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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|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|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|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>
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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.
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 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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