A validation of the Antarctic Mesoscale Prediction System us

Details of Research

TitleA validation of the Antarctic Mesoscale Prediction System using self-organizing maps and high-density observations from SNOWWEBAbstractThis study compares high-resolution output (1.1-km horizontal grid length) from twice-daily forecasts produced by the Antarctic Mesoscale Prediction System (AMPS) with a dense observational network east of Ross Island. Covering 10 000 km2, 15 SNOWWEB stations significantly increased the number of observation stations in the area to 19 during the 2014-15 austral summer. Collocated vÌirtual stations Ìcreated from AMPS output are combined with observations, producing a single dataset of zonal and meridional wind components used to train a self-organizing map (SOM). The resulting SOM is used to individually classify the observational and AMPS datasets, producing a time series of classifications for each dataset directly comparable to the other. Analysis of class composites shows two dominant weather patterns: low but directionally variable winds and high but directionally constant winds linked to the Ross Ice Shelf airstream (RAS). During RAS events the AMPS and SNOWWEB data displayed good temporal class alignment with good surface wind correlation. SOM analysis shows that AMPS did not accurately forecast surface-level wind speed or direction during light wind conditions where synoptic forcing was weak; however, it was able to forecast the low wind period occurrence accurately. Coggins' regimes provide synoptic-scale context and show a reduced synoptic pressure gradient during these classes, increasing reliance on the ability of Polar WRF to resolve mesoscale dynamics. Available initialization data have insufficient resolution for the region's complex topography, which likely impacts performance. The SOM analysis methods used are shown to be effective for model validation and are widely applicable. Copyright 2016 American Meteorological Society.AcknowledgementsThis study was partially supported by a grant from the New Zealand Antarctic Research Institute, scholarships from both the Department of Physics and Astronomy at the University of Canterbury and Kelly TarltonÅ› (administered by Antarctica New Zealand), and significant logistics support from Antarctica New Zealand. The authors appreciate the support of the University of Wisconsin--Madison Automatic Weather Station Program forthe AWS observational dataset (NSF Grants ANT-0944018, ANT-1245663, ANT-0943952, and ANT-1245737). The authors also appreciate the work of the Byrd Polar Research Center at Ohio State University and the National Center for Atmospheric Research for developing, running, and providing output from AMPS (retrieved from both the NCAR/UCAR FTP server and the Earth System Grid).

Details

1st AuthorJolly, B.AuthorJolly, B.McDonald, A.Coggins, J.Zawar-Reza, P.Cassano, J.Lazzara, M.Graham, G.Plank, G.Petterson, O.Dale, E.Year2016JournalMonthly Weather ReviewVolume144Number9Pages3181-3200DOI10.1175/MWR-D-15-0447.1URLhttps://www.scopus.com/inward/recor.....3a42da2c0649393657f75a201KeywordsClassification (of information)Climate changeComplex networksConformal mappingForecastingSelf organizing mapsWeather information servicesWind, AntarcticaAutomatic weather stationsGeographic locationMesoscale forecastingNumerical weather prediction/forecastingStatistical techniquesTopographic effects, Weather forecasting, rank5Author KeywordsAntarcticaAutomatic weather stationsCirculation/ DynamicsClassificationForecastingGeographic location/entityMathematical and statistical techniquesMesoscale forecastingNumerical weather prediction/forecastingObservational techniques and algorithmsTopographic effectsProgrammeK055 - Dynamics in the Antarctic Middle Atmosphere - MF calibration

Other

TypeArticleCitationJolly, B., McDonald, A., Coggins, J., Zawar-Reza, P., Cassano, J., Lazzara, M., Graham, G., Plank, G., Petterson, O. and Dale, E. (2016). A validation of the Antarctic Mesoscale Prediction System using self-organizing maps and high-density observations from SNOWWEB. Monthly Weather Review, 144(9): 3181-3200