Optimising orography for global high-resolution simulations Article Swipe
Birgit Sützl
,
Annelize van Niekerk
,
Anton Beljaars
,
Pedro Maciel
,
Margarita Choulga
,
Martin Janoušek
,
Bennoît Vannière
,
Richard Forbes
,
Gianpaolo Balsamo
,
Irina Sandu
,
Peter Dueben
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/ems2024-259
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/ems2024-259
The model’s mean orography acts as the boundary condition for the model dynamics and the drag from resolved orographic gravity waves can have a significant impact on the large-scale atmospheric circulation in weather and climate models. As we approach km-scale horizontal resolutions in global models, more of the orographic spectrum and the impact of orography becomes resolved. Benefits of increased resolution, for example better prediction of orographic rain, can only be harvested if the resolution of the mean orography field is also increased. However, even at kilometre scale, some of the orographic variance will not be represented on the model grid and must be parameterised.Initial simulations for Destination Earth’s global Digital Twin at 4.4 km horizontal resolution have shown a negative wind bias over Eastern Asia and increasing forecast error compared to the operational 9 km model, indicating that the higher resolution of resolved orography causes additional small horizontal-scale orographic gravity waves breaking above the mid-latitude jet, which affects the global circulation. Therefore, we focused on improving the mean orography processing and sub-grid scale orography parameterisations with the aim to find a scale-independent formulation that maintains good forecast skill at operational model resolutions and profits from increased resolution at kilometre scale.The processing of the source data was simplified and harmonised across resolutions using conservative interpolation of the source dataset. A new source dataset for surface elevation with 30 m resolution is used. A small increase to the spectral filtering of mean orography showed an improvement in forecast skill over the Tibetan plateau, and the updated fields describing sub-grid orographic features yield a more consistent behaviour across different resolutions. However, the sub-grid orography has significantly changed, and the parameters of the orographic parameterisation schemes needed to be optimised again considering an appropriate formulation across resolutions. We present a new approach to this parameter re-tuning, using Bayesian parameter optimisation, which enables an efficient workflow for simultaneously optimising several interdependent parameters.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/ems2024-259
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400372339
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400372339Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/ems2024-259Digital Object Identifier
- Title
-
Optimising orography for global high-resolution simulationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-05Full publication date if available
- Authors
-
Birgit Sützl, Annelize van Niekerk, Anton Beljaars, Pedro Maciel, Margarita Choulga, Martin Janoušek, Bennoît Vannière, Richard Forbes, Gianpaolo Balsamo, Irina Sandu, Peter DuebenList of authors in order
- Landing page
-
https://doi.org/10.5194/ems2024-259Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/ems2024-259Direct OA link when available
- Concepts
-
Orography, Resolution (logic), High resolution, Environmental science, Climatology, Computer science, Meteorology, Remote sensing, Geography, Geology, Artificial intelligence, PrecipitationTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4400372339 |
|---|---|
| doi | https://doi.org/10.5194/ems2024-259 |
| ids.doi | https://doi.org/10.5194/ems2024-259 |
| ids.openalex | https://openalex.org/W4400372339 |
| fwci | 0.0 |
| type | preprint |
| title | Optimising orography for global high-resolution simulations |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10466 |
| topics[0].field.id | https://openalex.org/fields/19 |
| topics[0].field.display_name | Earth and Planetary Sciences |
| topics[0].score | 0.9286999702453613 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1902 |
| topics[0].subfield.display_name | Atmospheric Science |
| topics[0].display_name | Meteorological Phenomena and Simulations |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C194507410 |
| concepts[0].level | 3 |
| concepts[0].score | 0.9507776498794556 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q901704 |
| concepts[0].display_name | Orography |
| concepts[1].id | https://openalex.org/C138268822 |
| concepts[1].level | 2 |
| concepts[1].score | 0.4976790249347687 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1051925 |
| concepts[1].display_name | Resolution (logic) |
| concepts[2].id | https://openalex.org/C3020199158 |
| concepts[2].level | 2 |
| concepts[2].score | 0.455628365278244 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q210521 |
| concepts[2].display_name | High resolution |
| concepts[3].id | https://openalex.org/C39432304 |
| concepts[3].level | 0 |
| concepts[3].score | 0.4092920422554016 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[3].display_name | Environmental science |
| concepts[4].id | https://openalex.org/C49204034 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3561844229698181 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q52139 |
| concepts[4].display_name | Climatology |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.34903767704963684 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C153294291 |
| concepts[6].level | 1 |
| concepts[6].score | 0.2669990658760071 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[6].display_name | Meteorology |
| concepts[7].id | https://openalex.org/C62649853 |
| concepts[7].level | 1 |
| concepts[7].score | 0.25865522027015686 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[7].display_name | Remote sensing |
| concepts[8].id | https://openalex.org/C205649164 |
| concepts[8].level | 0 |
| concepts[8].score | 0.24949043989181519 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[8].display_name | Geography |
| concepts[9].id | https://openalex.org/C127313418 |
| concepts[9].level | 0 |
| concepts[9].score | 0.24511289596557617 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[9].display_name | Geology |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.09371474385261536 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C107054158 |
| concepts[11].level | 2 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q25257 |
| concepts[11].display_name | Precipitation |
| keywords[0].id | https://openalex.org/keywords/orography |
| keywords[0].score | 0.9507776498794556 |
| keywords[0].display_name | Orography |
| keywords[1].id | https://openalex.org/keywords/resolution |
| keywords[1].score | 0.4976790249347687 |
| keywords[1].display_name | Resolution (logic) |
| keywords[2].id | https://openalex.org/keywords/high-resolution |
| keywords[2].score | 0.455628365278244 |
| keywords[2].display_name | High resolution |
| keywords[3].id | https://openalex.org/keywords/environmental-science |
| keywords[3].score | 0.4092920422554016 |
| keywords[3].display_name | Environmental science |
| keywords[4].id | https://openalex.org/keywords/climatology |
| keywords[4].score | 0.3561844229698181 |
| keywords[4].display_name | Climatology |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.34903767704963684 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/meteorology |
| keywords[6].score | 0.2669990658760071 |
| keywords[6].display_name | Meteorology |
| keywords[7].id | https://openalex.org/keywords/remote-sensing |
| keywords[7].score | 0.25865522027015686 |
| keywords[7].display_name | Remote sensing |
| keywords[8].id | https://openalex.org/keywords/geography |
| keywords[8].score | 0.24949043989181519 |
| keywords[8].display_name | Geography |
| keywords[9].id | https://openalex.org/keywords/geology |
| keywords[9].score | 0.24511289596557617 |
| keywords[9].display_name | Geology |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.09371474385261536 |
| keywords[10].display_name | Artificial intelligence |
| language | en |
| locations[0].id | doi:10.5194/ems2024-259 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.5194/ems2024-259 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5056498081 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9638-5643 |
| authorships[0].author.display_name | Birgit Sützl |
| authorships[0].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Birgit Sützl |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[1].author.id | https://openalex.org/A5017933122 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4706-1295 |
| authorships[1].author.display_name | Annelize van Niekerk |
| authorships[1].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Annelize van Niekerk |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[2].author.id | https://openalex.org/A5055141479 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2794-2631 |
| authorships[2].author.display_name | Anton Beljaars |
| authorships[2].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Anton Beljaars |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[3].author.id | https://openalex.org/A5013696347 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2834-2208 |
| authorships[3].author.display_name | Pedro Maciel |
| authorships[3].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Pedro Maciel |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[4].author.id | https://openalex.org/A5039170598 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-4393-931X |
| authorships[4].author.display_name | Margarita Choulga |
| authorships[4].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Margarita Choulga |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[5].author.id | https://openalex.org/A5005999706 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-9629-3757 |
| authorships[5].author.display_name | Martin Janoušek |
| authorships[5].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Martin Janoušek |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[6].author.id | https://openalex.org/A5108952493 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Bennoît Vannière |
| authorships[6].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Bennoît Vannière |
| authorships[6].is_corresponding | True |
| authorships[6].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[7].author.id | https://openalex.org/A5012391383 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-3596-8287 |
| authorships[7].author.display_name | Richard Forbes |
| authorships[7].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Richard Forbes |
| authorships[7].is_corresponding | True |
| authorships[7].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[8].author.id | https://openalex.org/A5021304034 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-1745-3634 |
| authorships[8].author.display_name | Gianpaolo Balsamo |
| authorships[8].countries | CH |
| authorships[8].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[8].affiliations[1].institution_ids | https://openalex.org/I1335690228 |
| authorships[8].affiliations[1].raw_affiliation_string | World Meteorological Organization (WMO), Geneva, Switzerland |
| authorships[8].institutions[0].id | https://openalex.org/I1335690228 |
| authorships[8].institutions[0].ror | https://ror.org/011pjwf87 |
| authorships[8].institutions[0].type | government |
| authorships[8].institutions[0].lineage | https://openalex.org/I1335690228 |
| authorships[8].institutions[0].country_code | CH |
| authorships[8].institutions[0].display_name | World Meteorological Organization |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Gianpaolo Balsamo |
| authorships[8].is_corresponding | True |
| authorships[8].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany, World Meteorological Organization (WMO), Geneva, Switzerland |
| authorships[9].author.id | https://openalex.org/A5056101463 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-1215-3288 |
| authorships[9].author.display_name | Irina Sandu |
| authorships[9].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Irina Sandu |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[10].author.id | https://openalex.org/A5016316012 |
| authorships[10].author.orcid | https://orcid.org/0000-0002-4610-3326 |
| authorships[10].author.display_name | Peter Dueben |
| authorships[10].affiliations[0].raw_affiliation_string | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| authorships[10].author_position | last |
| authorships[10].raw_author_name | Peter Dueben |
| authorships[10].is_corresponding | True |
| authorships[10].raw_affiliation_strings | European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5194/ems2024-259 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Optimising orography for global high-resolution simulations |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10466 |
| primary_topic.field.id | https://openalex.org/fields/19 |
| primary_topic.field.display_name | Earth and Planetary Sciences |
| primary_topic.score | 0.9286999702453613 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1902 |
| primary_topic.subfield.display_name | Atmospheric Science |
| primary_topic.display_name | Meteorological Phenomena and Simulations |
| related_works | https://openalex.org/W2362926307, https://openalex.org/W2174923245, https://openalex.org/W2017205623, https://openalex.org/W2336274196, https://openalex.org/W1973963008, https://openalex.org/W2038110265, https://openalex.org/W4243415743, https://openalex.org/W3117249865, https://openalex.org/W4212954839, https://openalex.org/W3190051883 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.5194/ems2024-259 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.5194/ems2024-259 |
| primary_location.id | doi:10.5194/ems2024-259 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.5194/ems2024-259 |
| publication_date | 2024-07-05 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.9 | 134 |
| abstract_inverted_index.A | 232 |
| abstract_inverted_index.a | 23, 119, 181, 261, 296 |
| abstract_inverted_index.m | 228 |
| abstract_inverted_index.30 | 227 |
| abstract_inverted_index.As | 36 |
| abstract_inverted_index.We | 294 |
| abstract_inverted_index.an | 243, 289, 309 |
| abstract_inverted_index.as | 5 |
| abstract_inverted_index.at | 85, 112, 189, 198 |
| abstract_inverted_index.be | 70, 95, 103, 285 |
| abstract_inverted_index.if | 72 |
| abstract_inverted_index.in | 31, 42, 245 |
| abstract_inverted_index.is | 80, 230 |
| abstract_inverted_index.km | 114, 135 |
| abstract_inverted_index.of | 46, 53, 58, 65, 75, 89, 142, 202, 215, 239, 278 |
| abstract_inverted_index.on | 26, 97, 165 |
| abstract_inverted_index.to | 131, 179, 235, 284, 299 |
| abstract_inverted_index.we | 37, 163 |
| abstract_inverted_index.4.4 | 113 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.aim | 178 |
| abstract_inverted_index.and | 13, 33, 50, 101, 126, 171, 193, 208, 252, 275 |
| abstract_inverted_index.can | 21, 68 |
| abstract_inverted_index.for | 9, 61, 106, 223, 312 |
| abstract_inverted_index.has | 272 |
| abstract_inverted_index.new | 220, 297 |
| abstract_inverted_index.not | 94 |
| abstract_inverted_index.the | 6, 10, 14, 27, 47, 51, 73, 76, 90, 98, 132, 139, 154, 159, 167, 177, 203, 216, 236, 249, 253, 269, 276, 279 |
| abstract_inverted_index.was | 206 |
| abstract_inverted_index.Asia | 125 |
| abstract_inverted_index.Twin | 111 |
| abstract_inverted_index.acts | 4 |
| abstract_inverted_index.also | 81 |
| abstract_inverted_index.bias | 122 |
| abstract_inverted_index.data | 205 |
| abstract_inverted_index.drag | 15 |
| abstract_inverted_index.even | 84 |
| abstract_inverted_index.find | 180 |
| abstract_inverted_index.from | 16, 195 |
| abstract_inverted_index.good | 186 |
| abstract_inverted_index.grid | 100 |
| abstract_inverted_index.have | 22, 117 |
| abstract_inverted_index.jet, | 156 |
| abstract_inverted_index.mean | 2, 77, 168, 240 |
| abstract_inverted_index.more | 45, 262 |
| abstract_inverted_index.must | 102 |
| abstract_inverted_index.only | 69 |
| abstract_inverted_index.over | 123, 248 |
| abstract_inverted_index.some | 88 |
| abstract_inverted_index.that | 138, 184 |
| abstract_inverted_index.this | 300 |
| abstract_inverted_index.will | 93 |
| abstract_inverted_index.wind | 121 |
| abstract_inverted_index.with | 176, 226 |
| abstract_inverted_index.above | 153 |
| abstract_inverted_index.again | 287 |
| abstract_inverted_index.error | 129 |
| abstract_inverted_index.field | 79 |
| abstract_inverted_index.model | 11, 99, 191 |
| abstract_inverted_index.rain, | 67 |
| abstract_inverted_index.scale | 173 |
| abstract_inverted_index.shown | 118 |
| abstract_inverted_index.skill | 188, 247 |
| abstract_inverted_index.small | 147, 233 |
| abstract_inverted_index.used. | 231 |
| abstract_inverted_index.using | 212, 303 |
| abstract_inverted_index.waves | 20, 151 |
| abstract_inverted_index.which | 157, 307 |
| abstract_inverted_index.yield | 260 |
| abstract_inverted_index.across | 210, 265, 292 |
| abstract_inverted_index.better | 63 |
| abstract_inverted_index.causes | 145 |
| abstract_inverted_index.fields | 255 |
| abstract_inverted_index.global | 43, 109, 160 |
| abstract_inverted_index.higher | 140 |
| abstract_inverted_index.impact | 25, 52 |
| abstract_inverted_index.model, | 136 |
| abstract_inverted_index.needed | 283 |
| abstract_inverted_index.scale, | 87 |
| abstract_inverted_index.showed | 242 |
| abstract_inverted_index.source | 204, 217, 221 |
| abstract_inverted_index.Digital | 110 |
| abstract_inverted_index.Eastern | 124 |
| abstract_inverted_index.Tibetan | 250 |
| abstract_inverted_index.affects | 158 |
| abstract_inverted_index.becomes | 55 |
| abstract_inverted_index.climate | 34 |
| abstract_inverted_index.dataset | 222 |
| abstract_inverted_index.enables | 308 |
| abstract_inverted_index.example | 62 |
| abstract_inverted_index.focused | 164 |
| abstract_inverted_index.gravity | 19, 150 |
| abstract_inverted_index.models, | 44 |
| abstract_inverted_index.models. | 35 |
| abstract_inverted_index.present | 295 |
| abstract_inverted_index.profits | 194 |
| abstract_inverted_index.schemes | 282 |
| abstract_inverted_index.several | 315 |
| abstract_inverted_index.surface | 224 |
| abstract_inverted_index.updated | 254 |
| abstract_inverted_index.weather | 32 |
| abstract_inverted_index.Bayesian | 304 |
| abstract_inverted_index.Benefits | 57 |
| abstract_inverted_index.However, | 83, 268 |
| abstract_inverted_index.approach | 38, 298 |
| abstract_inverted_index.boundary | 7 |
| abstract_inverted_index.breaking | 152 |
| abstract_inverted_index.changed, | 274 |
| abstract_inverted_index.compared | 130 |
| abstract_inverted_index.dataset. | 218 |
| abstract_inverted_index.dynamics | 12 |
| abstract_inverted_index.features | 259 |
| abstract_inverted_index.forecast | 128, 187, 246 |
| abstract_inverted_index.increase | 234 |
| abstract_inverted_index.km-scale | 39 |
| abstract_inverted_index.negative | 120 |
| abstract_inverted_index.plateau, | 251 |
| abstract_inverted_index.resolved | 17, 143 |
| abstract_inverted_index.spectral | 237 |
| abstract_inverted_index.spectrum | 49 |
| abstract_inverted_index.sub-grid | 172, 257, 270 |
| abstract_inverted_index.variance | 92 |
| abstract_inverted_index.workflow | 311 |
| abstract_inverted_index.behaviour | 264 |
| abstract_inverted_index.condition | 8 |
| abstract_inverted_index.different | 266 |
| abstract_inverted_index.efficient | 310 |
| abstract_inverted_index.elevation | 225 |
| abstract_inverted_index.filtering | 238 |
| abstract_inverted_index.harvested | 71 |
| abstract_inverted_index.improving | 166 |
| abstract_inverted_index.increased | 59, 196 |
| abstract_inverted_index.kilometre | 86, 199 |
| abstract_inverted_index.maintains | 185 |
| abstract_inverted_index.optimised | 286 |
| abstract_inverted_index.orography | 3, 54, 78, 144, 169, 174, 241, 271 |
| abstract_inverted_index.parameter | 301, 305 |
| abstract_inverted_index.resolved. | 56 |
| abstract_inverted_index.scale.The | 200 |
| abstract_inverted_index.Therefore, | 162 |
| abstract_inverted_index.additional | 146 |
| abstract_inverted_index.consistent | 263 |
| abstract_inverted_index.describing | 256 |
| abstract_inverted_index.harmonised | 209 |
| abstract_inverted_index.horizontal | 40, 115 |
| abstract_inverted_index.increased. | 82 |
| abstract_inverted_index.increasing | 127 |
| abstract_inverted_index.indicating | 137 |
| abstract_inverted_index.optimising | 314 |
| abstract_inverted_index.orographic | 18, 48, 66, 91, 149, 258, 280 |
| abstract_inverted_index.parameters | 277 |
| abstract_inverted_index.prediction | 64 |
| abstract_inverted_index.processing | 170, 201 |
| abstract_inverted_index.re-tuning, | 302 |
| abstract_inverted_index.resolution | 74, 116, 141, 197, 229 |
| abstract_inverted_index.simplified | 207 |
| abstract_inverted_index. A | 219 |
| abstract_inverted_index.Destination | 107 |
| abstract_inverted_index.appropriate | 290 |
| abstract_inverted_index.atmospheric | 29 |
| abstract_inverted_index.circulation | 30 |
| abstract_inverted_index.considering | 288 |
| abstract_inverted_index.formulation | 183, 291 |
| abstract_inverted_index.improvement | 244 |
| abstract_inverted_index.large-scale | 28 |
| abstract_inverted_index.operational | 133, 190 |
| abstract_inverted_index.parameters. | 317 |
| abstract_inverted_index.represented | 96 |
| abstract_inverted_index.resolution, | 60 |
| abstract_inverted_index.resolutions | 41, 192, 211 |
| abstract_inverted_index.significant | 24 |
| abstract_inverted_index.simulations | 105 |
| abstract_inverted_index.circulation. | 161 |
| abstract_inverted_index.conservative | 213 |
| abstract_inverted_index.mid-latitude | 155 |
| abstract_inverted_index.resolutions. | 267, 293 |
| abstract_inverted_index.interpolation | 214 |
| abstract_inverted_index.optimisation, | 306 |
| abstract_inverted_index.significantly | 273 |
| abstract_inverted_index.interdependent | 316 |
| abstract_inverted_index.simultaneously | 313 |
| abstract_inverted_index.horizontal-scale | 148 |
| abstract_inverted_index.parameterisation | 281 |
| abstract_inverted_index.Earth’s | 108 |
| abstract_inverted_index.model’s | 1 |
| abstract_inverted_index.parameterisations | 175 |
| abstract_inverted_index.scale-independent | 182 |
| abstract_inverted_index.parameterised.Initial | 104 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5108952493, https://openalex.org/A5016316012, https://openalex.org/A5021304034, https://openalex.org/A5056498081, https://openalex.org/A5039170598, https://openalex.org/A5013696347, https://openalex.org/A5012391383 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I1335690228 |
| citation_normalized_percentile.value | 0.16769969 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |