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E. Reimer, U. Cubasch, A. Claußnitzer, I. Langer P. Névir Institut für Meteorologie Freie Universität Berlin Statistical-dynamical methods for scale dependent.

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Presentation on theme: "E. Reimer, U. Cubasch, A. Claußnitzer, I. Langer P. Névir Institut für Meteorologie Freie Universität Berlin Statistical-dynamical methods for scale dependent."— Presentation transcript:

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

2 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 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.

3 Convective and stratiform cloud types Separation of cloud types for convective and stratiform precipitation analysis 1. cumulus 2. cumulonimbus cumulus mediocris 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 StratusNimbostratusCumulonimbus

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

5 Precipitation median for cloud classes derived from Meteosat and synoptic observations Precipitation [mm/h] > 7 cumulus cumulonimbus cumulus (WMO-type 8) stratusfractus stratus Stratocumulus (WMO-type altocumulus Altocumulus with alto- /nimbostratus

6 Interpolationscheme for precipitation analysis Precipitation scheme using simple linear Interpolation: f 0 = precipitation amount [mm/h] at Gridpoint g i = Weight f i = precipitation amount from observation w 0 = cloud weight t gridpoint w i = cloud weight at observation site di distance between gridpoint r 0 and observation r i and w is the weight (shown above) next step: statistical analysis scheme

7 Beispiel einer Niederschlagsanalyse vom 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), ERTELs 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. Workstep: Investigation of the vertically integrated DSI-field, including information of the vertical humidity profiles and liquid water content. Cooperation with QUEST Correlation: DSI² / Precipitation area mean from LM-output data

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. Cumulonimbus α = 1.21α = 1.84 Nimbostratus α = 2.13α = 2.10 Stratus α = 2.48α = 3.0 Result: Workstep: Further investigation of extreme precipitation (temporal resolution of minutes) Blackforest Brandenburg α Blackforest < α Brandenburg, more extrem values in the Blackforest area

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 Analyse Tagessumme des Niederschlags Monatssumme des Niederschlags für den 12. August 2002


15 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

16 Danke

17 12 August UTC 25 km1 km / 500 m 7 km 3-hourly rainfall (WMO data)hourly rainfall (WMO data)Rainfall network of Berlin (based on minutely data) Scale Dependent Analysis of Precipitation convective stratiform Mean absolute error year 2002 (LM vs. OBS) WMO synoptic observations Satellite data (Meteosat, NOAA) 60 rain gauges in Berlin (5 min) 76 rain gauges in Berlin (1 day) MAE (mm/h) Data Basis

18 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

19 Blackforest Brandenburg stratiform convective MAE 2004 (Juni, Juli, August) for the Blackforest and Brandenburg overestimated by LM Mean = [mm/1h] Mean = [mm/1h] Mean = [mm/1h] Mean = [mm/1h]

20 DSI: 00 UTC +12h Rain: 00 UTC +12h Analysis chart: 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. Result: Predicted DSI-field has the same filament-like precipitation structures. DSI-forecasts as a new precipitation forecast tool

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