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Abstract-Wind products from geostationary satellites have been generated for over 20 years and are now used in numerical weather prediction systems. However, geostationary satellites are of limited utility poleward of the midlatitudes. This study demonstrates the feasibility of deriving high latitude tropospheric wind information from polar-orbiting satellites. The methodology employed is based on the algorithms currently used with geostationary satellites, modified for use with the Moderate-Resolution Imaging Spectroradiometer (MODIS) infrared window and water vapor bands. These bands provide wind information throughout the troposphere in both clear and cloudy conditions. The project presents some unique challenges, including the irregularity of temporal sampling, varying viewing geometries, and uncertainties in wind vector height assignment as a result of low atmospheric water vapor amounts and thin clouds. A 30-day case study dataset has been produced and is being used in model impact studies. Preliminary results are encouraging: when the MODIS winds are assimilated in the European Centre for Medium Range Weather Forecasts (ECMWF) system and the NASA Data Assimilation Office system, forecasts of the geopotential height for the Arctic, the Northern Hemisphere extratropics, and the Antarctic are improved significantly.

On 12 October 1998, it was the 25th anniversary of the Man computer Interactive Data Access System (McIDAS). On that date in 1973, McIDAS was first used operationally by scientists as a tool for data analysis. Over the last 25 years, McIDAS has undergone numerous architectural changes in an effort to keep pace with changing technology. In its early years, significant technological breakthroughs were required to achieve the functionality needed by atmospheric scientists. Today McIDAS is challenged by new Internet-based approaches to data access and data display. The history and impact of McIDAS, along with some of the lessons learned, are presented here.

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.