Tropical cyclone forecast model: Difference between revisions

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A '''tropical cyclone forecast model''' is a computer program that uses [[meteorology|meteorological]] data to [[weather forecasting|forecast]] aspects of the future state of [[tropical cyclone]]s. There are three types of models: statistical, dynamical, or combined statistical-dynamic.<ref name="NHCmodel"/> Dynamical models utilize powerful [[supercomputer]]s with sophisticated [[mathematical model]]ing software and meteorological data to [[numerical weather prediction|calculate future weather conditions]]. [[Statistical model]]s forecast the evolution of a tropical cyclone in a simpler manner, by extrapolating from historical datasets, and thus can be run quickly on platforms such as [[personal computer]]s. Statistical-dynamical models use aspects of both types of forecasting. Four primary types of forecasts exist for tropical cyclones: [[tropical cyclone track forecasting|track]], intensity, [[storm surge]], and [[tropical cyclone rainfall climatology|rainfall]]. Dynamical models were not developed until the 1970s and the 1980s, with earlier efforts focused on the storm surge problem.

Track models did not show [[forecast skill]] when compared to statistical models until the 1980s. Statistical-dynamical models were used from the 1970s into the 1990s. Early models use data from previous model runs while late models produce output after the official hurricane forecast has been sent. The use of [[consensus forecast|consensus]], [[ensemble forecasting|ensemble]], and superensemble forecasts lowers errors more than any individual forecast model. Both consensus and superensemble forecasts can use the guidance of global and regional models runs to improve the performance more than any of their respective components. Techniques used at the [[Joint Typhoon Warning Center]] indicate that superensemble forecasts are a very powerful tool for track forecasting.

==Statistical guidance==

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

The first dynamical hurricane track forecast model, the Sanders Barotropic Tropical Cyclone Track Prediction Model (SANBAR),<ref>{{cite journal|author=R.W. Burpee|name-list-style=amp |year=2008|title=The Sanders Barotropic Tropical Cyclone Track Prediction Model (SANBAR)|journal=Meteorological Monographs|volume=33|issue=55 |pages=233-240233–240|doi=10.1175/0065-9401-33.55.233|doi-access=free|bibcode=2008MetMo..33..233B }}</ref> was introduced in 1970 and was used by the National Hurricane Center as part of its operational track guidance through 1989. It was based on a simplified set of atmospheric dynamical equations (the equivalent barotropic formulation) using a deep layer-mean wind.

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|access-date=2011-03-15|publisher=[[National Oceanic and Atmospheric Administration]]|page=2|archive-date=21 July 2011|archive-url=https://web.archive.org/web/20110721060003/http://slosh.nws.noaa.gov/sloshPub/pubs/SLOSH_TR48.pdf|url-status=dead}}</ref> In 1978, the first full-physics 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.|author-link=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|doi-access=free}}</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 |name-list-style=amp |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|author-link=Geophysical Fluid Dynamics Laboratory|title=Operational Hurricane Track and Intensity Forecasting|publisher=[[National Oceanic and Atmospheric Administration]]|date=2011-01-28|access-date=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 |name-list-style=amp |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|doi-access=free}}</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]]|access-date=2008-05-05|archive-url=https://web.archive.org/web/20110606101836/http://amsglossary.allenpress.com/glossary/search?p=1&query=beta+effect&submit=Search|archive-date=6 June 2011|url-status=dead}}</ref> The larger the cyclone, the larger the impact of the beta effect is likely to be.<ref name="NAVY">{{cite web|publisher=[[United States Navy]]|url=http://www.nrlmry.navy.mil/~chu/chap4/se100.htm|archive-url=https://web.archive.org/web/19991007015715/http://www.nrlmry.navy.mil/%7Echu/chap4/se100.htm|url-status=dead|archive-date=7 October 1999|title=Section 1. Influences on Tropical Cyclone Motion|access-date=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|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|access-date=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>

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==Consensus methods==

[[File:WRF rita spread2.jpg|thumb|upright=1.3|''Top'': WRF model simulation of [[Hurricane Rita]] tracks. ''Bottom'': The spread of NHC multi-model ensemble forecast.]]

Using a consensus of forecast models reduces forecast error.<ref name="TBK">{{cite web|author=Kimberlain, Todd|url=http://www.wpc.ncep.noaa.gov/research/TropicalTalk.ppt|title=Tropical cyclone motion and intensity talk|date=June 2007|access-date=2007-07-21|publisher=[[Hydrometeorological Prediction Center]]}}</ref> Trackwise, the GUNA model is a consensus of the interpolated versions of the GFDL, UKMET with quality control applied to the cyclone tracker, United States Navy NOGAPS, and GFS models. The version of the GUNA corrected for model biases is known as the CGUN. The TCON consensus is the GUNA consensus plus the Hurricane WRF model. The version of the TCON corrected for model biases is known as the TCCN. A lagged average of the last two runs of the members within the TCON plus the ECMWF model is known as the TVCN consensus. The version of the TVCN corrected for model biases is the TVCC consensus.<ref name="NHCmodel"/>

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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|archive-date=21 July 2011|archive-url=https://web.archive.org/web/20110721055820/http://www.emc.ncep.noaa.gov/mmb/SREF/2222289_WAF_Feb-2010.official.PDF|url-status=dead}}</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>

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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&q=Florida+State+Superensemble+hurricane+book&pg=PA532|title=Predictability of weather and climate|author1=Palmer, Tim |author2=Renate Hagedorn |name-list-style=amp |year=2006|publisher=Cambridge University Press|isbn=978-0-521-84882-4|access-date=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|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|journal=Weather and Forecasting|volume=22|doi=10.1175/WAF991.1|issue=3|bibcode = 2007WtFor..22..671S |doi-access=free}}</ref>

==Sunspot theory==