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, commonly called the cryosphere, exist at all latitudes and in about one hundred countries. Not only does the cryosphere and its characterization and distribution play a significant role in weather forecast and climate, it also has profound socio-economic value due to its role in water resources and its impact on transportation, hazards, recreation, fisheries, hunting, herding, and agriculture. 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).

Ice identification is relatively straightforward through the use of a grouped threshold technique. Ice concentration is derived via a so-called tie point analysis. A local search window is used to derive ice and water tie points, with additional synoptic corrections if applicable. 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. For ice thickness and age estimation, a One-dimensional Thermodynamic Ice Model (OTIM) was first developed based on the surface energy balance at thermo-equilibrium that contains all components of the surface energy balance to estimate sea and lake ice thickness. 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.