Satellite -based tropical cyclone intensity estimation using NOAA -KLM series advanced microwave sounding unit (AMSU) data.

摘要

Satellite-borne passive microwave radiometers are well suited to monitor tropical cyclones (TCs) by virtue of their ability to assess changes in tropospheric warm core structure in the presence of clouds. The temporal variability in TC warm core size, structure, and magnitude provide vital information on changes in kinematic structure and minimum sea level pressure (MSLP) through implicit thermodynamic and dynamic constraints. In this study, the efficacy of a hybrid-statistical algorithm capable of estimating MSLP using Advanced Microwave Sounding Unit (AMSU) temperature (AMSU-A) and moisture sounder (AMSU-B) data is demonstrated. The proposed AMSU TC intensity estimation algorithm addresses tropospheric warm anomaly (UTWA) sub-sampling through explicit convolution of an analytic function approximating the horizontal distribution of the TC UTWA and the AMSU-A antenna gain pattern. Differences between observed AMSU-A 54.94 GHz upper tropospheric limb-corrected brightness temperatures (T b) and those of a forward model based on the convolution function are minimized through maximum likelihood regression using known and estimated radiometric noise, TC position and structure function errors.;Dependent data set results using 1999 Atlantic (ATL) and eastern Pacific (EPAC) basin aircraft reconnaissance and AMSU-A observation pairs (n = 22) indicate significantly increased correlation between scan geometry/diffraction-corrected AMSU-A 54.94 GHz TC UTWA and MSLP (R2 = 0.9) VS. using no correction (R2 = 0.7). Linear least squares regression coefficients derived from the 1999 dependent sample were used to predict TC MSLP using a fully automated, objective processing scheme in multiple ocean basins during 2000 and a limited number of cases in early 2001. ATL/EPAC independent test results (n = 31) indicate that substantial improvements in correlation between AMSU-A TC UTWA and MSLP (R2 = 0.94 vs. 0.80), predicted MSLP mean error of 6.2 hPa vs. 7.5 hPa, and reduced standard deviation of 8.0 hPa vs. 9.9 hPa) are possible using the proposed AMSU TC intensity estimation scheme. Comparison of AMSU ATL/EPAC independent test results with subjective Dvorak MSLP estimate mean error and standard deviation (7.8 hPa +/- 7.6 hPa, n = 31) analyses indicates slightly superior AMSU MSLP estimate mean error with near equivalent variance.