Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02-05

AUTHORS

R. Manzanas, J. M. Gutiérrez, J. Bhend, S. Hemri, F. J. Doblas-Reyes, V. Torralba, E. Penabad, A. Brookshaw

ABSTRACT

This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)—e.g. quantile mapping—to more sophisticated ensemble recalibration (RC) methods—e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value—with respect to the raw model outputs—beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods. More... »

PAGES

1-19

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-019-04640-4

DOI

http://dx.doi.org/10.1007/s00382-019-04640-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111934813


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institute of Physics of Cantabria", 
          "id": "https://www.grid.ac/institutes/grid.469953.4", 
          "name": [
            "Meteorology Group, Institute of Physics of Cantabria (IFCA), CSIC-University of Cantabria, 39005, Santander, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Manzanas", 
        "givenName": "R.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Physics of Cantabria", 
          "id": "https://www.grid.ac/institutes/grid.469953.4", 
          "name": [
            "Meteorology Group, Institute of Physics of Cantabria (IFCA), CSIC-University of Cantabria, 39005, Santander, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Guti\u00e9rrez", 
        "givenName": "J. M.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Federal Office of Meteorology and Climatology", 
          "id": "https://www.grid.ac/institutes/grid.469494.2", 
          "name": [
            "Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bhend", 
        "givenName": "J.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Federal Office of Meteorology and Climatology", 
          "id": "https://www.grid.ac/institutes/grid.469494.2", 
          "name": [
            "Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hemri", 
        "givenName": "S.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Instituci\u00f3 Catalana de Recerca i Estudis Avan\u00e7ats", 
          "id": "https://www.grid.ac/institutes/grid.425902.8", 
          "name": [
            "Barcelona Supercomputing Center (BSC), Barcelona, Spain", 
            "ICREA, Pg. Llu\u00eds Companys, 23 08010, Barcelona, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Doblas-Reyes", 
        "givenName": "F. J.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Barcelona Supercomputing Center", 
          "id": "https://www.grid.ac/institutes/grid.10097.3f", 
          "name": [
            "Barcelona Supercomputing Center (BSC), Barcelona, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Torralba", 
        "givenName": "V.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "European Centre for Medium-Range Weather Forecasts", 
          "id": "https://www.grid.ac/institutes/grid.42781.38", 
          "name": [
            "European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Penabad", 
        "givenName": "E.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "European Centre for Medium-Range Weather Forecasts", 
          "id": "https://www.grid.ac/institutes/grid.42781.38", 
          "name": [
            "European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brookshaw", 
        "givenName": "A.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/2014gl061146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000298037"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr-d-11-00075.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000390037"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/qj.2975", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006924236"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0442(1994)007<1513:lsstcp>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011785264"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0477(1999)080<2313:sotehs>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012066826"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr3402.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013837450"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsif.2013.1162", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014506299"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1600-0870.2005.00104.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015724036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0450(1969)008<0985:assfpf>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018743296"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2013jd020680", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019603863"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0450(1973)012<0595:anvpot>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023865831"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0442(2003)016<4145:otrsop>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025442885"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00704-009-0134-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027965373", 
          "https://doi.org/10.1007/s00704-009-0134-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/2008mwr2431.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029104429"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0434(2000)015<0559:dotcrp>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031058864"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr2904.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031533304"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr2904.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031533304"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2009rg000314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032135615"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2009rg000314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032135615"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gloplacha.2016.12.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033055634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gloplacha.2016.12.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033055634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gloplacha.2016.12.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033055634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-013-1683-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033076144", 
          "https://doi.org/10.1007/s00382-013-1683-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gloplacha.2006.11.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038421763"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/qj.828", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039601605"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/wcc.217", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043013835"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/2008mwr2773.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051378925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00401706.1968.10490530", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058283932"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jcli-d-15-0868.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063455258"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr-d-14-00210.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063455969"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/15-aoas843", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064395040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3402/tellusa.v57i3.14665", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071280277"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3402/tellusa.v57i3.14672", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071280284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jamc-d-16-0204.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083939687"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-017-3668-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084517707", 
          "https://doi.org/10.1007/s00382-017-3668-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-017-3668-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084517707", 
          "https://doi.org/10.1007/s00382-017-3668-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jcli-d-16-0652.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084787621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cliser.2017.04.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084917017"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cliser.2017.06.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086044292"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.scitotenv.2017.08.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091143478"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.5249", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091517741"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nclimate3418", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092504071", 
          "https://doi.org/10.1038/nclimate3418"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nclimate3418", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092504071", 
          "https://doi.org/10.1038/nclimate3418"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cliser.2017.11.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093125547"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.5462", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101718699"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-018-4226-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103924891", 
          "https://doi.org/10.1007/s00382-018-4226-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-018-4226-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103924891", 
          "https://doi.org/10.1007/s00382-018-4226-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-018-4226-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103924891", 
          "https://doi.org/10.1007/s00382-018-4226-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-018-4226-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103924891", 
          "https://doi.org/10.1007/s00382-018-4226-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.envsoft.2018.09.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107161595"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.5878", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107393014"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-02-05", 
    "datePublishedReg": "2019-02-05", 
    "description": "This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)\u2014e.g. quantile mapping\u2014to more sophisticated ensemble recalibration (RC) methods\u2014e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and M\u00e9t\u00e9o France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value\u2014with respect to the raw model outputs\u2014beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00382-019-04640-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "type": "Periodical"
      }
    ], 
    "name": "Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset", 
    "pagination": "1-19", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "5b3c5badfa3ad5aebf0e05548d9f07c5161704e53afd76be85a165054c554fd9"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00382-019-04640-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111934813"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00382-019-04640-4", 
      "https://app.dimensions.ai/details/publication/pub.1111934813"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:02", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000331_0000000331/records_105412_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00382-019-04640-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00382-019-04640-4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00382-019-04640-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00382-019-04640-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00382-019-04640-4'


 

This table displays all metadata directly associated to this object as RDF triples.

240 TRIPLES      21 PREDICATES      66 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00382-019-04640-4 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author Nbc6a5cc010784d659a3de6335ee8c822
4 schema:citation sg:pub.10.1007/s00382-013-1683-2
5 sg:pub.10.1007/s00382-017-3668-z
6 sg:pub.10.1007/s00382-018-4226-z
7 sg:pub.10.1007/s00704-009-0134-9
8 sg:pub.10.1038/nclimate3418
9 https://doi.org/10.1002/2013jd020680
10 https://doi.org/10.1002/2014gl061146
11 https://doi.org/10.1002/joc.5249
12 https://doi.org/10.1002/joc.5462
13 https://doi.org/10.1002/joc.5878
14 https://doi.org/10.1002/qj.2975
15 https://doi.org/10.1002/qj.828
16 https://doi.org/10.1002/wcc.217
17 https://doi.org/10.1016/j.cliser.2017.04.001
18 https://doi.org/10.1016/j.cliser.2017.06.004
19 https://doi.org/10.1016/j.cliser.2017.11.003
20 https://doi.org/10.1016/j.envsoft.2018.09.009
21 https://doi.org/10.1016/j.gloplacha.2006.11.030
22 https://doi.org/10.1016/j.gloplacha.2016.12.009
23 https://doi.org/10.1016/j.scitotenv.2017.08.010
24 https://doi.org/10.1029/2009rg000314
25 https://doi.org/10.1080/00401706.1968.10490530
26 https://doi.org/10.1098/rsif.2013.1162
27 https://doi.org/10.1111/j.1600-0870.2005.00104.x
28 https://doi.org/10.1175/1520-0434(2000)015<0559:dotcrp>2.0.co;2
29 https://doi.org/10.1175/1520-0442(1994)007<1513:lsstcp>2.0.co;2
30 https://doi.org/10.1175/1520-0442(2003)016<4145:otrsop>2.0.co;2
31 https://doi.org/10.1175/1520-0450(1969)008<0985:assfpf>2.0.co;2
32 https://doi.org/10.1175/1520-0450(1973)012<0595:anvpot>2.0.co;2
33 https://doi.org/10.1175/1520-0477(1999)080<2313:sotehs>2.0.co;2
34 https://doi.org/10.1175/2008mwr2431.1
35 https://doi.org/10.1175/2008mwr2773.1
36 https://doi.org/10.1175/jamc-d-16-0204.1
37 https://doi.org/10.1175/jcli-d-15-0868.1
38 https://doi.org/10.1175/jcli-d-16-0652.1
39 https://doi.org/10.1175/mwr-d-11-00075.1
40 https://doi.org/10.1175/mwr-d-14-00210.1
41 https://doi.org/10.1175/mwr2904.1
42 https://doi.org/10.1175/mwr3402.1
43 https://doi.org/10.1214/15-aoas843
44 https://doi.org/10.3402/tellusa.v57i3.14665
45 https://doi.org/10.3402/tellusa.v57i3.14672
46 schema:datePublished 2019-02-05
47 schema:datePublishedReg 2019-02-05
48 schema:description This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)—e.g. quantile mapping—to more sophisticated ensemble recalibration (RC) methods—e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value—with respect to the raw model outputs—beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.
49 schema:genre research_article
50 schema:inLanguage en
51 schema:isAccessibleForFree false
52 schema:isPartOf sg:journal.1049631
53 schema:name Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset
54 schema:pagination 1-19
55 schema:productId N0e3a9a8a915a4098b7052883dbe27222
56 N86c6597162b8435eb587018b0104e4cc
57 Nd9f0035a81cc41feb47de4339d26edec
58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111934813
59 https://doi.org/10.1007/s00382-019-04640-4
60 schema:sdDatePublished 2019-04-11T09:02
61 schema:sdLicense https://scigraph.springernature.com/explorer/license/
62 schema:sdPublisher N464137a6c44c443fa41c211e8a2af364
63 schema:url https://link.springer.com/10.1007%2Fs00382-019-04640-4
64 sgo:license sg:explorer/license/
65 sgo:sdDataset articles
66 rdf:type schema:ScholarlyArticle
67 N0e3a9a8a915a4098b7052883dbe27222 schema:name dimensions_id
68 schema:value pub.1111934813
69 rdf:type schema:PropertyValue
70 N1b5235e701864b039b3aa8c59485cb52 rdf:first Ndf57196ec0f64450acda6c4d805b862e
71 rdf:rest Ne6b7faa119484cd69757325e08ce57d1
72 N2c0576f27a7542abbe0ce6661c9354e9 rdf:first N37bddcf70318427bb2fcf9e71ece98c2
73 rdf:rest rdf:nil
74 N37bddcf70318427bb2fcf9e71ece98c2 schema:affiliation https://www.grid.ac/institutes/grid.42781.38
75 schema:familyName Brookshaw
76 schema:givenName A.
77 rdf:type schema:Person
78 N4051b6a7a10b49328193e5046ee2cd1c rdf:first Nc2a80708518247dba3a01556b7d9a18d
79 rdf:rest N2c0576f27a7542abbe0ce6661c9354e9
80 N41aa318b6efd4dd7a0428d4a62471298 schema:affiliation https://www.grid.ac/institutes/grid.469494.2
81 schema:familyName Bhend
82 schema:givenName J.
83 rdf:type schema:Person
84 N464137a6c44c443fa41c211e8a2af364 schema:name Springer Nature - SN SciGraph project
85 rdf:type schema:Organization
86 N679b9bdcd42a4da0baca3811412f03c3 schema:affiliation https://www.grid.ac/institutes/grid.469494.2
87 schema:familyName Hemri
88 schema:givenName S.
89 rdf:type schema:Person
90 N6af6de827b874d0fb1641086c545064e schema:affiliation https://www.grid.ac/institutes/grid.10097.3f
91 schema:familyName Torralba
92 schema:givenName V.
93 rdf:type schema:Person
94 N701859cb3b804256ad7ef453dd3844dd rdf:first N679b9bdcd42a4da0baca3811412f03c3
95 rdf:rest N1b5235e701864b039b3aa8c59485cb52
96 N86c6597162b8435eb587018b0104e4cc schema:name doi
97 schema:value 10.1007/s00382-019-04640-4
98 rdf:type schema:PropertyValue
99 Nbc6a5cc010784d659a3de6335ee8c822 rdf:first Nbc6a6d226aa7477b866d754133448df5
100 rdf:rest Nc7b0ddb60c3d475abae50af137e913ad
101 Nbc6a6d226aa7477b866d754133448df5 schema:affiliation https://www.grid.ac/institutes/grid.469953.4
102 schema:familyName Manzanas
103 schema:givenName R.
104 rdf:type schema:Person
105 Nc2a80708518247dba3a01556b7d9a18d schema:affiliation https://www.grid.ac/institutes/grid.42781.38
106 schema:familyName Penabad
107 schema:givenName E.
108 rdf:type schema:Person
109 Nc7b0ddb60c3d475abae50af137e913ad rdf:first Ne1b27737517f4c6697acb6964b01bdd1
110 rdf:rest Nd8809d06f17b47209fe35a09504d6644
111 Nd8809d06f17b47209fe35a09504d6644 rdf:first N41aa318b6efd4dd7a0428d4a62471298
112 rdf:rest N701859cb3b804256ad7ef453dd3844dd
113 Nd9f0035a81cc41feb47de4339d26edec schema:name readcube_id
114 schema:value 5b3c5badfa3ad5aebf0e05548d9f07c5161704e53afd76be85a165054c554fd9
115 rdf:type schema:PropertyValue
116 Ndf57196ec0f64450acda6c4d805b862e schema:affiliation https://www.grid.ac/institutes/grid.425902.8
117 schema:familyName Doblas-Reyes
118 schema:givenName F. J.
119 rdf:type schema:Person
120 Ne1b27737517f4c6697acb6964b01bdd1 schema:affiliation https://www.grid.ac/institutes/grid.469953.4
121 schema:familyName Gutiérrez
122 schema:givenName J. M.
123 rdf:type schema:Person
124 Ne6b7faa119484cd69757325e08ce57d1 rdf:first N6af6de827b874d0fb1641086c545064e
125 rdf:rest N4051b6a7a10b49328193e5046ee2cd1c
126 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
127 schema:name Earth Sciences
128 rdf:type schema:DefinedTerm
129 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
130 schema:name Atmospheric Sciences
131 rdf:type schema:DefinedTerm
132 sg:journal.1049631 schema:issn 0930-7575
133 1432-0894
134 schema:name Climate Dynamics
135 rdf:type schema:Periodical
136 sg:pub.10.1007/s00382-013-1683-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033076144
137 https://doi.org/10.1007/s00382-013-1683-2
138 rdf:type schema:CreativeWork
139 sg:pub.10.1007/s00382-017-3668-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1084517707
140 https://doi.org/10.1007/s00382-017-3668-z
141 rdf:type schema:CreativeWork
142 sg:pub.10.1007/s00382-018-4226-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1103924891
143 https://doi.org/10.1007/s00382-018-4226-z
144 rdf:type schema:CreativeWork
145 sg:pub.10.1007/s00704-009-0134-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027965373
146 https://doi.org/10.1007/s00704-009-0134-9
147 rdf:type schema:CreativeWork
148 sg:pub.10.1038/nclimate3418 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092504071
149 https://doi.org/10.1038/nclimate3418
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1002/2013jd020680 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019603863
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1002/2014gl061146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000298037
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1002/joc.5249 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091517741
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1002/joc.5462 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101718699
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1002/joc.5878 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107393014
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1002/qj.2975 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006924236
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1002/qj.828 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039601605
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1002/wcc.217 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043013835
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.cliser.2017.04.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084917017
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.cliser.2017.06.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086044292
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.cliser.2017.11.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093125547
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.envsoft.2018.09.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107161595
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/j.gloplacha.2006.11.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038421763
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/j.gloplacha.2016.12.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033055634
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/j.scitotenv.2017.08.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091143478
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1029/2009rg000314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032135615
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1080/00401706.1968.10490530 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058283932
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1098/rsif.2013.1162 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014506299
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1111/j.1600-0870.2005.00104.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1015724036
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1175/1520-0434(2000)015<0559:dotcrp>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031058864
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1175/1520-0442(1994)007<1513:lsstcp>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011785264
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1175/1520-0442(2003)016<4145:otrsop>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025442885
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1175/1520-0450(1969)008<0985:assfpf>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018743296
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1175/1520-0450(1973)012<0595:anvpot>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023865831
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1175/1520-0477(1999)080<2313:sotehs>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012066826
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1175/2008mwr2431.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029104429
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1175/2008mwr2773.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051378925
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1175/jamc-d-16-0204.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083939687
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1175/jcli-d-15-0868.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063455258
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1175/jcli-d-16-0652.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084787621
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1175/mwr-d-11-00075.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000390037
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1175/mwr-d-14-00210.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063455969
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1175/mwr2904.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031533304
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1175/mwr3402.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013837450
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1214/15-aoas843 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064395040
220 rdf:type schema:CreativeWork
221 https://doi.org/10.3402/tellusa.v57i3.14665 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071280277
222 rdf:type schema:CreativeWork
223 https://doi.org/10.3402/tellusa.v57i3.14672 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071280284
224 rdf:type schema:CreativeWork
225 https://www.grid.ac/institutes/grid.10097.3f schema:alternateName Barcelona Supercomputing Center
226 schema:name Barcelona Supercomputing Center (BSC), Barcelona, Spain
227 rdf:type schema:Organization
228 https://www.grid.ac/institutes/grid.425902.8 schema:alternateName Institució Catalana de Recerca i Estudis Avançats
229 schema:name Barcelona Supercomputing Center (BSC), Barcelona, Spain
230 ICREA, Pg. Lluís Companys, 23 08010, Barcelona, Spain
231 rdf:type schema:Organization
232 https://www.grid.ac/institutes/grid.42781.38 schema:alternateName European Centre for Medium-Range Weather Forecasts
233 schema:name European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
234 rdf:type schema:Organization
235 https://www.grid.ac/institutes/grid.469494.2 schema:alternateName Federal Office of Meteorology and Climatology
236 schema:name Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
237 rdf:type schema:Organization
238 https://www.grid.ac/institutes/grid.469953.4 schema:alternateName Institute of Physics of Cantabria
239 schema:name Meteorology Group, Institute of Physics of Cantabria (IFCA), CSIC-University of Cantabria, 39005, Santander, Spain
240 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...