GPS radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate(COSMIC) mission were used to validate the measurements of the advanced microwave sounding unit-A(AMSU-A) in the lower stratosphere from different satellites. AMSU-A observations from two different calibrations—the pre-launch operational and post-launch simultaneous nadir overpass(SNO) calibrations—were compared to microwave brightness temperatures(Tb)simulated from COSMIC data. Observations from three satellites(NOAA-15,-16, and-18) were used in the comparison. The results showed that AMSU-A Tb measurements from both calibrations and from all three NOAA satellites were underestimated in the lower stratosphere,and that the biases were larger in polar winters, especially over the southern high latitudes. In comparison to operational calibration, the SNO-calibrated AMSU-A data produced much smaller biases relative to the COSMIC data.The improvement due to SNO calibration was quantified by a Ratio index, which measured the bias changes from operational to SNO calibrations relative to the biases between the operational-calibrated AMSU-A data and the COSMIC data. The Ratio values were 70 % for NOAA-15and [80 % for NOAA-18 and-16, indicating that the SNO calibration method significantly reduced AMSU-A biases and effectively improved AMSU-A data quality.
A technique for estimating tropical cyclone(TC) intensity over the Western North Pacific utilizing FY-3Microwave Imager(MWRI) data is developed. As a first step, we investigated the relationship between the FY-3 MWRI brightness temperature(TB) parameters, which are computed in concentric circles or annuli of different radius in different MWRI frequencies, and the TC maximum wind speed(Vmax) from the TC best track data. We found that the parameters of lower frequency channels' minimum TB, mean TB and ratio of pixels over the threshold TB with a radius of 1.0 or 1.5 degrees from the center give higher correlation. Then by applying principal components analysis(PCA)and multiple regression method, we established an estimation model and evaluated it using independent verification data, with the RMSE being 13 kt. The estimated Vmax is always stronger in the early stages of development, but slightly weaker toward the mature stage, and a reversal of positive and negative bias takes place with a boundary of around 70 kt. For the TC that has a larger error, we found that they are often with less organized and asymmetric cloud pattern, so the classification of TC cloud pattern will help improve the acuracy of the estimated TC intensity, and with the increase of statistical samples the accuracy of the estimated TC intensity will also be improved.
