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Enregistrement W2346306282 · doi:10.1002/wea.2730

Understanding the weather 2015

2016· article· en· W2346306282 sur OpenAlexaboutno aff
Steven Keates

Notice bibliographique

RevueWeather · 2016
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFlood Risk Assessment and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMeteorologyEnvironmental scienceClimatologyGeographyGeology

Résumé

récupéré en direct d'OpenAlex

2015 was turning out to be an uneventful year of weather across the UK, but then along came a succession of late autumn and winter storms, ensuring that the year concluded in a quite extraordinary and at times devastating manner. As always though, global weather events made the headlines throughout, with a particularly strong El Niño probably stealing the show. This meeting, organised and chaired by Kirsty Burgess (Met Office), brought together a range of talks examining notable weather events and trends throughout 2015 in the UK, around the world, and in space. Jenny Rourke (Met Office) started by focussing on notable weather in the UK in 2015, primarily associated with the named storms from October to December. Around the world, January and February saw massive flooding in Malawi, and then Cyclone Pam caused devastation in Vanuatu in March. Summer across the northern hemisphere brought significant heatwaves to Pakistan, parts of Europe (including a new record July maximum temperature in the UK) and northwestern North America. Heatwaves continued into autumn in the western USA, with devastating wildfires breaking out. In late October, Hurricane Patricia became the strongest ever tropical cyclone recorded in the eastern Pacific Ocean. To conclude, two tropical cyclones made landfall in Yemen in early November, another unprecedented event. Weather in 2015 in the UK was summarised by Tim Legg (Met Office). After a relatively unremarkable year, the last 2 months stole the headlines. The ‘highlight’ of this is probably the 341.1mm of rain falling in a 24h period (1800–1800 utc; 4/5 December), a new record in the UK. In fact, many parts of the country recorded a rainfall deficit up to October, but the onset of the deluge in November and December meant 2015 ended up being the sixth wettest since 1910. December itself turned out to be the wettest and warmest on record for the UK, and was also the wettest and warmest calendar month relative to average ever recorded in the UK. Expanding from this, Capel Curig in Gwynedd saw 77% of its annual rainfall in the period 1 November–13 January. Staying with the theme of rainfall, we then visited Peru, with Rebecca Emerton (University of Reading/ECMWF) investigating how El Niño acts as an early indicator of flooding here. Studies of river flow across Peru over 110 years reveal that discharges are, on average, higher following an El Niño. When sea surface temperatures are at their highest in the far eastern Pacific, we find the strongest correlations with flood events in Peru, and the 2015/2016 El Niño was third strongest in this part of the Pacific. As El Niño is somewhat predictable, even at seasonal timescales, actions can be taken by the relevant authorities to mitigate against the effects of flooding. ECMWF forecast the development of a strong El Niño early in the year. Additionally, the seasonal forecast from ECMWF from November 2015 highlights the risk of well-above average rainfall in the region. The Global Flood Awareness System (GloFAS) also suggested a medium to high level of flooding across a range of catchments in Peru. Moving from flooding to fire, Mark Parrington (ECMWF) spoke about global fire activity and emissions in 2015. He introduced the Copernicus Atmospheric Monitoring Service (CAMS), an EU-funded programme for environmental monitoring. This initiative covers many environmental issues, including air quality, with wildfires contributing significant numbers of pollutants. He covered two case studies of wildfire from North America and Indonesia. The USA saw a record fire season, with more than 10 million acres burned, and there was well-above average fire activity in Canada too. Smoke plumes from these fires were transported as far as Western Europe and the Arctic, and carbon monoxide was sampled by numerous aircraft on transatlantic flights, and measurements generally correlated well with forecast models. Indonesia was also badly affected by wildfire, exacerbated by dry conditions brought on by El Niño, with the smoke and haze almost totally shrouding Kalimantan. More carbon dioxide was emitted by Indonesian fires in 2015 than Japan emits in an entire year. We then turned back to the UK for a closer look at one of the more memorable days of the summer. Nick Silkstone and Matthew Lewis (Met Office) described and explained the formation of three severe thunderstorms that broke out across northern England following on from a short but record-breaking heatwave on 1 July. The latest developments in radar capability as part of the Radar Renewal Project were highlighted, namely Doppler and Dual Polarisation radar, as well as how Aircraft Meteorological Data Relays (AMDARs; essentially vertical cross-sections of the atmosphere) and social media were used for nowcasting and post-event analysis. Each storm was unique and complex in its formation, and this talk highlighted how these new techniques could improve short-term forecasting in the future. Planet Earth certainly saw tropical storms aplenty in 2015, and Fernando Prates (ECMWF) took us on a worldwide round-up of events. The North Atlantic was quieter than average, but Fred became the first hurricane to directly impact Cape Verde. Hurricane Joaquín was the strongest October hurricane to affect the Bahamas since 1866. With El Niño playing a significant role, the East and Central Pacific had a particularly active year. In October, Hurricane Patricia became the strongest hurricane ever recorded in the Western Hemisphere, striking Mexico's coast as a category 5 storm. The Northwest Pacific also saw above-average activity, with Typhoon Soudelor heavily impacting Taiwan and eastern China. Cyclone Chapala became the longest-lived severe cyclone in the North Indian Ocean and was the first cyclone to strike Yemen. Finally, Cyclone Pam became the second strongest storm on record in the South Pacific and led to Vanuatu's worst natural disaster in March. Forecasting in the twenty-first century is no longer confined to Earth. Andrew Sibley (Met Office) took us through the highlights of the Space Weather year. Currently in Solar Cycle 24, we have had the lowest solar cycle, measured by the smoothed sunspot number, for 86 years. Together with research from a recent Royal Astronomical Society meeting, this raises questions about the strength of the next two solar cycles. Geomagnetic storms are known to have an impact on the national grid, aviation and satellite communications, but also generate aurorae. In 2015 there were two severe geomagnetic storms. It was shown how, with use of satellite data from the Lagrange 1 position, and models, we can predict the arrival of these high speed plasma cloud events into the Earth's magnetosphere. In both case studies, in March and June, the arrival time was earlier than forecast, but factoring the enormous distance, the shortage of data, and mind-blowing speeds involved, exact precision is difficult at present. Space weather forecasting is an area of science that continues to develop.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,068
Score d'incertitude au seuil0,227

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,008
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0080,007
Science ouverte0,0010,004
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0680,029

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,042
Tête enseignante GPT0,251
Écart entre enseignants0,210 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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