Estimation of surface latent heat fluxes from IRS-P4/MSMR satellite data

The brightness temperatures of the Microwave sensor MSMR (Multichannel Scanning Microwave Radiometer) launched in May 1999 onboard Indian Oceansat-1 IRS-P4 are used to develop a direct retrieval method for latent heat flux by multivariate regression technique. The MSMR measures the microwave radiances at 8 channels at frequencies of 6.6, 10.7, 18 and 21 GHz at both vertical and horizontal polarizations. It is found that the surface LHF (Latent Heat Flux) is sensitive to all the channels. The coefficients were derived using the National Centre for Environmental Prediction (NCEP) reanalysis data of three months: July, September, November of 1999. The NCEP daily analyzed latent heat fluxes and brightness temperatures observed by MSMR were used to derive the coefficients. Validity of the derived coefficients was checked within situ observations over the Indian Ocean and with NCEP analyzed LHF for global points. The LHF derived directly from the MSMR brightness temperature (Tb) yielded an accuracy of 35 watt/m². LHF was also computed by applying bulk formula using the geophysical parameters extracted from MSMR. In this case the errors were higher apparently due to the errors involved in derivation of the geophysical parameters.     

Intercomparison of IRS-P4-MSMR derived geophysical products with DMSP-SSM/I and TRMM-TMI finished products

In this paper, MSMR geophysical products like Integrated Water Vapour (IWV), Ocean Surface Wind Speed (OWS) and Cloud Liquid Water (CLW) in different grids of 50, 75 and 150 kms are compared with similar products available from other satellites like DMSP-SSM/I and TRMMTMI. MSMR derived IWV, OWS and CLW compare well with SSM/I and TMI finished products. Comparison of MSMR derived CLW with that derived from TMI and SSM/I is relatively in less agreement. This is possibly due to the use of 37 GHz in SSM/I and TMI that is highly sensitive to CLW, while 37 GHz channels are not available on MSMR. Monthly comparison of MSMR geophysical products with those from TMI is all carried out for climatological purpose. The monthly comparisons were much better compared to instantaneous comparisons. In this paper, details of the data analysis and comparison results are presented. The usefulness of the MSMR vis-à-vis other sensors is also discussed.     

Estimation of heavy rainfall during cyclonic storms from microwave observations using nonlinear approach over Indian Ocean

In this study, an effort has been made to study heavy rainfall events during cyclonic storms over Indian Ocean. This estimate is based on microwave observations from tropical rainfall measuring mission (TRMM) Microwave Imager (TMI). Regional scattering index (SI) developed for Indian region based on measurements at 19-, 21- and 85-GHz brightness temperature and polarization corrected temperature (PCT) at 85?GHz have been utilized in this study. These PCT and SI are collocated against Precipitation Radar (PR) onboard TRMM to establish a relationship between rainfall rate, PCT and SI. The retrieval technique using both linear and nonlinear regressions has been developed utilizing SI, PCT and the combination of SI and PCT. The results have been compared with the observations from PR. It was found that a nonlinear algorithm using combination of SI and PCT is more accurate than linear algorithm or nonlinear algorithm using either SI or PCT. Statistical comparison with PR exhibits the correlation coefficients (CC) of 0.68, 0.66 and 0.70, and root mean square error (RMSE) of 1.78, 1.96 and 1.68?mm/h from the observations of SI, PCT and combination of SI and PCT respectively using linear regressions. When nonlinear regression is used, the CC of 0.73, 0.71, 0.79 and RMSE of 1.64, 1.95, 1.54?mm/h are observed from the observations of SI, PCT and combination of SI and PCT, respectively. The error statistics for high rain events (above 10?mm/h) shows the CC of 0.58, 0.59, 0.60 and RMSE of 5.07, 5.47, 5.03?mm/h from the observations of SI, PCT and combination of SI and PCT, respectively, using linear regression, and on the other hand, use of nonlinear regression yields the CC of 0.66, 0.64, 0.71 and RMSE of 4.68, 5.78 and 4.02?mm/h from the observations of SI, PCT and combined SI and PCT, respectively.     

Artificial neural network approach for estimation of surface specific humidity and air temperature using multifrequency scanning microwave radiometer

Microwave sensor MSMR (Multifrequency Scanning Microwave Radiometer) data onboard Oceansat-1 was used for retrieval of monthly averages of near surface specific humidity (Q _a) and air temperature (T _a) by means of Artificial Neural Network (ANN). The MSMR measures the microwave radiances in 8 channels at frequencies of 6.6, 10.7, 18 and 21 GHz for both vertical and horizontal polarizations. The artificial neural networks (ANN) technique is employed to find the transfer function relating the input MSMR observed brightness temperatures and output (Q _a andT _a) parameters. Input data consist of nearly 28 months (June 1999 – September 2001) of monthly averages of MSMR observed brightness temperature and surface marine observations ofQ _a andT _a from Comprehensive Ocean-Atmosphere Data Set (COADS). The performance of the algorithm is assessed with independent surface marine observations. The results indicate that the combination of MSMR observed brightness temperatures as input parameters provides reasonable estimates of monthly averaged surface parameters. The global root mean square (rms) differences are 1.0‡C and 1.1 g kg⁻¹ for air temperature and surface specific humidity respectively.     

Radiative transfer simulations for the MADRAS imager of Megha-Tropiques

This paper reports the radiative transfer simulations for the passive microwave radiometer onboard the proposed Indian climate research satellite Megha-Tropiques due to be launched in 2011. These simulations have been performed by employing an in-house polarized radiative transfer code for raining systems ranging from depression and tropical cyclones to the Indian monsoon. For the sake of validation and completeness, simulations have also been done for the Tropical Rainfall Measuring Mission (TRMM)’s Microwave Imager (TMI) of the highly successful TRMM mission of NASA and JAXA. The paper is essentially divided into two parts: (a) Radiometer response with specific focus on high frequency channels in both the radiometers is discussed in detail with a parametric study of the effect of four hydrometeors (cloud liquid water, cloud ice, precipitating water and precipitating ice) on the brightness temperatures. The results are compared with TMI measurements wherever possible. (b) Development of a neural network-based fast radiative transfer model is elucidated here. The goal is to speed up the computational time involved in the simulation of brightness temperatures, necessitated by the need for quick and online retrieval strategies. The neural network model uses hydrometeor profiles as inputs and simulates spectral microwave brightness temperature at multiple frequencies as output. A huge database is generated by executing the in-house radiative transfer code for seven different cyclones occurred in North Indian Ocean region during the period 2001–2006. A part of the dataset is used to train the network while the remainder is used for testing purposes. For the purpose of testing, a typical scene from the southwest monsoon rain is also considered. The results obtained are very encouraging and show that the neural network is able to mimic the underlying physics of the radiative transfer simulations with a correlation coefficient of over 99%.     

Polarized microwave forward model simulations for tropical storm <Emphasis Type="Italic">Fanoos</Emphasis>

In the present study, forward radiative transfer simulations are carried out for the tropical cyclone Fanoos that hit the coast off south India in December 2005. The in-house radiative transfer package used for this study employs the doubling and adding method to calculate radiances leaving the top of the one dimensional precipitating atmosphere. The particle drop size distribution is assumed to follow a modified gamma distribution in respect of the cloud liquid water and cloud ice water content. For precipitation, the Marshall-Palmer particle size distribution is used. All the hydrometeor particles are assumed to be spherical and Lorentz Mie theory is used to evaluate the interaction parameters like absorption, scattering coefficients and polarized scattering matrix. In order to validate the drop size distributions and interaction parameter calculations, the simulated brightness temperatures are compared with the TMI measured brightness temperatures for all the channels. For carrying out this exercise, vertical hydrometeors retrieved by TMI are used as input. The differences between simulated and measured brightness temperatures are found to be within ±10%. The maximum difference in the brightness temperatures between the present work and the Eddington model which the TRMM algorithm employs is about 4.5K. This may become significant when retrieval of precipitation is attempted by combining the forward model with a suitable retrieval strategy, under tropical conditions.     

卫星资料在暴雨数值模拟中的应用研究

分析研究了2001年5月15日～8月15日3个月GMS卫星资料在湖南资水流域实时数值预报中的应用以及将TRMM(Tropical Rainfall Measuring Mission)卫星上的TMI(Microwave Imager)雨水资料适时融入数值模式改变当时模式中雨水分布场,数值模拟还研究了发生在淮河流域的10次暴雨过程。结果表明:资水流域3个月的实时预报效果良好,准确预报出其中出现的3次致洪暴雨和1次特大暴雨;对淮河流域暴雨,由于TMI资料空间分辨率较高,能够很好地反映中小尺度系统的空间结构,加入模式后使得模拟出来的降雨强度,雨量中心时空分布更接近实际情况,10次暴雨过程的TS评分较不使用TMI资料更好。     

A diagnosis of rainfall over South America during 1997/98 El Niño and 1998/99 La Niña events: Comparison between TRMM PR and GPCP rainfall estimates

A comparison between TRMM PR rainfall estimates and rain gauge data from ANEEL and combined gauge/satellite data from GPCP over South America (SA) is made. In general, the annual and seasonal regional characteristics of rainfall over SA are qualitatively well reproduced by TRMM PR and GPCP. It is found that over most of SA GPCP exceeds TRMM PR rainfall. The largest positive differences between GPCP and TRMM PR data occur in the north SA, northwestern and central Amazonia. However, there are regions where GPCP rainfall is lower than TRMM PR, particularly in the Pacific coastal regions and in southern Brazil. We suggest that the cause for the positive differences GPCP minus TRMM PR rainfall are related to the fact that satellite observations based on infrared radiation and outgoing longwave radiance sensors overestimate convective rainfall in GPCP and the cause for the negative differences are due to the random errors in TRMM PR. Rainfall differences in the latter phases of the 1997/98 El Niño and 1998/99 La Niña are analyzed. The results showed that the rainfall anomalies are generally higher in GPCP than in TRMM PR, however, as in the mean annual case, there are regions where the rainfall in GPCP is lower than in TRMM PR. The higher positive (negative) differences between the rainfall anomalies in GPCP and TRMM PR, which occur in the central Amazonia (southern Brazil), are reduced (increased) in the El Niño event. This is due to the fact that during the El Niño episode the rainfall decreases in the central Amazonia and increases in the southern Brazil. Consequently, the overestimation of the convective rainfall by GPCP is reduced and the overestimation of the rainfall by TRMM PR is increased in these two regions, respectively.     

Atmospheric correction for sea surface temperature retrieval from single thermal channel radiometer data onboard Kalpana satellite

An atmospheric correction method has been applied on sea surface temperature (SST) retrieval algorithm using Very High Resolution Radiometer (VHRR) single window channel radiance data onboard Kalpana satellite (K-SAT). The technique makes use of concurrent water vapour fields available from Microwave Imager onboard Tropical Rainfall Measuring Mission (TRMM/TMI) satellite. Total water vapour content and satellite zenith angle dependent SST retrieval algorithm has been developed using Radiative Transfer Model [MODTRAN ver3.0] simulations for Kalpana 10.5–12.5 μm thermal window channel. Retrieval of Kalpana SST (K-SST) has been carried out for every half-hourly acquisition of Kalpana data for the year 2008 to cover whole annual cycle of SST over Indian Ocean (IO). Validation of the retrieved corrected SST has been carried out using near-simultaneous observations of ship and buoys datasets covering Arabian Sea, Bay of Bengal and IO regions. A significant improvement in Root Mean Square Deviation (RMSD) of K-SST with respect to buoy (1.50–1.02 K) and to ship datasets (1.41–1.19 K) is seen with the use of near real-time water vapour fields of TMI. Furthermore, comparison of the retrieved SST has also been carried out using near simultaneous observations of TRMM/TMI SST over IO regions. The analysis shows that K-SST has overall cold bias of 1.17 K and an RMSD of 1.09 K after bias correction.     

Satellite-based rainfall estimation and discharge measurement of Middle Indus River,Pakistan

The impacts of floods and droughts are intensified by climate change, lack of preparedness, and coordination. The average rainfall in study area is ranging from 200 to 400 mm per year. Rain gauge generally provides very accurate measurement of point rain rates and the amounts of rainfall but due to scarcity of the gauge locations provides very general information of the area on regional scale. Recognizing these practical limitations, it is essential to use remote sensing techniques for measuring the quantity of rainfall in the Middle Indus. In this research, Tropical Rainfall Measuring Mission (TRMM) estimation can be used as a proxy for the magnitude of rainfall estimates from classical methods (rain gauge), quantity, and its spatial distribution for Middle Indus river basin. In order to use TRMM satellite data for discharge measurement, its accuracy is determined by statistically comparing it with in situ gauged data on daily and monthly bases. The daily R ² value (0.42) is significantly lower than monthly R ² value (0.82), probably due to the time of summation of TRMM 3-hourly precipitation data into daily estimates. Daily TRMM data from 2003 to 2012 was used as input forcing in Soil and Water Assessment Tool (SWAT) hydrological model along with other input parameters. The calibration and validation results of SWAT model give R ² = 0.72 and 0.73 and Nash-Sutcliffe coefficient of efficiency = 0.69 and 0.65, respectively. Daily and monthly comparison graphs are generated on the basis of model discharge output and observed data.     

Western disturbances seen with AMSU-B and infrared sensors

Western disturbances (WD) of winter and pre-monsoon seasons are the important sources of rainfall in the Indo-Gangetic plains. WDs are troughs or circulations in the westerly winds modified by the Himalayas. Operationally, WDs are monitored using infrared (IR) and water vapour (WV) images. Advanced Microwave Sounding Unit-B (AMSU-B), flying onboard the NOAA satellites, also allows WDs to be monitored in five microwave frequencies. Two are in water vapour window (89, 150 GHz) and three are absorption channels (centred at 183.31 GHz). Unlike the top of cloud view in IR or WV, AMSU-B radiances show the effect of moisture and hydrometeors in different layers. Two cases of WD (17 April 2001 and 18–19 February 2003) are discussed using the microwave data from AMSU-B and the IR and WV data from Meteosat-5. The aim here is to demonstrate the skill of AMSU-B in delineating structure of WDs. In particular, the cold intrusion and the moist conveyor belts are examined. It was found that the multi-channel view of the AMSU-B permits a better understanding of the moist structures seen in the conveyor belts. The à trous wavelet transform is used to clearly bring out mesoscale features in WDs. AMSU-B brings out intense convection as a large depression of BTs (>50K) at 150/176 GHz, cirrus and moist bands at 180/182 GHz. Mesoscale convection lines within WDs that last short time are shown here for the first time only in the AMSU-B images. Large-scale cirrus features are separated using the à trous wavelet transform. Lastly, it is shown that there is a good likeness in the rain contours in the 3-h rain 3B42 (computed from TRMM and other data) to AMSU-B depressions in BT. Overall, AMSU-B shows better skill in delineating the structure of clouds and rain in WDs.     

卫星降雨数据在高山峡谷地区的代表性与可靠性

以长江上游金沙江流域典型高山峡谷地区为研究对象,用该区域地面观测降雨量数据对TRMM PR 3B42 V6产品进行了3 h、日、月3个时间尺度的有效性评估,旨在为开展区域卫星与地面降水数据融合的流域水文模拟及预报奠定数据基础。分别采用了线性回归方法分析降雨量相关性、经验正交函数-奇异值分解方法(EOF-SVD)分析降雨量主要模态空间分布特征、相对偏差B_ias、错报率R_FA和探测率P_D指标对该卫星产品进行了精度评定。研究结果表明:研究区该卫星产品与地面观测数据在3个时间尺度存在显著的线性时间和空间相关性,但相关程度随时间尺度的减小而减弱;二者在空间分布上总体具有一致性特征,但在高海拔、大坡度区域表现出较为显著的差异;相对偏差指标显示2008-2010年降雨量均值相对偏差在±10%的概率密度百分数为36.08%;随高程的增加,卫星数据R_FA呈逐渐增加趋势变化,P_D呈逐渐减小趋势变化;总体上小雨对误差的贡献最大,大雨峰值误差贡献次之,时段降雨量偏差随时间尺度的增加逐渐减小,而随高程的增加卫星数据的探测精度下降。因此,对于类似的高山峡谷流域,要应用该卫星产品进行日、3 h尺度水文模拟及预报,有必要对流域卫星数据和地面观测数据进行融合,充分发挥两种数据的优势。     

TRMM卫星降水数据在洣水流域径流模拟中的应用

采用地面雨量站点观测降水作为基准数据,评估热带降雨观测计划（TRMM）最新一代卫星降水产品3B42V7的精度;利用站点和卫星两种降水数据驱动栅格新安江模型,采用SCEM-UA算法考虑模型参数不确定性,进行流量过程模拟,评估TRMM 3B42V7在流域水文模拟和预报中的应用能力。数据精度评估显示:在平均意义上,TRMM 3B42V7日降水精度较高,较站点观测低估了6.68%;但在绝对值意义上,TRMM 3B42V7日降水精度较低,绝对偏差达到57.76%;TRMM 3B42V7经过了地面月降水量偏差校准,其精度在月尺度上有较大提高。径流模拟结果表明:TRMM 3B42V7模拟的日径流过程精度较低,有部分洪峰没有捕捉到,但仍能表征径流的日变化特征;月尺度上模拟径流与实测径流吻合较好,能表征径流的季节性和年内变化特征;计算的日尺度和月尺度95%置信区间包含大部分实测流量过程。     

Using cloud water path and cloud top temperature for estimating convective and stratiform rainfall from SEVIRI daytime data

A satellite rainfall retrieval technique is proposed here. The relationships of rain rate with each of cloud water path (CWP) and cloud top temperature (CTT) are investigated. The CWP and CTT are retrieved from SEVIRI data (spinning enhanced visible and infrared imager), and corresponding rain rates are measured by weather radar. The rain rates are compared to corresponding CWP and then to corresponding CTT. The investigation demonstrates an exponential functional dependency between rain rates and CWP for low and moderate rain rates (stratiform rainfall). Conversely, the rain rates are more closely related to CTT for high rain rates (convective rainfall). Therefore, two separate relationships are established for rain rate retrievals. The results show rain rates estimated by the developed scheme are in good correlation with those observed by weather radar.     

Assimilation of IRS-P4 (MSMR) meteorological data in the NCMRWF global data assimilation system

Oceansat-1 was successfully launched by India in 1999, with two payloads, namely Multi-frequency Scanning Microwave Radiometer (MSMR) and Ocean Color Monitor (OCM) to study the biological and physical parameters of the ocean. The MSMR sensor is configured as an eight-channel radiometer using four frequencies with dual polarization. The MSMR data at 75 km resolution from the Oceansat-I have been assimilated in the National Centre for Medium Range Weather Forecasting (NCMRWF) data assimilation forecast system. The operational analysis and forecast system at NCMRWF is based on a T80L18 global spectral model and Spectral Statistical Interpolation (SSI) scheme for data analysis. The impact of the MSMR data is seen globally, however it is significant over the oceanic region where conventional data are rare. The dry-nature of the control analyses have been removed by utilizing the MSMR data. Therefore, the total precipitable water data from MSMR has been identified as a very crucial parameter in this study. The impact of surface wind speed from MSMR is to increase easterlies over the tropical Indian Ocean. Shifting of the positions of westerly troughs and ridges in the south Indian Ocean has contributed to reduction of temperature to around 30‡S.     

基于NoSREx实验的积雪演化及微波辐射分析研究

在被动微波雪水当量反演中,积雪物理参数随时间的变化特征影响着反演精度,为理解积雪随时间演化的特征及其对微波辐射亮温的影响,本研究选用2009—2013年北欧积雪实验（Nordic Snow Radar Experiment, NoSREx）积雪地面观测和微波辐射测量数据,通过雪深和温度把雪期分为积累期（10月—次年2月）、稳定期（2—4月）和消融期（4—5月）,发现各个雪期的积雪演化特征为：雪颗粒形状在积累期前期以融态颗粒（Melt Forms, MF）为主,积累期后期和稳定期以圆形颗粒、片状颗粒、深霜为主,消融期以MF为主;整个雪季底层雪粒径从小变大再变小的过程,粒径最大值出现在稳定期的2至3月,约为2.5~4.0 mm,均出现在近地表雪层,而表层粒径较小且较为稳定。通过雪深和微波亮度差（18~37 GHz）的关系分析,表明亮温差在不同雪期对于雪深的依赖关系不同,在积累期和稳定期,雪深变化与亮温差变化具有明显的正相关;在消融期由于积雪融化的影响,其相关性较差;基于多层积雪微波辐射模型（MEMLS）构建了一维微波辐射模拟环境,模拟表明MEMLS模型在3个雪期的垂直极化10.65 GHz和18.7 GHz模拟结果较37 GHz和90 GHz更好;10.65 GHz V极化在入射角为50°且稳定期时,微波亮温模拟均方根误差（RMSE）结果最小,为2.49 K。3个雪期90 GHz模拟结果水平极化优于垂直极化,由于受表层积雪变化影响,90 GHz模拟结果较不稳定,尤其是消融期时,RMSE最小也达到了42.7 K。本研究有助于理解积雪随时间演化的特征及其对微波辐射模拟的影响,表明在被动微波雪水当量反演算法中,针对不同积雪期需要考虑积雪演化动态过程。     

Impact of satellite observed microwave SST on the simulation of tropical cyclones

The impact of realistic representation of sea surface temperature (SST) on the numerical simulation of track and intensity of tropical cyclones formed over the north Indian Ocean is studied using the Weather Research and Forecast (WRF) model. We have selected two intense tropical cyclones formed over the Bay of Bengal for studying the SST impact. Two different sets of SSTs were used in this study: one from TRMM Microwave Imager (TMI) satellite and other is the weekly averaged Reynold’s SST analysis from National Center for Environmental Prediction (NCEP). WRF simulations were conducted using the Reynold’s and TMI SST as model boundary condition for the two cyclone cases selected. The TMI SST which has a better temporal and spatial resolution showed sharper gradient when compared to the Reynold’s SST. The use of TMI SST improved the WRF cyclone intensity prediction when compared to that using Reynold’s SST for both the cases studied. The improvements in intensity were mainly due to the improved prediction of surface latent and sensible heat fluxes. The use of TMI SST in place of Reynold’s SST improved cyclone track prediction for Orissa super cyclone but slightly degraded track prediction for cyclone Mala. The present modeling study supports the well established notion that the horizontal SST gradient is one of the major driving forces for the intensification and movement of tropical cyclones over the Indian Ocean.     

青藏高原积雪深度和雪水当量的被动微波遥感反演

利用1993年1月份的SSM/I亮度温度数据反演了青藏高原的雪水当量,首先使用被动微波SSM/I数据19和37GHz的水平极化数据来反演雪深,根据积雪时间的函数来计算实时的雪密度,由雪的深度和密度计算出雪水当量.最后,利用SSM/I数据的19和37GHz的垂直极化亮度温度梯度对计算出的雪水当量进行回归分析,得到了利用SSM/I数据直接反演雪水当量的算法.     

Comparison of TRMM-based flood indices for Gaziantep,Turkey

Floods are the most common natural disasters threatening the welfare of humanity. Gaziantep, a city located in a semi-arid region of Turkey, is occasionally flooded, and in May 2014, a flood not only caused property damage, but also resulted in the death of a lady who became trapped in flood waters. The fatality and property damage of flash floods arise from the limited response time for remediation. Despite improvements in numerical weather predictions, forecasting flash floods is not easy. Due to its frequent observations, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) real-time (RT) 3B42RT data are tested for Gaziantep flood predictions in this study. During TRMM era, six floods occurred in Gaziantep. Three-hourly 3B42RT data covering the 2000- to 2014-year period indicated high rain rates during months in which floods were observed. Also daily variation of rainfall was well represented. High-intensity rain (HIR), cumulative distribution functions (CDF) and Gaziantep Flood Index (GAFI) indices are developed for flood characterization. HIR, calculated as 10 mm/h, detected October and December of 2010 floods. CDFs with 99, 98.5, 95 and 91.3% indicated 4 floods occurred in August 2005, June 2007, October 2010 and December 2010, respectively. GAFI was able to detect 4 out of 6 occurrences (August 2005, June 2007, October 2010 and December 2010) as values ranging from 1 to 2.63 are selected for monthly precipitation. In the missed occurrence, 3B42RT did not indicate any rainfall. Although only rain rates are used in flood characterization, the results are promising, and the simplicity of the methodology favors its usage. Also, methodology can easily be implemented to TRMM following missions such as Global Precipitation Measurement Mission.     

Daily rainfall statistics of TRMM and CMORPH: A case for trans-boundary Gandak River basin

Satellite precipitation products offer an opportunity to evaluate extreme events (flood and drought) for areas where rainfall data are not available or rain gauge stations are sparse. In this study, daily precipitation amount and frequency of TRMM 3B42V.7 and CMORPH products have been validated against daily rain gauge precipitation for the monsoon months (June–September or JJAS) from 2005–2010 in the trans-boundary Gandak River basin. The analysis shows that the both TRMM and CMORPH can detect rain and no-rain events, but they fail to capture the intensity of rainfall.