Background Error Covariance Matrix Applied to the Global Data Assimilation System at CPTEC: Single Observation Experiments Article Swipe
Carlos Frederico Bastarz
,
Dirceu Luís Herdies
,
Luiz Fernando Sapucci
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.8227433
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.8227433
The background error covariance matrix represents a key component of a data assimilation system. It can be shown mathematically that the analysis increments are directly proportional to the covariance matrix. As a result it is correct to state that the performance of a data assimilation system is related to the characteristics of the covariance matrix, in terms of: horizontal and vertical length scales, standard deviations and variances. Considering the information that the data assimilation system uses the observations to correct the model forecasts weighting the model and observations errors, thus the use of an unadjusted covariance matrix can impact the resulting analysis at a great level. At CPTEC efforts has been made in order to adjust the covariance matrix for its application at the operations. In this work it is presented a conceptual overview on the subject, enlightening the importance of the background error covariance matrix and how it is treated in an operational data assimilation system. Furthermore, it is also made a quantitative and qualitative characterization of the background error covariance matrix calculated using the CPTEC global forecast model and the differences in the resulting analysis increments.
Related Topics
Concepts
Metadata
- Type
- dataset
- Language
- en
- Landing Page
- https://doi.org/10.6084/m9.figshare.8227433
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394308137
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4394308137Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.6084/m9.figshare.8227433Digital Object Identifier
- Title
-
Background Error Covariance Matrix Applied to the Global Data Assimilation System at CPTEC: Single Observation ExperimentsWork title
- Type
-
datasetOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Carlos Frederico Bastarz, Dirceu Luís Herdies, Luiz Fernando SapucciList of authors in order
- Landing page
-
https://doi.org/10.6084/m9.figshare.8227433Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.6084/m9.figshare.8227433Direct OA link when available
- Concepts
-
Data assimilation, Covariance matrix, Assimilation (phonology), Covariance, Matrix (chemical analysis), Environmental science, Statistics, Computer science, Mathematics, Algorithm, Meteorology, Physics, Chemistry, Chromatography, Philosophy, LinguisticsTop 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/W4394308137 |
|---|---|
| doi | https://doi.org/10.6084/m9.figshare.8227433 |
| ids.doi | https://doi.org/10.6084/m9.figshare.8227433 |
| ids.openalex | https://openalex.org/W4394308137 |
| fwci | |
| type | dataset |
| title | Background Error Covariance Matrix Applied to the Global Data Assimilation System at CPTEC: Single Observation Experiments |
| 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 | 0.9941999912261963 |
| 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 |
| topics[1].id | https://openalex.org/T11405 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.9890999794006348 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1910 |
| topics[1].subfield.display_name | Oceanography |
| topics[1].display_name | Geophysics and Gravity Measurements |
| topics[2].id | https://openalex.org/T10029 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9291999936103821 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2306 |
| topics[2].subfield.display_name | Global and Planetary Change |
| topics[2].display_name | Climate variability and models |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C24552861 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8461899757385254 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2670177 |
| concepts[0].display_name | Data assimilation |
| concepts[1].id | https://openalex.org/C185142706 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6286654472351074 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1134404 |
| concepts[1].display_name | Covariance matrix |
| concepts[2].id | https://openalex.org/C75649859 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6223994493484497 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q148877 |
| concepts[2].display_name | Assimilation (phonology) |
| concepts[3].id | https://openalex.org/C178650346 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5326900482177734 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q201984 |
| concepts[3].display_name | Covariance |
| concepts[4].id | https://openalex.org/C106487976 |
| concepts[4].level | 2 |
| concepts[4].score | 0.44501766562461853 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q685816 |
| concepts[4].display_name | Matrix (chemical analysis) |
| concepts[5].id | https://openalex.org/C39432304 |
| concepts[5].level | 0 |
| concepts[5].score | 0.41863423585891724 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[5].display_name | Environmental science |
| concepts[6].id | https://openalex.org/C105795698 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3893522024154663 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[6].display_name | Statistics |
| concepts[7].id | https://openalex.org/C41008148 |
| concepts[7].level | 0 |
| concepts[7].score | 0.38427042961120605 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[7].display_name | Computer science |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.38212883472442627 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C11413529 |
| concepts[9].level | 1 |
| concepts[9].score | 0.35015442967414856 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[9].display_name | Algorithm |
| concepts[10].id | https://openalex.org/C153294291 |
| concepts[10].level | 1 |
| concepts[10].score | 0.2789769172668457 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[10].display_name | Meteorology |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.182757169008255 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C185592680 |
| concepts[12].level | 0 |
| concepts[12].score | 0.12396949529647827 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[12].display_name | Chemistry |
| concepts[13].id | https://openalex.org/C43617362 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0567929744720459 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q170050 |
| concepts[13].display_name | Chromatography |
| concepts[14].id | https://openalex.org/C138885662 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[14].display_name | Philosophy |
| concepts[15].id | https://openalex.org/C41895202 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[15].display_name | Linguistics |
| keywords[0].id | https://openalex.org/keywords/data-assimilation |
| keywords[0].score | 0.8461899757385254 |
| keywords[0].display_name | Data assimilation |
| keywords[1].id | https://openalex.org/keywords/covariance-matrix |
| keywords[1].score | 0.6286654472351074 |
| keywords[1].display_name | Covariance matrix |
| keywords[2].id | https://openalex.org/keywords/assimilation |
| keywords[2].score | 0.6223994493484497 |
| keywords[2].display_name | Assimilation (phonology) |
| keywords[3].id | https://openalex.org/keywords/covariance |
| keywords[3].score | 0.5326900482177734 |
| keywords[3].display_name | Covariance |
| keywords[4].id | https://openalex.org/keywords/matrix |
| keywords[4].score | 0.44501766562461853 |
| keywords[4].display_name | Matrix (chemical analysis) |
| keywords[5].id | https://openalex.org/keywords/environmental-science |
| keywords[5].score | 0.41863423585891724 |
| keywords[5].display_name | Environmental science |
| keywords[6].id | https://openalex.org/keywords/statistics |
| keywords[6].score | 0.3893522024154663 |
| keywords[6].display_name | Statistics |
| keywords[7].id | https://openalex.org/keywords/computer-science |
| keywords[7].score | 0.38427042961120605 |
| keywords[7].display_name | Computer science |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.38212883472442627 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/algorithm |
| keywords[9].score | 0.35015442967414856 |
| keywords[9].display_name | Algorithm |
| keywords[10].id | https://openalex.org/keywords/meteorology |
| keywords[10].score | 0.2789769172668457 |
| keywords[10].display_name | Meteorology |
| keywords[11].id | https://openalex.org/keywords/physics |
| keywords[11].score | 0.182757169008255 |
| keywords[11].display_name | Physics |
| keywords[12].id | https://openalex.org/keywords/chemistry |
| keywords[12].score | 0.12396949529647827 |
| keywords[12].display_name | Chemistry |
| keywords[13].id | https://openalex.org/keywords/chromatography |
| keywords[13].score | 0.0567929744720459 |
| keywords[13].display_name | Chromatography |
| language | en |
| locations[0].id | doi:10.6084/m9.figshare.8227433 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | dataset |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.6084/m9.figshare.8227433 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5054573216 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-5402-7979 |
| authorships[0].author.display_name | Carlos Frederico Bastarz |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Carlos Frederico Bastarz |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5081471498 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2872-8453 |
| authorships[1].author.display_name | Dirceu Luís Herdies |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Dirceu Luis Herdies |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5071794597 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8420-8033 |
| authorships[2].author.display_name | Luiz Fernando Sapucci |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Luiz Fernando Sapucci |
| authorships[2].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.6084/m9.figshare.8227433 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Background Error Covariance Matrix Applied to the Global Data Assimilation System at CPTEC: Single Observation Experiments |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| 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 | 0.9941999912261963 |
| 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/W4391897620, https://openalex.org/W2394447903, https://openalex.org/W2354237351, https://openalex.org/W2890621699, https://openalex.org/W2064064686, https://openalex.org/W4238891807, https://openalex.org/W2170838091, https://openalex.org/W115305104, https://openalex.org/W2886934452, https://openalex.org/W1489099099 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.6084/m9.figshare.8227433 |
| 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 | |
| best_oa_location.raw_type | dataset |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.6084/m9.figshare.8227433 |
| primary_location.id | doi:10.6084/m9.figshare.8227433 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | dataset |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.6084/m9.figshare.8227433 |
| publication_date | 2019-01-01 |
| publication_year | 2019 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 7, 11, 32, 43, 104, 132, 163 |
| abstract_inverted_index.As | 31 |
| abstract_inverted_index.At | 107 |
| abstract_inverted_index.In | 126 |
| abstract_inverted_index.It | 15 |
| abstract_inverted_index.an | 94, 153 |
| abstract_inverted_index.at | 103, 123 |
| abstract_inverted_index.be | 17 |
| abstract_inverted_index.in | 56, 113, 152, 184 |
| abstract_inverted_index.is | 35, 47, 130, 150, 160 |
| abstract_inverted_index.it | 34, 129, 149, 159 |
| abstract_inverted_index.of | 10, 42, 52, 93, 141, 168 |
| abstract_inverted_index.on | 135 |
| abstract_inverted_index.to | 27, 37, 49, 79, 115 |
| abstract_inverted_index.The | 1 |
| abstract_inverted_index.and | 60, 66, 87, 147, 165, 181 |
| abstract_inverted_index.are | 24 |
| abstract_inverted_index.can | 16, 98 |
| abstract_inverted_index.for | 120 |
| abstract_inverted_index.has | 110 |
| abstract_inverted_index.how | 148 |
| abstract_inverted_index.its | 121 |
| abstract_inverted_index.key | 8 |
| abstract_inverted_index.of: | 58 |
| abstract_inverted_index.the | 21, 28, 40, 50, 53, 69, 72, 77, 81, 85, 91, 100, 117, 124, 136, 139, 142, 169, 176, 182, 185 |
| abstract_inverted_index.use | 92 |
| abstract_inverted_index.also | 161 |
| abstract_inverted_index.been | 111 |
| abstract_inverted_index.data | 12, 44, 73, 155 |
| abstract_inverted_index.made | 112, 162 |
| abstract_inverted_index.that | 20, 39, 71 |
| abstract_inverted_index.this | 127 |
| abstract_inverted_index.thus | 90 |
| abstract_inverted_index.uses | 76 |
| abstract_inverted_index.work | 128 |
| abstract_inverted_index.CPTEC | 108, 177 |
| abstract_inverted_index.error | 3, 144, 171 |
| abstract_inverted_index.great | 105 |
| abstract_inverted_index.model | 82, 86, 180 |
| abstract_inverted_index.order | 114 |
| abstract_inverted_index.shown | 18 |
| abstract_inverted_index.state | 38 |
| abstract_inverted_index.terms | 57 |
| abstract_inverted_index.using | 175 |
| abstract_inverted_index.adjust | 116 |
| abstract_inverted_index.global | 178 |
| abstract_inverted_index.impact | 99 |
| abstract_inverted_index.length | 62 |
| abstract_inverted_index.level. | 106 |
| abstract_inverted_index.matrix | 5, 97, 119, 146, 173 |
| abstract_inverted_index.result | 33 |
| abstract_inverted_index.system | 46, 75 |
| abstract_inverted_index.correct | 36, 80 |
| abstract_inverted_index.efforts | 109 |
| abstract_inverted_index.errors, | 89 |
| abstract_inverted_index.matrix, | 55 |
| abstract_inverted_index.matrix. | 30 |
| abstract_inverted_index.related | 48 |
| abstract_inverted_index.scales, | 63 |
| abstract_inverted_index.system. | 14, 157 |
| abstract_inverted_index.treated | 151 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.analysis | 22, 102, 187 |
| abstract_inverted_index.directly | 25 |
| abstract_inverted_index.forecast | 179 |
| abstract_inverted_index.overview | 134 |
| abstract_inverted_index.standard | 64 |
| abstract_inverted_index.subject, | 137 |
| abstract_inverted_index.vertical | 61 |
| abstract_inverted_index.component | 9 |
| abstract_inverted_index.forecasts | 83 |
| abstract_inverted_index.presented | 131 |
| abstract_inverted_index.resulting | 101, 186 |
| abstract_inverted_index.weighting | 84 |
| abstract_inverted_index.background | 2, 143, 170 |
| abstract_inverted_index.calculated | 174 |
| abstract_inverted_index.conceptual | 133 |
| abstract_inverted_index.covariance | 4, 29, 54, 96, 118, 145, 172 |
| abstract_inverted_index.deviations | 65 |
| abstract_inverted_index.horizontal | 59 |
| abstract_inverted_index.importance | 140 |
| abstract_inverted_index.increments | 23 |
| abstract_inverted_index.represents | 6 |
| abstract_inverted_index.unadjusted | 95 |
| abstract_inverted_index.variances. | 67 |
| abstract_inverted_index.Considering | 68 |
| abstract_inverted_index.application | 122 |
| abstract_inverted_index.differences | 183 |
| abstract_inverted_index.increments. | 188 |
| abstract_inverted_index.information | 70 |
| abstract_inverted_index.operational | 154 |
| abstract_inverted_index.operations. | 125 |
| abstract_inverted_index.performance | 41 |
| abstract_inverted_index.qualitative | 166 |
| abstract_inverted_index.Furthermore, | 158 |
| abstract_inverted_index.assimilation | 13, 45, 74, 156 |
| abstract_inverted_index.enlightening | 138 |
| abstract_inverted_index.observations | 78, 88 |
| abstract_inverted_index.proportional | 26 |
| abstract_inverted_index.quantitative | 164 |
| abstract_inverted_index.mathematically | 19 |
| abstract_inverted_index.characteristics | 51 |
| abstract_inverted_index.characterization | 167 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |