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Enregistrement W1910731447 · doi:10.1002/wea.2524

Future measurements for climate monitoring

2015· article· en· W1910731447 sur OpenAlexaboutno aff
Mike Brettle

Notice bibliographique

RevueWeather · 2015
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueGeophysics and Gravity Measurements
Établissements canadiensnon disponible
Organismes subventionnairesBritish Antarctic Survey
Mots-clésRadiosondeMeteorologyClimatologyEnvironmental scienceGlobal climateComputer sciencePresentation (obstetrics)TelecommunicationsClimate changeGeographyGeology

Résumé

récupéré en direct d'OpenAlex

This was a society main meeting organised by Stephen Burt of the Special Interest Group on Observing Systems. The aim was to address how meteorological measurements have been used to measure climate in the past and how this might be done in the future, be it with traditional or new techniques. There were 59 attendees. Ian Strangeways, FRMetS, started by reviewing meteorological observations going back to the 1850s. Some key measurements are still made by instruments little changed over this period. However, apparently minor changes might be significant in the long term; for example, replacing wooden Stevenson screens with plastic ones that might age in different ways. Afterwards Ian was asked who is responsible for the quality of global networks: in principle, it is the responsibility of the organisation operating each station. Ian's presentation was appropriately followed by Steve Colwell, of the British Antarctic Survey, describing the Global Climate Observing System (GCOS). This comprises many networks, each focussing on particular measurements. For example, the GCOS Upper Air Network (GUAN) is a network of radiosonde stations, and a subset of this is the GCOS Reference Upper Air Network (GRUAN), comprising stations making higher quality observations. The WMO have defined a number of Essential Climate Variables (ECVs) to be measured. These were referred to by several speakers. Although operated to standards defined by the WMO, these networks are funded and operated by individual weather services and institutes, and may serve other functions, such as weather forecasting. The status of these networks is mixed. For example, many African GTN-R (Global Terrestrial Network for Rivers) stations stopped reporting in the 1980s. The GRUAN network, on the other hand, should grow from 17 to 30 stations. The WMO is collecting more metadata on GCOS sites and categorising stations on a scale of 1–5. Only stations scoring 1–3 contribute to GCOS. Steve was asked more about the role of the GCOS organisation since it does not itself own any stations. This is essentially coordination; for example, in arranging for comparisons of different techniques for measuring the same variable at one site. Giles Harrison (University of Reading) described how climate change studies will benefit from higher quality reference ­radiosondes at GRUAN sites improving measurement of low absolute humidity. They are expensive, though, and may only be flown once a week or so. Future radiosoundings could measure a lot more parameters than at present. Measurements of atmospheric electricity, turbulence and other parameters that are not only interesting in their own right but give proxy data on cloud depth might be collected. However, there are longer-term threats to the widespread use of radiosondes, such as a shortage of helium in 20–30 years and environmental issues (radiosondes are rarely recycled). Howard J. Diamond of NOAA (via a live internet link) described the US Climate Reference Network (USCRN). This is a network specifically designed to measure climate change on a national scale and was first deployed operationally in 2004. It consists of 132 stations across the USA and sites in Russia and Canada for comparison purposes. They are installed in areas likely to have stable conditions for decades, such as National Parks. Each station measures several ECVs to follow changes in the US climate over 50 years. Some measurements are made in triplicate, in particular temperature, with three separate aspirated sensors. Howard said he believed aspirated measurements were generally superior, but the main interest is trends not absolute values. USCRN temperatures followed variations from networks using non-aspirated sensors well, but generally reported lower values. The USCRN has thorough metadata documentation, careful maintenance and quality control, resulting in a system addressing questions related to climate beyond temperature trends themselves. For example, USCRN data are very useful for satellite calibration and studies of phenology. Elizabeth Kent (National Oceanography Centre) gave a presentation on ocean measurements. The marine observation record stretches back hundreds of years. Until the 1970s all measurements were from ships or coastal stations, but since then moored and drifting buoys and profiling floats have improved coverage in spatial and temporal resolution. A key ­feature of marine ­observations is the rarity of time series from fixed locations. Elizabeth showed changes in the mix of types of surface temperature measurements available over time. Buoy measurements are providing an increasing fraction of measurements available. She also showed differences in temperatures measured by different techniques. Generally these seem to be converging. An abrupt change in the offset between satellite and buoy temperature measurements around 1994 was explained by changes in the satellites in operation. In general, despite its importance and the length of data available, funding for routine ocean measurements is often uncertain. Research ships, for example, are expensive but have importance beyond their own ­measurements by providing ­calibration measurements when in the vicinity of other sources of data. Nevertheless new parameters are being measured, for example CO2 partial pressure from the Surface Ocean CO2 ATlas (SOCAT) database. This attracted a question about possible correlations with rainfall, but Elizabeth explained that rainfall measurements at sea are very difficult and are only available as a ship passes a given location. Finally, Don Grainger (University of Oxford) gave a presentation on satellite measurements, in particular how a well calibrated, long-term record of ECVs is being constructed from satellite data as part of the ESA Climate Change Initiative. The ESA ECV dataset includes variables categorised as aerosol and cloud properties, ozone, greenhouse gases, land and ocean colour, sea level and sea surface temperature. More satellite-based measurements may be available in the future as lower cost, compact ‘CubeSat’ satellites become available. Overall I found this a very positive meeting. The presentations covered a range of observations, but a common theme was improvements in quality and thorough collection of metadata. It left me with optimism for the future of global climate monitoring.

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)0,007
score de la tête « metaresearch » (Gemma)0,012
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: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,060
Score d'incertitude au seuil0,199

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,012
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0030,002
Communication savante0,0070,011
Science ouverte0,0020,006
Intégrité de la recherche0,0030,006
Charge utile insuffisante (le modèle a refusé de juger)0,0600,024

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 Opus0,097
Tête enseignante GPT0,264
Écart entre enseignants0,167 · 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
GenreEmpirique

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

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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