GFS analysis data represents the best estimate of the current atmospheric state, whereas GFS forecast data predicts future states.
the verdict
INSUFFICIENT LEANING
refutedsupported
the weight of evidence
2 sources for · 0 against
The retrieved evidence partially references GFS analysis data for tracking and initial conditions, but does not provide sufficient source text to fully establish the complete definition comparing GFS analysis and forecast data.
Abstract Several reanalysis data sets are being used for understanding the role of environmental factors controlling tropical cyclones (TCs) evolution. Six reanalysis data sets, namely, European Center for Medium‐range Weather Forecast (ECMWF) ERA‐Interim (ERAI) reanalysis, Global Forecast System (GFS) analysis, Japan Meteorological Agency's 55‐year reanalysis projects reanalysis (JRA55), Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA2) reanalysis, NCEP Climate Forecast System Reanalysis (CFSR), and fifth generation of ECMWF atmospheric reanalysis of global climate (ERA5), have been evaluated for the representation of track, intensity, and structure of 28 TCs which occurred over North Indian Ocean (NIO) during the period 2006–2015. The errors in track, intensity, and minimum sea level pressure (MSLP) of TCs are estimated with respect to the best track data set of India Meteorological Department (IMD). The representation of inner core structure of TCs has been compared. The smallest error in the position of TCs center, MSLP, and maximum wind speed is found in GFS analysis followed by ERA5 and CFSR reanalysis, respectively. GFS and CFSR data sets capture the most intense stages of the TCs followed by the ERA5 data set, while the other three are unable to obtain intensification beyond the severe cyclonic storm stage. The structures of TCs are better represented in GFS analysis followed by ERA5 reanalysis. However, GFS analysis represents early
Hurricane Leslie (2018) was a non-tropical system that lasted for a long time undergoing several transitions between tropical and extratropical states. Its trajectory was highly uncertain and difficult to predict. Here the extratropical transition of Leslie is simulated using the Model for Prediction Across Scales (MPAS) with two different sets of initial conditions (IC): the operational analysis of the Integrate Forecast System (IFS) and the Global Forecast System (GFS).
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