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.

In this paper rain estimation capability of MSMR is explored. MSMR brightness temperature data of six channels corresponding to three frequencies of 10, 18 and 21 GHz are colocated with the TRMM Microwave Imager (TMI) derived rain rates to find a new empirical algorithm for rain rate by multiple regression. Multiple correlation analysis involving various combinations of channels in linear and non-linear forms and rain rate from TMI is carried out, and thus the best possible algorithm for rain rate measurement was identified which involved V and H polarized brightness temperature measurements at 10 and 18 GHz channels. This algorithm explained about 82 per cent correlation (r) with rain rate, and 1.61 mm h^-1 of error of estimation.

Further, this algorithm is used for generating global average rain rate map for two contrasting months of August (2000) and January (2001) of northern and southern hemispheric summers, respectively. MSMR derived monthly averaged rain rates are compared with similar estimates from TRMM Precipitation Radar (PR), and it was found that MSMR derived rain rates match well, quantitatively and qualitatively, with that from PR.

The nature of the inherent temporal variability of surface winds is analyzed by comparison of winds obtained through different measurement methods. In this work, an auto-correlation analysis of a time series data of surface winds measuredin situ by a deep water buoy in the Indian Ocean has been carried out. Hourly time series data available for 240 hours in the month of May, 1999 were subjected to an auto-correlation analysis. The analysis indicates an exponential fall of the autocorrelation in the first few hours with a decorrelation time scale of about 6 hours. For a meaningful comparison between satellite derived products andin situ data, satellite data acquired at different time intervals should be used with appropriate ‘weights’, rather than treating the data as concurrent in time. This paper presents a scheme for temporal weighting using the auto-correlation analysis. These temporal ‘weights’ can potentially improve the root mean square (rms) deviation between satellite andin situ measurements. A case study using the TRMM Microwave Imager (TMI) and Indian Ocean buoy wind speed data resulted in an improvement of about 10%.