Biometeorología en el Grupo de Meteorología de Santander

Trabajo presentado al Primer Workshop en Biometeorología Ciudad de Santander celebrado en octubre de 2011.Peer reviewe

Process-conditioned bias correction for seasonal forecasting: a case-study with ENSO in Peru

This work assesses the suitability of a first simple attempt for process-conditioned bias correction in the context of seasonal forecasting. To do this, we focus on the northwestern part of Peru and bias correct 1- and 4-month lead seasonal predictions of boreal winter (DJF) precipitation from the ECMWF System4 forecasting system for the period 1981–2010. In order to include information about the underlying large-scale circulation which may help to discriminate between precipitation affected by different processes, we introduce here an empirical quantile–quantile mapping method which runs conditioned on the state of the Southern Oscillation Index (SOI), which is accurately predicted by System4 and is known to affect the local climate. Beyond the reduction of model biases, our results show that the SOI-conditioned method yields better ROC skill scores and reliability than the raw model output over the entire region of study, whereas the standard unconditioned implementation provides no added value for any of these metrics. This suggests that conditioning the bias correction on simple but well-simulated large-scale processes relevant to the local climate may be a suitable approach for seasonal forecasting. Yet, further research on the suitability of the application of similar approaches to the one considered here for other regions, seasons and/or variables is needed.This work has received funding from the MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R). The authors are grateful to SENAMHI for the observational data, which are publicly available from http://www.senamhi.gob.pe/?p=data-historica, and to the European Center for Medium-Range Weather Forecast (ECMWF), for the access to the System4 seasonal forecasting hindcast

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

This work presents a comprehensive intercomparison of diferent 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 diferent 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 Ofce-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with diferent skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods efectively 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 afects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613)

Assessing multidomain overlaps and grand nnsemble generation in CORDEX regional projections

ABSTRACT: The Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative has made available an enormous amount of regional climate projections in different domains worldwide. This information is crucial for the development of adaptation strategies and policy-making. A relevant open issue in this context is assessing the potential multidomain conflicts that may result in overlapping regions and developing appropriate ensemble methods trying to make the most of all available information. This work addresses this timely topic by focusing on precipitation over the Mediterranean region, a first illustrative case study that is encompassed by both the Euro- and Africa-CORDEX domains. We focus on several mean, extreme, and temporal indices and use variance decomposition to assess the separate contribution of the domain and models to the climate change signal, concluding that the contribution of the domain alone is nearly negligible (below urn:x-wiley:grl:media:grl60267:grl60267-math-0001 in all cases). Nevertheless, for some cases, the combined model/domain effect triggers up to urn:x-wiley:grl:media:grl60267:grl60267-math-0002 of the total variance.This work has been funded by the Spanish R+D Program of the Ministry of Economy and Competitiveness, through projects MULTI-SDM (CGL2015-66583-R) and INSIGNIA (CGL2016-79210-R), cofunded by the European Regional Development Fund (ERDF/FEDER)

Superconducting light generator for large offshore wind turbines

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How to create an operational multi-model of seasonal forecasts?

Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multi-model seasonal forecasts based on a scientific assessment performed within the framework of the EU Copernicus Climate Change Service (C3S) Quality Assurance for Multi-model Seasonal Forecast Products (QA4Seas) contract C3S 51 lot 3. Our results underpin the importance of processing raw ensemble forecasts differently depending on the final forecast product needed. While ensemble forecasts benefit a lot from bias correction using climate conserving recalibration, this is not the case for the intrinsically bias adjusted multi-category probability forecasts. The same applies for multi-model combination. In this paper, we apply simple, but effective, approaches for multi-model combination of both forecast formats. Further, based on existing literature we recommend to use proper scoring rules like a sample version of the continuous ranked probability score and the ranked probability score for the verification of ensemble forecasts and multi-category probability forecasts, respectively. For a detailed global visualization of calibration as well as bias and dispersion errors, using the Chi-square decomposition of rank histograms proved to be appropriate for the analysis performed within QA4Seas.The research leading to these results is part of the Copernicus Climate Change Service (C3S) (Framework Agreement number C3S_51_Lot3_BSC), a program being implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. Francisco Doblas-Reyes acknowledges the support by the H2020 EUCP project (GA 776613) and the MINECO-funded CLINSA project (CGL2017-85791-R)

Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.This study was partially supported by the SPECS and EUPORIAS projects, funded by the European Commission through the Seventh Framework Programme for Research under grant agreements 308378 and 308291, respectively. JMG acknowledges partial support from the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER)

Zientzia eskola egutegia 2022 (euskara)

El proyecto “Calendario Científico Escolar 2022” ha consistido en la elaboración de un calendario dirigido al alumnado de educación primaria y secundaria obligatoria. Cada día se ha recogido un aniversario científico o tecnológico como, por ejemplo, nacimientos de personas de estos ámbitos o conmemoraciones de hallazgos destacables. Además, el calendario se acompaña de una guía didáctica con orientaciones para el aprovechamiento educativo transversal del calendario en las clases, incluyendo actividades adaptadas a cada rango de edad y al alumnado con necesidades especiales.Proyecto FCT-20-16375 de la Fundación Española para la Ciencia y la Tecnología (FECYT); Agencia Estatal de Investigación (España); Ministerio de Ciencia e Innovación; Consejo Superior de Investigaciones Científicas; Universidad de León; Instituto de Ganadería de Montaña (IGM, CSIC-ULE); Cátedra de Cultura Científica de la Universidad del País Vasco/ Euskal Herriko Unibertsitatea (UPV/EHU); Delegación del CSIC en Castilla y León; Unidade de Divulgación Científica e Cultural - Universidade da Coruña; Academia de la Llingua Asturiana; Casa Árabe; Alliance Française de Gijón; University of California-Davis; Teagasc; CSIC-Representación Illes Balears; Balearic Islands Coastal Observing and Forecasting System (SOCIB); Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, CSIC-UIB); Casa de la Ciència de Valencia (CSIC); Federación Española de Esperanto; Asociación Cultural Nogará Religada; Universidad de Zaragoza; Europa Laica; Museo Didáctico e Interactivo de Ciencias de la Vega Baja del Segura (MUDIC VBS-CV); Universidad Miguel Hernández; PuraVida Software.Mujeres con Ciencia; Asociaţia Secular-Umanistă din România; Instituto Geológico y Minero de España (IGME); Centro de Biología Molecular Severo Ochoa (CSIC-UAM); Asociación Española para el Avance de la Ciencia (AEAC); Centro de Investigación del Cáncer (CIC, CSIC-USAL); Discapacitodos; Universitat de les Illes Balears (UIB); Escuela de Estudios Hispano-americanos (CSIC); PRISMA – Asociación para la diversidad afectivo-sexual y de género en ciencia, tecnología e innovación; Instituto de Recursos Naturales y Agrobiología de Salamanca (IRNASA, CSIC); Círculo Escéptico; Civiencia; Universidad Autónoma de Madrid; Escuela de Estudios Árabes (CSIC); Evento Ciencia.Peer reviewe

Calendariu científicu escolar 2022 (asturianu)

El proyecto “Calendario Científico Escolar 2022” ha consistido en la elaboración de un calendario dirigido al alumnado de educación primaria y secundaria obligatoria. Cada día se ha recogido un aniversario científico o tecnológico como, por ejemplo, nacimientos de personas de estos ámbitos o conmemoraciones de hallazgos destacables. Además, el calendario se acompaña de una guía didáctica con orientaciones para el aprovechamiento educativo transversal del calendario en las clases, incluyendo actividades adaptadas a cada rango de edad y al alumnado con necesidades especiales.Proyecto FCT-20-16375 de la Fundación Española para la Ciencia y la Tecnología (FECYT); Agencia Estatal de Investigación (España); Ministerio de Ciencia e Innovación; Consejo Superior de Investigaciones Científicas; Universidad de León; Instituto de Ganadería de Montaña (IGM, CSIC-ULE); Cátedra de Cultura Científica de la Universidad del País Vasco/ Euskal Herriko Unibertsitatea (UPV/EHU); Delegación del CSIC en Castilla y León; Unidade de Divulgación Científica e Cultural - Universidade da Coruña; Academia de la Llingua Asturiana; Casa Árabe; Alliance Française de Gijón; University of California-Davis; Teagasc; CSIC-Representación Illes Balears; Balearic Islands Coastal Observing and Forecasting System (SOCIB); Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, CSIC-UIB); Casa de la Ciència de Valencia (CSIC); Federación Española de Esperanto; Asociación Cultural Nogará Religada; Universidad de Zaragoza; Europa Laica; Museo Didáctico e Interactivo de Ciencias de la Vega Baja del Segura (MUDIC VBS-CV); Universidad Miguel Hernández; PuraVida SoftwareMujeres con Ciencia; Asociaţia Secular-Umanistă din România; Instituto Geológico y Minero de España (IGME); Centro de Biología Molecular Severo Ochoa (CSIC-UAM); Asociación Española para el Avance de la Ciencia (AEAC); Centro de Investigación del Cáncer (CIC, CSIC-USAL); Discapacitodos; Universitat de les Illes Balears (UIB); Escuela de Estudios Hispano-americanos (CSIC); PRISMA – Asociación para la diversidad afectivo-sexual y de género en ciencia, tecnología e innovación; Instituto de Recursos Naturales y Agrobiología de Salamanca (IRNASA, CSIC); Círculo Escéptico; Civiencia; Universidad Autónoma de Madrid; Escuela de Estudios Árabes (CSIC); Evento Ciencia.Peer reviewe

Calendari científic escolar 2022 (català)

El proyecto “Calendario Científico Escolar 2022” ha consistido en la elaboración de un calendario dirigido al alumnado de educación primaria y secundaria obligatoria. Cada día se ha recogido un aniversario científico o tecnológico como, por ejemplo, nacimientos de personas de estos ámbitos o conmemoraciones de hallazgos destacables. Además, el calendario se acompaña de una guía didáctica con orientaciones para el aprovechamiento educativo transversal del calendario en las clases, incluyendo actividades adaptadas a cada rango de edad y al alumnado con necesidades especiales.Proyecto FCT-20-16375 de la Fundación Española para la Ciencia y la Tecnología (FECYT); Agencia Estatal de Investigación (España); Ministerio de Ciencia e Innovación; Consejo Superior de Investigaciones Científicas; Universidad de León; Instituto de Ganadería de Montaña (IGM, CSIC-ULE); Cátedra de Cultura Científica de la Universidad del País Vasco/ Euskal Herriko Unibertsitatea (UPV/EHU); Delegación del CSIC en Castilla y León; Unidade de Divulgación Científica e Cultural - Universidade da Coruña; Academia de la Llingua Asturiana; Casa Árabe; Alliance Française de Gijón; University of California-Davis; Teagasc; CSIC-Representación Illes Balears; Balearic Islands Coastal Observing and Forecasting System (SOCIB); Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, CSIC-UIB); Casa de la Ciència de Valencia (CSIC); Federación Española de Esperanto; Asociación Cultural Nogará Religada; Universidad de Zaragoza; Europa Laica; Museo Didáctico e Interactivo de Ciencias de la Vega Baja del Segura (MUDIC VBS-CV); Universidad Miguel Hernández; PuraVida SoftwareMujeres con Ciencia; Asociaţia Secular-Umanistă din România; Instituto Geológico y Minero de España (IGME); Centro de Biología Molecular Severo Ochoa (CSIC-UAM); Asociación Española para el Avance de la Ciencia (AEAC); Centro de Investigación del Cáncer (CIC, CSIC-USAL); Discapacitodos; Universitat de les Illes Balears (UIB); Escuela de Estudios Hispano-americanos (CSIC); PRISMA – Asociación para la diversidad afectivo-sexual y de género en ciencia, tecnología e innovación; Instituto de Recursos Naturales y Agrobiología de Salamanca (IRNASA, CSIC); Círculo Escéptico; Civiencia; Universidad Autónoma de Madrid; Escuela de Estudios Árabes (CSIC); Evento Ciencia.Peer reviewe