The Variational Assimilation Method for the Retrieval of Humidity Profiles with the Wind-Profiling Radar

Abstract

Recently, a humidity estimation technique was developed by using the turbulence echo characteristics detected with a wind-profiling radar. This study is concerned with improvement of the retrieval algorithm for delineating a humidity profile from the refractive index gradient (M) inferred from the echo power. To achieve a more precise estimate of humidity, a one-dimensional variational method is adopted. Because the radar data provide only the absolute value of M, its sign must be determined in the retrieval. A statistical probability for the sign of M [Pr(z)] is introduced to the cost function of the variational method to determine the optimum result with reduced calculation cost. GPS-derived integrated water vapor (IWV) was assimilated together with the radar-derived |M| for constraining the signs of |M| to agree with the radar-derived IWV and the GPS-derived IWV. Humidity profiles were retrieved from the Middle and Upper Atmosphere (MU) radar?Radio Acoustic Sounding System (RASS) data for July?August 1999 using the first guess calculated from the time interpolation of radiosonde results. The |M| profiles from the MU radar?RASS were assimilated at 21 height layers between 1.5 and 7.5 km. A genetic algorithm is employed to find the global optimum. The humidity profiles are retrieved with the same vertical resolution as that of the observation values. The precision of the retrieval result using the new method is superior to that of the conventional method. The difference between the analysis and simultaneous radiosonde results was related to a large error in the first guess. The sensitivity of the analysis result to the shape of the Pr(z) profile was investigated, and the result appears to be insensitive to the profile of Pr(z). The improvement over the conventional method is especially evident for the case of a large error in the first guess.