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E. Reimer, U. Cubasch, A. Claußnitzer, I. Langer P. Névir Institut für Meteorologie

Statistical-dynamical methods for scale dependent model evaluation and short term precipitation forecasting (STAMPF / FU-Berlin). E. Reimer, U. Cubasch, A. Claußnitzer, I. Langer P. Névir Institut für Meteorologie Freie Universität Berlin.

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E. Reimer, U. Cubasch, A. Claußnitzer, I. Langer P. Névir Institut für Meteorologie

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  1. Statistical-dynamical methods for scale dependent model evaluation and short term precipitation forecasting (STAMPF / FU-Berlin) E. Reimer, U. Cubasch, A. Claußnitzer, I. Langer P. Névir Institut für Meteorologie Freie Universität Berlin

  2. The central focus of this project is a scale dependent evaluation of precipitation forecasts of the LMK / LME using dynamical, and statistical parameters as well as cloud properties. • Separation of stratiform and convective precipitation and objective analysis combing WMO observations, rain gauge data and Meteosat-8 cloud data. • Analyse of (convective) precipitation by high resolution data from Berlin rain gauge stations n combination to satellite data and radar data. • Process-oriented dynamical evaluation of precipitation forecasts using the Dynamic State Index (DSI) • Statistical diagnostics of precipitation fields by means of scaling exponents, or Shannon`s information entropy • Participation in campaign COPS/GOP in 2007 in Southwest Germany and Germany

  3. Convective and stratiform cloud types • Separation of cloud types for convective and stratiform precipitation analysis • 1. cumulus2. cumulonimbus● cumulusmediocris ● cumulonimbus calvus● cumulus congestus ● cumulonimbus capilatus● cumulus and stratocumulus (weight by 33%) • 3. stratiform● cumulus and stratocumulus (weight by 67%) ● stratus nebulosus ● stratus fractus ● nimbostratus Cumulonimbus Nimbostratus Stratus

  4. Cloud classification from Meteosat-7 data for 12. August 2002 13 UTC

  5. Precipitation median for cloud classes derived from Meteosat and synoptic observations

  6. Interpolationscheme for precipitation analysis Precipitation scheme using simple linear Interpolation: f0= precipitation amount [mm/h] atGridpoint gi= Weight fi = precipitation amount from observation w0 = cloud weight t gridpoint wi = cloud weight at observation site di distance between gridpoint r0 and observation ri and w is the weight (shown above) next step: statistical analysis scheme

  7. Beispiel einer Niederschlagsanalyse vom 12.8.2002 Niederschlagssumme Niederschlagswahr- Niederschlagssumme ohne Satellitenkorrektur scheinlichkeit aus Meteosat mit Satellitenkorrektur

  8. Process-oriented dynamical evaluation with Dynamic State Index (DSI) The DSI locally combines information from energy (B), ERTEL’s potential vorticity (Π) and entropy (θ).DSI describes all non-stationary / diabatic processes! Result: High correlation (40-60%) between DSI² and LM-precipitation shows, that the DSI is a dynamical threshold parameter for rainfall processes. Threshold: stationary, adiabatic solution of the primitive equations. Correlation: DSI² / Precipitation area mean from LM-output data Workstep: Investigation of the vertically integrated DSI-field, including information of the vertical humidity profiles and liquid water content. Cooperation with „QUEST“

  9. Statistical evaluation of precipitation through scaling exponent Scaling exponent α is a statistical parameter which indicates probability of extreme precipitation. Smaller values of α characterise distributions with high intensity tails. Workstep: Further investigation of extreme precipitation (temporal resolution of minutes) Blackforest Brandenburg Cumulonimbus α = 1.21 α = 1.84 Nimbostratus α = 2.13 α = 2.10 Stratus α = 2.48 α = 3.0 αBlackforest < αBrandenburg, more extrem values in the Blackforest area Result:

  10. Convective rain intensity versus duration obeys a power law! Result: Explanation using Turbulence Theory of Kolmogorov and Richardson Workstep:Testing the hypothesis that the turbulent momentum flux (friction velocity), the mixing ratio r, energy dissipation and accelerations determine the maximum rain intensity in convective cloud layers (COPS).

  11. 5min - Messungen

  12. Tagesmessungen

  13. Niederschlagssummen (mm) vom 12.8.2002Analyse Tagessumme des Niederschlags Monatssumme des Niederschlags für den 12. August 2002

  14. Arbeiten und Aussicht • Weitere Aufbereitung der Berliner Niederschlagsdaten 2006 und 2007 • Kontrolle der Niederschlagsmessungen über 5-Minuten- und Tagessummen • Verwendung der Radarechos für die Analyse der 5-Minutensummen im 500m bis 1km Gitter • Auswertung der Intensitäten für verschiedene Zeitintervalle • Teilnahme an GOP • Berücksichtigung der Windprofile aus dem LMK und Beobachtungen • Einbeziehung von Niederschlagsprofilen vom Vertikalradar (Peters, Hamburg) für 2007 • Vergleich der Messungen und Radardaten mit LMK (2,8km Gitter) des DWD für 2002 jetzt und 2007

  15. Danke

  16. Scale Dependent Analysis of Precipitation 12 August 2002 20 UTC 3-hourly rainfall (WMO data) hourly rainfall (WMO data) Rainfall network of Berlin (based on minutely data) 25 km 1 km / 500 m 7 km Mean absolute error year 2002 (LM vs. OBS) Data Basis stratiform MAE (mm/h) convective • WMO synoptic observations • Satellite data (Meteosat, NOAA) • 60 rain gauges in Berlin (5 min) • 76 rain gauges in Berlin (1 day)

  17. Mean absolute error (2004) for different forecast period (LM forecast- FUB analysis) MAE of convective precipitation is greater than stratiform precipitation Total precipitation is dominated by the convective precipitation

  18. MAE 2004 (Juni, Juli, August) for the Blackforest and Brandenburg Blackforest Brandenburg stratiform Mean = 0.059 [mm/1h] Mean = 0.075 [mm/1h] convective overestimated by LM Mean = 0.1751 [mm/1h] Mean = 0.1332 [mm/1h]

  19. DSI-forecasts as a new precipitation forecast tool Rain: 00 UTC +12h DSI: 00 UTC +12h Analysis chart: 21.09.04 Result: Predicted DSI-field has the same filament-like precipitation structures. Workstep: Exploring the precipitation forecast skill of the DSI by comparison the correlation of the DSI on different isentropic levels with LMK precipitation forecasts. / This workstep will also be extended to the special case studies.

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