JP2.16
Sea and Lake Ice Characteristics from GOES-R ABI
Xuanji Wang1, Yinghui Liu1, and Jeffrey R. Key2
1Cooperative
Institute for Meteorological Satellite Studies (CIMSS) / Space Science and Engineering Center (SSEC), UW-Madison, Madison, Wisconsin
2Center for Satellite Applications and Research, NOAA/NESDIS, Madison, Wisconsin
Introduction
MODIS True Color Image
Ice Surface Temperature (K)
Ice Concentration (%)
Sea and lake ice concentration and thickness affect the exchange of heat, energy,
mass, and momentum between the atmosphere and the underlying water body. Ice
and snow as primary components of the cryosphere can exist at all latitudes and in
about one hundred countries (Fig.1). In this study, retrieval algorithms for ice
identification, concentration, thickness, and age estimation are developed for the
next generation Geostationary Operational Environmental Satellite (GOES-R)
Advanced Baseline Imager (ABI). An overview of the ice characterization
algorithms, preliminary results, and validation studies will be shown here with
applications to SEVIRI, AVHRR, and MODIS data as a proxy for the future
GOES-R ABI.
Ice Motion (cm/s)
Ice Thickness (m)
Fig. 6. The submarine track during the SCICEX experiment in 1999 (left), the comparison of ice thickness
cumulative frequency distribution from OTIM and submarine sonar (ice draft, middle), and a point-to-point
comparison of the ice thickness from OTIM, PIOMAS, and submarine (right) during that period along the
submarine track. OTIM ice thickness products were produced with AVHRR data.
Ice Age/Type
Fig. 1. Examples of the cryosphere (left) and its global distribution (right).
Sea and Lake Ice Concentration and Extent
Sea and lake ice is detected through the use of a grouped threshold technique. A
pixel is identified as sea ice when it has a normalized difference snow index
greater than 0.4 (NDSI = (R0.865-R1.61)/(R0.865+R1.61)), a visible reflectance at
0.865 μm greater than 0.11, and a visible reflectance at 0.645 μm greater than 0.10
(Fig.2). For ice/snow surface temperature (IST) retrieval from the AVHRR and
MODIS that is applicable to GOES-R ABI, we use the equation
where Ts is the estimated surface temperature (K), T11 and T12 are the brightness
temperatures (K) at 11 μm (AVHRR channel 4, MODIS band 31, GOES-R ABI
band 14) and 12 μm (AVHRR channel 5, MODIS band 32, GOES-R ABI band 15)
and θ is the sensor scan angle. Coefficients a, b, c, and d are derived for the
following temperature ranges: T11 < 240K, 240K < T11 < 260K, T11 > 260K.
This has been adapted to SEVERI. Ice concentration is derived via a tie point
analysis. A local search window is used to derive ice and water tie points (Fig. 3),
with additional synoptic corrections on the ice tie point. Ice extent is determined
based on ice concentration. During the day, both visible reflectance and calculated
ice surface temperature (IST) are used to derive ice concentration, while only IST
is used during nighttime. Preliminary results are shown in Fig. 4, 5, and 7.
Fig. 4. MODIS Aqua true color image on February 24, 2008 over the Great Lakes, derived ice surface skin temperature
(K), ice concentration (%), ice thickness (m), and ice type/age. Ice motion (lower-left) is from February 27, 2003.
Fig. 7. MODIS true color image
over the Caspian Sea (upperleft) on January 27, 2006 and the
timely corresponding ice surface
temperature (upper-middle), sea
ice concentration (upper-right),
sea ice thickness (lower-middle),
and ice age (lower-right) from
SEVIRI data on the same date.
Sea Ice Motion
Fig. 5. MODIS Aqua true color image (left) on March 31, 2006 over Kara Sea in the Arctic, derived surface skin
temperature (K) (middle), and ice concentration (%) (right).
An algorithm developed by C. Fowler, J. Emery, and J. Maslanik (IEEE Geoscience and Remote Sensing
Letters,Vol.1, No. 2, pp.71-74, April 2004) was adopted in this study. It is basically a statistical method;
maximum-cross correlation (MCC) is employed for ice motion calculations. Fig. 9 gives an example over the
Arctic based on MODIS data.
Sea and Lake Ice Thickness and Age
A One-dimensional Thermodynamic Ice Model (OTIM) was developed based on the surface energy balance at thermoequilibrium that contains all components of the surface energy balance to estimate sea and lake ice thickness. The
foundational equation for energy conservation at the top surface (ice or snow) is
(1-αs) Fr – I0 – Fup + Fdn + Fs + Fe + Fc = 0
Fig. 2. Reflectance means for samples of ice, clouds, and water (Riggs et al. 1999).
Fig. 3. Distribution of 640 nm reflectance for an ice/water scene. The
ice/water threshold reflectance (0.336) and the water tie point (0.083) are
indicated. (Appel and Jensen, Fresh water ice VIIRS ATBD, 2002)
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where αs is surface ice or snow broadband albedo, Fr is surface downward solar radiation, I0 is the solar radiation passing
through the ice interior, Fup and Fdn are surface upward and downward longwave radiations, respectively. Fs, Fe, and Fc
are surface turbulent sensible, latent, and conductive heat fluxes, respectively. Then, based on the ice thickness, eight
categories of ice “age” are defined: new, nilas (0.00~0.10 m), grey (0.10~0.15 m), grey-white (0.15~0.30 m), first-year
thin (0.30~0.70 m), first-year medium (0.70~1.20 m), first-year thick (1.20~1.80 m), and old ice including second-year
and multi-year ice (> 1.80 m). The thicker categories are for sea ice only. The current version of the OTIM was
compared with the ice draft data measured by submarine upward looking sonar during the Scientific Ice Expedition
(SCICEX) in 1999, ice thickness data measured by Canadian meteorological stations over 2002~2004, and the
simulated ice thickness data from Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS). Preliminary
testing of the ice characteristic retrieval algorithms with AVHRR, MODIS, and SEVIRI is promising as shown in Fig. 4,
6, and 7.
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Fig. 8. Retrieved ice motion with MRF model surface winds on 2 Nov, 2007 over Laptev Sea in the Arctic (left
two pictures), and retrieved ice motion vectors with MODIS data and surface wind for March 8, 2008 (right two
pictures). Orientation is the same on both pictures. Both motions show similar directions, with ice motion to right
of the wind direction. The speed of ice movement is around 2% of the wind speed. 11 micron Brightness
Temperature is displayed as the background, with lower value for blue color.
*
*Note: Some of photos were taken by people not associated with this work. See copyright information below. None of the photos can be used
commercially. 1: K. Claffey; 2: J. Key; 3: Feenicks Polarbear Image Gallery; 4: Photographer unknown; 5: Thomas D. Mangelsen, from "Images of
Nature" (a calendar); 6: Copyright Corel Corporation, From "The Arctic", a Corel Professional Photos CDROM.
This poster does not reflect the views or policy of the GOES-R Program Office.
89th Annual Meeting, 11—16 January 2009, Phoenix, AZ