The apparent cooling trend in observed global mean temperature series from radiosonde records relative to Microwave Sounding Unit (MSU) radiances has been a long-standing problem in upper-air climatology. It is very likely caused by a warm bias of radiosonde temperatures in the 1980s, which has been reduced over time with better instrumentation and correction software. The warm bias in the MSU-equivalent lower stratospheric (LS) layer is estimated as 0.6 Ϯ 0.3 K in the global mean and as 1.0 Ϯ 0.3 K in the tropical (20°S-20°N) mean. These estimates are based on comparisons of unadjusted radiosonde data, not only with MSU data but also with background forecast (BG) temperature time series from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and with two new homogenized radiosonde datasets. One of the radiosonde datasets [Radiosonde Observation Correction using Reanalyses (RAOBCORE) version 1.4] employs the BG as reference for homogenization, which is not strictly independent of MSU data. The second radiosonde dataset uses the dates of the breakpoints detected by RAOBCORE as metadata for homogenization. However, it relies only on homogeneous segments of neighboring radiosonde data for break-size estimation. Therefore, adjustments are independent of satellite data.Both of the new adjusted radiosonde time series are in better agreement with satellite data than comparable published radiosonde datasets, not only for zonal means but also at most single stations. A robust warming maximum of 0.2-0.3K (10 yr)Ϫ1 for the 1979-2006 period in the tropical upper troposphere could be found in both homogenized radiosonde datasets. The maximum is consistent with mean temperatures of a thick layer in the upper troposphere and upper stratosphere (TS), derived from M3U3 radiances. Inferred from these results is that it is possible to detect and remove most of the mean warm bias from the radiosonde records, and thus most of the trend discrepancy compared to MSU LS and TS temperature products.The comprehensive intercomparison also suggests that the BG is temporally quite homogeneous after 1986. Only in the early 1980s could some inhomogeneities in the BG be detected and quantified.

This article describes progress in the homogenization of global radiosonde temperatures with updated versions of the Radiosonde Observation Correction Using Reanalyses (RAOBCORE) and Radiosonde Innovation Composite Homogenization (RICH) software packages. These are automated methods to homogenize the global radiosonde temperature dataset back to 1958. The break dates are determined from analysis of time series of differences between radiosonde temperatures (obs) and background forecasts (bg) of climate data assimilation systems used for the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and the ongoing interim ECMWF Re-Analysis (ERA-Interim).RAOBCORE uses the obs2bg time series also for estimating the break sizes. RICH determines the break sizes either by comparing the observations of a tested time series with observations of neighboring radiosonde time series (RICH-obs) or by comparing their background departures (RICH-t). Consequently RAOBCORE results may be influenced by inhomogeneities in the bg, whereas break size estimation with RICH-obs is independent of the bg. The adjustment quality of RICH-obs, on the other hand, may suffer from large interpolation errors at remote stations. RICH-t is a compromise that substantially reduces interpolation errors at the cost of slight dependence on the bg. Adjustment uncertainty is estimated by comparing the three methods and also by varying parameters in RICH. The adjusted radiosonde time series are compared with recent temperature datasets based on (Advanced) Microwave Sounding Unit [(A)MSU] radiances. The overall spatiotemporal consistency of the homogenized dataset has improved compared to earlier versions, particularly in the presatellite era. Vertical profiles of temperature trends are more consistent with satellite data as well.

This paper presents a method to detect and correct occurring biases in observational mean sea level pressure (MSLP) data, which was developed within the Mesoscale Alpine Climate Dataset [MESOCLIM; i.e., 3-hourly MSLP, potential and equivalent potential temperature Vienna Enhanced Resolution Analysis (VERA) analyses for a 3000 km 3 3000 km area centered over the Alps during 1971Alps during -2005 project. There are many reasons for a change of a measurement site's performance, for example, a change in the instrumentation, a slight modification of the site's place or position, or a different way of data processing (pressure reduction). To get an estimate for these artificial influences in the data, deviations for each reporting station at each point of time were calculated, using a piecewise functional fitting approach that is based on a variational algorithm. In this algorithm first-and second-order spatial derivatives are minimized using the tested stations neighbor stations and furthermore their neighbors. The resulting time series of deviations for each station were then tested with a ''standard normal homogeneity test'' to detect changes in the mean deviation. With the knowledge of these ''break points,'' biascorrection estimates for each station were calculated. These correction estimates are constant between the detected break points because the method does not detect different slopes in trends. Application of these correction estimates yields in smoother fields and a more homogenous distribution of trends.

MetGIS is an innovative Java-based, combined Meteorological and Geographic Information System, with a specific emphasis on snow and mountain weather. This constantly upgraded prediction scheme has been developed within the framework of a number of interdisciplinary international research projects. A principal focus of the system is the automated production of high-resolution, downscaled forecast maps of meteorological parameters such as precipitation, fresh snow amounts, the snow limit, the form of precipitation, wind and air temperature.The geographic part of the system includes topographies relying on data bases such as SRTM (Shuttle Radar Topographic Mission) and representations of roads, rivers, railway lines, political borders and cities. On top of these, partly linked to terrain features, down-scaled meteorological information can be visualized in a variety of display styles. Meteorological forecast data of any numerical model with common output data formats can be used as a starting point for the downscaling procedures. Currently, the real-time output of the GFS (Global Forecast System of the US National Weather Service) is used as a base for MetGIS  forecasts. Verification results are quite encouraging so far. Mean absolute errors are in the range of 1.3-3°C for 36 h temperature forecasts, and around 80% of the 24 h forecasts predicted correctly, if the precipitation will be below or above 1 mm.