The climate of India is dominated by monsoon systems. The remotely sensed estimates obtained from the Tropical Rainfall Measuring Mission (TRMM) are used to examine the most of the Indian monsoon systems. This study deals with the diurnal and spatial variation of precipitation over the Indian region. The precipitation data from TRMM Multi-satellite Precipitation Analysis (TMPA), blended from a variety of sources (including rain gauges over land) and having both daily and 3- hourly output are being used for evaluation of the Numerical Weather Prediction models Basu (2007) of National Centre for Medium Range Weather Forecasting. The precipitation obtained from TRMM 3B42 for this study period has a spatial resolution of 0.25º X 0.25º latitude-longitude. The 3-hourly averaged values are centered at the middle of each 3 hr period. South Asian regions are dominated by seasonal climatic fluctuations and the major rainy season is the southwest monsoon season. In addition to the seasonal fluctuations, Indian summer monsoon is modulated by diurnal fluctuations; nature of diurnal variation of rainfall varies from place to place and depends upon the locations, topography of the region. Diurnal variation of rain-rate, frequency of rain, conditional rain rate, and maximum and minimum rain occurrence is studied. Over Indian tropical region, maximum rainfall over land and Bay of Bengal regions is observed during the late-afternoon and early-morning period, respectively. Drizzle or less rainfall occur frequently in the morning over most land areas, whereas convective activity occurs during the afternoon. The model predicted diurnal cycle of precipitation peaks too early (by ~3h) and the amplitude is too strong over Indian land region and tropical ocean region.
Emergence of extensively large computational facilities have enabled the scientific world to use earth system models for
understating the prevailing dynamics of the earth's atmosphere, ocean and cryosphere and their inter relations. The sea
ice in the arctic and the Antarctic has been identified as one of the main proxies to study the climate changes. The rapid
sea-ice melting in the Arctic and disappearance of multi-year sea ice has become a matter of concern. The earth system
models couple the ocean, atmosphere and sea-ice in order to bring out the possible inter connections between these three
very important components and their role in the changing climate. The Indian monsoon is seen to be subjected to nonlinear
changes in the recent years. The rapid ice melt in the Arctic sea ice is apparently linked to the changes in the
weather and climate of the Indian subcontinent. The recent findings reveal the relation between the high events occurs in
the Indian subcontinent and the Arctic sea ice melt episodes. The coupled models are being used in order to study the
depth of these relations. However, the models have to be validated extensively by using measured parameters. The
satellite measurements of sea-ice starts from way back in 1979. There have been many data sets available since then.
Here in this study, an evaluation of the existing data sets is conducted. There are some uncertainties in these data sets. It
could be associated with the absence of a single sensor for a long period of time and also the absence of accurate in-situ
measurements in order to validate the satellite measurements.
Simulation and prediction of Indian monsoon rainfall at scales from days-to-season is a challenging task for numerical modelling community worldwide. Gridded estimates of daily rainfall data are required for both land and oceanic regions for model validation, process studies and in turn for model development. Due to recent developments in satellite meteorology, it has become possible to produce realistic near real-time gridded rainfall datasets at operational basis by combining satellite estimates with rain gauge values and other available in-situ observations. Microwave and space based radar based estimates of rainfall has revolutionised the preparation of rainfall datasets especially for tropics. However, a variety of multi-satellite products are available over Indian monsoon region from a variety of sources. Popular products like TRMM TMPA3B42 (RT and V7), GsMaP, CPC/RFE, GPCP and GPM are available to end users in various space/time scales for applications and model validation. In this study, we show the representation and skill of monsoon rainfall from a variety of multi-satellite products over the Indian region. The bias and skill of multi-satellite rainfall are evaluated against gauge based observations. It was found that the TRMM based TMPA was one of the best dataset for Indian monsoon region. Attempt is made to compare the latest GPM based data with other products. The GPM based rainfall product is seen to be superior compared to TRMM.
Aquarius is mission that aims to measure Sea surface salinity (SSS) from the space in order to provide the global salinity for climate studies. Accurate estimation of SSS is useful for the hydrological cycle, oceanographic processes, and climate. Recently the new version (V4) of Aquarius data releases with the improving the quality of the data and achieving the mission accuracy requirement globally on monthly scale. This paper highlight the results of recently release Aquarius V4, and version 3 (V3) data with Argo observations on monthly time scale from 2012-2014 periods. The spatial distribution of mean SSS shows that both products capture the SSS variation very well. The Aquarius V4 SSS showed minor improvement over the Aquarius V3 SSS with less root mean square error over the central & eastern equatorial Indian Ocean (EEIO) & part of Bay of Bengal (BOB). The frequency distribution is also improved in Aquarius V4 compare to Aquarius V3 average over the different regions. However, both the versions overestimates/underestimates the frequency of low/high salinity values.
KEYWORDS: Satellites, Meteorology, Microwave radiation, Atmospheric modeling, Data modeling, Statistical analysis, Meteorological satellites, Climatology, Clouds, System on a chip
For study of Asian monsoon, IR based estimates from Kalpana-1, Meteosat-5 and
microwave based estimates from TRMM rainfall data are very useful, particularly for the oceanic
regions. Satellite only estimates have biases, but are able to represent the large-scale monsoon
rainfall features. IR estimates are unable to capture the heavy rain over the west coast of India.
Even the TRMM values are underestimating the heavy rainfall over the west coast of India.
Inclusion of gauge data (over land and island) improves the representation of daily rainfall.
Gauge data corrects the biases in the satellite estimates. The number of gauge stations available
in real time is less, and for a better analysis of the large-scale rainfall field at least the full set of
540 stations from India will be very useful. For meso-scale analysis a much denser network of
gauge stations will be required. These gauge data will be very useful to correct the current biases
in the satellite estimates.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.