Comparison of microwave satellite humidity data and radiosonde profiles: A case study

Abstract

[1] This article documents a case study comparing radiosonde humidity data to Advanced Microwave Sounding Unit (AMSU) satellite humidity data. The study had two goals: first, to develop a robust method for such a comparison, and second, to check the quality and mutual consistency of radiosonde data, radiative transfer model, and AMSU data. The radiosonde data used are Vaisala RS80 data from the station Lindenberg of the German Weather Service (DWD), which have been subject to several corrections compared to the standard data processing. The radiative transfer model is the Atmospheric Radiative Transfer Simulator (ARTS), and the AMSU data are those of the satellites NOAA 15 and 16 for the time periods 2001 and 2002. The comparison was done in radiance space, using a radiative transfer model to simulate AMSU radiances from the radiosonde data. The overall agreement is very good, with radiance biases below 1.5 K and standard deviations below 2 K. The main source of ''noise'' in the comparison is atmospheric inhomogeneity on the 10-km scale. While the radiosonde correction performed at Lindenberg significantly reduces the bias between simulated and measured AMSU radiance, there still remains a slope in the radiance difference. Possible reasons for this were investigated. Most likely, the radiosondes underestimate the relative humidity under extremely dry conditions, showing 0 %RH when the true value is 2-4 %RH.

Intercomparison of general purpose clear sky atmospheric radiative transfer models for the millimeter/submillimeter spectral range

“…This cannot be done for a model in general, but rather individually for each application and configuration. For the case of the down-looking instrument AMSU and the ARTS model, e.g., this has already been done [Buehler et al, 2004].…”

A practical demonstration on AMSU retrieval precision for upper tropospheric humidity by a non-linear multi-channel regression method

“…The humidity data from Lindenberg have undergone several corrections and quality control (Leiterer et al, 1997;Buehler et al, 2004b). Typical temperature and relative humidity profiles are shown in Fig.…”

“…In addition, the sonde drifts during its ascent, so the radiosonde data might not be representative of the launch site. The issue of matching radiosondes and AMSU-B pixels at Lindenberg was also discussed by Buehler et al (2004b), the selection of the training set follows some of the ideas presented there. The basic idea is to select a set of pixels within 50 km of the radiosonde launch based on considerations of atmospheric homogeneity and time differences.…”

“…As mentioned before, the radiosonde humidity data suffer from problems, so a regression dataset based on co-locations of radiosondes and AMSU pixels will clearly be suffering from biases. Nevertheless, some radiosonde stations have made an effort to improve their humidity data, allowing the data to be used for validation if a reasonable co-location between radiosonde launches and AMSU pixels can be achieved, as was the case in Buehler et al (2004b). Here we will follow the same approach and use high quality data from the same radiosonde station to check the precision when using measured AMSU radiances.…”

The COST 723 Action

“…Buehler et al (2004) describe a comparison between microwave satellite humidity data and radiosondes, focusing on one radiosonde station, the reference station of the DWD in Lindenberg. Good agreement was found for radiosonde data that incorporated correction factors, but a residual dry bias of a few percent remained for extremely dry conditions.…”