About the NIC IMS Products
Interactive Multisensor Snow and Ice Mapping System (IMS)
The National Oceanic and Atmospheric Administration / National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has an extensive history of monitoring snow and ice coverage. Accurate monitoring of global snow/ice cover is a key component in the study of climate and global change as well as daily weather forecasting.
The Polar and Geostationary Operational Environmental Satellite programs (POES/GOES) operated by NESDIS provide invaluable visible and infrared spectral data in support of these efforts. Clear-sky imagery from both the POES and the GOES sensors show snow/ice boundaries very well; however, the visible and infrared techniques may suffer from persistent cloud cover near the snowline, making observations difficult (Ramsay, 1995). The microwave products (DMSP and AMSR-E) are unobstructed by clouds and thus can be used as another observational platform in most regions. Synthetic Aperture Radar (SAR) imagery also provides all-weather, near daily capacities to discriminate sea and lake ice. With several other derived snow/ice products of varying accuracy, such as those from NCEP and the NWS NOHRSC, it is highly desirable for analysts to be able to interactively compare and contrast the products so that a more accurate composite map can be produced.
The Satellite Analysis Branch (SAB) of NESDIS first began generating Northern Hemisphere Weekly Snow and Ice Cover analysis charts derived from the visible satellite imagery in November, 1966. The spatial and temporal resolutions of the analysis (190 km and 7 days, respectively) remained unchanged for the product's 33-year lifespan. However, these resolutions and other shortcomings had been shown to cause errors in the National Meteorological Center's Numerical Weather Prediction (NWP) models (Mitchell, 1993). The weekly update often missed changes in snowcover which occurred on a daily basis. Further, erroneous snowcover in the NWP models contributed to significant errors in low-level air temperatures forecasts, leading to inaccurate predictions of rainfall versus snowfall (Murphy, 1993).