[1] A comparison between the fast radiative transfer model Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-7) and the physical radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS) was carried out. Radiances were simulated for the sounding channels of the Advanced Microwave Sounding Unit B (AMSU-B) for the whole globe for a single time of a single day (1 January 2000, 0000 UT). Temperature, pressure, and specific humidity profiles from the reanalysis data set ERA-40 of the European Centre for Medium-Range Weather Forecasts (ECMWF) were used as input for both models; geopotential height profiles were also used but only as input for ARTS. The simulations were made for two different surface emissivities, 0.60 and 0.95. The low surface emissivity case exhibits the larger radiance differences. Although the global values of the mean difference and standard deviation are small (for example, the global mean difference for channel 18 is 0.014 K and the standard deviation is 0.232 K), the examination of the geographical distribution of the differences shows that large positive or negative values are observed over dry regions of high northern and southern latitudes and over dry elevated regions. The origin of these differences was found to be due to errors introduced by the transmittance parametrization used in RTTOV.

A Monte Carlo method is used to study the propagation of temperature uncertainties into relative humidity with respect to ice (RHi) calculated from specific humidity. For a flat specific humidity distribution and Gaussian temperature uncertainties the resulting RHi distribution drops exponentially at high RHi values—much slower than a Gaussian. This agrees well with the RHi distribution measured by the Microwave Limb Sounder (MLS), which means that such remotely measured RHi distributions can be explained, at least partly, by temperature uncertainties.

Abstract-This letter presents a cautionary note on the assumption of Gaussian behavior for upper tropospheric humidity (UTH) derived from satellite data in climatological studies, which can introduce a wet bias in the climatology. An example study using European Centre for Medium-Range Weather Forecasts reanalysis data shows that this wet bias can reach up to 6 %RH, which is significant for climatological applications. A simple Monte Carlo approach demonstrates that these differences and their link to the variability of brightness temperatures are due to a log-normal distribution of the UTH. This problem can be solved by using robust estimators such as the median instead of the arithmetic mean.

Abstract. Recently, the reprocessed Advanced Television Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder (ATOVS) tropospheric water vapour and temperature data record was released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). ATOVS observations from infrared and microwave sounders onboard the National Oceanic and Atmospheric Agency (NOAA)-15-19 satellites and EUMETSAT's Meteorological Operational (Metop-A) satellite have been consistently reprocessed to generate 13 years (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) of global water vapour and temperature daily and monthly means with a spatial resolution of 90 km × 90 km. The data set is referenced under the following digital object identifier (DOI): doi:10.5676/EUM_SAF_CM/WVT_ATOVS/V001. After preprocessing, a maximum likelihood solution scheme was applied to the observations to simultaneously infer temperature and water vapour profiles. In a postprocessing step, an objective interpolation method (Kriging) was applied to allow for gap filling. The product suite includes total precipitable water vapour (TPW), layer-integrated precipitable water vapour (LPW) and layer mean temperature for five tropospheric layers between the surface and 200 hPa, as well as specific humidity and temperature at six tropospheric levels between 1000 and 200 hPa. To our knowledge, this is the first time that the ATOVS record (1998-now) has been consistently reprocessed (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) to retrieve water vapour.TPW and LPW products were compared to corresponding products from the Global Climate Observing System (GCOS) Upper-Air Network (GUAN) radiosonde observations and from the Atmospheric Infrared Sounder (AIRS) version 5 satellite data record. TPW shows a good agreement with the GUAN radiosonde data: average bias and root mean square error (RMSE) are −0.2 and 3.3 kg m −2 , respectively. For LPW, the maximum absolute (relative) bias and RMSE values decrease (increase) strongly with height. The maximum bias and RMSE are found at the lowest layer and are −0.7 and 2.5 kg m −2 , respectively. While the RMSE relative to AIRS is generally smaller, the TPW bias relative to AIRS is larger, with dominant contributions from precipitating areas. The consistently reprocessed ATOVS data record exhibits improved quality and stability relative to the operational CM SAF products when compared to the TPW from GUAN radiosonde data over the period 2004-2011. Finally, it became evident that the change in the number of satellites used for the retrieval combined with the use of the Kriging leads to breakpoints in the ATOVS data record; therefore, a variability analysis of the data record is not recommended for the time period from