[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.

[1] 183.31 GHz observations from the Advanced Microwave Sounding Unit B (AMSU-B) instruments onboard the NOAA 15, 16, and 17 satellites were used to derive a new data set of Upper Tropospheric Humidity (UTH). The data set consist of monthly median and mean data on a 1.5°latitude-longitude grid between 60°S and 60°N, and covers the time period of January 2000 to February 2007. The data from all three instruments are very consistent, with relative difference biases of less than 4% and relative difference standard deviations of 7%. Radiometric contributions by high ice clouds and by the Earth's surface affect the measurements in certain areas. The uncertainty due to clouds is estimated to be up to approximately 10%RH in areas with deep convection. The uncertainty associated with contamination from surface emission can exceed 10%RH in midlatitude winter, where the data therefore should be regarded with caution. Otherwise the surface influence appears negligible. The paper also discusses the UTH median climatology and seasonal cycle, which are found to be broadly consistent with UTH climatologies from other sensors. Finally, the paper presents an initial validation of the new data set against IR satellite data and radiosonde data. The observed biases of up to 9%RH (wet bias relative to HIRS) were found to be broadly consistent with expectations based on earlier studies. The observed standard deviations against all other data sets were below 6%RH. The UTH data are available to the scientific community on http://www.sat.ltu.se.

Abstract. The paper presents a cloud filtering method for upper tropospheric humidity (UTH) measurements at 183.31±1.00 GHz. The method uses two criteria: a viewing angle dependent threshold on the brightness temperature at 183.31±1.00 GHz, and a threshold on the brightness temperature difference between another channel and 183.31±1.00 GHz. Two different alternatives, using 183.31±3.00 GHz or 183.31±7.00 GHz as the other channel, are studied. The robustness of this cloud filtering method is demonstrated by a mid-latitudes winter case study. The paper then studies different biases on UTH climatologies. Clouds are associated with high humidity, therefore the possible dry bias introduced by cloud filtering is discussed and compared to the wet biases introduced by the clouds radiative effect if no filtering is done. This is done by means of a case study, and by means of a stochastic cloud database with representative statistics for midlatitude conditions. Both studied filter alternatives perform nearly equally well, but the alternative using 183.31±3.00 GHz as other channel is preferable, because that channel is less likely to see the Earth's surface than the one at 183.31±7.00 GHz. The consistent result of all case studies and for both filter alternatives is that both cloud wet bias and cloud filtering dry bias are modest for microwave data. The recommended strategy is to use the cloud filtered data as an estimate for the true all-sky UTH value, but retain the unfiltered data to have an estimate of the cloud induced uncertainty. The focus of the paper is on midlatitude data, since atmospheric data to test the filter for that case were readily available. The filter is expected to be applicable also to subtropical and tropical data, but should be further validated with case studies similar to the one presented here for those cases.

Abstract. The paper presents a cloud filtering method for upper tropospheric humidity (UTH) measurements at 183.31±1.00 GHz. The method uses two criteria: The difference between the brightness temperatures at 183.31±7.00 and 183.31±1.00 GHz, and a threshold for the brightness temperature at 183.31±1.00 GHz. The robustness of this cloud filter is demonstrated by a mid-latitudes winter case-study. The paper then studies different biases on UTH climatologies. Clouds are associated with high humidity, therefore the dry bias introduced by cloud filtering is discussed and compared to the wet biases introduced by the clouds radiative effect if no filtering is done. This is done by means of a case study, and by means of a stochastic cloud database with representative statistics for midlatitude conditions. The consistent result is that both cloud wet bias (0.8% RH) and cloud filtering dry bias (–2.4% RH) are modest for microwave data, where the numbers given are for the stochastic cloud dataset. This indicates that for microwave data cloud-filtered UTH and unfiltered UTH can be taken as error bounds for errors due to clouds. This is not possible for the more traditional infrared data, since the radiative effect of clouds is much stronger there. The focus of the paper is on midlatitude data, since atmospheric data to test the filter for that case were readily available. The filter is expected to be applicable also to subtropical and tropical data, but should be further validated with case studies similar to the one presented here for those cases.