E. Reimer, U. Cubasch, A. Claußnitzer, I. LangerP. Névir

Institut für Meteorologie Freie Universität Berlin

Statistical-dynamical methods for scale dependent model evaluation and short term

precipitation forecasting (STAMPF / FU-Berlin)

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.

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

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

Precipitation median for cloud classes derived from Meteosat and synoptic observations

Precipitation [mm/h]

0-0.9 1-1.9 2-2.9 3-3.9 4.-4.9 5.-5.9 6-6.9 > 7

cumulus 0.047 0.022 0.023 0.015 0.017 0.009 0.005 0.007

cumulonimbus 0.066 0.067 0.082 0.092 0.10 0.10 0.091 0.10

cumulus (WMO-type 8)

0.042 0.024 0.022 0.016 0.016 0.012 0.011 0.009

stratusfractus 0.10 0.025 0.021 0.005 0.008 0.008 0.005 0.0007

stratus 0.09 0.089 0.082 0.062 0.048 0.032 0.024 0.011

Stratocumulus (WMO-type8

0.064 0.042 0.034 0.025 0.017 0.013 0.014 0.005

altocumulus 0.055 0.064 0.066 0.079 0.076 0.050 0.056 0.049

Altocumulus with alto-/nimbostratus

0.080 0.084 0.077 0.067 0.049 0.033 0.020 0.011

Interpolationscheme for precipitation analysis

Precipitation scheme using simple linear Interpolation:

, ,

f0 = precipitation amount [mm/h] at Gridpointgi = Weightfi = precipitation amount from observationw0 = cloud weight t gridpointwi = 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

Beispiel einer Niederschlagsanalyse vom 12.8.2002

Niederschlagssumme Niederschlagswahr- Niederschlagssumme ohne Satellitenkorrektur scheinlichkeit aus Meteosat mit Satellitenkorrektur

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!

January7

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 verticallyintegrated DSI-field, including informationof the vertical humidity profiles and liquidwater content. Cooperation with „QUEST“

Correlation: DSI² / Precipitationarea mean from LM-output data

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 = 1.21 α = 1.84

Nimbostratus α = 2.13 α = 2.10

Stratus α = 2.48 α = 3.0

Result:

Workstep: Further investigation of extremeprecipitation (temporal resolution of minutes)

Blackforest Brandenburg

αBlackforest < α Brandenburg, more extrem values in the Blackforest area

Convective rain intensity versus duration obeys apower law!

Result: Explanation using TurbulenceTheory of Kolmogorov and Richardson

Workstep: Testing the hypothesis that the turbulent momentum flux (frictionvelocity), the mixing ratio r, energy dissipation and accelerations determine the maximum rain intensity in convective cloud layers (COPS).

5min - Messungen

Tagesmessungen

Niederschlagssummen (mm) vom 12.8.2002Analyse

Tagessumme des Niederschlags Monatssumme des Niederschlagsfür den 12. August 2002

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

Danke

12 August 2002 20 UTC

25 km 1 km / 500 m7 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

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

Blackforest Brandenburg

stratiform

convective

MAE 2004 (Juni, Juli, August) for the Blackforest and Brandenburg

overestimated by LM

Mean = 0.075 [mm/1h]

Mean = 0.1751 [mm/1h]

Mean = 0.059 [mm/1h]

Mean = 0.1332 [mm/1h]

DSI total precipitation analysis chartDSI: 00 UTC +12h Rain: 00 UTC +12h Analysis chart: 21.09.04

Workstep: Exploring the precipitation forecast skill of the DSI by comparisonthe correlation of the DSI on different isentropic levels with LMK precipitationforecasts. / 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