Application of low- resolution ETA model data to provide guidance to high impact weather in complex terrain Juha Kilpinen Finnish Meteorological Institute.

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)

19.04.23 2Kilpinen-Bazo-Jacome-Metzger

19.04.23Kilpinen-Bazo-Jacome-Metzger 3

EUMETCAL training tools used

User-interface for the verifcation system at SENAMHI

19.04.23Kilpinen-Bazo-Jacome-Metzger 4

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.

19.04.23 5Kilpinen-Bazo-Jacome-Metzger

• 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

19.04.23Kilpinen-Bazo-Jacome-Metzger 6

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

19.04.23Kilpinen-Bazo-Jacome-Metzger 7

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)

• …

19.04.23Kilpinen-Bazo-Jacome-Metzger 8

Stations in focus:

Cusco 3399 m Pucallpa 154 m Andahuaylas 2866 m Huanuco 1859 m Lima 13 mTacna 452 m

Machu Picchu 19.04.23Kilpinen-Bazo-Jacome-Metzger 9

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

19.04.23Kilpinen-Bazo-Jacome-Metzger 16

Precipitation forecasts

Machu Picchu 2010

19.04.23Kilpinen-Bazo-Jacome-Metzger 17

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

19.04.23 18Kilpinen-Bazo-Jacome-Metzger

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

19.04.23Kilpinen-Bazo-Jacome-Metzger 19