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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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Christoph zingerle and pertti nurmi

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

  • 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


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)



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


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


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


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


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


full resolution, 30.4.2004 (10.8µ)

after re-sampling, 30.4.2004 (10.8µ)


simulated, 30.4.2004 (10.8µ)

observed, 30.4.2004 (10.8µ)


Difference:

observed - simulated

Difference > 40 K:

Model error?


simulated

observed

Summary distributions

relative frequency of Tb

observed / simulated Tb


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


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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