Rainfall analysis for Indian monsoon region using the merged rain gauge observations and satellite estimates: Evaluation of monsoon rainfall features (original) (raw)
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Variability of Indian summer monsoon rainfall in daily data from gauge and satellite
Journal of Geophysical Research, 2009
It has long been thought that tropical rainfall retrievals from satellites have large errors. Here we show, using a new daily 1 degree gridded rainfall data set based on about 1800 gauges from the India Meteorology Department (IMD), that modern satellite estimates are reasonably close to observed rainfall over the Indian monsoon region. Daily satellite rainfalls from the Global Precipitation Climatology Project (GPCP 1DD) and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) are available since 1998. The high summer monsoon (June-September) rain over the Western Ghats and Himalayan foothills is captured in TMPA data. Away from hilly regions, the seasonal mean and intraseasonal variability of rainfall (averaged over regions of a few hundred kilometers linear dimension) from both satellite products are about 15% of observations. Satellite data generally underestimate both the mean and variability of rain, but the phase of intraseasonal variations is accurate. On synoptic timescales, TMPA gives reasonable depiction of the pattern and intensity of torrential rain from individual monsoon low-pressure systems and depressions. A pronounced biennial oscillation of seasonal total central India rain is seen in all three data sets, with GPCP 1DD being closest to IMD observations. The new satellite data are a promising resource for the study of tropical rainfall variability.
Long‐Term High‐Resolution Gauge Adjusted Satellite Rainfall Product Over India
Earth and Space Science
This study aims to create a 21-year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland. The targeted resolutions of the GSMaP are hourly and 0.1°× 0.1°. The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) daily gauge analysis (0.5°× 0.5°) and Indian Meteorological Department (IMD) daily gridded rainfall product (0.25°× 0.25°) were utilized to generate two long-term rainfall products, GSMaP CPC and GSMaP IMD rainfall, respectively. After preliminary verification of the GSMaP CPC and GSMaP IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon (ISM) periods of 2000-2020 with comparisons of other merged rainfall products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results suggest GSMaP IMD has a smaller root-mean-square difference (RMSD) and higher correlation than GSMaP CPC, evaluated against independent rainfall products. In the threehour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP IMD with respect to GSMaP CPC throughout the day. The statistics against the hourly dense rain gauge network in Karnataka suggests that the GSMaP IMD is more effective in capturing large spatiotemporal rainfall variation over India. Thus, validation results with the independent sources suggest that GSMaP IMD rainfall generally improved over GSMaP CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India.
This work presents the validation of satellite (TMPA and IMERG) rainfall products against the India Meteorological Department (IMD) gridded data sets (0.25° × 0.25°) of dense network of rain gauges distributed over the monsoon core region of India. The validation uses the data sets covering the 20 years (1998-2017) and detects the time series bias; inter annual variations and Intra Seasonal Oscillations (ISO). The bias in the two data sets is found to be very less over the core region compared to whole India. The correlation between daily rainfall IMD and satellite is found to be +0.88 which is of 99% confidence level. The dominant periodicities in the rainfall patterns of IMD and satellite are Madden Julie Oscillation (30-60 days) and local oscillations (less than 20 days) are conspicuous and the normalized power varies from year to year. During the El Niño and La Niña years, the normalized power of rainfall pattern is low and high in satellite data sets which infer the suppressed and strongest activity of MJO over Indian Ocean that modulates the rainfall pattern over India.
Journal of Hydrometeorology, 2015
This paper evaluates the seasonal (winter, premonsoon, monsoon, and postmonsoon) performance of seven precipitation products from three different sources: gridded station data, satellite-derived data, and reanalyses products over the Indian subcontinent for a period of 10 years (1997/98–2006/07). The evaluated precipitation products are the Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE), the Climate Prediction Center unified (CPC-uni), the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM) post-real-time research products (3B42-V6 and 3B42-V7), the Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). Several verification measures are employed to assess the accuracy of the data. All datasets capture the large-scale characteristics of the seasonal mean precipitation distribution...
Climate Dynamics, 2015
for Research and Applications (NASA-MERRA), premonsoon with Japanese 25 years Re Analysis (JRA-25), and post-monsoon with climate forecast system reanalysis (CFSR) reanalysis datasets. Among the reanalysis datasets, European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) shows good comparison followed by CFSR, NASA-MERRA, and JRA-25. Further, for the first time, with high resolution and long-term IMD data, the spatial distribution of trends is estimated using robust regression analysis technique on the annual and seasonal rainfall data with respect to different regions of India. Significant positive and negative trends are noticed in the whole time series of data during the monsoon season. The northeast and west coast of the Indian region shows significant positive trends and negative trends over western Himalayas and north central Indian region.
MAUSAM, 2021
The study provides a concise and synthesized documentation of the current level of skill of the satellite (3B42RT, 3B42V-6, KALPANA-1) products over Indian regions based on the data gathered during the summer monsoon seasons of 2006, 2007 and 2008. The inter-comparison of satellite products with the rain gauge observations suggests that the TRMM 3B42V6 product could distinctly capture characteristic features of the summer monsoon, such as north–south oriented belt of heavy rainfall along the Western Ghats with sharp gradient of rainfall between the west coast heavy rain region and the rain shadow region to the east, pockets of heavy rainfall along the location of monsoon trough, over the east central parts of the country, over north-east India, along the foothills of Himalayas and over the north Bay of Bengal. The KALPANA-1 and 3B42RT products reproduce only the broadest features of mean monsoon seasonal rainfall. The near real-time products 3B42RT and KALPANA-1 underestimate the or...
Quarterly Journal of the Royal Meteorological Society, 2017
This study investigates 1) the role of orography in precipitation along and upstream of the Western Ghats (WG), and 2) a diurnal cycle of precipitation in western India during the summer monsoon, using a high-resolution meteorological model and a network of surface rain gauges over the WG. The Weather Research and Forecasting model (WRF-ARW) was used to simulate the 2008, 2009, and 2010 summer monsoons at 5 km horizontal grid spacing and allows resolved convection, with initial and boundary conditions provided by ERA-Interim. The highest daily mean precipitation is found immediate to the WG escarpment and coastal plain between 11.5 •-18 • N, but areas receiving the most rainfall do not necessarily receive it most frequently. The greatest percentage of rainy days occurs over the escarpment of the WG and slightly inland, corresponding with topography, and high percentages (over 75%) of rainy days occur along the coast, along the coastal plain and the WG. These findings are in agreement with several recent studies using high spatial resolution TRMM precipitation data. Analysis of WRF output at time increments of 30 minutes reveals a clear diurnal pattern of rainfall with an early morning maximum offshore, and afternoon maxima over inland regions that occur later in the day with distance inland. A weak land breeze circulation is observed, as nocturnal cooling of the land surface results in deceleration of westerly flow upstream of the WG. Offshore moisture convergence and destabilization of low-level air results in the offshore morning maximum. Rainfall maxima over inland regions indicate that while orography is the primary impetus for lift, rainfall is also convectively driven. Analysis of convective parameters and land surface variables such as soil moisture and latent and sensible heat fluxes support this weak land-sea breeze circulation embedded in prevailing westerly monsoonal flow.
Worldwide agriculture is sensitive to short-term changes in weather and to seasonal, annual and longer-term variations in climate. The variations in the meteorological parameters have overriding influence on the agricultural systems. It is widely believed that developing countries such as India will be impacted more severely than developed countries, where, about 56% of the net cultivated area is rain-fed that largely depends on monsoon rainfall. Thus accurate estimation of rainfall is crucial for crop yield assessment, water resource management and flood and drought monitoring for the area. But, traditional precipitation records are thought to be rarely complete, to analyze these limitations a comparison between rain gauge observations and satellitebased estimates such as the Tropical Rainfall Measuring Mission (TRMM) is carried out in this paper. Different statistical tools like coefficient of determination, Mean Bias Error, Root Mean Square Error and NRMSE are used to have validation of TRMM data with rain gauge data. TRMM data found overestimated to the rain gauge data with 58.17 % of error, capturing 60.6 % of variability in spatial distribution with traditional rain gauges. It is concluded from the analysis that there is some dissimilarity in the spatial distribution between the two which may due to lack of ground rain gauge stations at some remote areas or diversified topography of the district area. Hence, it may be useful in estimating rainfall particularly in regions where no gauge observations available and therefore such measurements are useful for many water related applications.