[1] In the present study, an attempt was made to estimate rainfall by synergistically analyzing collocated thermal infrared (TIR) brightness temperatures from Meteosat along with rainfall estimates from active microwave precipitation radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) over Indian land and oceanic regions. In this study, we used broad and frequent TIR measurements from a geostationary satellite for rainfall estimation, calibrating them with sparse but more accurate PR rain rates. To make the algorithm robust, we used a two-step procedure. First, a cloud classification scheme was applied to TIR measurements using the 6.7 mm water vapor channel and TIR radiances to delineate the rain-bearing clouds. Next, the concurrent TIR and PR observations were used to establish a regression relation between them. The relationship thus established was used to estimate rainfall from TIR measurements by applying it to rain-producing systems during southwest and northeast monsoons and tropical cyclones. Comparisons were made with TRMM-merged (3B42 V6) data, Global Precipitation Climatology Project (GPCP) monthly rain rate data, ground-based rain gauge observations from automatic weather stations, and Doppler weather radar over India. The results from combined infrared and microwave sensors were in very good agreement with the ground-based measurements, TRMM-3B42 V6, as well as GPCP.Citation: Mishra, A., R. M. Gairola, A. K. Varma, and V. K. Agarwal (2010), Remote sensing of precipitation over Indian land and oceanic regions by synergistic use of multisatellite sensors,

The major scientific objective of the Megha-Tropiques (MT) satellite, an Indian Space Research Organisation (ISRO)-Centre National d'Études Spatiales (CNES) collaborative project, is to understand the energy and water cycles in the global tropical region. With its 20 • inclined orbit, it will frequently measure radiation emitted by the Earth-Atmosphere System in the visible, infrared and microwave spectrum through its four sensors on board. Various geophysical parameters, namely water vapour, cloud liquid water and surface winds over oceanic regions, and the rainfall, humidity profile and top-of-atmosphere radiative fluxes over land as well as over oceanic regions will be derived from the measurements made by these instruments. This article deals with the efforts made by ISRO to develop algorithms for deriving these geophysical parameters from the microwave imager and sounder, mentioning the pre-launch specifications with prelude examples from existing space-borne sensors of similar types. The sensor-specific algorithms are presented in different sections.

India launched a geostationary satellite INSAT-3D on 26 July 2013 with an objective to monitor the Earth's surface using various spectral channels of meteorological importance. INSAT-3D retrieved Hydro-Estimator (HE) rainfall is compared with Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 merged rainfall and in situ observations from the India Meteorological Department (IMD). Results suggest that INSAT-3D HE rainfall is of reasonably good quality and hence can be used for various meteorological applications. The Weather Research and Forecasting (WRF) model and its four-dimensional variational (4D-Var) data assimilation system are used to assimilate the INSAT-3D retrieved highresolution HE rainfall product. Two parallel experiments are performed daily with and without assimilation of rainfall observations during the entire month of July 2014. Results show that assimilation of INSAT-3D rainfall makes a good improvement in temperature and wind speed forecasts, and a marginal improvement in water vapour mixing ratio forecasts. Prediction of rainfall is also found to be improved with the use of INSAT-3D rainfall over control experiments. Additionally, one case-study is performed to assess the impact of INSAT-3D rainfall on an unprecedented high rainfall event over Ahmedabad, India on 15 November 2014. Results show that the WRF model experiment with assimilation of INSAT-3D rainfall is able to capture the heavy rainfall episode, which otherwise was unpredictable.

[1] Surface rain radar data from areas surrounding Japan and in the tropics are used to study the variability of rainfall over an area similar to a satellite microwave radiometric instantaneous field of view (FOV). The results of such variability are applied to radiative transfer simulations to modify the brightness temperature versus rain rate relationship. First, the surface radar data from different geographical regions are used to develop a relationship between fractional rain cover and average rain rate over microwave radiometric FOV-sized areas. This resultant relationship is also compared with spaceborne Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations. It is found that the rainfall type (convective or stratiform) accounts for the most of variations in this relationship. The sub-FOV rainfall variability is then investigated using the same surface radar data sets. The conditional probability distribution function of precipitation within the FOV-sized area is parameterized in terms of average rain of the area. The form of the model function does not depend upon the characteristics of the two radars and their geographical locations and can be parameterized by a combination of two lognormal distributions. By analyzing collocated TRMM Microwave Imager (TMI) and PR data, it is shown that the averaged relationship between observed brightness temperatures and rain rates at 19.3 and 37 GHz shows a significant disagreement with that simulated by a plane-parallel radiative transfer model for convective rain events, although it shows a better agreement for stratiform rain events. Presumably, this disagreement is mainly caused by variability of the rain field within satellite instantaneous FOV. With the application of the fractional rain area and the probability distribution function derived from this study to the radiative transfer simulations, we are able to bring results of the radiative transfer simulations much closer to the observations.

The horizontal distribution of rain rates within an area comparable to the pixel size of satellite microwave radiometers and the grid size of numerical weather prediction models has been studied over the global Tropics using three years of the Tropical Rainfall Measuring Mission satellite precipitation radar (PR) data. The global distribution of rain-rate standard deviation derived from the PR data suggests that the horizontal variability of rain rates is largely influenced by two factors: surface type (land or ocean) and latitudinal location (tropical or extratropical). Except for light stratiform rain, the land–ocean contrast seems to be the dominant feature for the differences in conditional probability density functions (PDFs) of rain rate. That is, oceanic rain-rate distribution is narrower when the rain rate is low, but becomes broader when the rain rate is high. For light stratiform rain, there is no clear difference among the rain-rate PDFs for rain events over land and ocean. The latitudinal variation of rain-rate PDFs seems to be greater for heavy rain than for light rain. In particular, there is no measurable difference in overland convective rain-rate PDFs between the Tropics and extratropics. Based on three years of observational data, two attributes, fractional rain cover and conditional rain-rate PDFs, are parameterized as a function of 0.25° × 0.25° areal rain rate. These parameterizations are particularly useful in satellite microwave rainfall retrieval and assimilation of satellite microwave radiance data in numerical weather prediction models.