Tropical cyclone forecast model: Difference between revisions

Revision as of 10:20, 23 October 2022

Revision as of 00:50, 16 December 2022

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[[File:Irene13.gif|right|thumb|A NOAA prediction for [[Hurricane Irene (2011)|Hurricane Irene]] ]]

The [[Hurricane Weather Research and Forecasting model|Hurricane Weather Research and Forecasting]] (HWRF) model is a specialized version of the [[Weather Research and Forecasting model|Weather Research and Forecasting]] (WRF) model and is used to [[weather forecasting|forecast]] the track and [[tropical cyclone scales|intensity]] of [[tropical cyclone]]s. The model was developed by the [[National Oceanic and Atmospheric Administration]] (NOAA), the [[United States Naval Research Laboratory|U.S. Naval Research Laboratory]], the [[University of Rhode Island]], and [[Florida State University]].<ref>{{cite web|publisher=[[University Corporation for Atmospheric Research|UCAR]] press release|url=http://www.ucar.edu/news/releases/2006/wrf.shtml|title=Weather Forecast Accuracy Gets Boost with New Computer Model|access-date=2007-07-09|url-status=dead|archive-url=https://web.archive.org/web/20070519183407/http://www.ucar.edu/news/releases/2006/wrf.shtml|archive-date=19 May 2007}}</ref> It became operational in 2007.<ref name="NOAA Magazine Article 2885">{{cite web|publisher=[[National Oceanic and Atmospheric Administration|NOAA Magazine]]|url=http://www.noaanews.noaa.gov/stories2007/s2885.htm|title=New Advanced Hurricane Model Aids NOAA Forecasters|access-date= 2007-07-09}}</ref> Despite improvements in track forecasting, predictions of the intensity of a tropical cyclone based on numerical weather prediction continue to be a challenge, since statistical methods continue to show higher skill over dynamical guidance.<ref>{{cite journal|last=Rappaport|first=Edward N. |author2=Franklin, James L. |author3=Avila, Lixion A. |author4=Baig, Stephen R. |author5=Beven, John L. |author6=Blake, Eric S. |author7=Burr, Christopher A. |author8=Jiing, Jiann-Gwo |author9=Juckins, Christopher A. |author10=Knabb, Richard D. |author11=Landsea, Christopher W. |author12=Mainelli, Michelle |author13=Mayfield, Max |author14=McAdie, Colin J. |author15=Pasch, Richard J. |author16=Sisko, Christopher |author17=Stewart, Stacy R. |author18=Tribble, Ahsha N. |title=Advances and Challenges at the National Hurricane Center|journal=Weather and Forecasting|date=April 2009|volume=24|issue=2|pages=395–419|doi=10.1175/2008WAF2222128.1|bibcode=2009WtFor..24..395R|citeseerx=10.1.1.207.4667 |s2cid=14845745 }}</ref> Other than the specialized guidance, global guidance such as the GFS, [[Unified Model]] (UKMET), NOGAPS, Japanese Global Spectral Model (GSM), [[European Centre for Medium-Range Weather Forecasts]] model, France's Action de Recherche Petite Echelle Grande Echelle (ARPEGE) and Aire Limit´ee Adaptation Dynamique Initialisation (ALADIN) models, India's [[National Centre for Medium Range Weather Forecasting]] (NCMRWF) model, Korea's Global Data Assimilation and Prediction System (GDAPS) and Regional Data Assimilation and Prediction System (RDAPS) models, Hong Kong/China's Operational Regional Spectral Model (ORSM) model, and Canadian [[Global Environmental Multiscale Model]] (GEM) model are used for track and intensity purposes.<ref name="models"/>

===Timeliness===

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==Ensemble methods==

{{further|Ensemble forecasting}}

No model is ever perfectly accurate because it is impossible to learn exactly everything about the atmosphere in a timely enough manner, and atmospheric measurements that are taken are not completely accurate.<ref>{{cite journal|last=Epstein|first=E.S.|title=Stochastic dynamic prediction|journal=[[Tellus A|Tellus]]|date=December 1969|volume=21|issue=6|pages=739–759|doi=10.1111/j.2153-3490.1969.tb00483.x|bibcode=1969Tell...21..739E}}</ref> The use of the ensemble method of forecasting, whether it be a multi-model ensemble, or numerous ensemble members based on the global model, helps define the uncertainty and further limit errors.<ref>{{cite web|url=http://www.atmos.washington.edu/~ens/pdf/WEM_WKSHP_2004.epgrimit.pdf|title=Redefining the Ensemble Spread-Skill Relationship from a Probabilistic Perspective|author1=Grimit, Eric P.|author2=Mass, Clifford F.|publisher=[[University of Washington]]|date=October 2004|access-date=2010-01-02|archive-url=https://web.archive.org/web/20081012094121/http://www.atmos.washington.edu/~ens/pdf/WEM_WKSHP_2004.epgrimit.pdf|archive-date=12 October 2008|url-status=dead}}</ref><ref>{{cite journal|url=http://www.emc.ncep.noaa.gov/mmb/SREF/2222289_WAF_Feb-2010.official.PDF|title=Fog Prediction From a Multimodel Mesoscale Ensemble Prediction System|author1=Zhou, Binbin |author2=Du, Jun |volume=25|issue=1|date=February 2010|access-date=2011-01-02|journal=[[Weather and Forecasting]]|pages=303–322|doi=10.1175/2009WAF2222289.1|bibcode=2010WtFor..25..303Z|s2cid=4947206 }}</ref>

The JMA has produced an 11-member ensemble forecast system for typhoons known as the Typhoon Ensemble Prediction System (TEPS) since February 2008, which is run out to 132&nbsp;hours into the future. It uses a lower resolution version (with larger grid spacing) of its GSM, with ten perturbed members and one non-perturbed member. The system reduces errors by an average of {{convert|40|km|mi}} five days into the future when compared to its higher resolution GSM.<ref>{{cite web|url=http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/techrev/text11-2.pdf|title=Outline of the Typhoon Ensemble Prediction System at the Japan Meteorological Agency|author1=Yamaguchi, Munehiko |author2=Takuya Komori |name-list-style=amp |pages=14–15|date=2009-04-20|access-date=2011-03-15|publisher=[[Japan Meteorological Agency]]}}</ref>