German Meteorological Service DWD

Hohenpeißenberg, Germany

German Meteorological Service DWD

Hohenpeißenberg, Germany
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Bennartz R.,Vanderbilt University | Bennartz R.,University of Wisconsin - Madison | Hoschen H.,German Meteorological Service DWD | Picard B.,Collecte Localisation Satellite CLS | And 13 more authors.
Atmospheric Measurement Techniques | Year: 2017

The microwave radiometers (MWRs) on board the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new total column water vapour (TCWV) and wet tropospheric correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERA-Interim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time. However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retrieved WTC is superior to purely ERA-Interim-derived WTC for all satellites and for the entire time series. Even compared to the European Space Agency's (ESA) operational WTC retrievals, which incorporate in addition to MWR additional observational data, the here-described dataset shows improvements in particular for the mid-latitudes and for the two earlier satellites, ERS-1 and ERS-2. The dataset is publicly available under doi:10.5676/DWD-EMIR/V001 (Bennartz et al., 2016). © Author(s) 2017.


Rieder H.E.,ETH Zurich | Jancso L.M.,ETH Zurich | Jancso L.M.,University of Innsbruck | di Rocco S.,ETH Zurich | And 11 more authors.
Tellus, Series B: Chemical and Physical Meteorology | Year: 2011

We apply methods from extreme value theory to identify extreme events in high (termed EHOs) and low (termed ELOs) total ozone and to describe the distribution tails (i.e. very high and very low values) of five long-term European ground-based total ozone time series. The influence of these extreme events on observed mean values, long-term trends and changes is analysed. The results show a decrease in EHOs and an increase in ELOs during the last decades, and establish that the observed downward trend in column ozone during the 1970-1990s is strongly dominated by changes in the frequency of extreme events. Furthermore, it is shown that clear 'fingerprints' of atmospheric dynamics (NAO, ENSO) and chemistry [ozone depleting substances (ODSs), polar vortex ozone loss] can be found in the frequency distribution of ozone extremes, even if no attribution is possible from standard metrics (e.g. annual mean values). The analysis complements earlier analysis for the world's longest total ozone record at Arosa, Switzerland, confirming and revealing the strong influence of atmospheric dynamics on observed ozone changes. The results provide clear evidence that in addition to ODS, volcanic eruptions and strong/moderate ENSO and NAO events had significant influence on column ozone in the European sector. © 2011 The Authors Tellus B © 2011 John Wiley & Sons A/S.


Pleskachevsky A.,German Aerospace Center | Gebhardt C.,German Aerospace Center | Rosenthal W.,German Aerospace Center | Lehner S.,German Aerospace Center | And 6 more authors.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2015

Remote sensing Synthetic Aperture Radar (SAR) data from TerraSAR-X and Tandem-X (TS-X and TD-X) satellites have been used for validation and verification of newly developed coastal forecast models in the German Bight of the North Sea. The empirical XWAVE algorithm for estimation of significant wave height has been adopted for coastal application and implemented for NRT services. All available TS-X images in the German Bight collocated with buoy measurements (6 buoys) since 2013 were processed and analysed (total of 46 scenes/passages with 184 StripMap images). Sea state estimated from series of TS-X images cover strips with length of ∼200km and width of 30km over the German Bight from East-Frisian Islands to the Danish coast. The comparisons with results of wave prediction model show a number of local variations due to variety in bathymetry and wind fronts.


Lehner S.,German Aerospace Center | Pleskachevsky A.,German Aerospace Center | Gebhardt C.,German Aerospace Center | Rosenthal W.,German Aerospace Center | And 3 more authors.
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2015

Remote sensing Synthetic Aperture Radar (SAR) data from TerraSAR-X and Tandem-X (TS-X and TD-X) satellites have been used for validation and verification of newly developed coastal forecast models in the German Bight of the North Sea. The empirical XWAVE algorithm for estimation of significant wave height from X-band satellite data has been adopted for coastal application. All available TS-X images in the German Bight collocated with measurements of 6 buoys since 2013 were processed and analysed (53 passages with 196 StripMap images). One TS-X overflight covers strips with length of ∼200km and width of 30km over the German Bight from East-Frisian Islands to the Danish coast with a sequence of 3-6 StripMap images. The comparisons with results of wave prediction model show a number of local variations due to variety in bathymetry and wind fronts. The developed Sea State Processor SSP includes XWAVE, pre-filtering of ships and other artefacts and checking results was implemented for NRT services. The comparison with in-situ buoy measurements results in Scatter Index of 20% and RMSE of 0.25m. © 2015 IEEE.


Dalelane C.,German Meteorological Service DWD | Deutschlander T.,German Meteorological Service DWD
Weather and Climate Extremes | Year: 2013

The article examines extreme temperature events defined as the exceedances of several high quantiles of temperature anomalies in regional climate model data over Germany as an example for the analysis of meteorological extremes using a two step-nonparametric approach. In the first step we estimate the intensities of the Poisson point processes of temperature extremes using a kernel estimator which so far has only rarely been used in climatology. Its advantages include robustness against model selection errors, simple computability and intuitive interpretability. In the second step we aggregate the pointwise intensity curves by means of functional cluster analysis to form regions, where the exceedance probabilities of the quantiles evolve similarly over time. A distinct gradient from Northwest to Southeast is found in the data with frequencies of exceedance of the 99% quantile of more than 1000% at the end of the 21st century compared to the control period 1961-2000. © 2013 The Authors.

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