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ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course

ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course. 1 to 4 July 2013. ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course. The infrared spectrum, measurement, modelling and information content. Sensors. High Spectral Resolution IR sounders on Polar Spacecraft

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ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course

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  1. ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course 1 to 4 July 2013

  2. ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course The infrared spectrum, measurement, modelling and information content

  3. Sensors

  4. High Spectral Resolution IR sounders on Polar Spacecraft (GEO covered in winds lecture)

  5. What do we mean by high spectral resolution ?

  6. Why is high spectral resolution important ? HIRS 19 channels IASI 8461 IASI HIRS

  7. AIRS IASI CrIS 15 um 6 um 4 um

  8. IASI v CrIS

  9. Infrared Atmospheric Sounding Interferometer (IASI) Cross-track Infrared Sounder (CrIS)

  10. IASI has higher spectral resolution compared to CrIS

  11. IASI has higher instrument noise compared to CrIS

  12. CrIS has stronger inter-channel correlations than IASI Observation error (K) Observation error (K) IASI CrIS Inter-channel correlation Inter-channel correlation

  13. Case study: The assimilation of IASI

  14. The assimilation of IASI results in a significant reduction of forecast error NH EU SH US

  15. What does IASI measure (and what do we assimilate)

  16. The infrared spectrum of atmospheric radiation 9.6 um 3.7 um 15 um 6.4 um 4.2um

  17. The infrared spectrum of atmospheric radiation The long-wave temperature sounding band 9.6 um 3.7 um 6.4 um 4.2um 15 um ~ 200 channels assimilated

  18. Zoom of long-wave temperature sounding channel usagefor IASI

  19. The infrared spectrum of atmospheric radiation The long-wave ozone sounding band 9.6 um 3.7 um 6.4 um 4.2um 15 um ~ 20 channels assimilated

  20. The infrared spectrum of atmospheric radiation The long-wave water vapour sounding band 9.6 um 3.7 um 6.4 um 4.2um 15 um ~ 10 channels assimilated

  21. The infrared spectrum of atmospheric radiation The short-wave temperature sounding band 9.6 um 3.7 um 6.4 um 4.2um 15 um ~ 0 channels assimilated – IASI noise high

  22. The infrared spectrum of atmospheric radiation The surface sensing channels (window channels) 9.6 um 3.7 um 6.4 um 4.2um 15 um channels mostly used for cloud detection

  23. Issues specific to IR • Ambiguity • Nonlinearity • Clouds • Channel selection (PC)

  24. Ambiguity in infrared radiance observations

  25. When the absorbing gas is itself a variable

  26. AMBIGUITY BETWEEN GEOPHYSICAL VARIABLES • When the primary absorber in a sounding channel is a well mixed gas (e.g. oxygen) the radiance essentially gives information about variations in the atmospherictemperature profile only. • When the primary absorber is not well mixed (e.g. water vapour, ozone or CO2) the radiance gives ambiguous information about the temperature profile and the absorber distribution. This ambiguity must be resolved by : • differential channel sensitivity • synergistic use of well mixed channels (constraining the temperature) • the background error covariance (+ physical constraints)

  27. When sounding channels are sensitive to the surface

  28. Atmospheric sounding channels …selecting channels where there is no contribution from the surface…. Surface reflection/ scattering Surface emission Cloud/rain contribution + + + +...

  29. Surface sensing Channels (passive) …selecting channels where there is no contribution from the atmosphere…. Surface reflection/ scattering Surface emission Cloud/rain contribution + + + +... Screen data to remove clouds / rain

  30. AMBIGUITY WITH SURFACE AND CLOUDS By placing sounding channels in parts of the spectrum where the absorption is weak we obtain temperature (and humidity) information from the lower troposphere (low peaking weighting functions). BUT … These channels (obviously) become more sensitive tosurface emission and the effects of cloud and precipitation. In most cases surface or cloud contributions will dominate the atmospheric signal in these channels and it is difficult to use the radiance data safely (i.e. we may alias a cloud signal as a temperature adjustment) K(z)

  31. Practical session

  32. Metview web pages: • https://software.ecmwf.int/metview • Download (Open Source) • Documentation and tutorials available • Metview articles in recent ECMWF newsletters • email: • Metview: metview@ecmwf.int

  33. RTTOV Radiative Transfer Model Y radiances RTTOV X (z)

  34. Task 1: Overlay Tropical and Polar IASI spectra • View the map of TCWV (visualise PLOT_TCWV) • Select a Tropical profile ( edit / save profile extractor) • Select a Polar profile ( edit / save profile extractor) • Edit / visualise IASI SPECTRUM (with tropical.nc) • Edit / drop IASI SPECTRUM (with polar.nc)

  35. Task 1: Overlay Tropical and Polar IASI spectra

  36. Task 2: Overlay Tropical and Polar IASI channel 272 weighting function • Edit / visualise IASI JACOBIAN (with tropical.nc) • Edit / drop IASI JACOBIAN (with polar.nc) • Change channels and do similar ?

  37. Task 3: Tropical and Polar IASI weighting functions

  38. Task 3: Overlay Tropical and Polar IASI matrix of jacobians • Edit / visualise IASI MATRIX JAC (with tropical.nc) • Edit / drop IASI MATRIX JAC (with polar.nc)

  39. Task 3: Overlay Tropical and Polar IASI matrix of jacobians

  40. End…Questions ?

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