Comparison of TMI and AMSR-E sea surface temperatures with Argo near-surface temperatures over the global oceans

Satellite-derived sea surface temperatures（SSTs） from the tropical rainfall measuring mission（TRMM）microwave imager（TMI） and the advanced microwave scanning radiometer for the earth observing system（AMSR-E） were compared with non-pumped near-surface temperatures（NSTs） obtained from Argo profiling floats over the global oceans. Factors that might cause temperature differences were examined, including wind speed, columnar water vapor, liquid cloud water, and geographic location. The results show that both TMI and AMSR-E SSTs are highly correlated with the Argo NSTs; however, at low wind speeds, they are on average warmer than the Argo NSTs. The TMI performs slightly better than the AMSR-E at low wind speeds, whereas the TMI SST retrievals might be poorly calibrated at high wind speeds. The temperature differences indicate a warm bias of the TMI/AMSR-E when columnar water vapor is low, which can indicate that neither TMI nor AMSR-E SSTs are well calibrated at high latitudes. The SST in the Kuroshio Extension region has higher variability than in the Kuroshio region. The variability of the temperature difference between the satellite-retrieved SSTs and the Argo NSTs is lower in the Kuroshio Extension during spring. At low wind speeds, neither TMI nor AMSR-E SSTs are well calibrated, although the TMI performs better than the AMSR-E.

Evaluating a satellite-based sea surface temperature by shipboard survey in the Northwest Indian Ocean

A summer-time shipboard meteorological survey is described in the Northwest Indian Ocean. Shipboard observations are used to evaluate a satellite-based sea surface temperature（SST）, and then find the main factors that are highly correlated with errors. Two satellite data, the first is remote sensing product of a microwave, which is a Tropical Rainfall Measuring Mission Microwave Imager（TMI）, and the second is merged data from the microwave and infrared satellite as well as drifter observations, which is Operational Sea Surface Temperature and Sea Ice Analysis（OSTIA）. The results reveal that the daily mean SST of merged data has much lower bias and root mean square error as compared with that from microwave products. Therefore the results support the necessary of the merging infrared and drifter SST with a microwave satellite for improving the quality of the SST. Furthermore, the correlation coefficient between an SST error and meteorological parameters, which include a wind speed, an air temperature, a relative humidity, an air pressure, and a visibility. The results show that the wind speed has the largest correlation coefficient with the TMI SST error. However, the air temperature is the most important factor to the OSTIA SST error. Meanwhile,the relative humidity shows the high correlation with the SST error for the OSTIA product.

ESTIMATION OF CLOUD LIQUID WATER OVER THE HUBEX AREA USING THE TRMM MICROWAVE IMAGER

The ability of the Tropical Rainfall Measuring Mission Microwave Imager(TRMM/TMI)for cloud liquid water(CLW)retrieval has been demonstrated in this study.Due to the great sensitivity of the TMI 85.5 GHz channels to CLW,the liquid water path(LWP)of nonprecipitating clouds over land can be successfully estimated using the VDISORT model based on the iteration steps.Both the vertical-polarized 85. 5 GHz single-channel method and the polarization-difference of 85.5 GHz method were applied to the LWP estimates over land regions during the Huaihe River Basin Energy and Water Cycle Experiment(HUBEX)in China.The retrieval results show reasonable agreement with the ground-based microwave radiometer measurements.When the surface emissivity or skin temperature is difficult to be made sure,the polarization-difference method shows advantages of providing estimates of LWP especially for low clouds because of its extremely insensitiveness to the surface skin temperature.

SATELLITE MICROWAVE REMOTE SENSING OF FLOODING

The ability of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for flooding and soil wetness detection has been demonstrated in this study.On the basis of TMI measurements,four methods,the classification method,the soil wetness index (SWI) method. the polarization difference index (PDI) method,and the polarization ratio index (PRI) method, were brought out to monitor flooding and study soil wetness in the Changjiang and Huaihe River Basins during the summer 1998.Compared with the images provided by L-band Synthetic Aperture Radar (L-SAR) and Radar Satellite (Radarsat) and the figures derived from daily rainfall data based on the Z-index method,the detection of flooding and soil wetness by TMI was proved to be feasible.