Rainfall Estimation from Combined Observations Using KALPANA-IR and TRMM- Precipitation Radar Measurements over Indian Region

In the present study an attempt has been made to improve the rainfall estimation technique developed recently by Mishra et al. (2009a, 2009b) based on KALPANA and Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) data over the Indian land and oceanic region. The algorithm for rainfall estimation was basically based on synergistically analyzing the thermal infra-red radiances from Kalpana/INSAT data along with the high resolution, horizontal and vertical rainfall estimates from PR. Presently the augmentation is based on the data base of precipitable water and relative humidity from National Centre for Environmental Prediction-Global forecast System (NCEP-GFS) data as a background field to correct for the biases in earlier algorithm. The algorithm is tested for many case studies of monsoon rainfall over India and adjoining oceanic regions. The rainfall from the present scheme is compared with the standard TRMM-3B42 rain product. The validation with the Automatic Weather Station (AWS) rain gauge and the Global Precipitation and Climatology Project (GPCP) version 2 rain products shows that the present scheme is able to retrieve the rainfall with a very good accuracy. These studies are aimed at the rainfall retrievals in near future from both INSAT-3D and Megha-Tropiques, IR and MW imagers respectively.     

Assessment of Kalpana-1 Rainfall Product over Indian Meteorological Sub-Divisions During the Summer Monsoon Season

In this paper, Kalpana-1 derived INSAT Multispectral Rainfall Algorithm (IMSRA) rainfall estimates are compared with two multisatellite rainfall products namely, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 and Global Satellite Mapping of Precipitation (GSMaP), and India Meteorological Department (IMD) surface rain gauge (SRG)-based rainfall at meteorological sub-divisional scale over India. The performance of the summer monsoon rainfall of 2013 over Indian meteorological sub-divisions is assessed at different temporal scales. Comparison of daily accumulated rainfall over India from IMSRA shows a linear correlation of 0.72 with TMPA-3B42 and 0.70 with GSMaP estimates. IMSRA is capable to pick up daily rainfall variability over the monsoon trough region as compared to TMPA-3B42 and GSMaP products, but underestimates moderate to heavy rainfall events. Satellite-derived rainfall maps at meteorological sub-divisional scales are in reasonably good agreement with IMD-SRG based rainfall maps with some exceptions. However, IMSRA performs better than GSMaP product at meteorological sub-divisional scale and comparable with TMPA data. All the satellite-derived rainfall products underestimate orographic rainfall along the west coast, the Himalayan foothills and over the northeast India and overestimate rainfall over the southeast peninsular India. Overall results suggest that IMSRA estimates have potential for monsoon rainfall monitoring over the Indian meteorological sub-divisions and can be used for various hydro-meteorological applications.     

Diurnal rainfall variability over the Upper Blue Nile Basin: A remote sensing based approach

In this study we aim to assess the diurnal cycle of rainfall across the Upper Blue Nile (UBN) basin using satellite observations from Tropical Rainfall Measuring Mission (TRMM). Seven years (2002–2008) of Precipitation Radar (PR) and TRMM Microwave Imager (TMI) data are used and analyses are based on GIS operations and simple statistical techniques. Observations from PR and TMI reveal that over most parts of the basin area, the rainfall occurrence and conditional mean rain rate are highest between mid- and late-afternoon (15:00–18:00 LST). Exceptions to this are the south-west and south-eastern parts of the basin area and the Lake Tana basin where midnight and early morning maxima are observed. Along the Blue Nile River gorge the rainfall occurrence and the conditional mean rain rate are highest during the night (20:00–23:00 LST). Orographic effects by large scale variation of topography, elevation and the presence of the UBN river gorge were assessed taking two transects across the basin. Along transects from north to south and from east to west results indicate increased rainfall with increase of elevation whereas areas on the windward side of the high mountain ranges receive higher amount of rainfall than areas on the leeward side. As such, mountain ranges and elevation affect the rainfall distribution resulting in rain shadow effect in the north-eastern parts of Choke-mountain and the ridges in the north-east of the basin. Moreover, a direct relation between rainfall occurrence and elevation is observed specifically for 17:00–18:00 LST. Further, results indicate that the rainfall distribution in the deeply incised and wide river gorge is affected with relatively low rainfall occurrence and low mean rainfall rates in the gorge areas. Seasonal mean rainfall depth is highest in the south-west area and central highlands of the basin while areas in the north, north-east and along the Blue Nile gorge receive the least amount of rainfall. Statistical results of this work show that the diurnal cycle of rainfall occurrence from TRMM estimates show significant correlation with the ground observations at 95% confidence level. In the UBN basin, the PR conditional mean rain rate estimates are closer to the ground observations than the TMI. Analysis on mean wet season rainfall amount indicates that PR generally underestimates and TMI overestimates the ground observed rainfall.     

A Case Study: Heavy Rainfall Event Comparison Between Daily Satellite Rainfall Estimation Products with IMD Gridded Rainfall Over Peninsular India During 2015 Winter Monsoon

India Meteorological department (IMD) used INSAT-3D Metrological Satellite Imager data to drive two type rainfall estimation products viz-Hydro Estimate (HE) and INSAT Multi-Spectral Rainfall Algorithm (IMSRA) on half hourly rainfall rate and daily accumulated rainfall in millimeter (mm). Integrated Multi-Satellite Retrieval for GPM (IMERG) product is being derived by NASA and JAXA by using Global Precipitation Mission (GPM) satellites data. IMSRA and GPM (IMERG) are gridded data at 10 km spatial resolution and HE is available at pixel level (4 km at Nadir). IMD provides gridded rainfall data at 0.25° × 0.25° resolution which is based on wide coverage of 6955 actual observation. In present study, validation of INSAT-3D based Hydro Estimator (HE), INSAT Multi-Spectral Rainfall Algorithm (IMSRA) and Integrated Multi-Satellite Retrieval for GPM (IMERG) of Global Precipitation Mission (GPM) satellites are carried out with IMD gridded data set for heavy rainfall event during winter monsoon, over peninsular India (November–December 2015). In validation, Nash–Sutcliffe efficiencies (NSE), RMSE, Correlation, Skilled scores are calculated at grid level for heavy and very heavy rain categories and the values of NSE of HE (? 32.36, ? 3.12), GPM (? 68.67, ? 2.39) and IMSRA (? 0.02, 0.28) on 16th November 2015 and HE (? 13.65, ? 1.69), GPM (? 43.79, ? 2.94) and IMSRA (? 1.08, ? 1.60) on 2nd Dec 2015, for heavy and very heavy rainfall. On both days, HE is showing better rainfall estimate compare to GPM for Heavy rainfall and GPM showing better estimation for very heavy rainfall events. In all the cases IMSRA is underestimating, if daily rain fall exceeded 75 mm.     

Evaluation of Precipitation Features in High-Frequency SSM/I Measurements Over Indian Land and Oceanic Regions

Evaluation of the Special Sensor Microwave Imager (SSM/I) precipitation features is presented from various passes over Indian oceanic and land regions under the framework of radiative transfer simulations for both emission and scattering atmospheres. There is considerable uncertainty in the interpretation of the SSM/I high-frequency scattering features for development of rainfall algorithms. Specifically large areas of very low emissivity regimes showing false rain signatures due to the presence of low brightness temperatures (TBs) that are often present in the vicinity of colder sea surface areas in the 85-GHz TB from SSM/I. In this connection, the Polarization Corrected Temperature (PCT) defined by Spencer using the 85-GHz channels, vertical (V) and horizontal (H), has been studied to delineate these surface effects. These false scattering signatures, once corrected using PCT as a suitable parameter, significantly improve the quality of the SSM/I-derived precipitation areas. These results are also confirmed with the cloud optical depth data from the Moderate Resolution Imaging Spectroradiometer sensor onboard the Aqua satellite and the standard merged rain product (geostationary infrared and Tropical Rainfall Measuring Mission (TRMM) microwave data) 3B42 from TRMM. This letter is aimed for selecting PCT as one of the suitable predictor variables for the development of operational rainfall retrieval algorithm for the Indo-French Megha-Tropiques satellite's microwave radiometer at high frequencies.      

Effect of Long Waves on Ku-Band Ocean Radar Backscatter at Low Incidence Angles Using TRMM and Altimeter Data

This letter uses a large ocean satellite data set to document relationships between Ku-band radar backscatter (sigma_o) of the sea surface, near-surface wind speed (U), and ocean wave height (SWH). The observations come from satellite crossovers of the Tropical Rainfall Mapping Mission (TRMM) Precipitation Radar (PR) and two satellite altimeters, namely: 1) Jason-1 and 2) ENVISAT. At these nodes, we obtain TRMM clear-air normalized radar cross-section data along with coincident altimeter-derived significant wave height. Wind speed estimates come from the European Centre for Medium-Range Weather Forecast. TRMM PR is the first satellite to measure low incidence Ku-band ocean backscatter at a continuum of incidence angles from 0deg to 18deg. This letter utilizes these global ocean data to assess hypotheses developed in past theoretical and field studies.     

Investigating flash floods potential areas using ASCAT and TRMM satellites in the Western Cape Province,South Africa

The aim of the study was to evaluate flash flood potential areas in the Western Cape Province of South Africa, by integrating remote sensing products of high rainfall intensity, antecedent soil moisture and topographic wetness index (TWI). Rainfall has high spatial and temporal variability, thus needs to be quantified at an area in real time from remote sensing techniques unlike from sparsely distributed, point gauge network measurements. Western Cape Province has high spatial variation in topography which results in major differences in received rainfall within areas not far from each other. Although high rainfall was considered as the major cause of flash flood, also other contributing factors such as topography and antecedent soil moisture were considered. Areas of high flash flood potential were found to be associated with high rainfall, antecedent precipitation and TWI. Although TRMM 3B42 was found to have better rainfall intensity accuracy, the product is not available in near real time but rather at a rolling archive of three months; therefore, Multi- sensor precipitation estimate rainfall estimates available in near real time are opted for flash flood events. Advanced Scatterometer (ASCAT) soil moisture observations were found to have a reasonable r value of 0.58 and relatively low MAE of 3.8 when validated with in situ soil moisture measurements. The results of this study underscore the importance of ASCAT and TRMM satellite datasets in mapping areas at risk of flooding.     

Inter-comparison of satellite rainfall products for representing rainfall diurnal cycle over the Nile basin

In this study, the authors inter-compared the performance of three satellite-rainfall products in representing the diurnal cycle of rain occurrence and rain rate over the Nile basin in eastern Africa. These products are the real time (RT) and post-real-time (PRT) (bias adjusted) versions of Tropical Rainfall Measuring Mission (TRMM) and other sources product known as TRMM-3B42 and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) product which is based on the CPC morphing technique (CMORPH). The rainfall diurnal cycles are re-produced using these products with specific focus on assessing effects of geographic location and topographic features. The performance of the satellite products in representing rainfall diurnal cycle shows large variation over the Nile basin. The products overestimate rain occurrence over the lakes, islands, and shores and underestimate occurrence over mountain tops. Overall, CMORPH performs better than TRMM-3B42 RT and TRMM-3B42 PRT in capturing the diurnal cycle of rain rate in Lake Tana basin. However, the difference between the two products is very small for Lake Victoria basin, where both products perform more favorably. Over most of the Nile basin areas, the use of fine versus coarse temporal and spatial resolution of the CMORPH product showed large differences for diurnal cycle of rain occurrence than that of rain rate. Results also show that the bias adjustment of TRMM-3B42 product does not necessarily bring improvements probably since the adjustments are not performed based on local rain gauge data.     

Flood forecasting in Niger-Benue basin using satellite and quantitative precipitation forecast data

Availability of reliable, timely and accurate rainfall data is constraining the establishment of flood forecasting and early warning systems in many parts of Africa. We evaluated the potential of satellite and weather forecast data as input to a parsimonious flood forecasting model to provide information for flood early warning in the central part of Nigeria. We calibrated the HEC-HMS rainfall-runoff model using rainfall data from post real time Tropical Rainfall Measuring Mission (TRMM) Multi satellite Precipitation Analysis product (TMPA). Real time TMPA satellite rainfall estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) rainfall products were tested for flood forecasting. The implication of removing the systematic errors of the satellite rainfall estimates (SREs) was explored. Performance of the rainfall-runoff model was assessed using visual inspection of simulated and observed hydrographs and a set of performance indicators. The forecast skill was assessed for 1–6 days lead time using categorical verification statistics such as Probability Of Detection (POD), Frequency Of Hit (FOH) and Frequency Of Miss (FOM). The model performance satisfactorily reproduced the pattern and volume of the observed stream flow hydrograph of Benue River. Overall, our results show that SREs and rainfall forecasts from weather models have great potential to serve as model inputs for real-time flood forecasting in data scarce areas. For these data to receive application in African transboundary basins, we suggest (i) removing their systematic error to further improve flood forecast skill; (ii) improving rainfall forecasts; and (iii) improving data sharing between riparian countries.     

Spatial analysis of human-induced vegetation productivity decline over eastern Africa using a decade (2001–2011) of medium resolution MODIS time-series data

Climate variation and land transformations related to exploitative land uses are among the main drivers of vegetation productivity decline and ongoing land degradation in East Africa. We combined analysis of vegetation trends and cumulative rain use efficiency differences (CRD), calculated from 250-m MODIS NDVI time-series data, to map vegetation productivity loss over eastern Africa between 2001 and 2011. The CRD index values were furthermore used to discern areas of particular severe vegetation productivity loss over the observation period. Monthly 25-km Tropical Rainfall Measuring Mission (TRMM) data metrics were used to mask areas of rainfall declines not related to human-induced land productivity loss. To provide insights on the productivity decline, we linked the MODIS-based vegetation productivity map to land transformation processes using very high resolution (VHR) imagery in Google Earth (GE) and a Landsat-based land-cover change map. In total, 3.8 million ha experienced significant vegetation loss over the monitoring period. An overall agreement of 68% was found between the rainfall-corrected MODIS productivity decline map and all reference pixels discernable from GE and the Landsat map. The CRD index showed a good potential to discern areas with ‘severe’ vegetation productivity losses under high land-use intensities.     

Detecting Drought-Prone Areas of Rice Agriculture Using a MODIS-Derived Soil Moisture Index

This study examined the use of remote sensing in detecting and assessing drought in Iloilo Province, Philippines. A remote sensing-based soil moisture index (SMI), rainfall anomaly data from the Tropical Rainfall Measuring Mission (TRMM), and rice production departure (P_d ) data were used for drought detection and validation. The study was conducted using two drought years (2001, 2005) and one non-drought year (2002). According to SMI data, the drought distribution was classified into four major groups. SMI values > 0.3 were considered not to be drought and SMI values ≤ 0.3 were classified as slight, moderate, and severe drought. Results based on SMI revealed that the study area experienced drought in 2001 and 2005, while 2002 exhibited no drought. On the other hand, TRMM-based rainfall anomaly data revealed negative values in 2001 and 2005 and positive values in 2002. Below-normal P_d values were observed in 2005 and above-normal values in 2002, whereas nearly normal values prevailed in 2001. Yield indicator data were crucial for the assessment of drought impacts on rice production. In most cases, the pattern of rice production and productivity revealed that the decline in the production or productivity of rice for a particular year coincided with lower SMI values and greater rainfall departure or negative anomaly.     

Annual variations in water storage and precipitation in the Amazon Basin

We combine satellite gravity data from the gravity recovery and climate experiment (GRACE) and precipitation measurements from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center’s (CPC) Merged Analysis of Precipitation (CMAP) and the Tropical Rainfall Measuring Mission (TRMM), over the period from mid-2002 to mid-2006, to investigate the relative importance of sink (runoff and evaporation) and source (precipitation) terms in the hydrological balance of the Amazon Basin. When linear and quadratic terms are removed, the time-series of land water storage variations estimated from GRACE exhibits a dominant annual signal of 250 mm peak-to-peak, which is equivalent to a water volume change of ~1,800 km³. A comparison of this trend with accumulated (i.e., integrated) precipitation shows excellent agreement and no evidence of basin saturation. The agreement indicates that the net runoff and evaporation contributes significantly less than precipitation to the annual hydrological mass balance. Indeed, raw residuals between the de-trended water storage and precipitation anomalies range from ±40 mm. This range is consistent with stream-flow measurements from the region, although the latter are characterized by a stronger annual signal than our residuals, suggesting that runoff and evaporation may act to partially cancel each other.     

基于杨赤中推估法的降雨空间插值应用研究

杨赤中推估法是一种以二项系数加权游动平均为基础,按照迭代法几何滤波过程建立推估数学模型的最小二乘估值方法,它能够基于少量已知数据点取得好的插值效果。针对有限的降雨观测站点和分布不规则的雨量特征点数据,本文基于杨赤中滤波与推估法空间插值,以湖南省降雨观测站点数据为实例数据,实例研究了建立降雨估值数学模型和实现插值计算的过程。首先,按照逐步增大游动半径进行滤波建立杨赤中特征函数,选用负幂指数函数模型拟合特征函数,建立降雨估值数学模型;然后,设计了杨赤中空间插值计算的自动化流程,实现降雨观测变量规律性变化的最优估值,并构建出研究区连续分布的降雨空间信息;最后,将其与TRMM(Tropical Rainfall Measuring Mission)和DEM(Digital Elevation Model)数据作为协变量进行协同克里金插值的结果进行对比验证。结果表明:杨赤中滤波与推估法在不需要其他协变量的情况下,可以取得较好的插值模拟结果,证实了该方法针对少量且不规则分布的降雨观测点数据插值的可行性与有效性。     

联合GRACE与TRMM探测阿富汗水储量能力及其发生洪水的可能性

联合重力恢复和气候探测任务(gravity recovery and climate experiment,GRACE)确定的陆地水储量变化以及降水测量卫星任务(tropical rainfall measuring mission satellite,TRMM)提供的降水观测数据,探测局部地区发生洪水的可能性,是一种非常有用的遥测方法。本文提出了一种改进的方法来探测阿富汗陆地水储量能力及其发生洪水的可能性。首先,根据GRACE数据确定的陆地水储量变化获取改进的水储量不足,进而估计阿富汗水储量能力;其次,联合TRMM降水数据,建立阿富汗洪水因子模型;最后,将阿富汗洪水因子结果与中国气象局国家气候中心观测图进行对比。结果表明,洪水因子与中国气象局国家气候中心观测结果基本吻合,并从时间和空间角度真实地反应了阿富汗地区发生的洪水。因此,联合GRACE和TRMM卫星观测数据可探测阿富汗发生洪水的可能性,并为研究区域洪水预警提供了新的有利工具。     

The global climate monitor system: from climate data-handling to knowledge dissemination

This paper summarizes our work on building a data model and a geovisualization tool that provides access to global climate data: the Global Climate Monitor Web Viewer. Linked to this viewer, a complete set of climate-environmental indicators capable of displaying climate patterns on a global scale that is accessible to any potential user (scientists and laypeople) will be built and published using the same online application. The data currently available correspond to the CRU TS3.21 version of the Climate Research Unit (University of East Anglia) database – a product that provides data at a spatial resolution of half of a degree in latitude and longitude, spanning January 1901 to December 2012, on a monthly basis. Since January 2013, the datasets feeding the system have been the GHCN-CAMS temperature dataset and the Global Precipitation Climatology Centre (GPCC) First Guess precipitation dataset. Climatologists, hydrologists, planners and non-experts users such as media workers, policymakers, non-profit organizations, teachers or students, can access useful climatological information through the Global Climate Monitor system.     

降雨对Ku波段旋转扇形波束散射计影响

星载散射计测量的归一化后向散射截面是有关海面毛细重力波的大小和方向的函数:散射计的回波强度与海面毛细重力波的振幅成正比;风向对后向散射系数具有调制作用。因此,可以利用散射计的数据,根据地球物理模型反演出具有高精确度、无雨和中低风速条件下的海面风场矢量。然而高达10%的散射计测量数据会受到降雨影响(Nie,etal.,2008),尤其工作在Ku波段的散射计。降雨对海面散射幅度的影响主要包括:(1)降雨对雷达波的衰减和散射;(2)降雨改变海洋水面形态和海面粗糙度。     

Evaluation of satellite-derived agro-climate variables in the Northern Great Plains of the United States

The climate of the United States Northern Great Plains region is highly variable. Modelling of agriculture in this region and similar locations depends on the availability and quality of satellite and ground data for agro-climate variables. We evaluated tropical rainfall measuring mission (TRMM) multi-satellite preparation analysis (TMPA) precipitation, atmospheric infrared sounder (AIRS) surface air temperature, and AIRS relative air humidity (RH). A significant bias was found within the temperature and RH products and no bias but an insufficient rain event detection skill in the precipitation product (probability of detection ～0.3). A linear correction of the temperature product removed the bias as well as lowered the root mean square deviation (RMSD). The bias-corrections for RH led to increased RMSD or worse correlation. For precipitation, the correlation between the satellite product and ground data improved if cumulative precipitation or only precipitation during the growing season was used.     

Satellite-derived rainfall thresholds for landslide early warning in Bogowonto Catchment,Central Java,Indonesia

Satellite rainfall products for landslide early warning prediction have been spotlighted by several researchers, in the last couple of decades. This study investigates the use of TRMM and ERA-Interim data, for the determination of rainfall thresholds and the prediction of precipitation, respectively, to be used for landslide early warning purposes at the Bogowonto catchment, Central Java, Indonesia. A landslide inventory of 218 landslides for the period of 2003–2016 was compiled, and rainfall data were retrieved for the landslide locations, as given by 6 ground stations, TRMM, and ERA-Interim data. First, rainfall data from the three different sources was compared in terms of correlation and extreme precipitation indices. Second, a procedure for the calculation of rainfall thresholds for landslide occurrence was followed consisting of four steps: i) the TRMM-based rainfall data was reconstructed for selected dates and locations characterized by landslide occurrence and non-occurrence; ii) the antecedent daily rainfall was calculated for 3, 5, 10, 15, 20 and 30 days for the selected dates and locations; iii) two-parameter daily rainfall-antecedent rainfall thresholds were calculated for the aforementioned dates; after analysis of the curves the optimum number of antecedent rainfall days was selected; and (iv) empirical rainfall thresholds for landslide occurrence were determined. The procedure was repeated for the entire landslide dataset, differentiating between forested and built-up areas, and between landslide occurrence in four temporal periods, in relation to the monsoon. The results indicated that TRMM performs well for the detection of very heavy precipitation and can be used to indicate the extreme rainfall events that trigger landslides. On the contrary, as ERA-Interim failed to detect those events, its applicability for LEWS remains limited. The 15-day antecedent rainfall was indicated to mostly affect the landslide occurrence in the area. The rainfall thresholds vary for forested and built-up areas, as well as for the beginning, middle and end of the rainy season.     

Estimation of surface runoff in Malattar sub-watershed using SCS-CN method

Nowadays watershed management plays a vital role in water resources engineering. Watershed based on water resources management is necessary to plan and conserve the available resources. Remote Sensing (RS) and Geographic Information System (GIS) techniques can be effectively used to manage spatial and non spatial database that represent the hydrologic characteristics of the watershed use as realistically as possible. The present study area is Malattar subwatershed (4C2B2) lies in the region Gudiyattam Block, Vellore District, Tamil Nadu. The daily rainfall data of Gudiyattam rain gauge station (1971–2007) was collected and used to predict the daily runoff from the watershed using Soil Conservation Service — Curve Number (SCS — CN) method (USDA, 1972) and GIS. Monthly and annual runoff have been calculated from the monthly rainfall data for the years of 1971 to 2007 in the watershed area. The average minimum and maximum rainfall for the years of 1971 to 2007 is 35.30 mm and 111.61 mm respectively and average runoff for the year of 1971 to 2007 is 31.87 mm³ and 47.04 mm³ respectively. The developed rainfall-runoff model is used to understand the watershed and its runoff flow characteristics.