Operational aspects of machine learning in a met-service Article Swipe
Mark A. Liniger
,
D. Cattani
,
Benoît Crouzy
,
Daniele Nerini
,
Lionel Moret
,
Christian Sigg
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/ems2021-181
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/ems2021-181
<p>Machine Learning has a big potential for various tasks along the whole value chain of a national Met-Service. Indeed, many research groups, private and national weather services have started to explore the possibilities and first real-time operational implementations are in place already. However, the building up of the expertise is difficult, large amounts of data have to be made available in an efficient way and the necessary tools have special and demanding requirements concerning infrastructure and maintenance. Also, the transition from research results towards operational tools being operated in realtime is a particular challenge. Not least, trust from end-users must be built, while trying to avoid falling into the short-term hype trap.</p><p>In this presentation, we want to present some examples of machine-learning at MeteoSwiss that are in operational use or soon to be. This includes the use in a measurement system to identify pollen species, the quality control of meteorological observations, the postprocessing of numerical weather forecasts and the condensation of weather forecast information for the meteorologists. These examples have different characteristics and cover a wide range of applications, but also share some common properties. We want to juxtapose these properties with the incentives and conditions how machine learning methods are developed and employed in a more research oriented context like in academia. It turns out that an operational setup of machine learning has very different requirements than machine learning in a research context. The identification of these differences, but also the similarities, could help to understand the challenge of bringing research results into operation and how to alleviate this challenge in the future.</p>
Related Topics
Concepts
Context (archaeology)
Implementation
Computer science
Incentive
Service (business)
Quality (philosophy)
Presentation (obstetrics)
Artificial intelligence
Operations research
Machine learning
Software engineering
Engineering
Business
Epistemology
Microeconomics
Paleontology
Biology
Economics
Philosophy
Marketing
Medicine
Radiology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/ems2021-181
- OA Status
- gold
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- 10
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All OpenAlex metadata
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https://openalex.org/W4248629703Canonical identifier for this work in OpenAlex
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https://doi.org/10.5194/ems2021-181Digital Object Identifier
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Operational aspects of machine learning in a met-serviceWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
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2021-06-18Full publication date if available
- Authors
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Mark A. Liniger, D. Cattani, Benoît Crouzy, Daniele Nerini, Lionel Moret, Christian SiggList of authors in order
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https://doi.org/10.5194/ems2021-181Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.5194/ems2021-181Direct OA link when available
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| abstract_inverted_index.oriented | 208 |
| abstract_inverted_index.realtime | 89 |
| abstract_inverted_index.research | 20, 81, 207, 232, 251 |
| abstract_inverted_index.services | 26 |
| abstract_inverted_index.species, | 144 |
| abstract_inverted_index.academia. | 212 |
| abstract_inverted_index.alleviate | 258 |
| abstract_inverted_index.available | 59 |
| abstract_inverted_index.challenge | 248, 260 |
| abstract_inverted_index.demanding | 71 |
| abstract_inverted_index.developed | 201 |
| abstract_inverted_index.different | 170, 225 |
| abstract_inverted_index.efficient | 62 |
| abstract_inverted_index.end-users | 98 |
| abstract_inverted_index.expertise | 48 |
| abstract_inverted_index.forecasts | 156 |
| abstract_inverted_index.juxtapose | 188 |
| abstract_inverted_index.necessary | 66 |
| abstract_inverted_index.numerical | 154 |
| abstract_inverted_index.operation | 254 |
| abstract_inverted_index.potential | 5 |
| abstract_inverted_index.real-time | 35 |
| abstract_inverted_index.MeteoSwiss | 123 |
| abstract_inverted_index.challenge. | 93 |
| abstract_inverted_index.concerning | 73 |
| abstract_inverted_index.conditions | 195 |
| abstract_inverted_index.difficult, | 50 |
| abstract_inverted_index.incentives | 193 |
| abstract_inverted_index.particular | 92 |
| abstract_inverted_index.properties | 190 |
| abstract_inverted_index.short-term | 109 |
| abstract_inverted_index.transition | 79 |
| abstract_inverted_index.understand | 246 |
| abstract_inverted_index.information | 163 |
| abstract_inverted_index.measurement | 139 |
| abstract_inverted_index.operational | 36, 84, 127, 218 |
| abstract_inverted_index.properties. | 184 |
| abstract_inverted_index.Met-Service. | 17 |
| abstract_inverted_index.condensation | 159 |
| abstract_inverted_index.differences, | 238 |
| abstract_inverted_index.maintenance. | 76 |
| abstract_inverted_index.requirements | 72, 226 |
| abstract_inverted_index.applications, | 178 |
| abstract_inverted_index.observations, | 150 |
| abstract_inverted_index.possibilities | 32 |
| abstract_inverted_index.presentation, | 113 |
| abstract_inverted_index.similarities, | 242 |
| abstract_inverted_index.identification | 235 |
| abstract_inverted_index.infrastructure | 74 |
| abstract_inverted_index.meteorological | 149 |
| abstract_inverted_index.postprocessing | 152 |
| abstract_inverted_index.characteristics | 171 |
| abstract_inverted_index.implementations | 37 |
| abstract_inverted_index.meteorologists. | 166 |
| abstract_inverted_index.machine-learning | 121 |
| abstract_inverted_index.&lt;p&gt;Machine | 0 |
| abstract_inverted_index.future.&lt;/p&gt; | 263 |
| abstract_inverted_index.trap.&lt;/p&gt;&lt;p&gt;In | 111 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.20880246 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |