Short Range Ensemble Prediction System Verification over Greece Petroula Louka, Flora Gofa Hellenic National Meteorological Service.

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Short Range Ensemble Prediction System Verification over Greece
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Short Range Ensemble Prediction System Verification over Greece Petroula Louka, Flora Gofa Hellenic National Meteorological Service

COSMO 9th General Meeting HNMS involvement in COSMO-SREPS Task 6 HNMS involvement in COSMO-SREPS Task 6  Verification of LM-COSMO ensemble forecasts for Autumn cases of 72-hour forecast horizon, 16 members Verification domain: Greece Data used: SYNOP data covering Greece Parameters verified: o2m temperature oMean Sea Level Pressure (MSLP) oPrecipitation Statistical analysis of the results Statistical analysis of the results

COSMO 9th General Meeting COSMO-SREPS domain COSMO-SREPS domain

COSMO 9th General Meeting Greek SYNOP stations 30 stations covering Greece

COSMO 9th General Meeting Statistical analysis methods Statistical analysis methods  For continuous parameters such as Temperature and MSLP BiasRMSE  For non-continuous parameters (precipitation) Deterministic approach oMulti-category contingency tables (limits: 0-0.1, , , >9.0 mm) oPOD, FAR, ETS, etc Probabilistic approach (e.g., ROC diagrams) (average of all 16 members)

COSMO 9th General Meeting Point selection Point selection Closest point Interpolated value (1,1)(1,2)(1,3) (2,1)(2,3)(2,2) (3,3)(3,2)(3,1)

COSMO 9th General Meeting COSMO-SREPS members COSMO-SREPS members

COSMO 9th General Meeting Temperature & MSLP Statistical analysis Temperature & MSLP Statistical analysis  Bias and RMSE averaged over all forecast members and stations

COSMO 9th General Meeting 2m Temperature – Bias 2m Temperature – Bias

COSMO 9th General Meeting 2m Temperature – RMSE 2m Temperature – RMSE

COSMO 9th General Meeting 2m Temperature – Bias 2m Temperature – Bias

COSMO 9th General Meeting 2m Temperature – RMSE 2m Temperature – RMSE

COSMO 9th General Meeting Mean 2m Temperature SYNOP

COSMO 9th General Meeting MSLP – Bias MSLP – Bias

COSMO 9th General Meeting MSLP – RMSE MSLP – RMSE

COSMO 9th General Meeting MSLP – Bias MSLP – Bias

COSMO 9th General Meeting MSLP – RMSE MSLP – RMSE

COSMO 9th General Meeting Mean MSLP SYNOP Mean MSLP SYNOP

COSMO 9th General Meeting COSMO-SREPS members COSMO-SREPS members

COSMO 9th General Meeting Precipitation Statistical analysis Precipitation Statistical analysis  Contingency table  Probability Of Detection (POD) to examine the occurrence of the event  False Alarm Ratio (FAR)  Threat Score (TS) to examine the performance of rare events Limits used: mm, mm, mm, >9.0 mm

COSMO 9th General Meeting Event Forecast Event observed YesNo Total YesHit False alarm Fc Yes NoMiss Correct rejection Fc No Τ otalObs YesObs NoSum total Event Forecast Event observed YesNo Total Yesaba+b Nocdc+d Totala+cb+da+b+c+d=n Precipitation Contingency table Precipitation Contingency table 4x4 table for each 6-hour forecast

COSMO 9th General Meeting Precipitation – Example of contingency table Precipitation – Example of contingency table

COSMO 9th General Meeting Precipitation – POD mm Precipitation – POD mm IFS GME UKMO NCEP

COSMO 9th General Meeting Precipitation – POD mm Precipitation – POD mm IFS GME UKMO NCEP

COSMO 9th General Meeting Precipitation – FAR 0-0.1mm Precipitation – FAR 0-0.1mm IFS GME UKMO NCEP

COSMO 9th General Meeting Precipitation – FAR mm Precipitation – FAR mm IFS GME UKMO NCEP

COSMO 9th General Meeting Precipitation – POD Precipitation – POD Tiedtke, pat_len500 Kain-Fritsch, pat_len500 Tiedtke, tur_len1000, pat_len500 Tiedtke, pat_len10000

COSMO 9th General Meeting Precipitation – FAR Precipitation – FAR Tiedtke, pat_len500 Kain-Fritsch, pat_len500 Tiedtke, tur_len1000, pat_len500 Tiedtke, pat_len10000

COSMO 9th General Meeting Example of Precipitation field 28.0mm 21.0mm 03/11/06LM3 Period LM3: IFS, tiedtke (T), tur_len (1000), pat_len (500)

COSMO 9th General Meeting Example of Precipitation field 28.0mm 21.0mm 03/11/06LM7 Period LM7: GME, tiedtke (T), tur_len (1000), pat_len (500)

COSMO 9th General Meeting Example of Precipitation field 21.0mm 28.0mm 03/11/06LM11 Period LM11: NCEP, tiedtke (T), tur_len (1000), pat_len (500)

COSMO 9th General Meeting Example of Precipitation field 21.0mm 28.0mm 03/11/06LM15 Period LM15: UKMO, tiedtke (T), tur_len (1000), pat_len (500)

COSMO 9th General Meeting Lightnings observed

COSMO 9th General Meeting Satellite image 03/11/2006 Geostationary 00UTC

COSMO 9th General Meeting Example of Precipitation field 03/11/06LM3 Period LM3: IFS, tiedtke (T), tur_len (1000), pat_len (500)

COSMO 9th General Meeting Example of Precipitation field 03/11/06LM7 Period LM7: GME, tiedtke (T), tur_len (1000), pat_len (500) 0.0mm >5mm

COSMO 9th General Meeting Example of Precipitation field 03/11/06LM11 Period LM11: NCEP, tiedtke (T), tur_len (1000), pat_len (500)

COSMO 9th General Meeting Example of Precipitation field 03/11/06LM15 Period LM15: UKMO, tiedtke (T), tur_len (1000), pat_len (500) 0.5mm >5mm

COSMO 9th General Meeting Lightnings observed

COSMO 9th General Meeting Satellite image 04/11/2006 Geostationary 12UTC

COSMO 9th General Meeting Summary – Suggestions  In general, averaged BIAS and RMSE values showed a small overestimation for MSLP and statistically acceptable values (~ 2C for 2m Temperature and <5 mb for MSLP) apart from a few specific forecasts for which RMSE is large compared to the mean value.

COSMO 9th General Meeting Summary – Suggestions  The sample used is statistically small for extracting conclusive information regarding the precipitation forecast  The available results showed that Precipitation amounts are generally overestimated The influence of the different initial conditions on the forecasted precipitation field is evident The influence of convective scheme and turbulent length scale is important mainly on forecasting accurately the presence of a precipitation event (POD value)

COSMO 9th General Meeting Future plans  Application of the existed statistical methods to a larger sample  Extend the statistics to include other meteorological parameters  Investigation of precipitation ensemble forecasts using probabilistic approach (ROC diagrams, etc)  Possibility of examining the performance of ensemble forecasting on upper air meteorology