geometry from MFG and GPM), poor relationship between warm rain (light rain) and IR brightness temperature, and weak characterization of orographic rain from IR signature. We validated hourly rainfall estimated from the present approach with independent observations from GPM. We also validated daily rainfall from this approach with rain gauge based product from India Meteorological Department (IMD). Present technique shows a Correlation Coefficient (CC) of 0.76, a bias of −2.72 mm, a Root Mean Square Error (RMSE) of 10.82 mm, Probability of Detection (POD) of 0.74, False Alarm Ratio (FAR) of 0.34 and a Skill score of 0.36 with daily rainfall from rain gauge based product of IMD at 0.25° resolution. However, FAR reduces to 0.24 for heavy rainfall events. Validation results with rain gauge observations reveal that present technique outperforms available satellite based rainfall estimates for monitoring heavy rainfall over Indian region.     

雷达回波强度拼图的定量估测降水及其效果检验

为了得到精度更高的高时空分辨率格点定量估测降水量,需要将雷达资料和雨量计降水量资料进行有效的综合利用。利用广东6部多普勒雷达的回波强度拼图资料和稠密的自动雨量计降水强度观测资料,采用概率密度法建立Z-R关系进行雷达降水估计,并采用客观订正方法利用雨量计资料对雷达降水估计进行校准。交叉检验表明：利用雨量计对雷达降水估计进行客观方法订正可以取得比单纯用雨量计资料进行OI插值好的效果。OI雷达订正法较优。     

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。     

Tropical Precipitation Estimated by GPCP and TRMM PR Observations

In this study, tropical monthly mean precipitation estimated by the latest Global Precipitation Climatology Project （GPCP） version 2 dataset and Tropical Rainfall Measurement Mission Precipitation Radar （TRMM PR） are compared in temporal and spatial scales in order to comprehend tropical rainfall climatologically. Reasons for the rainfall differences derived from both datasets are discussed. Results show that GPCP and TRMM PR datasets present similar distribution patterns over the Tropics but with some differences in amplitude and location. Generally, the average difference over the ocean of about 0.5 mm d^-1 is larger than that of about 0.1 mm d^-1 over land. Results also show that GPCP tends to underestimate the monthly precipitation over the land region with sparse rain gauges in contrast to regions with a higher density of rain gauge stations. A Probability Distribution Function （PDF） analysis indicates that the GPCP rain rate at its maximum PDF is generally consistent with the TRMM PR rain rate as the latter is less than 8 mm d^-1. When the TRMM PR rain rate is greater than 8 mm d^-1, the GPCP rain rate at its maximum PDF is less by at least 1 mm d^-1 compared to TRMM PR estimates. Results also show an absolute bias of less than 1 mm d^-1 between the two datasets when the rain rate is less than 10 mm d^-1. A large relative bias of the two datasets occurs at weak and heavy rain rates.     

青藏高原与四川盆地夏季降水日变化的对比分析

采用2006-2008年自动气象站和2002-2008年TRMM（Tropical Rainfall Measurement Mis-sion）多卫星降水分析（Munti-satellite Precipitation Analysis,TMPA）的夏季（6～8月）逐时降水量资料,分析了青藏高原（下称高原）及周边地区夏...     

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.     

基于雷达—雨量计降水融合方法提高极端降水监测能力

高时空分辨率、高精度的降水产品对于极端降水的监测以及防灾减灾具有重要意义。地面雨量计提供点尺度降水精确观测,但无法精细化捕捉对流性强降水的空间分布。雷达观测可以精细地刻画降水的空间分布特征,但雷达定量估计降水（QPE,quantitative precipitation estimation）产品估测精度易受雷达观测偏差和Z–R（雷达反射率—降水率）关系等因素影响。因此,本文开展高时空分辨率的雷达—雨量计降水融合算法研究,集成雨量计观测和雷达定量估计降水产品各自的优点。该算法主要步骤包括:雨量站观测数据格点化、局地雨量计订正雷达QPE和雷达—雨量计降水融合三个部分。首先利用克里金插值方法,对雨量站观测的降水进行插值,得到格点降水信息;再通过局地雨量计订正方法系统性地订正雷达QPE产品,以提高雷达QPE产品精度;最后,结合降水类型,通过雷达—雨量计降水融合算法,产生高时空分辨率、高精度的雷达—雨量计降水融合产品。通过郑州“21·7”暴雨、台风“烟花”和2021年8月随州暴雨三个典型的极端降水个例,对雷达—雨量计降水融合算法产生的雷达—雨量计降水融合产品进行了系统地评估和分析。结果表明,在不同的极端降水个例和不同的降水时段,雷达—雨量计降水融合产品精度上优于雷达QPE产品,且在降水的空间分布上较雨量站观测格点插值产品更能精细地刻画降水的结构特征。表明算法得到的雷达—雨量计降水融合产品的准确性较高,对极端降水有较好地捕捉和监测能力。     

湖南省雨滴谱仪与常规降水观测对比分析

湖南省97个国家气象站自2017年开始陆续安装了雨滴谱仪,2018年1月1日起进行平行观测。为分析评估其探测降水量的准确性,选取湖南省12个国家站2018年雨滴谱仪观测资料和自动站翻斗雨量计小时降水资料,从总体观测误差、不同降水量级下观测误差和累积降水量观测误差3个方面进行对比分析,结果表明：（1）雨滴谱仪小时降水量和翻斗雨量计小时降水量存在显著的相关性,R2平均为0.94,其中南岳站R2最低为0.90,浏阳站R2最高为0.98,12个站的小时降水量绝对偏差均值为0.34mm;（2）当小时降水量Rh<1.0mm时,各站雨滴谱仪降水量较翻斗雨量计降水量平均偏大0.05mm,且平均差值绝对值均在0.2mm以下;当1.0mm≤Rh<2.6mm时,大部分站点雨滴谱降水量均大于或与翻斗雨量计降水量相当;当2.6mm≤Rh<5.0mm时,株洲和南岳站雨滴谱降水量较翻斗雨量计降水量明显偏小,武冈和娄底站雨滴谱仪降水量则明显偏高;当5.0mm≤Rh<8.0mm时,除株洲和南岳站外,其它各站雨滴谱降水量均大于或与翻斗雨量计降水量相当;当8.0mm≤Rh<16.0mm 时,除株洲和南岳站雨滴谱仪降水量偏小外,其他各站雨滴谱仪降水量均较翻斗雨量计降水量偏大;当Rh≥16.0mm时,雨滴谱仪降水量偏差明显变大,平均偏差绝对值达到3.570mm;（3）雨滴谱仪累计降水量和翻斗雨量计累计降水量变化趋势基本一致,除汨罗和南岳站外,雨滴谱仪累计降水量常表现为偏多。通过分析可见,湖南省雨滴谱仪雨量观测有较好可靠性,可为强降水监测预警、人工影响天气及降水数据订正等提供数据支撑。