GLORIA

GEOMAR Library Ocean Research Information Access

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Book
    Book
    Karlsruhe : Institut für Meteorologie und Klimaforschung Universität Karlsruhe (TH)
    Keywords: Hochschulschrift ; Niederschlagsmessung ; Wetterradar ; Extinktion
    Type of Medium: Book
    Pages: IV, 341 S. , Ill., graph. Darst., Kt.
    Series Statement: Wissenschaftliche Berichte des Instituts für Meteorologie und Klimaforschung 35
    RVK:
    RVK:
    Language: German
    Note: Zugl.: Karlsruhe, Univ., Diss., 2004
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2024-03-06
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉The usually short lifetime of convective storms and their rapid development during unstable weather conditions makes forecasting these storms challenging. It is necessary, therefore, to improve the procedures for estimating the storms' expected life cycles, including the storms' lifetime, size, and intensity development. We present an analysis of the life cycles of convective cells in Germany, focusing on the relevance of the prevailing atmospheric conditions. Using data from the radar‐based cell detection and tracking algorithm KONRAD of the German Weather Service, the life cycles of isolated convective storms are analysed for the summer half‐years from 2011 to 2016. In addition, numerous convection‐relevant atmospheric ambient variables (e.g., deep‐layer shear, convective available potential energy, lifted index), which were calculated using high‐resolution COSMO‐EU assimilation analyses (0.0625°), are combined with the life cycles. The statistical analyses of the life cycles reveal that rapid initial area growth supports wider horizontal expansion of a cell in the subsequent development and, indirectly, a longer lifetime. Specifically, the information about the initial horizontal cell area is the most important predictor for the lifetime and expected maximum cell area during the life cycle. However, its predictive skill turns out to be moderate at most, but still considerably higher than the skill of any ambient variable is. Of the latter, measures of midtropospheric mean wind and vertical wind shear are most suitable for distinguishing between convective cells with short lifetime and those with long lifetime. Higher thermal instability is associated with faster initial growth, thus favouring larger and longer living cells. A detailed objective correlation analysis between ambient variables, coupled with analyses discriminating groups of different lifetime and maximum cell area, makes it possible to gain new insights into their statistical connections. The results of this study provide guidance for predictor selection and advancements of nowcasting applications.〈/p〉
    Description: 〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Based on a combination of data of the cell tracking algorithm KONRAD of the German Weather Service and COSMO‐EU model analyses for the summer half‐years from 2011 to 2016, statistical relationships between storm attributes (lifetime and maximum horizontal area), and ambient variables as well as the storms' history are quantified. The initial growth of the cell area is a better indicator of the lifetime and maximum area than ambient variables are. Of the latter, measures of the midtropospheric wind and vertical wind shear, in particular, are most suitable for distinguishing between convective cells with short and long lifetimes, whereas higher convective instability favours larger cells. 〈boxed-text position="anchor" id="qj4505-blkfxd-0001" content-type="graphic" xml:lang="en"〉〈graphic position="anchor" id="jats-graphic-1" xlink:href="urn:x-wiley:00359009:media:qj4505:qj4505-toc-0001"〉 〈/graphic〉 〈/boxed-text〉〈/p〉
    Description: Bundesministerium für Digitales und Verkehr http://dx.doi.org/10.13039/100008383
    Keywords: ddc:551.6 ; convective storms ; life cycle ; multisource data ; nowcasting ; statistics ; weather prediction
    Language: English
    Type: doc-type:article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Publication Date: 2022-03-31
    Description: The local ensemble transform Kalman filter (LETKF) suggested by Hunt et al., 2007 is a very popular method for ensemble data assimilation. It is the operational method for convective‐scale data assimilation at Deutscher Wetterdienst (DWD). At DWD, based on the LETKF, three‐dimensional volume radar observations are assimilated operationally for the operational ICON‐D2. However, one major challenge for the LETKF is the situation where observations show precipitation (reflectivity) whereas all ensemble members do not show such reflectivity at a given point in space. In this case, there is no sensitivity of the LETKF with respect to the observations, and the analysis increment based on the observed reflectivity is zero. The goal of this work is to develop a targeted covariance inflation (TCI) for the assimilation of 3D‐volume radar data based on the LETKF, adding artificial sensitivity and making the LETKF react properly to the radar observations. The basic idea of the TCI is to employ an additive covariance inflation as entrance point for the LETKF. Here, we construct perturbations to the simulated observation which are used by the core LETKF assimilation step. The perturbations are constructed such that they exhibit a correlation between humidity and reflectivity. This leads to a change in humidity in such a way that precipitation is more likely to occur. We describe and demonstrate the theoretical basis of the method. We then present a case study where targeted covariance inflation leads to a clear improvement of the LETKF and precipitation forecast. All examples are based on the German radar network and the ICON‐D2 model over Central Europe.
    Description: The goal of this work is to develop a targeted covariance inflation (TCI) for the assimilation of 3D‐volume radar data based on the local ensemble transform Kalman filter (LETKF), adding artificial sensitivity and making the LETKF react properly to the radar observations. Perturbations to the simulated observations are constructed such that they exhibit an empirically derived correlation between humidity and reflectivity. This leads to a change in humidity in such a way that precipitation is more likely to occur.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:551.5
    Language: English
    Type: doc-type:article
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 4
    Publication Date: 2021-09-27
    Description: To account for model error on multiple scales in convective-scale data assimilation, we incorporate the small-scale additive noise based on random samples of model truncation error and combine it with the large-scale additive noise based on random samples from global climatological atmospheric background error covariance. A series of experiments have been executed in the framework of the operational Kilometre-scale ENsemble Data Assimilation system of the Deutscher Wetterdienst for a 2-week period with different types of synoptic forcing of convection (i.e., strong or weak forcing). It is shown that the combination of large- and small-scale additive noise is better than the application of large-scale noise only. The specific increase in the background ensemble spread during data assimilation enhances the quality of short-term 6-hr precipitation forecasts. The improvement is especially significant during the weak forcing period, since the small-scale additive noise increases the small-scale variability which may favor occurrence of convection. It is also shown that additional perturbation of vertical velocity can further advance the performance of combination.
    Keywords: 551.5 ; additive noise ; model truncation error ; multiscale ; radar data assimilation ; probabilistic forecasts
    Language: English
    Type: map
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...