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’.

The need for highly accurate atmospheric wind observations is a high priority in the science community, particularly for numerical weather prediction (NWP). To address this need, this study leverages Aeolus wind lidar level-2B data provided by the European Space Agency (ESA) as a potential comparison standard to better characterize atmospheric motion vector (AMV) bias and uncertainty. AMV products from geostationary (GEO) and low Earth orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August–September 2019. Winds from two Aeolus observing modes are compared with AMVs, namely Rayleigh-clear (RAY; derived from the molecular scattering signal) and Mie-cloudy (MIE; derived from the particle scattering signal). Quality-controlled (QC'd) Aeolus winds are co-located with QC'd AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean co-location differences (MCDs) and the standard deviation (SD) of those differences (SDCDs) are determined and analyzed. As shown in other comparison studies, the level of agreement between AMV and Aeolus wind velocities (HLOSVs) varies with the AMV type, geographic region, and height of the co-located winds, as well as with the Aeolus observing mode. In terms of global statistics, QC'd AMVs and QC'd Aeolus HLOSVs are highly correlated for both observing modes. Aeolus MIE winds are shown to have great potential value as a comparison standard to characterize AMVs, as MIE co-locations generally exhibit smaller biases and uncertainties compared to RAY co-locations. Aeolus RAY winds contribute a substantial fraction of the total SDCDs in the presence of clouds where co-location/representativeness errors are also large. Stratified comparisons with Aeolus HLOSVs are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, the Arctic, and at mid- to upper-levels in clear and cloudy scenes. AMVs in the SH/Antarctic generally exhibit larger-than-expected MCDs and SDCDs, most probably due to larger AMV height assignment errors and co-location/representativeness errors in the presence of high wind speeds and strong vertical wind shear, particularly for RAY comparisons.

For more than 15 years, polar winds from the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery have been generated by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS). These datasets are a NOAA National Environmental Satellite, Data, and Information Service (NESDIS) operational satellite product that is used at more than 10 major numerical weather prediction (NWP) centers worldwide. The MODIS polar winds product is composed of both infrared window (IR-W) and water vapor (WV) tracked features. The WV atmospheric motion vectors (AMV) yield a better spatial distribution than the IR-W since both cloud and clear-sky features can be tracked in the WV images. As the new generation polar satellite-era begins with the Suomi National Polar-orbiting Partnership (S-NPP), there is currently no WV channel on the Visible/Infrared Imager/Radiometer Suite (VIIRS), resulting in a data gap with only IR-W derived AMVs possible. This scenario presents itself as an opportunity to evaluate hyperspectral infrared moisture retrievals from consecutive overlapping satellite polar passes to extract atmospheric motion from clear-sky regions on constant (and known) pressure surfaces, i.e., estimating winds in retrieval space rather than radiance space. Perhaps most significantly, this method has the potential to provide vertical wind profiles, as opposed to the current MODIS-derived single-level AMVs. In this study, the winds technique is applied to Atmospheric Infrared Sounder (AIRS) moisture retrievals from NASA’s Aqua satellite. The resulting winds are assimilated into the Goddard Earth Observing System Model, Version 5 (GEOS-5). The results are encouraging, as the AIRS retrieval polar AMVs have a similar quality as the MODIS AMVs and exhibit a positive impact in the hemispheric Day 4.5 to 6.5 forecasts for a one-month experiment in July 2012.

The use of atmospheric motion vectors (AMVs) in NWP models continues to be an important source of information in data-sparse regions. These AMVs are derived from a time sequence of images from geostationary and polar-orbiting satellites. NWP centers have documented positive impact on model forecasts not only in regions where the AMVs are measured, but elsewhere as well. One example is the effect of the Moderate Resolution Imaging Spectroradiometer (MODIS) polar winds on forecasts in the middle and lower latitudes. Using a preoperational version of NCEP’s Global Forecast System (GFS), an experiment was run during August and September 2004, with and without the MODIS polar winds. Several cases within this period were analyzed to determine how winds poleward of 70° latitude affect the height and wind fields into lower latitudes. From the five cases examined, it was determined that the addition of the polar winds modifies the mass balance in synoptic-scale waves near the polar jet streams. This change in mass balance is evident in differences in the ageostrophic wind, which has an effect on the speed and amplitude of baroclinic waves that extends from the jet stream into lower latitudes in later forecast times. These results reveal the substantial impact that polar-only observations may have on the predictability of global weather systems.

At 08:45 local time (02:45 GMT) on 26 December 2004, 1 h and 45 min after the Sumatra Earthquakes of magnitude 9.3, a devastating tsunami struck the east coast of Sri Lanka. Nearly 2 h and 30 min after the wave hit the coast, a weather satellite passed over Sri Lanka's coastal zone providing a rare glimpse of internal waves along the continental slope due to this tsunami. The satellite imagery indicates wave‐like features from the tsunami being reflected, diffracted, and scattered off the steep continental slope and submarine canyons adjacent to Sri Lanka. The energetic wave and its modification to internal waves possibly eroded sediment from the sea floor and transported it to the sea surface. Solitary features generated by internal waves can explain the observed pattern. Future modelling approaches considering these nonlinear interactions would be required for a better understanding of the tsunami behaviour in the coastal zone, where its destructive effects are most prominent.

The next‐generation geostationary satellites are expected to have hyperspectral infrared (IR) sounders, providing hemispheric coverage of satellite‐derived vertical profiles of temperature, moisture, and wind in clear skies and above clouds. Derivation of winds, or atmospheric motion vectors (AMVs), from IR hyperspectral sounders was first demonstrated using Aqua Atmospheric Infrared Sounder retrievals. The AMVs on discrete pressure levels (3D winds) provided, for the first time, vertical profiles of wind information in the polar regions. Since then, the capability has been extended to tracking features in global profile retrievals of humidity and ozone derived from Cross‐track Infrared Sounder (CrIS) and Infrared Atmospheric Sounding Interferometer radiances. And, it is now demonstrated for the first time globally using retrievals at single field‐of‐view resolution from successive overpasses of three CrIS instruments on NOAA‐21, NOAA‐20, and SNPP flying in formation. 3D winds from polar‐orbiting satellites can provide all‐latitude (“global”) coverage giving insight into capabilities when all geostationary satellites are equipped with IR sounders.