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MSG Battlespace Products Including Icing

MSG Battlespace Products Including Icing. Stan Kidder. Purpose of Seminar. To update the Meteosat Second Generation (MSG) products reported at the last annual review (Adelphi, 15-17 Nov 2005) To demonstrate application of the products to a current battlespace

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MSG Battlespace Products Including Icing

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  1. MSG Battlespace Products Including Icing Stan Kidder

  2. Purpose of Seminar • To update the Meteosat Second Generation (MSG) products reported at the last annual review (Adelphi, 15-17 Nov 2005) • To demonstrate application of the products to a current battlespace • To outline future work to improve the products

  3. The Battlespace • Resources, such as MSG data, • Need to be applied quickly

  4. The Battlespace • Resources, such as MSG data, • Need to be applied quickly • To the Battlespace, which can be anywhere

  5. MSG Channels

  6. Products To Be Discussed MSG Cloud Product 10.8 µm Brightness Temperature Dust Product Precipitating Cores Skin Temperature Cloud Mask Cloud Phase / Icing

  7. Web Link • Connect to this link if you can, but it is not necessary http://products.cira.colostate.edu/MSG/Mideast • If you connect (now or later) you will see something like this:

  8. Warning! • The products displayed on the Web site are experimental, non-operational products which can change at any time. • The site will soon be passworded. If you would like to continue viewing it, please send me email: kidder@cira.colostate.edu

  9. Daytime MSG Cloud Product • (Red, Green, Blue) = 255*(A1.6, A0.8, A0.6) • Liquid water clouds are highly reflective at all three wavelengths and therefore appear white • Ice clouds are highly reflective at 0.8 and 0.6 µm, but poorly reflective at 1.6 µm. They therefore appear cyan in the resulting image.

  10. Nighttime MSG Cloud Product • 3.9 µm albedo • Liquid water clouds are reflective at 3.9 µm and therefore appear white • Thin ice clouds transmit radiation from below and therefore appear to have a negative albedo (and are black in the imagery) • Some soils (northern Sinai) are bright as is thick cirrus

  11. 10.8 µm Brightness Temperature • A standard product—one of several products that a forecaster might want to look at to interpret the scene • Clouds colder than −20°C are colored in 10 K increments

  12. Dust Product • EUMETSAT Product • R=T12.0 – T10.8G=T10.8 – T8.7 B=T10.8 • Dust is pink and moves • Low clouds, unfortunately, are also pink and they move • Thin cirrus is blue • Thick cirrus is dark red

  13. Precipitating Cores • Identifies deep, cold clouds which are likely to be precipitating. • Water vapor is used to screen out low clouds • Precipitation clouds (green) are those for which T10.8 – T6.2 < 11 K (an empirically determined threshold).

  14. Skin Temperature • Uses the algorithm of Price (1984), modified for MSG channels • Tskin = T10.8 + 2.5 (T10.4 – T12.0)

  15. Cloud Mask • Uses 8.7 µm channel • The warmest pixel in the previous 10 days is used as a background • Pixels colder than the background are cloudy • Over land, DT = 8 K • Over water, DT = 4 K • Some clouds are missed

  16. Cloud Phase / Icing • Starts with the 8.7 µm cloud mask • Ice clouds (white) are those for which • T10.8 < −30°C (day or night) or • Clouds are “cyan” in MSG Cloud Product (day) • Clouds are “black” in MSG Cloud Product (night)

  17. Cloud Phase / Icing • All clouds which are not ice clouds are liquid water clouds • Warm liquid water clouds (>0°C, yellow) are safe to fly in • Cold liquid water clouds (≤0°C, red) represent an icing hazard This product needs work. Some clouds are missed, and some are incorrectly typed.

  18. Future Work • CloudSat and CALIPSO offer a golden opportunity to verify these (or any) cloud algorithms.

  19. MODIS Imagery CloudSat and CALIPSO ground track

  20. CALIPSO Imagery Cirrus Mid-level Clouds North South MODIS Image

  21. CloudSat Imagery Mid-level Clouds MODIS Image

  22. Summary & Conclusions • A set of cloud products using MSG data was developed quickly to support a current battlespace (I started work on this project on 24 Aug 2006) • Development of the products reveals deficiencies in current algorithms • CloudSat and CALIPSO data offer a way forward

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