Tropical cyclone forecast model: Difference between revisions

Revision as of 15:12, 18 January 2019

Revision as of 13:16, 19 February 2019

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==Statistical guidance==

[[File:Isabel2003rcliper.jpg|thumb|right|r-CLIPER for [[Hurricane Isabel]] (2003)]]

The first statistical guidance used by the [[National Hurricane Center]] was the Hurricane Analog Technique (HURRAN), which was available in 1969. It used the newly developed [[HURDAT|North Atlantic tropical cyclone database]] to find storms with similar tracks. It then shifted their tracks through the storm's current path, and used ___location, direction and speed of motion, and the date to find suitable analogs. The method did well with storms south of the [[25th parallel north|25th parallel]] which had not yet turned northward, but poorly with systems near or after recurvature.<ref name="models">{{cite book|pages=288–292|url=https://books.google.com/books?id=6gFiunmKWWAC&pg=PA297&dq=hours+time+used+to+run+ECMWF+model#v=onepage&q=hours%20time%20used%20to%20run%20ECMWF%20model&f=false|title=Global Perspectives on Tropical Cyclones: From Science to Mitigation|author1=Chan, Johnny C. L. |author2=Jeffrey D. Kepert |lastauthoramp=yes |year=2010|publisher=World Scientific|isbn=978-981-4293-47-1|accessdate=2011-02-24}}</ref> Since 1972, the [[Climatology and Persistence]] (CLIPER) statistical model has been used to help generate tropical cyclone track forecasts. In the era of skillful dynamical forecasts, CLIPER is now being used as the baseline to show model and forecaster skill.<ref>{{cite journal|url=http://rammb.cira.colostate.edu/resources/docs/Statistical_Knaff.pdf|pages=80–81|title=Statistical, 5-Day Tropical Cyclone Intensity Forecasts Derived from Climatology and Persistence|volume=18|date=February 2003|accessdate=2011-02-25|publisher=[[American Meteorological Society]]|journal=Weather and Forecasting|doi=10.1175/1520-0434(2003)018<0080:SDTCIF>2.0.CO;2|issn=1520-0434|last1=Knaff|first1=John A.|last2=Demaria|first2=Mark|last3=Sampson|first3=Charles R.|last4=Gross|first4=James M.|bibcode = 2003WtFor..18...80K }}</ref> The Statistical Hurricane Intensity Forecast (SHIFOR) has been used since 1979 for tropical cyclone intensity forecasting. It uses climatology and persistence to predict future intensity, including the current [[Julian day]], current cyclone intensity, the cyclone's intensity 12&nbsp;hours ago, the storm's initial latitude and longitude, as well as its zonal (east-west) and meridional (north-south) components of motion.<ref name="models"/>

A series of statistical-dynamical models, which used regression equations based upon CLIPER output and the latest output from [[primitive equations|primitive equation]] models run at the National Meteorological Center, then [[National Centers for Environmental Prediction]], were developed between the 1970s and 1990s and were named NHC73, NHC83, NHC90, NHC91, and NHC98.<ref name="NHCmodel"/><ref name="Simpson">{{cite book|url=https://books.google.com/books?id=P7DnIb2XNg0C&pg=PA111&lpg=PA111&dq=QLM+quasi+tropical+cyclone+model+book#v=onepage&q=QLM%20quasi%20tropical%20cyclone%20model%20book&f=false|page=110|author=Simpson, Robert H.|title=Hurricane!: coping with disaster : progress and challenges since Galveston, 1900|publisher=[[American Geophysical Union]]|year=2003|accessdate=2011-02-25|isbn=978-0-87590-297-5|authorlink=Robert Simpson (meteorologist)}}</ref> Within the field of [[tropical cyclone track forecasting]], despite the ever-improving dynamical model guidance which occurred with increased computational power, it was not until the decade of the 1980s when [[numerical weather prediction]] showed [[Forecast skill|skill]], and until the 1990s when it consistently outperformed statistical or simple dynamical models.<ref>{{cite web|url=http://www.nhc.noaa.gov/verification/verify6.shtml|publisher=[[National Hurricane Center]]|date=2010-04-20|accessdate=2011-01-02|author=Franklin, James|title=National Hurricane Center Forecast Verification|authorlink=James Franklin (meteorologist)}}</ref> In 1994, a version of SHIFOR was created for the northwest Pacific Ocean for [[typhoon]] forecasting, known as the Statistical Typhoon Intensity Forecast (STIFOR), which used the 1971–1990 data for that region to develop intensity forecasts out to 72&nbsp;hours into the future.<ref>{{cite web|url=http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=ADA290588|title=A Regression Model For the Western North Pacific Tropical Cyclone Intensity Forecast|author=Chu, Jan-Hwa|publisher=[[United States Naval Research Laboratory]]|date=November 1994|accessdate=2011-03-15}}</ref>

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{{See also|History of numerical weather prediction}}

During 1972, the first model to forecast storm surge along the [[continental shelf]] of the United States was developed, known as the [[Special Program to List the Amplitude of Surges from Hurricanes]] (SPLASH).<ref>{{cite web|url=http://slosh.nws.noaa.gov/sloshPub/pubs/SLOSH_TR48.pdf|title=SLOSH: Sea, lake, and Overland Surges from Hurricanes. NOAA Technical Report NWS 48|author=Jelesnianski, C. P., J. Chen, and W. A. Shaffer|date=April 1992|accessdate=2011-03-15|publisher=[[National Oceanic and Atmospheric Administration]]|page=2}}</ref> In 1978, the first hurricane-tracking model based on [[Atmospheric dynamics#Dynamic meteorology|atmospheric dynamics]] – the movable fine-mesh (MFM) model – began operating.<ref name="Shuman W&F">{{cite journal|last=Shuman|first=Frederick G.|authorlink=Frederick Gale Shuman|title=History of Numerical Weather Prediction at the National Meteorological Center|journal=[[Weather and Forecasting]]|date=September 1989|volume=4|issue=3|pages=286–296|issn=1520-0434|doi=10.1175/1520-0434(1989)004<0286:HONWPA>2.0.CO;2|bibcode=1989WtFor...4..286S}}</ref> The Quasi-Lagrangian Limited Area (QLM) model is a multi-level primitive equation model using a [[Cartesian coordinate system|Cartesian]] grid and the [[Global Forecast System]] (GFS) for boundary conditions.<ref name="models"/> In the early 1980s, the assimilation of satellite-derived winds from water vapor, infrared, and visible satellite imagery was found to improve tropical cyclones track forecasting.<ref>{{cite journal|url=http://www.bom.gov.au/amm/docs/1996/lemarshall2.pdf|page=275|title=Tropical Cyclone ''Beti'' – an Example of the Benefits of Assimilating Hourly Satellite Wind Data|author1=Le Marshall |author2=J. F. |author3=L. M. Leslie |author4=A. F. Bennett |last-author-amp=yes |journal=Australian Meteorological Magazine|volume=45|year=1996}}</ref> The [[Geophysical Fluid Dynamics Laboratory]] (GFDL) hurricane model was used for research purposes between 1973 and the mid-1980s. Once it was determined that it could show skill in hurricane prediction, a multi-year transition transformed the research model into an operational model which could be used by the [[National Weather Service]] for both track and intensity forecasting in 1995.<ref>{{cite web|url=http://www.gfdl.noaa.gov/operational-hurricane-forecasting|author=[[Geophysical Fluid Dynamics Laboratory]]|title=Operational Hurricane Track and Intensity Forecasting|publisher=[[National Oceanic and Atmospheric Administration]]|date=2011-01-28|accessdate=2011-02-25}}</ref> By 1985, the Sea Lake and Overland Surges from Hurricanes (SLOSH) Model had been developed for use in areas of the [[Gulf of Mexico]] and near the United States' East coast, which was more robust than the SPLASH model.<ref>{{cite journal|author1=Jarvinen B. J. |author2=C. J. Neumann |lastauthoramp=yes |year=1985|title=An evaluation of the SLOSH storm surge model|journal=Bulletin of the American Meteorological Society|volume=66|issue=11 |pages=1408–1411|bibcode=1985BAMS...66.1408.|doi=10.1175/1520-0477-66.11.1408}}</ref>

The [[Beta Advection Model]] (BAM) has been used operationally since 1987 using steering winds averaged through the 850 hPa to 200 hPa layer and the Beta effect which causes a storm to drift northwest due to differences in the [[coriolis effect]] across the tropical cyclone.<ref>{{cite web|author=Glossary of Meteorology|date=June 2000|url=http://amsglossary.allenpress.com/glossary/search?p=1&query=beta+effect&submit=Search|title=Beta Effect|publisher=[[American Meteorological Society]]|accessdate=2008-05-05}}</ref> The larger the cyclone, the larger the impact of the beta effect is likely to be.<ref name="NAVY">{{cite web|author=[[United States Navy]]|url=http://www.nrlmry.navy.mil/~chu/chap4/se100.htm|title=Section 1. Influences on Tropical Cyclone Motion|accessdate=2011-02-25|year=2011}}</ref> Starting in 1990, three versions of the BAM were run operationally: the BAM shallow (BAMS) average winds in an 850 hPa to 700 hPa layer, the BAM Medium (BAMM) which uses average winds in an 850 hPa to 400 hPa layer, and the BAM Deep (BAMD) which is the same as the pre-1990 BAM.<ref name="Simpson"/> For a weak hurricane without well-developed central thunderstorm activity, BAMS works well, because weak storms tend to be steered by low-level winds.<ref name="NHCmodel"/> As the storm grows stronger and associated thunderstorm activity near its center gets deeper, BAMM and BAMD become more accurate, as these types of storms are steered more by the winds in the upper-level. If the forecast from the three versions is similar, then the forecaster can conclude that there is minimal uncertainty, but if the versions vary by a great deal, then the forecaster has less confidence in the track predicted due to the greater uncertainty.<ref name="ensbook">{{cite book|url=https://books.google.com/books?id=6RQ3dnjE8lgC&pg=PA261#v=onepage&q&f=false|title=Numerical Weather and Climate Prediction|author=Warner, Thomas Tomkins |publisher=[[Cambridge University Press]]|year=2010|isbn=978-0-521-51389-0|pages=266–275|accessdate=2011-02-11}}</ref> Large differences between model predictions can also indicate wind shear in the atmosphere, which could affect the intensity forecast as well.<ref name="NHCmodel"/>

Tested in 1989 and 1990, The Vic Ooyama Barotropic (VICBAR) model used a [[spline (mathematics)|cubic-B spline]] representation of variables for the objective analysis of observations and solutions to the shallow-water prediction equations on nested domains, with the boundary conditions defined as the global forecast model.<ref>

{{cite journal | doi = 10.1175/1520-0493(1992)120<1628:ANSMFH>2.0.CO;2 | year = 1992 | volume = 120 | pages = 1628–1643 | title = A Nested Spectral Model for Hurricane Track Forecasting | author = Demaria, Mark | journal = Monthly Weather Review | last2 = Aberson | first2 = Sim D. | last3 = Ooyama | first3 = Katsuyuki V. | last4 = Lord | first4 = Stephen J. | issn = 1520-0493 | issue = 8|bibcode = 1992MWRv..120.1628D }}</ref> It was implemented operationally as the Limited Area Sine Transform Barotropic (LBAR) model in 1992, using the GFS for boundary conditions.<ref name="models"/> By 1990, Australia had developed its own storm surge model which was able to be run in a few minutes on a personal computer.<ref>{{cite journal|title=Computer Techniques: A Real-Time System For Forecasting Tropical Cyclone Storm Surges|author=Hubbert, Graeme D., Greg J. Holland, Lance M. Leslie, Michael J. Manton|date=March 1991|publisher=[[American Meteorological Society]]|pages=86–87|journal=Weather and Forecasting|issn=1520-0434|volume=6|issue=1|doi=10.1175/1520-0434(1991)006<0086:ARTSFF>2.0.CO;2|bibcode=1991WtFor...6...86H}}</ref> The [[Japan Meteorological Agency]] (JMA) developed its own Typhoon Model (TYM) in 1994,<ref>{{cite book|url=https://books.google.com/books?id=De3JAP1N_wEC&pg=PA172&lpg=PA172&dq=japan+meteorological+agency+typhoon+model+book#v=onepage&q=japan%20meteorological%20agency%20typhoon%20model%20book&f=false|title=Early warning systems for natural disaster reduction|page=172|author1=Zschau, Jochen |author2=Andreas N. Küppers |lastauthoramp=yes |year=2003|publisher=Springer|isbn=978-3-540-67962-2|accessdate=2011-03-16}}</ref> and in 1998, the agency began using its own dynamic [[storm surge]] model.<ref>{{cite web|url=http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/techrev/text11-3.pdf|title=Outline of the Storm Surge Prediction Model at the Japan Meteorological Agency|page=25|author=Higaki, Masakazu, Hironori Hayashibara, and Futoshi Nozaki|date=2009-04-20|accessdate=2011-03-15|publisher=[[Japan Meteorological Agency]]}}</ref>

[[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|accessdate=2007-07-09|deadurl=yes|archiveurl=https://web.archive.org/web/20070519183407/http://www.ucar.edu/news/releases/2006/wrf.shtml|archivedate=19 May 2007|df=dmy-all}}</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|accessdate= 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 statiscal 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 }}</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==

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|accessdate=2010-01-02}}</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|accessdate=2011-01-02|journal=[[Weather and Forecasting]]|publisher=[[American Meteorological Society]]|pages=303–322|doi=10.1175/2009WAF2222289.1|bibcode=2010WtFor..25..303Z}}</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 |lastauthoramp=yes |pages=14–15|date=2009-04-20|accessdate=2011-03-15|publisher=[[Japan Meteorological Agency]]}}</ref>

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The Florida State Super Ensemble (FSSE) is produced from a suite of models which then uses statistical regression equations developed over a training phase to reduce their biases, which produces forecasts better than the member models or their mean solution. It uses 11&nbsp;global models, including five developed at [[Florida State University]], the Unified Model, the GFS, the NOGAPS, the United States Navy NOGAPS, the Australian Bureau of Meteorology Research Centre (BMRC) model, and Canadian [[Recherche en Prévision Numérique]] (RPN) model. It shows significant skill in track, intensity, and rainfall predictions of tropical cyclones.<ref>{{cite book|pages=532–545|url=https://books.google.com/books?id=c-rY28QQCj8C&pg=PA532&dq=Florida+State+Superensemble+hurricane+book#v=onepage&q=Florida%20State%20Superensemble%20hurricane%20book&f=false|title=Predictability of weather and climate|author1=Palmer, Tim |author2=Renate Hagedorn |lastauthoramp=yes |year=2006|publisher=Cambridge University Press|isbn=978-0-521-84882-4|accessdate=2011-02-26}}</ref>

The Systematic Approach Forecast Aid (SAFA) was developed by the Joint Typhoon Warning Center to create a selective consensus forecast which removed more erroneous forecasts at a 72‑hour time frame from consideration using the United States Navy NOGAPS model, the GFDL, the Japan Meteorological Agency's global and typhoon models, as well as the UKMET. All the models improved during SAFA's five-year history and removing erroneous forecasts proved difficult to do in operations.<ref>{{cite journal|url=http://journals.ametsoc.org/doi/pdf/10.1175/WAF991.1|title=Notes and Correspondence: Operational Evaluation of a Selective Consensus in the Western North Pacific Basin|author=Sampson, Charles R., John A. Knaff, and Edward M. Fukada|pages=671–675|date=June 2007|accessdate=2011-03-15|journal=Weather and Forecasting|volume=22|doi=10.1175/WAF991.1|issue=3|bibcode = 2007WtFor..22..671S }}</ref>

==Sunspot theory==

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==Hurricane forecast model accuracy==

The accuracy of hurricane forecast models can vary significantly from storm to storm. For some storms the factors affecting the hurricane track are relatively straightforward, and the models are not only accurate but they produce similar forecasts, while for other storms the factors affecting the hurricane track are more complex and different models produce very different forecasts.<ref>{{cite web|url=http://www.hurricanescience.org/science/forecast/models/modelskill/|title=Hurricanes: Science and Society: Hurricane Forecast Model Accuracy|first=|last=[NULL]|publisher=}}</ref>

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