slide1 n.
Download
Skip this Video
Download Presentation
Juha Kilpinen Finnish Meteorological Institute (FMI), Finland

Loading in 2 Seconds...

play fullscreen
1 / 19

Juha Kilpinen Finnish Meteorological Institute (FMI), Finland - PowerPoint PPT Presentation


  • 246 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Juha Kilpinen Finnish Meteorological Institute (FMI), Finland' - vivien


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

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 senamhi
Co-operation between FMI and SENAMHI

Institutional development

  • Among the items:
  • Forecast verification (training, application development: an entry level verification system)
  • Post-processing of NWP output (training, application development: testing of Kalman filtering for NWP data)

Kilpinen-Bazo-Jacome-Metzger

slide3

EUMETCAL training tools used

Kilpinen-Bazo-Jacome-Metzger

introduction
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.

Kilpinen-Bazo-Jacome-Metzger

slide6

Data

  • 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

Kilpinen-Bazo-Jacome-Metzger

methods post processing kalman filter
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

Kilpinen-Bazo-Jacome-Metzger

methods verification
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)

Kilpinen-Bazo-Jacome-Metzger

slide9
Stations in focus:Cusco 3399 m Pucallpa 154 m Andahuaylas 2866 m Huanuco 1859 m Lima 13 mTacna 452 m Machu Picchu

Kilpinen-Bazo-Jacome-Metzger

slide10

Temperature verification

Cusco (near Machu Picchu) 3399 m

machu picchu 2010
Machu Picchu 2010

Kilpinen-Bazo-Jacome-Metzger

categorical results machu picchu 2010
Categorical results: Machu Picchu 2010

Kilpinen-Bazo-Jacome-Metzger

conclusions
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

Kilpinen-Bazo-Jacome-Metzger

ad