Abstract
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’.
Highlights
The use of satellite-derived cloud displacements to infer atmospheric motion (AMVs or Atmospheric Motion Vectors) has been investigated since the first weather satellites were launched
This threshold is used in all AMV datasets to avoid the processing of ‘low-quality AMVs’; in some elements of the Intercomparison, an additional ‘Common Quality Indicator (CQI) threshold’ of 80% (CQI >= 80%) is used to verify its impact on the improvement of the validation parameters in the different AMV datasets
Results show an approximate Gaussian distribution of the variable ‘AMV best-fit pressure level—AMV pressure level’ for all centers, which is more consistent than the one found in the previous ‘AMV Intercomparison’ [15]. This might indicate that the JMA AMV height assignment method has a stronger dependency on the Numerical Weather Prediction (NWP) model background
Summary
The use of satellite-derived cloud displacements to infer atmospheric motion (AMVs or Atmospheric Motion Vectors) has been investigated since the first weather satellites were launched. The following is a brief chronology on the generation of geostationary AMVs from the primary producers:
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