application of low- resolution eta model data to provide guidance to high impact weather in complex...
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Application of low-resolution ETA model data to provide guidance to high impact weather in complex terrain
Juha Kilpinen
Finnish Meteorological Institute (FMI), Finland
Juan Bazo, Gerardo Jacome & Luis Metzger
Servicio National de Meteorologia e Hidrologia (SENAMHI), Peru
Co-operation between FMI and SENAMHIInstitutional development
• Among the items:
1. Forecast verification (training, application development: an entry level verification system)
2. Post-processing of NWP output (training, application development: testing of Kalman filtering for NWP data)
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EUMETCAL training tools used
User-interface for the verifcation system at SENAMHI
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Introduction• Peruvian ETA-model data is available for guidance at Peruvian
Hydro-meteorological service (SENAMHI). The application of rather low resolution (22 km grid) data is not straight forward in complex terrain in terms of topography, climatic zones and sharp land-sea gradient.
• The applicability of the data is evaluated and if some problems are encountered a solution will be searched. So far data has been partly verified and tests with Kalman filter have started. The test data includes maximum and minimum temperature.
• Another focus is the heavy precipitation in Machu Picchu area resulting flooding and danger to life and property.
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• ETA-model• Resolution 22 km
• 18 vertical levels
• Output 6 h interval up to 72 h
• Kain Frish cumulus parameterization
• the GFS global model data is used for the lateral boundaries
• No data assimilation is made
• Test data periods: temperature 2009-2010
• Precipitation 2010
• Observations (21 stations) Tumbes
Piura
Huánuco
Pucallpa
Lima
Andahuaylas
Cuzco
Tacna
Chiclayo
Chimbote
Cajamarca
Tarapoto
Yurimaguas
Iquitos
Tingo Maria
Trujillo
Ayacucho
Puerto Maldonado
Arequipa
Juliaca
Pisco
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Data
Methods – post-processing/Kalman filter• Only for temperature
forecasts: • Two state parameters
• TKalman = B1 + B2 * TETA
• Optimized estimation of measurement noise R and system noise Q
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Methods - verification• For temperature
forecasts: • ME (Mean Error)
• RMSE (Root Mean Squared Error)
• HR (Hit Rate) (not shown)
• Also other scores
• For precipitation forecasts (categories):
• B (Bias)
• PC (Percent correct)
• POD (Probability of Detection)
• FAR (False Alarm Rate)
• KSS (Kuipers Skill Score)
• TS (Threat Score)
• ETS (Equility Threat Score)
• …
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Stations in focus:
Cusco 3399 m Pucallpa 154 m Andahuaylas 2866 m Huanuco 1859 m Lima 13 mTacna 452 m
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TMAX ME ME_KAL RMSE RMSE_KAL
Day 1 -7.09 -0.33 7.61 2.99
Day 2 -7.07 -0.34 7.68 3.00
Day 3 -7.09 -0.37 7.84 3.06
TMIN ME ME_KAL RMSE RMSE_KAL
Day 1 -2.88 -0.16 4.00 2.35
Day 2 -2.12 -0.13 3.50 2.36
Day 3 -2.00 -0.12 3.48 2.38
Temperature verification
Cusco (near Machu Picchu) 3399 m
TMAX ME ME_KAL RMSE RMSE_KAL
Day 1 -3.00 -0,10 4,01 2,36
Day 2 -2.99 -0,10 4,29 2,52
Day 3 -3.18 -0,09 4,58 2,52
TMIN ME ME_KAL RMSE RMSE_KAL
Day 1 1,58 0,13 3,20 2,68
Day 2 2,68 0,16 3,90 2,68
Day 3 2,82 0,18 4,08 2,70
Pucallpa 154m
TMAX ME ME_KAL RMSE RMSE_KAL
Day 1 0,56 -0,015 3,42 3,12
Day 2 0,76 0,005 3,45 3,13
Day 3 0,79 0,025 3,50 3,09
TMIN ME ME_KAL RMSE RMSE_KAL
Day 1 3,43 0,15 4,59 3,31
Day 2 4,92 0,26 5,76 3,42
Day 3 5,12 0,27 5,92 3,38
Andahuaylas 2866 m
TMAX ME ME_KAL RMSE RMSE_KAL
Day 1 -8,99 -0,58 9,24 2,38
Day 2 -8,84 -0,47 9,11 2,44
Day 3 -8,52 -0,53 8,90 2,50
TMIN ME ME_KAL RMSE RMSE_KAL
Day 1 -2,79 -0,18 3,47 2,12
Day 2 -1,50 -0,10 2,62 2,01
Day 3 -1,20 -0,14 2,69 2,06
Huanuco 1859 m
TMAX ME ME_KAL RMSE RMSE_KAL
Day 1 -1,25 -0,13 2,26 1,48
Day 2 -1,36 -0,10 2,24 1,46
Day 3 -1,48 -0,15 2,40 1,54
TMIN ME ME_KAL RMSE RMSE_KAL
Day 1 -0,44 -0,02 0,97 0,76
Day 2 -0,02 0,02 0,80 0,74
Day 3 -0,05 0,02 0,80 0,74
Lima 13 m
TMAX ME ME_KAL RMSE RMSE_KAL
Day 1 0,96 0,09 2,96 1,38
Day 2 0,92 0,04 3,46 1,40
Day 3 0,62 0,01 3,44 1,54
TMIN ME ME_KAL RMSE RMSE_KAL
Day 1 -1,02 -0,04 2,18 1,38
Day 2 0,74 0,10 1,97 1,33
Day 3 0,63 0,10 2,00 1,34
Tacna 452 m
Machu Picchu area verification of ETA model
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Precipitation forecasts
Machu Picchu 2010
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Categorical results: Machu Picchu 2010Day 1 Day 2
R>3mm
yes no yes no
yes 104 37 yes 101 43
no 47 158 no 41 152
R>15mm
yes no yes no
yes 13 19 yes 19 39
no 40 247 no 31 248
R>3mm R>15mm R>3mm R>15mmB 0,93 0,6 1,01 1,16
PC 0,76 0,83 0,75 0,79
POD 0,69 0,25 0,71 0,38
FAR 0,26 0,59 0,3 0,67
KSS 0,5 0,18 0,49 0,24
TS 0,55 0,18 0,54 0,21
ETS 0,23 0,11 0,22 0,12
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Conclusions• The preliminary results indicate that ETA model has
problems with temperature forecasts in most regions; in tropical rain forest, at mountains and near coastline affected by cold sea current. The application of Kalman filter was used to minimize systematic errors from the ETA-model and rather encouraging results was received.
• For precipitation forecasts ETA model is not able to forecast higher precipitation amounts correctly
• A higher resolution (e.g. WRF) NWP model would be a way to inprove the forecast quality in complex terrain
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