The 183-GHz water vapor absorption band measurements from the Advanced Microwave Sounding Unit B (AMSU-B) and Microwave Humidity Sounder (MHS) on board polar-orbiting satellites were processed to produce a bias-corrected, intersatellite calibrated microwave brightness temperature data set suitable for longterm climate monitoring. The data set generation involves radiative transfer simulations to adjust for biases arising from satellite orbital drift and difference in local observation time between satellites. − Grid resolution: 1.5°×1.5°. − Contained fields: inter-satellite calibrated 183.31±1 GHz channel monthly mean brightness temperatures [K]. − Time span: January 1999 December 2014.

Numerical weather prediction (NWP) today relies more and more on satellite data, both for assimilation and for evaluation. However, process-based analyses of the biases between observed and simulated satellite data, which go beyond a mere identification of the biases, are rare. The present study investigates a long-known bias (Bohme et al., 2011) between brightness temperatures (BTs) simulated from the regional NWP model COSMO-DE forecasts via RTTOV (Radiative Transfer for TOVS) and those observed by Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). The pivotal question is whether a novel two-moment cloud ice scheme, developed by Kohler (2013) primarily to improve the representation of ice nucleation processes, exhibits an improved performance with respect to this bias and, if that is so, to provide a process-based analysis which identifies the reasons for the improved behaviour.It is shown that the new two-moment cloud ice scheme reduces the BT bias distinctly and can therefore be considered an improvement in comparison to two standard schemes, the two-category ice scheme and the three-category ice scheme. The improvement in simulated BTs is due to a vertical redistribution of cloud ice to lower model levels. Sensitivity studies identify two of the introduced changes in the two-moment cloud ice scheme to be hand-in-hand responsible for most of the improved performance: the choice of heterogeneous ice nucleation scheme and the consideration of cloud ice sedimentation. Including only cloud ice sedimentation without changing the heterogeneous ice nucleation scheme has no distinct effect on cloud ice. Further sensitivity studies with varying aerosol number densities reveal a comparably small sensitivity, indicating that the use of a physically reasonable heterogeneous ice nucleation scheme is far more important than the exact knowledge of the actual aerosol number densities. (C) 2015 Elsevier B.V. All rights reserved.

The spatio-temporal variability of integrated water vapour (IWV) on small scales of less than 10 km and hours is assessed with data from the 2 months of the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE). The statistical intercomparison of the unique set of observations during HOPE (microwave radiometer (MWR), Global Positioning System (GPS), sun photometer, radiosondes, Raman lidar, infrared and near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellites Aqua and Terra) measuring close together reveals a good agreement in terms of random differences (standard deviation ≤1 kg m−2) and correlation coefficient (≥ 0.98). The exception is MODIS, which appears to suffer from insufficient cloud filtering. For a case study during HOPE featuring a typical boundary layer development, the IWV variability in time and space on scales of less than 10 km and less than 1 h is investigated in detail. For this purpose, the measurements are complemented by simulations with the novel ICOsahedral Nonhydrostatic modelling framework (ICON), which for this study has a horizontal resolution of 156 m. These runs show that differences in space of 3–4 km or time of 10–15 min induce IWV variabilities on the order of 0.4 kg m−2. This model finding is confirmed by observed time series from two MWRs approximately 3 km apart with a comparable temporal resolution of a few seconds. Standard deviations of IWV derived from MWR measurements reveal a high variability (> 1 kg m−2) even at very short time scales of a few minutes. These cannot be captured by the temporally lower-resolved instruments and by operational numerical weather prediction models such as COSMO-DE (an application of the Consortium for Small-scale Modelling covering Germany) of Deutscher Wetterdienst, which is included in the comparison. However, for time scales larger than 1 h, a sampling resolution of 15 min is sufficient to capture the mean standard deviation of IWV. The present study shows that instrument sampling plays a major role when climatological information, in particular the mean diurnal cycle of IWV, is determined.

The spatio-temporal variability of integrated water vapour (IWV) on small scales of less than 10 km and hours is assessed with data from the 2 months of the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)) Observational Prototype Experiment (HOPE). The statistical intercomparison of the unique set of observations during HOPE (microwave radiometer (MWR), Global Positioning System (GPS), sun photometer, radiosondes, Raman lidar, infrared and near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellites Aqua and Terra) measuring close together reveals a good agreement in terms of random differences (standard deviation ≤ 1 kgm) and correlation coefficient (≥ 0.98). The exception is MODIS, which appears to suffer from insufficient cloud filtering. For a case study during HOPE featuring a typical boundary layer development, the IWV variability in time and space on scales of less than 10 km and less than 1 h is investigated in detail. For this purpose, the measurements are complemented by simulations with the novel ICOsahedral Nonhydrostatic modelling framework (ICON), which for this study has a horizontal resolution of 156 m. These runs show that differences in space of 3–4 km or time of 10–15 min induce IWV variabilities on the order of 0.4 kgm. This model finding is confirmed by observed time series from two MWRs approximately 3 km apart with a comparable temporal resolution of a few seconds. Standard deviations of IWV derived from MWR measurements reveal a high variability (> 1 kg m) even at very short time scales of a few minutes. These cannot be captured by the temporally lower-resolved instruments and by operational numerical weather prediction models such as COSMO-DE (an application of the Consortium for Small-scale Modelling covering Germany) of Deutscher Wetterdienst, which is included in the comparison. However, for time scales larger than 1 h, a sampling resolution of 15 min is sufficient to capture the mean standard deviation of IWV. The present study shows that instrument sampling plays a major role when climatological information, in particular the mean diurnal cycle of IWV, is determined.