Loading in 2 Seconds...
Loading in 2 Seconds...
Validation. Sea and Lake Ice Thickness and Age for Use with GOES-R ABI. Xuanji Wang 1 , Jeffrey R. Key 2 , Yinghui Liu 1 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS) / Space Science and Engineering Center (SSEC), UW-Madison, Madison, Wisconsin
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Sea and Lake Ice Thickness and Age for Use with GOES-R ABI
Xuanji Wang1, Jeffrey R. Key2,Yinghui Liu1
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
Validation and Uncertainty
To estimate the performance and accuracy of the OTIM, we have used upward-looking sonar measurements of ice draft from U.S. Navy submarines, Canadian meteorological station measurements, and numerical model simulations as shown below.
Sea and Lake Ice Thickness and Age
A One-dimensional Thermodynamic Ice Model (OTIM) was developed based on the surface energy balance at thermo-equilibrium state that contains all components of the surface energy budget to estimate sea and lake ice thickness. The equation for energy conservation at the top of the surface (ice or snow) is
(1-αs) Fr – I0 – Fup + Fdn + Fs + Fe + Fc = Fa
where αs is surface ice/snow broadband albedo, Fris surface downward shortwave radiation, I0is shortwave radiation passing through the ice interior, Fup and Fdn are surface upward and downward longwave radiation, respectively. Fs, Fe, and Fcare surface turbulent sensible, latent, and conductive heat fluxes, Fais the residual absorbed energy that contributes to melting at or near the surface (zero in the absence of a phase change). 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 results are shown in Figures 1-3.
Fig. 4. The submarine track during the SCICEX experiment in 1999 with starting and ending dates marked on the plot (left), the comparison of ice thickness cumulative frequency distribution from OTIM, PIOMAS, and submarine sonar (middle), and the 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. Overall OTIM uncertainty is 0.31 m with respect to the mean ice thickness of 1.80 m from submarine. OTIM performs well when actual ice thickness is less than 3 meters. Submarine ice draft was converted to ice thickness by a factor of 1.11.
Fig. 1. MODIS Aqua true color image (left) on February 24, 2008 over Great Lakes, retrieved ice thickness in meter (middle), and ice age (right).
Fig. 5. Comparisons of ice thickness cumulative distribution (left) and point-to-point values (right) from OTIM , PIOMAS, and one station at Alert, Canada as shown in the upper-left corner of the plot.
Uncertainty Analysis and Sensitivity Study
In estimation of ice thickness by using the OTIM, many factors affect the accuracy of ice thickness. The uncertainties from all of the input controlling variables in the OTIM will propagate into ice thickness through parameterizations and model algorithms. Theoretically and mathematically speaking, ice thickness is actually a function of 11 variables that control the surface energy budget, which are surface ice/snow temperature (Ts), ice temperature (Ti), snow depth (hs), surface air relative humidity (R), surface wind speed (U), surface air pressure (Pa), surface shortwave albedo (αs), ice slab transmittance (Tr), surface downward shortwave radiative flux (Fr), cloud amount (C), surface residual heat flux (Fa). Note that surface longwave radiative fluxes are parameterized with those controlling variables in OTIM.
Fig. 2. MODIS true color image over the Caspian Sea on January 27, 2006 and the corresponding sea ice thickness in meter, and sea ice age from SEVIRI data on the same day.
Fig. 6. Sensitivity of ice thickness to expected uncertainties in the controlling variables from satellite retrievals for daytime case (left) and nighttime case (right) with reference ice thickness of 0.3 (red), 1 (black), and 1.8 (blue) meters. Plus signs are the ice thickness values for positive uncertainties in the indicated variables; minus signs show the direction of changes in ice thickness for a decrease in the controlling variable value.
Fig. 3. OTIM retrieved Arctic sea ice thickness distribution in March (left) and October (right) in 2004 with AVHRR Data at 04:00 local solar.
This poster does not reflect the views or policy of the GOES-R Program Office.
6th GOES Users’ Conference and 50th Anniversary Meteorological Satellite Experiment Symposium, 2-5 November 2009, Monona Terrace Convention Center, Madison, Wisconsin