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- Author or Editor: Rashmi Sharma x
- Journal of Atmospheric and Oceanic Technology x
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Abstract
A 2-yr (June 1999–June 2001) observation of ocean surface wind speed (SWS) and sea surface temperature (SST) derived from microwave radiometer measurements made by a multifrequency scanning microwave radiometer (MSMR) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is compared with direct measurements by Indian Ocean buoys. Also, for the first time SWS and SST values of the same period obtained from 40-yr ECMWF Re-Analysis (ERA-40) have been evaluated with these buoy observations. The SWS and SST are shown to have standard deviations of 1.77 m s−1 and 0.60 K for TMI, 2.30 m s−1 and 2.0 K for MSMR, and 2.59 m s−1 and 0.68 K for ERA-40, respectively. Despite the fact that MSMR has a lower-frequency channel, larger values of bias and standard deviation (STD) are found compared to those of TMI. The performance of SST retrieval during the daytime is found to be better than that at nighttime. The analysis carried out for different seasons has raised an important question as to why one spaceborne instrument (TMI) yields retrievals with similar biases during both pre- and postmonsoon periods and the other (MSMR) yields drastically different results. The large bias at low wind speeds is believed to be due to the poorer sensitivity of microwave emissivity variations at low wind speeds. The extreme SWS case study (cyclonic condition) showed that satellite-retrieved SWS captured the trend and absolute magnitudes as reflected by in situ observations, while the model (ERA-40) failed to do so. This result has direct implications on the real-time application of satellite winds in monitoring extreme weather events.
Abstract
A 2-yr (June 1999–June 2001) observation of ocean surface wind speed (SWS) and sea surface temperature (SST) derived from microwave radiometer measurements made by a multifrequency scanning microwave radiometer (MSMR) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is compared with direct measurements by Indian Ocean buoys. Also, for the first time SWS and SST values of the same period obtained from 40-yr ECMWF Re-Analysis (ERA-40) have been evaluated with these buoy observations. The SWS and SST are shown to have standard deviations of 1.77 m s−1 and 0.60 K for TMI, 2.30 m s−1 and 2.0 K for MSMR, and 2.59 m s−1 and 0.68 K for ERA-40, respectively. Despite the fact that MSMR has a lower-frequency channel, larger values of bias and standard deviation (STD) are found compared to those of TMI. The performance of SST retrieval during the daytime is found to be better than that at nighttime. The analysis carried out for different seasons has raised an important question as to why one spaceborne instrument (TMI) yields retrievals with similar biases during both pre- and postmonsoon periods and the other (MSMR) yields drastically different results. The large bias at low wind speeds is believed to be due to the poorer sensitivity of microwave emissivity variations at low wind speeds. The extreme SWS case study (cyclonic condition) showed that satellite-retrieved SWS captured the trend and absolute magnitudes as reflected by in situ observations, while the model (ERA-40) failed to do so. This result has direct implications on the real-time application of satellite winds in monitoring extreme weather events.
Abstract
Large-scale features of sea surface temperature, wind speed, water vapor, and cloud liquid water, derived from multifrequency scanning microwave radiometer (MSMR) on board Indian oceanographic satellite IRS-P4 could be identified during 15 June–23 August 1999. This is the period during which extensive validation was carried out. MSMR is the only sensor in orbit operating at 6.6 GHz. Average distribution of these parameters brings out large-scale atmospheric and oceanographic features. Zonal averages of these parameters were also studied to examine the consistency of MSMR data over larger spatial scales. Linear correlations between all parameters were also computed to check for the interconsistency of these parameters. The present analysis shows the potential use of MSMR products in studying the oceanographic and atmospheric phenomena.
Abstract
Large-scale features of sea surface temperature, wind speed, water vapor, and cloud liquid water, derived from multifrequency scanning microwave radiometer (MSMR) on board Indian oceanographic satellite IRS-P4 could be identified during 15 June–23 August 1999. This is the period during which extensive validation was carried out. MSMR is the only sensor in orbit operating at 6.6 GHz. Average distribution of these parameters brings out large-scale atmospheric and oceanographic features. Zonal averages of these parameters were also studied to examine the consistency of MSMR data over larger spatial scales. Linear correlations between all parameters were also computed to check for the interconsistency of these parameters. The present analysis shows the potential use of MSMR products in studying the oceanographic and atmospheric phenomena.
