Exploring the relationship between temperature forecast errors and Earth system variables Article Swipe
Melissa Ruiz‐Vásquez
,
O Sungmin
,
Alexander Brenning
,
Randal D. Koster
,
Gianpaolo Balsamo
,
Ulrich Weber
,
Gabriele Arduini
,
Ana Bastos
,
Markus Reichstein
,
René Orth
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/esd-13-1451-2022
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/esd-13-1451-2022
Accurate subseasonal weather forecasts, from 2 weeks up to a season, can help reduce costs and impacts related to weather and corresponding extremes. The quality of weather forecasts has improved considerably in recent decades as models represent more details of physical processes, and they benefit from assimilating comprehensive Earth observation data as well as increasing computing power. However, with ever-growing model complexity, it becomes increasingly difficult to pinpoint weaknesses in the forecast models' process representations which is key to improving forecast accuracy. In this study, we use a comprehensive set of observation-based ecological, hydrological, and meteorological variables to study their potential for explaining temperature forecast errors at the weekly timescale. For this purpose, we compute Spearman correlations between each considered variable and the forecast error obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) reforecasts at lead times of 1–6 weeks. This is done across the globe for the time period 2001–2017. The results show that temperature forecast errors globally are most strongly related with climate-related variables such as surface solar radiation and precipitation, which highlights the model's difficulties in accurately capturing the evolution of the climate-related variables during the forecasting period. At the same time, we find particular regions in which other variables are more strongly related to forecast errors. For instance, in central Europe, eastern North America and southeastern Asia, vegetation greenness and soil moisture are relevant, while in western South America and central North America, circulation-related variables such as surface pressure relate more strongly with forecast errors. Overall, the identified relationships between forecast errors and independent Earth observations reveal promising variables on which future forecasting system development could focus by specifically considering related process representations and data assimilation.
Related Topics
Concepts
Climatology
Environmental science
Precipitation
Forecast verification
Forecast skill
Variable (mathematics)
Global Forecast System
Meteorology
Quantitative precipitation forecast
Consensus forecast
Forecast period
Model output statistics
Vegetation (pathology)
Weather forecasting
Numerical weather prediction
Geography
Econometrics
Mathematics
Production (economics)
Geology
Mathematical analysis
Macroeconomics
Pathology
Net present value
Medicine
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/esd-13-1451-2022
- https://esd.copernicus.org/articles/13/1451/2022/esd-13-1451-2022.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 90
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307927231
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4307927231Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/esd-13-1451-2022Digital Object Identifier
- Title
-
Exploring the relationship between temperature forecast errors and Earth system variablesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-28Full publication date if available
- Authors
-
Melissa Ruiz‐Vásquez, O Sungmin, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, René OrthList of authors in order
- Landing page
-
https://doi.org/10.5194/esd-13-1451-2022Publisher landing page
- PDF URL
-
https://esd.copernicus.org/articles/13/1451/2022/esd-13-1451-2022.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://esd.copernicus.org/articles/13/1451/2022/esd-13-1451-2022.pdfDirect OA link when available
- Concepts
-
Climatology, Environmental science, Precipitation, Forecast verification, Forecast skill, Variable (mathematics), Global Forecast System, Meteorology, Quantitative precipitation forecast, Consensus forecast, Forecast period, Model output statistics, Vegetation (pathology), Weather forecasting, Numerical weather prediction, Geography, Econometrics, Mathematics, Production (economics), Geology, Mathematical analysis, Macroeconomics, Pathology, Net present value, Medicine, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 6Per-year citation counts (last 5 years)
- References (count)
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90Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.improving | 80 |
| abstract_inverted_index.instance, | 216 |
| abstract_inverted_index.potential | 101 |
| abstract_inverted_index.promising | 266 |
| abstract_inverted_index.radiation | 175 |
| abstract_inverted_index.relevant, | 232 |
| abstract_inverted_index.represent | 37 |
| abstract_inverted_index.variables | 97, 170, 191, 207, 243, 267 |
| abstract_inverted_index.accurately | 184 |
| abstract_inverted_index.considered | 120 |
| abstract_inverted_index.explaining | 103 |
| abstract_inverted_index.forecasts, | 4 |
| abstract_inverted_index.highlights | 179 |
| abstract_inverted_index.identified | 256 |
| abstract_inverted_index.increasing | 55 |
| abstract_inverted_index.particular | 202 |
| abstract_inverted_index.processes, | 42 |
| abstract_inverted_index.timescale. | 110 |
| abstract_inverted_index.vegetation | 226 |
| abstract_inverted_index.weaknesses | 69 |
| abstract_inverted_index.complexity, | 62 |
| abstract_inverted_index.considering | 278 |
| abstract_inverted_index.development | 273 |
| abstract_inverted_index.ecological, | 93 |
| abstract_inverted_index.forecasting | 194, 271 |
| abstract_inverted_index.independent | 262 |
| abstract_inverted_index.observation | 50 |
| abstract_inverted_index.reforecasts | 138 |
| abstract_inverted_index.subseasonal | 2 |
| abstract_inverted_index.temperature | 104, 160 |
| abstract_inverted_index.2001–2017. | 155 |
| abstract_inverted_index.Medium-Range | 132 |
| abstract_inverted_index.assimilating | 47 |
| abstract_inverted_index.considerably | 31 |
| abstract_inverted_index.correlations | 117 |
| abstract_inverted_index.difficulties | 182 |
| abstract_inverted_index.ever-growing | 60 |
| abstract_inverted_index.increasingly | 65 |
| abstract_inverted_index.observations | 264 |
| abstract_inverted_index.southeastern | 224 |
| abstract_inverted_index.specifically | 277 |
| abstract_inverted_index.assimilation. | 284 |
| abstract_inverted_index.comprehensive | 48, 89 |
| abstract_inverted_index.corresponding | 22 |
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| abstract_inverted_index.relationships | 257 |
| abstract_inverted_index.meteorological | 96 |
| abstract_inverted_index.precipitation, | 177 |
| abstract_inverted_index.climate-related | 169, 190 |
| abstract_inverted_index.representations | 75, 281 |
| abstract_inverted_index.observation-based | 92 |
| abstract_inverted_index.circulation-related | 242 |
| abstract_inverted_index.subseasonal-to-seasonal | 136 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5021304034, https://openalex.org/A5018403486, https://openalex.org/A5052018420, https://openalex.org/A5019118021, https://openalex.org/A5020381091, https://openalex.org/A5052091501, https://openalex.org/A5053860456 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I138925566, https://openalex.org/I154986956, https://openalex.org/I4210154168, https://openalex.org/I76198965 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.71202656 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |