Bruce H. Ramsay

The interactive multisensor snow and ice mapping system

The interactive multisensor snow and ice mapping system (IMS) was developed to give snow and ice analysts the tools, on one platform, to inspect visually the imagery and mapped data from various sensor sources to determine the presence of snow and ice and to depict snow- and ice-covered areas on a map on a daily basis, in one hour or less. Snow and ice analysts in the National Environmental Satellite, Data, and Information Service have been creating weekly maps showing the extent of snow cover for the Northern Hemisphere since 1966 using visible imagery from polar-orbiting and geostationary satellites and surface observations as data sources. The current process is mostly manual and time-consuming, taking up to 10 hours to produce a map during the snow season. Where cloud cover precludes an unobstructed view of an area during the entire week, the analysis from the previous week is carried forward. Each week the analyst draws a new map by hand, then digitizes the extent of snow and ice cover using an 89 × 89 line grid overlaid on a stereographic map of the Northern Hemisphere. The hand-drawn map is photocopied and distributed and the digitized map is saved to a file for use in National Weather Service numerical models and for archival storage. IMS was designed and built to replace and improve this process by producing a more accurate and timely product.

Real-time weekly global green vegetation fraction derived from advanced very high resolution radiometer-based NOAA operational global vegetation index (GVI) system

[1] To provide quality-improved and consistent real-time global green vegetation fraction (GVF) data products that are suitable for use in operational numerical weather, climate, and hydrological models, necessary processing steps are applied to the output data stream from the advanced very high resolution radiometer (AVHRR)-based NOAA operational global vegetation index (GVI) system. This paper reviewed the NOAA GVI data and described the algorithm to derive weekly updated real-time GVF from the normalized difference vegetation index (NDVI). The methodology description focuses on algorithm justification in an operational production context. The described algorithm was implemented in the global vegetation processing system (GVPS). The new global GVF data sets include the multiyear GVF weekly climatology and the real-time weekly GVF. Compared to the old 5 year GVF monthly climatology currently used in the operational National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) weather and climate models, the new data sets provide an overall higher vegetation value, real-time surface vegetation information, and numerous other improvements. The new GVF data set quality was partially assured by validation against Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at a few EOS land validation core sites and comparison with another independently processed NDVI data set. Impact of the new GVF data sets in numerical weather prediction (NWP) model was investigated using EMC mesoscale model simulations and concluded overall positive.