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Development of Satellite Products for the Battlespace. Stan Kidder and Adam Kankiewicz. Objective. Develop products based on satellite observations to allow a precise determination of the state of the battlespace. Outline. Meteosat Second Generation (MSG) products
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Development of Satellite Products for the Battlespace Stan Kidder and Adam Kankiewicz
Objective Develop products based on satellite observations to allow a precise determination of the state of the battlespace
Outline • Meteosat Second Generation (MSG) products • GOES Products in support of CLEX-10 • Validation of products using CloudSat and CALIPSO data
Meteosat Second Generation Products Reported in November 2005 on products developed for AFWA and delivered via a Lockheed/Martin contract. Recently applied (and updated) the algorithms to the Mideast and developed a real-time Web site (http://products.cira.colostate.edu/MSG/Mideast) for DoD use and comment. Gave a VTC seminar on the products on 12 September 2006
Products To Be Discussed MSG Cloud Product 10.8 µm Brightness Temperature Dust Product Precipitating Cores Skin Temperature Cloud Mask Cloud Phase / Icing
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. DoD Relevance: A theater-level awareness of the location and phase of clouds is important for DoD operations
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 Current Example
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 Current Example DoD Relevance: Brightness temperature is a time-tested product to aid EOTDA planning.
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 Current Example DoD Relevance: Dust storms continue to be a major problem for DoD operations
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). Current Example DoD Relevance: Heavy rain is a problem for aviation and for MASINT.
Skin Temperature • Uses the algorithm of Price (1984), modified for MSG channels • Tskin = T10.8 + 2.5 (T10.4 – T12.0) Current Example DoD Relevance: Skin temperature is important for infrared background and personnel health
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 Current Example DoD Relevance: A cloud/no cloud map is useful for generating other products.
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) DoD Relevance: Icing continues to be a problem for aircraft and UAV operations.
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 Current Example
GOES Products in Support of CLEX-10:A prototype for specialized operations • If one has access to the satellite data, products to support operations can be quickly set up. • http://amsu.cira.colostate.edu/CLEX10
Current Work • CloudSat and CALIPSO offer a golden opportunity to verify cloud algorithms.
Mixed-Phase Clouds Viewed By MODIS/CloudSat/CALIPSO A 7/19/06 22:45 UTC MODIS 11 µm Iraq Iran Saudi Arabia Persian Gulf B
Mixed-Phase Clouds Viewed By MODIS/CloudSat/CALIPSO A VIIRS Cloud Phase B
10 10 10 5 5 5 Height (km) Height (km) Height (km) 0 0 0 CloudSat Radar Reflectivity (dBZ) A B CloudSat Cloud Mask -30 -20 -10 0 CALIPSO 532 nm Backscatter
Summary & Conclusions • A set of cloud products can be quickly developed to support current contingencies • Examination of the real-time products often reveals deficiencies in the algorithms • CloudSat and CALIPSO data offer a way forward
Preliminary CloudSat Data AnalysisJuly 2006 Definitions: • Cloud — a range bin with Cloud_Mask >= 20 • Cloud Top— a cloudy range bin with a non-cloudy range bin immediately above it • Cloud-Top Temperature— the temperature in the ECMWF analysis at the same height as the cloud top
Mixed-phase defined as cloud tops with temps between 0°C and -45°C from ECMWF fields Few mixed-phase clouds in tropics and subtropics; many in the mid- and high latitudes Latitudinal Distribution
All latitudes Fairly uniform distribution in the troposphere Cloud-Top Height Distribution
Quite a uniform distribution with perhaps a few more at very cold and very warm temperatures Cloud-Top Temperature
Most mixed-phase clouds are thin The long tail is puzzling, perhaps an artifact of the analysis Cloud Thickness
Slightly fewer mixed-phase clouds at night (0130 LT) than in the daytime (1330 LT) Day/Night Distribution
Cloud = CloudSat Cloud Mask >= 20 Cloud-Top Temp between -5°C and -40°C All latitudes, day and night Probably shows ice crystal growth below cloud top Contoured Frequency by Altitude Diagram (CFAD)
Conclusions and Future Plans • We should be able to get a near global picture of mid-level, mixed-phase clouds using CloudSat, CALIPSO, and MODIS data • We will be analyzing CALIPSO data soon (They were released on Monday, Dec. 11) • A detailed study of CLEX-10/C3VP cases will take place • We hope to use these data to improve modeling of mid-level, mixed-phase clouds