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Utilisation of satellite data in the verification of HIRLAM cloud forecasts

Christoph Zingerle and Pertti Nurmi. Utilisation of satellite data in the verification of HIRLAM cloud forecasts. Contents. verification, the task the forecasting system HIRLAM observations = satellite data making forecast and observation comparable an example summary future. Task.

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Utilisation of satellite data in the verification of HIRLAM cloud forecasts

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  1. Christoph Zingerle and Pertti Nurmi Utilisation of satellite data in the verification of HIRLAM cloud forecasts

  2. Contents • verification, the task • the forecasting system HIRLAM • observations = satellite data • making forecast and observation comparable • an example • summary • future

  3. Task • Verification of the HIRLAM cloud forecast -detection of deficiencies in the cloud forecast scheme -feasibility of different approaches to verification using satellite data -methodology of verifying cloud forecasts and its operational implementation

  4. HIRLAM at FMI • HIgh Resolution Limited Area Model - FMI is running the reference HIRLAM (RCR) operationally -resolution 0.2 deg horizontal (438x336 grid points, ~ 22 km) 40 levels vertical (up to 10 hPa) - semi-lagrangian advection - 3D–Var analysis (no satellite data) - lateral boundary conditions from ECMWF -Denmark, Finland, Iceland, Ireland, Netherlands, Norway, Spain and Sweden (France)

  5. HIRLAM RCR domain

  6. Satellite Observations • satellite data at FMI from: - METEOSAT 7/8: high resolution (temporal and spatial) coarse resolution at the edges - like Finland - with limb darkening - NOAA polar orbiting satellites high spatial resolution coarse temporal resolution

  7. Observation – Forecast • Model to Satellite: • Satellite to Model: - transferring the observations to parameters forecasted by the model - Cloud classification scheme generally thresholding methods based on typical cloud properties - transferring the parameters forecasted by the model to observations - Radiative Transfer Model uses model data to simulate observed radiances and Tb’s

  8. Model to Satellite • Radiative Transfer Model (RTM) - RTTOV 7 - a fast RTM for the assimilation of satellite data calculates radiances (and Tb's) as seen by a satellite instrument uses profiles of temperature humidity cloud fraction cloud liquid water cloud ice water ozone surface properties

  9. Model to Satellite • 'synthetic' NOAA AVHRR image (10.8µ) - 24 h forecast from HIRLAM - AVHRR because of the high resolution provided even at the poles

  10. Observation Re-sampling • NOAAAVHRR satellite image - re-sampling needed - preprocessed (AAPP) AVHRR image (calibrated and navigated) pixel center in the grid-box corresponding to the HIRLAM grid pixel assigned to this grid-box - Assumptions: HIRLAM grid value represents average over all the values in box neighbouring pixels don't differ much from each other

  11. full resolution, 30.4.2004 (10.8µ) after re-sampling, 30.4.2004 (10.8µ)

  12. simulated, 30.4.2004 (10.8µ) observed, 30.4.2004 (10.8µ)

  13. Difference: observed - simulated Difference > 40 K: Model error?

  14. simulated observed Summary distributions relative frequency of Tb observed / simulated Tb

  15. Summary • RTTOV - a tool to simulate satellite measurements as close as possible - surface parameters and transmission (clouds) dependent • observations - simple re-sampling of NOAA AVHRR data is sufficient - re-sampling will be more sophisticated for other instruments • verification of HIRLAM - cloud forecast scheme not yet verified extensively - approach to verification looks promising

  16. Future • examine approaches to verification - satellite to observation approach (SAFNWC software) - pattern recognition methods • expand to other satellite data (instruments) - Meteosat data over Europe - polar orbiting satellites over Scandinavia and Nordic Countries • operational verification - refine methodology to verify cloud forecasts - improve the operational verification package of FMI

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