Sub-daily scale validation of satellite-based high-resolution rainfall products

Recent research efforts have been geared towards developing high-resolution rainfall products from satellites for hydrological applications. A necessary step in assessing the potential and utility of these products is to quantify the uncertainty associated with them at validation scales appropriate for hydrological applications. The main objective of this paper is to evaluate the accuracy of the widely-known PERSIANN-CCS high-resolution (hourly, 0.04° × 0.04°) satellite rainfall products against high-quality NEXRAD radar rainfall observations in the Little Washita watershed. Our results reveal that (1) PERSIANN-CCS shows high skills in reproducing the patterns of inter-annual rainfall variability on a monthly basis; (2) both at the hourly and storm scales, the performance statistics of PERSIANN-CCS exhibit large spread, suggesting that the quality of PERSIANN-CCS product is almost unique for each hour and storm; and (3) significant improvement in performance statistics is obtained as PERSIANN-CCS products are averaged to longer sub-daily time scales. The implications of our results are: (1) PERSIANN-CCS could be used with high confidence for inter-annual rainfall variability studies; (2) PERSIANN-CCS products need to be accompanied by corresponding hourly error estimates in order to provide meaningful error estimates for hydrological applications; and (3) research is needed to characterize the tradeoff between the quality of rainfall input and the space-time resolution of hydrological modeling, as a function of watershed size and hydrologic model complexity level.     

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.     

Rainfall yield characteristics of electrical storm observed in the Spanish Basque Country area during the period 1992–1996

This paper is focused on the study of rainfall yield characteristics of electrical storms observed over the Northern Iberian Peninsula during 1992–1996. To this aim Principal Components Analysis (PCA) and Self-Organizing Maps (SOM) method have been used. The SOM method is a group of artificial neural networks based on the topological properties of the human brain. Results clearly suggest that there exist three different meteorological patterns that are linked to the characteristics of electrical events found in the study area. In winter, most of the electrical events are formed under oceanic advection (NW air fluxes). On these cases, mean rainfall yield estimates reach values of 700 10⁴ m³ per cloud to ground lightning flash (CG flash). During summer most frequent electrical storms are associated to local instability shooting by surface heating with advection of humidity coming from the Iberian Peninsula. Under these meteorological situations, rain is scarcer if compared with oceanic events but lightning CG counts reach the maximum values found in the area (about 10 CG counts per 20 × 20 km² and day) giving this way the smallest rainfall yield with a mean value of 15 10⁴ m³ per CG flash. Iberian air fluxes associated with cold air in upper parts of the atmosphere represent the third meteorological pattern found. This pattern is most common in spring and autumn but is not unusual in the rest of the seasons. In those cases mean rainfall yield in the area is about 150 10⁴ m³ per CG flash. In all electrical episodes K instability index is greater than 15 °C but in the most lightning producing events, this index reaches in the area values greater than 24 °C. PCA results pointed out that there exists a relationship between rain and CG counts expressed by the first principal component computed from standardized data. However, we must notice that no event is solely linked to this axis, since a seasonal influence which decreases lightning production when rain increases is always present. Results found are of great interest for short term forecasting of flashfloods in mountainous areas like the Spanish Basque Country region.     

Estimating actual rainfall from satellite rainfall products

The lack of uncertainty measures in operational satellite rainfall (SR) products leads to a situation where users of the SR products know that there are significant errors in the products, but they have no quantitative information about the distribution of these errors. The authors propose a semiparametric model to characterize the conditional distribution of actual rainfall (AR) given measures from SR products. The model consists of two components: a conditional gamma density given each SR, and a smooth functional relationship between the gamma parameters and SR. The model is developed for monthly rainfall, estimated from a satellite with sampling frequency once a day, averaged over an area of 512 × 512 km² in the Mississippi River basin. The conditional distribution results are more informative than deterministic SR products since the whole conditional distribution enables users to take appropriate actions according to their own risk assessments and cost/benefit analyses.     

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.     

Analyses of summer lightning activity and precipitation in the Central and Eastern Mediterranean

The lightning activity and precipitation in two 3-hour time intervals in the grid boxes of 0.25 × 0.25° over East and Central Mediterranean during the summer of 2005 and 2006 are analysed. The results show that the frequency distribution of the precipitation amount is shifted towards larger values for the cases with lightning as compared with the cases without lightning. It was found that the number of cases with 3-hour accumulated rainfall greater than 10 mm was bigger when lightning occurred (65%) than when it was absent (35%). Investigation of diurnal and spatial distributions of lightning shows that the afternoon flash density peak is associated mainly with lightning over the land, which is in accordance with the results of earlier works. The early morning flash density peak is associated mainly with flashes over the sea. High correlation coefficients (0.89 during the morning hours and 0.98 during afternoon) were found between rain rate (mm/h) and average flash density (fl/km²) when flash density is averaged in logarithmic intervals of rain rate.     

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.     

Analysis of rainfall characteristics of the Madden–Julian oscillation using TRMM satellite data

Rainfall characteristics of the Madden–Julian oscillation (MJO) are analyzed primarily using tropical rainfall measuring mission (TRMM) precipitation radar (PR), TRMM microwave imager (TMI) and lighting imaging sensor (LIS) data. Latent heating structure is also examined using latent heating data estimated with the spectral latent heating (SLH) algorithm.The zonal structure, time evolution, and characteristic stages of the MJO precipitation system are described. Stratiform rain fraction increases with the cloud activity, and the amplitude of stratiform rain variation associated with the MJO is larger than that of convective rain by a factor of 1.7. Maximum peaks of both convective rain and stratiform rain precede the minimum peak of the outgoing longwave radiation (OLR) anomaly which is often used as a proxy for the MJO convection. Stratiform rain remains longer than convective rain until 4000 km behind the peak of the mature phase. The stratiform rain contribution results in the top-heavy heating profile of the MJO.Associated with the MJO, there are tri-pole convective rain top heights (RTH) at 10–11, 7 and 3 km, corresponding to the dominance of afternoon showers, organized systems, and shallow convections, respectively. The stratiform rain is basically organized with convective rain, having similar but slightly lower RTH and slightly lags the convective rain maximum. It is notable that relatively moderate (7 km) RTH is dominant in the mature phase of the MJO, while very tall rainfall with RTH over 10 km and lightning frequency increase in the suppressed phase. The rain-yield-per flash (RPF) varies about 20–100% of the mean value of 2–10 × 10⁹ kg fl⁻¹ over the tropical warm ocean and that of 2–5 × 10⁹ kg fl⁻¹ over the equatorial Islands, between the convectively suppressed phase and the active phase of MJO, in the manner that RPF is smaller in the suppressed phase and larger in the active phase.     

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。     

Spatial variability of rainfall at an experimental station in Niger,West Africa

Summary Variability of rainfall in the semi-arid regions can cause problems in evaluating experimental trials. To describe the spatial rainfall patterns over a large experimental station, rainfall was monitored during the 1986 and 1987 rainy seasons using 18 raingages over the 500 ha experimental station of ICRISAT Sahelian Center, in Niger, West Africa. Average relative variability of individual rain storms, defined as the percentage deviation from the mean, varied from 2 to 62%, while the variability over the rainy season was 17.1%. Isohyetal patterns of individual rain storms as well as seasonal totals showed distinct coherence in the spatial pattern over the station. The effects of total volume, duration, direction and intensity of storms and the time of year on the spatial correlations were analyzed. Storm value showed a large influence on the correlation decay with distance. Correlations in the W — E and SW — NE directions were higher in comparison to those in the N — S and NW — SE directions. Point rainfall measurements were better correlated with the network average rainfall than with the rainfall recorded at the meteorological station. Variograms among raingages revealed that the distance of independence was approximately 1 000 m for almost all storms. Use of a network of raingages over agricultural experiment stations reduces the average relative variability of areal rainfall estimates and provides a means to develop simple relations for estimation of point rainfall for individual applications.With 8 Figures     

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.     

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.     

Different data,different general circulations? A comparison of selected fields in NCEP/DOE AMIP-II and ECMWF ERA-40 reanalyses

Reanalysis datasets have been very popular for understanding the general circulation as well as verifying general circulation models. The most recent versions of global reanalysis datasets prepared by ECMWF (“ERA-40”) and NCEP (“NDRa2”) are examined in this article. The NDRa2 data are regridded to the resolution (2.5° × 2.5° longitude and latitude) of the ERA-40 public data. Primary variables that both relate to the atmosphere's general circulation and are readily available are compared and contrasted. Significant differences are found in the primary circulation variables and energetics. The zonal mean Hadley cells are stronger in ERA-40, but differences in temperature and moisture make the poleward heat transport by the cells more similar in the two datasets. The subtropical and polar night jet streams are stronger in ERA-40 data as is kinetic energy. The surface energy budgets differ in that ERA-40 data have greater sensible heat flux into the air, while NDRa2 data have greater latent heat flux. The result is NDRa2 has more moisture in the subtropics; ERA-40 data have more moisture in the tropics. Geographically, the two datasets have notable differences in their treatment of the intertropical convergence zone (ICZ). The ICZ over the Atlantic and eastern Pacific is narrower and stronger in ERA-40 data. The ICZ over the western Pacific and Indian oceans is generally stronger in NDRa2 data, one consequence is a stronger tropical easterly jet in NDRa2 data over the Indian Ocean in JJA. Both datasets have a double ICZ in the western half of the Pacific in DJF; in JJA ERA-40 retains that double ICZ but NDRa2 largely does not. Beyond the handling of the ICZ, the datasets differ in tropical zonal mean zonal wind, ERA-40 data in DJF has zonal mean upper troposphere tropical westerlies where NDRa2 data have easterlies; this difference may imply a different amount of interhemispheric communication. The datasets also have strong disagreements in regions of large-scale higher topography.     

广东台风特大暴雨预报

使用台风年鉴，天气图，天星云图等气象资料，对１９６０－１９９１年影响广东的３９例特大暴雨台风进行了对比分析，台风特大暴雨主要是由其云系中的中尺度强降水系统造成，归纳出形成发展的概仿模式和相应环境流场特征，建立预报思路与方法。１９９４年作１５次预报试验，其准确率达７３％。     

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.     

Scaling of tropical rainfall as observed by TRMM precipitation radar

We used a three-year (1998–2000) dataset of TRMM Precipitation Radar observations to investigate the scaling properties of spatial rainfall fields. This dataset allows consideration of spatial scales ranging from about 4.3 km to 138 km and short temporal scales corresponding to the sensor overpasses. The focus is on the marginal spatial moment scaling, which allows estimation of the scaling parameters from a single scene of data. Here we present a global perspective of the scaling properties of tropical rainfall in terms of its spatial variability, atmospheric forcing, predictability, and applicability. Our results reveal the following: 1) the scaling parameters exhibit strong variability associated with land/ocean contrast and mean precipitation at the synoptic scale; 2) there exists a one-to-one relationship between the scaling parameters and the large-scale spatial average rain rate of a universal functional form; 3) the majority of the scenes are consistent with the hypothesis of scale invariance at the moment orders of 0 and 2; 4) relatively there are more scale-invariant rain scenes over land than over ocean; and 5) for the scenes that are non-scale-invariant, deviation from scale-invariance mainly arises from the increasingly intermittent behavior of rainfall as spatial scale decreases. These results have important implications for the development and calibration of downscaling procedures designed to reproduce rainfall properties at different spatial scales and lead to a better understanding of the nature of tropical rainfall at various spatial resolutions.     

A new method to compare hourly rainfall between station observations and satellite products over central-eastern China

This study employs a newly defined regional-rainfall-event (RRE) concept to compare the hourly characteristics of warm-season (May-September) rainfall among rain gauge observations, China merged hourly precipitation analysis (CMPA-Hourly), and two commonly used satellite products (TRMM 3B42 and CMORPH). By considering the rainfall characteristics in a given limited area rather than a single point or grid, this method largely eliminates the differences in rainfall characteristics among different observations or measurements over central-eastern China. The results show that the spatial distribution and diurnal variation of RRE frequency and intensity are quite consistent among different datasets, and the performance of CMPA-Hourly is better than the satellite products when compared with station observations. A regional rainfall coefficient (RRC), which can be used to classify local rain and regional rain, is employed to represent the spatial spread of rainfall in the limited region defining the RRE. It is found that rainfall spread in the selected grid box is more uniform during the nocturnal to morning hours over central-eastern China. The RRC tends to reach its diurnal maximum several hours after the RRE intensity peaks, implying an intermediate transition stage from convective to stratiform rainfall. In the afternoon, the RRC reaches its minimum, implying the dominance of local convections on small spatial scale in those hours, which could cause large differences in rain gauge and satellite observations. Since the RRE method reflects the overall features of rainfall in a limited region rather than at a fixed point or in a single grid, the widely recognized overestimation of afternoon rainfall in satellite products is not obvious, and thus the satellite estimates are more reliable in representing sub-daily variation of rainfall from the RRE perspective. This study proposes a reasonable method to compare satellite products with rain gauge observations on the sub-daily scale, which also has great potential to be used in evaluating the spatiotemporal variation of cloud and rainfall in numerical models.     

青藏高原地区TRMM PR地面降雨率的修正

为掌握并改进青藏高原地区TRMM卫星降水雷达 (precipitation radar,PR) 地面降雨率准确度,统计分析了2005—2007年TRMM PR 2A25资料和逐小时地面雨量计,结果表明:青藏高原地区TRMM PR地面降雨率在层云降水时平均偏低35%,在对流云降水时平均偏高42%。Z-R关系的适用性是PR产生偏差的原因之一,研究将TRMM PR层云降水模型中20℃层Z-R关系的初始系数A和b分别修正为0.0288和0.6752,对流云降水模型中20℃层的初始系数A和b分别修正为0.0406和0.5809,得到两类降水模型0℃层与20℃层之间不同高度Z-R关系的更新系数。检验结果表明,修正降水模型后能够提高青藏高原地面降雨率测量的准确度。     

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.     

Resolving SSM/I-ship radar rainfall discrepancies from AIP-3

The third algorithm intercomparison project （AIP-3） involved rain estimates from more than 50 satellite rainfall algorithms and ground radar measurements within the Intensive Flux Array （IFA） over the equatorial western Pacific warm pool region during the Tropical Ocean Global Atmosphere coupled Ocean-Atmosphere Response Experiment （TOGA COARE）. Early results indicated that there was a sys- tematic bias between rainrates from satellite passive microwave and ground radar measurements. The mean rainrate from radar measurements is about 50% underestimated compared to that from passive microwave-based retrieval algorithms. This paper is designed to analyze rain patterns from the Florida State University rain retrieval algorithm and radar measurements to understand physically the rain discrep- ancies. Results show that there is a clear range-dependent bias associated with the radar measurements. However, this range-dependent systematical bias is almost eliminated with the corrected radar rainrates. Results suggest that the effects from radar attenuation correction, calibration and beam filling are the major sources of rain discrepancies. This study demonstrates that rain retrievals based on satellite mea- surements from passive microwave radiometers such as the Special Sensor of Microwave Imager （SSM/I） are reliable, while rain estimates from ground radar measurements are correctable.