Editing Weather forecasting (section)

==Techniques==
===Persistence===
The simplest method of forecasting the weather, persistence, relies upon today's conditions to forecast tomorrow's. This can be valid when the weather achieves a steady state, such as during the summer season in the tropics. This method strongly depends upon the presence of a stagnant weather pattern. Therefore, when in a fluctuating pattern, it becomes inaccurate. It can be useful in both short- and long-range forecast|long range forecasts.<ref>University of Illinois at Urbana-Champaign. [http://ww2010.atmos.uiuc.edu/(Gh)/guides/mtr/fcst/mth/prst.rxml "Persistence Forecasting: Today equals Tomorrow"] {{Webarchive|url=https://web.archive.org/web/20070220120718/http://ww2010.atmos.uiuc.edu/(Gh)/guides/mtr/fcst/mth/prst.rxml |date=February 20, 2007 }}. Retrieved February 16, 2007.</ref>

===Barometer===
Measurements of barometric pressure and the pressure tendency (the change of pressure over time) have been used in forecasting since the late 19th century.<ref>[[USA Today]]. [https://www.usatoday.com/weather/wbarocx.htm "Understanding air pressure"] {{Webarchive|url=https://web.archive.org/web/20120701185551/https://www.usatoday.com/weather/wbarocx.htm |date=July 1, 2012 }}. Retrieved May 25, 2008.</ref> The larger the change in pressure, especially if more than {{convert|3.5|hPa|mmHg|lk=on|abbr=on}}, the larger the change in weather can be expected. If the pressure drop is rapid, a [[Low pressure area|low pressure system]] is approaching, and there is a greater chance of rain. [[High pressure area|Rapid pressure rises]] are associated with improving weather conditions, such as clearing skies.<ref>Weather Doctor. [http://www.islandnet.com/~see/weather/eyes/barometer3.htm "Applying The Barometer To Weather Watching"] {{Webarchive|url=https://web.archive.org/web/20080509105153/http://www.islandnet.com/~see/weather/eyes/barometer3.htm |date=May 9, 2008 }}. Retrieved May 25, 2008.</ref>

===Observation===
[[File:marestail.jpg|thumb|Marestail shows moisture at high altitude, signalling the later arrival of wet weather.]]
Along with pressure tendency, the condition of the sky is one of the more important parameters used to forecast weather in mountainous areas. Thickening of cloud cover or the invasion of a higher cloud deck is indicative of rain in the near future. High thin [[cirrostratus cloud]]s can create [[halo (optical phenomenon)|halo]]s around the [[sun]] or [[moon]], which indicates an approach of a [[warm front]] and its associated rain.<ref>{{cite magazine |url=https://books.google.com/books?id=KtkDAAAAMBAJ&pg=PA148|magazine=[[Popular Mechanics]] |page=148 |title=Make Your Own Weather Forecasts |author=Dennis Eskow |date=March 1983 |volume=159 |issue=3 |access-date=April 2, 2011}}</ref> Morning [[fog]] portends fair conditions, as rainy conditions are preceded by wind or clouds that prevent fog formation. The approach of a line of [[thunderstorm]]s could indicate the approach of a [[cold front]]. Cloud-free skies are indicative of fair weather for the near future.<ref>Mark Moore (March 25, 2009). [https://web.archive.org/web/20090325034756/http://www.nwac.us/education_resources/Field_forecasting.pdf "Field Forecasting – A Short Summary"]. Retrieved February 15, 2012.</ref> A [[Bar (tropical cyclone)|bar]] can indicate a coming tropical cyclone. The use of sky cover in weather prediction has led to various [[weather lore]] over the centuries.<ref name=Skywatch />

===Nowcasting===
{{Main|Nowcasting (meteorology)}}
The forecasting of the weather for the following six hours is often referred to as nowcasting.<ref>Glossary of Meteorology. [http://glossary.ametsoc.org/wiki/Nowcast] {{Webarchive|url=https://web.archive.org/web/20150527035615/http://glossary.ametsoc.org/wiki/Nowcast|date=May 27, 2015}} Retrieved May 26, 2015.</ref> In this time range it is possible to forecast smaller features such as individual showers and thunderstorms with reasonable accuracy, as well as other features too small to be resolved by a computer model. A human given the latest radar, satellite and observational data will be able to make a better analysis of the small scale features present and so will be able to make a more accurate forecast for the following few hours.<ref>E-notes.com. [http://www.enotes.com/science-fact-finder/weather-climate/what-nowcasting Weather and Climate | What Is Nowcasting?] {{Webarchive|url=https://web.archive.org/web/20110905132837/http://www.enotes.com/science-fact-finder/weather-climate/what-nowcasting |date=September 5, 2011 }} Retrieved September 8, 2011.</ref> However, there are now [[expert system]]s using those data and mesoscale numerical model to make better extrapolation, including evolution of those features in time. [[Accuweather]] is known for a Minute-Cast, which is a minute-by-minute [[precipitation]] forecast for the next two hours.

===Atmospheric model===
{{Main|Atmospheric model}}
[[File:NAM 500 MB.PNG|thumb|An example of 500 [[millibar|mbar]] [[geopotential height]] prediction from a numerical weather prediction model]]
In the past, human forecasters were responsible for generating the weather forecast based upon available observations.<ref>[[NASA]]. [http://earthobservatory.nasa.gov/Library/WxForecasting/wx2.html "Weather Forecasting Through the Ages"] {{Webarchive|url=https://web.archive.org/web/20050910210732/http://earthobservatory.nasa.gov/Library/WxForecasting/wx2.html |date=September 10, 2005 }}. Retrieved May 25, 2008.</ref> Today, human input is generally confined to choosing a model based on various parameters, such as model biases and performance.<ref name="Klaus">Klaus Weickmann, Jeff Whitaker, Andres Roubicek and Catherine Smith (December 1, 2001). [http://www.cdc.noaa.gov/spotlight/12012001/ "The Use of Ensemble Forecasts to Produce Improved Medium Range (3–15&nbsp;days) Weather Forecasts"]. [[Climate Diagnostics Center]]. Retrieved February 16, 2007. {{Webarchive|url=https://web.archive.org/web/20090827021959/http://www.cdc.noaa.gov/spotlight/12012001/ |date=August 27, 2009 }}</ref> Using a consensus of forecast models, as well as ensemble members of the various models, can help reduce forecast error.<ref name="TBK">Todd Kimberlain (June 2007). [http://www.wpc.ncep.noaa.gov/research/TropicalTalk.ppt "TC Genesis, Track, and Intensity Forecating"] {{Webarchive|url=https://web.archive.org/web/20210227154914/http://www.wpc.ncep.noaa.gov/research/TropicalTalk.ppt |date=February 27, 2021 }}. PowerPoint. Retrieved July 21, 2007.</ref> However, regardless how small the average error becomes with any individual system, large errors within any particular piece of guidance are still possible on any given model run.<ref>Richard J. Pasch, Mike Fiorino, and [[Chris Landsea]]. [http://www.emc.ncep.noaa.gov/research/NCEP-EMCModelReview2006/TPC-NCEP2006.ppt "TPC/NHC'S Review of the NCEP Production Suite for 2006"]. Retrieved May 5, 2008.{{dead link|date=October 2017}}</ref> Humans are required to interpret the model data into weather forecasts that are understandable to the end user. Humans can use knowledge of local effects that may be too small in size to be resolved by the model to add information to the forecast. While increasing accuracy of forecasting models implies that humans may no longer be needed in the forecasting process at some point in the future, there is currently still a need for human intervention.<ref>{{cite journal |last1=Roebber |first1=P. J. |last2=Bosart |first2=L. F. |url=http://cat.inist.fr/?aModele=afficheN&cpsidt=2512901 |title=The complex relationship between forecasting skill and forecast value : A real-world analysis |journal=Weather and Forecasting |issn=0882-8156 |year=1996 |volume=11 |issue=4 |pages=544–559 |access-date=May 25, 2008 |bibcode=1996WtFor..11..544R |doi=10.1175/1520-0434(1996)011<0544:TCRBFS>2.0.CO;2 |s2cid=15191426 |doi-access=free |archive-date=August 16, 2011 |archive-url=https://web.archive.org/web/20110816214902/http://cat.inist.fr/?aModele=afficheN&cpsidt=2512901 |url-status=live }}</ref>

===Analog ===
The analog technique is a complex way of making a forecast, requiring the forecaster to remember a previous weather event that is expected to be mimicked by an upcoming event. What makes it a difficult technique to use is that there is rarely a perfect analog for an event in the future.<ref>[http://ww2010.atmos.uiuc.edu/(Gh)/guides/mtr/fcst/mth/oth.rxml "Other Forecasting Methods: climatology, analogue and numerical weather prediction"] {{Webarchive|url=https://web.archive.org/web/20070519200402/http://ww2010.atmos.uiuc.edu/(Gh)/guides/mtr/fcst/mth/oth.rxml |date=May 19, 2007 }}. Retrieved February 16, 2006.</ref> Some call this type of forecasting pattern recognition. It remains a useful method of observing rainfall over data voids such as oceans,<ref>Kenneth C. Allen. [https://web.archive.org/web/20070714214614/http://stinet.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADP006190 "Pattern Recognition Techniques Applied to the NASA-ACTS Order-Wire Problem"]. Retrieved February 16, 2007.</ref> as well as the forecasting of precipitation amounts and distribution in the future. A similar technique is used in medium range forecasting, which is known as teleconnections, when systems in other locations are used to help pin down the location of another system within the surrounding regime.<ref>Weather Associates, Inc. [http://www.weatherassociates.com/courses.htm "The Role of Teleconnections & Ensemble Forecasting in Extended- to Medium-Range Forecasting"]. Retrieved February 16, 2007. {{webarchive|url=https://web.archive.org/web/20070622105708/http://www.weatherassociates.com/courses.htm |date=June 22, 2007 }}</ref> An example of teleconnections are by using [[El Niño-Southern Oscillation]] (ENSO) related phenomena.<ref>Thinkquest.org. [http://library.thinkquest.org/20901/teleconnections.htm "Teleconnections: Linking El Niño with Other Places"]. Retrieved February 16, 2007. {{webarchive|url=https://web.archive.org/web/20070420054354/http://library.thinkquest.org/20901/teleconnections.htm |date=April 20, 2007 }}</ref>

=== Artificial intelligence ===
Initial attempts to use [[artificial intelligence]] began in the 2010s. [[Huawei]]'s Pangu-Weather model, [[Google]]'s GraphCast, WindBorne's WeatherMesh model, [[Nvidia]]'s FourCastNet, and the [[European Centre for Medium-Range Weather Forecasts]]' Artificial Intelligence/Integrated Forecasting System, or AIFS all appeared in 2022–2023. In 2024, AIFS started to publish real-time forecasts, showing specific skill at predicting hurricane tracks, but lower-performing on the intensity changes of such storms relative to physics-based models.<ref name=":02">{{Cite web |last=Berger |first=Eric |date=June 3, 2024 |title=No physics? No problem. AI weather forecasting is already making huge strides. |url=https://arstechnica.com/ai/2024/06/as-a-potentially-historic-hurricane-season-looms-can-ai-forecast-models-help/ |access-date=June 6, 2024 |website=Ars Technica |language=en-us}}</ref>

Such models use no physics-based atmosphere modeling or [[large language model]]s. Instead, they learn purely from data such as the [[ECMWF re-analysis]] ERA5.<ref>{{Cite web |last=Setchell |first=Helen |date=February 19, 2020 |title=ECMWF Reanalysis v5 |url=https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 |access-date=June 11, 2024 |website=ECMWF |language=en}}</ref> These models typically require far less compute than physics-based models.<ref name=":02" />

[[Microsoft]]'s Aurora system offers global 10-day weather and 5-day air pollution ([[Carbon dioxide|{{chem|CO|2}}]], [[NOx|NO]], [[Nitrogen dioxide|{{chem|NO|2}}]], [[Silicon dioxide|{{chem|SO|2}}]], [[Ozone|{{chem|O|3}}]], and particulates) forecasts with claimed accuracy similar to physics-based models, but at orders-of-magnitude lower cost. Aurora was trained on more than a million hours of data from six weather/climate models.<ref>{{Cite journal |last=Wong |first=Carissa |date=June 4, 2024 |title=Superfast Microsoft AI is first to predict air pollution for the whole world |url=https://www.nature.com/articles/d41586-024-01677-2 |journal=Nature |language=en |doi=10.1038/d41586-024-01677-2|pmid=38834696 |url-access=subscription }}</ref><ref>{{cite arXiv |last1=Bodnar |first1=Cristian |title=Aurora: A Foundation Model of the Atmosphere |date=May 28, 2024 |eprint=2405.13063 |last2=Bruinsma |first2=Wessel P. |last3=Lucic |first3=Ana |last4=Stanley |first4=Megan |last5=Brandstetter |first5=Johannes |last6=Garvan |first6=Patrick |last7=Riechert |first7=Maik |last8=Weyn |first8=Jonathan |last9=Dong |first9=Haiyu|class=physics.ao-ph }}</ref>

In 2024, a group of researchers at Google's DeepMind AI research laboratories published a paper in Nature to describe their machine-learning model, called GenCast, that is expected to produce more accurate forecasts than the best traditional weather forecasting systems.<ref>{{Cite journal |last=Price |first=Ilan | display-authors=etal | year=2025 |title=Probabilistic weather forecasting with machine learning  |journal=Nature |volume=637  |issue=8044 |pages=84–90 |language=en |doi=10.1038/s41586-024-08252-9 |pmid=39633054 |pmc=11666454 |bibcode=2025Natur.637...84P }}</ref>

In a study conducted using the AIFS, Lang et al. (2024) presented 30-day ensemble simulations of the Madden-Julia Oscillation.''<ref>{{Citation |last1=Lang |first1=Simon |title=AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score |date=2024 |arxiv=2412.15832 |last2=Alexe |first2=Mihai |last3=Clare |first3=Mariana C. A. |last4=Roberts |first4=Christopher |last5=Adewoyin |first5=Rilwan |last6=Bouallègue |first6=Zied Ben |last7=Chantry |first7=Matthew |last8=Dramsch |first8=Jesper |last9=Dueben |first9=Peter D.}}</ref>