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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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Statistical-dynamical methods for scale dependent model evaluation and short term precipitation forecasting (STAMPF / FU-Berlin)
E. Reimer, U. Cubasch, A. Claußnitzer, I. Langer
Institut für Meteorologie
Freie Universität Berlin
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.
Precipitation scheme using simple linear Interpolation:
f0= precipitation amount [mm/h] atGridpoint
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
Niederschlagssumme Niederschlagswahr- Niederschlagssumme
ohne Satellitenkorrektur scheinlichkeit aus Meteosat mit Satellitenkorrektur
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“
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)
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: 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).
Tagessumme des Niederschlags Monatssumme des Niederschlags
für den 12. August 2002
12 August 2002 20 UTC
3-hourly rainfall (WMO data)
hourly rainfall (WMO data)
Rainfall network of Berlin
(based on minutely data)
1 km / 500 m
Mean absolute error year 2002 (LM vs. OBS)
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
Mean = 0.059 [mm/1h]
Mean = 0.075 [mm/1h]
overestimated by LM
Mean = 0.1751 [mm/1h]
Mean = 0.1332 [mm/1h]
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.