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Seasonal Forecasting Climate Services for the Energy Industry
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Seasonal Forecasting Climate Services for the Energy Industry

A special issue of Climate (ISSN 2225-1154). This special issue belongs to the section "Weather, Events and Impacts".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 12782

Special Issue Editors

Prof. Dr. Alberto Troccoli

E-Mail Website
Guest Editor
1. School of Environmental Sciences, University of East Anglia (UEA), Norwich NR4 7TJ, UK
2. World Energy & Meteorology Council (WEMC), The Enterprise Centre, Norwich NR4 7TJ, UK
Interests: climate science; meteorological applications; climate services; energy management and planning; climate and society
Dr. Chaofan Li

E-Mail Website
Co-Guest Editor
Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: seasonal forcast; climate dynamics; monsoon variability

Special Issue Information

Dear Colleagues,

The energy industry is amongst the sectors increasingly impacted by climatic events such as heat waves, drought and storms. This is why the industry is seeking to mitigate its losses by making use of the latest advances in seasonal climate forecasting. In this context, the aim of climate services is to offer accurate seasonal climate forecast to help to reduce risk as well as cost. In turn, the optimal use of these forecasts should lead to a better supply–demand balance in the energy sector, therefore positively contributing to both climate change adaptation (forecasts represent soft adaptation measures) and mitigation.

Dynamical and statistical seasonal climate forecasts can bring value compared to the current use of simple climatological information. At its simplest, the latter entails using information from the past few years or considering analogous climatic situations. However, in general, the past is not a good indicator of the future, particularly when strong signals are present, such as in the case of heat waves.

A critical aspect in the uptake of climate services is the proper understanding of the requirements of the industry and how climate information can effectively and practically be used. This understanding ranges from the terminology used by the different actors to an appreciation of the decision-making process, to the co-design and co-development approaches, to the operationalisation of the service.

Several challenges still remain in order to make seasonal forecast climate services mainstream in the industry. This Special Issue therefore invites work that contributes toward the following targets:

  1. Demonstrating that dynamical and/or statistical models have sufficient additional information to perform better than current benchmarks (e.g., climatology);
  2. Understanding the limitations of using forecast over one or even a few years, even if they have a strong signal (e.g., a heat wave) as typically done for case studies;
  3. Identifying the benefits of using multi-model forecast combinations;
  4. Understanding the stages of decision making with reference to the specific role and use of climate information;
  5. Determining the improvements of co-design and co-development approaches;
  6. Operationalising and possibly commercialising a seasonal forecast climate service.

I/We look forward to receiving your contributions.

Prof. Dr. Alberto Troccoli
Dr. Chaofan Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Seasonal climate forecasts
  • Energy operations and management
  • Decision making under uncertainty
  • Tailored forecasts
  • Skill of forecasts
  • Multi-model combinations
  • Value of seasonal forecasts
  • Climate service co-design and co-development
  • Trial operational climate services

Published Papers (5 papers)

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Research

16 pages, 5550 KiB  
Article
The Added Value of Statistical Seasonal Forecasts
by Folmer Krikken, Gertie Geertsema, Kristian Nielsen and Alberto Troccoli
Climate 2024, 12(6), 83; https://doi.org/10.3390/cli12060083 - 4 Jun 2024
Viewed by 645
Abstract
Seasonal climate predictions can assist with timely preparations for extreme episodes, such as dry or wet periods that have associated additional risks of droughts, fires and challenges for water management. Timely warnings for extreme warm summers or cold winters can aid in preparing [...] Read more.
Seasonal climate predictions can assist with timely preparations for extreme episodes, such as dry or wet periods that have associated additional risks of droughts, fires and challenges for water management. Timely warnings for extreme warm summers or cold winters can aid in preparing for increased energy demand. We analyse seasonal forecasts produced by three different methods: (1) a multi-linear statistical forecasting system based on observations only; (2) a non-linear random forest model based on observations only; and (3) process-based dynamical forecast models. The statistical model is an empirical system based on multiple linear regression that is extended to include the trend over the previous 3 months in the predictors, and overfitting is further reduced by using an intermediate multiple linear regression model. This results in a significantly improved El Niño forecast skill, specifically in spring. Also, the Indian Ocean dipole (IOD) index forecast skill shows improvements, specifically in the summer and autumn months. A hybrid multi-model ensemble is constructed by combining the three forecasting methods. The different methods are used to produce seasonal forecasts (three-month means) for near-surface air temperature and monthly accumulated precipitation seasonal forecast with a lead time of one month. We find numerous regions with added value compared with multi-model ensembles based on dynamical models only. For instance, for June, July and August temperatures, added value is observed in extensive parts of both Northern and Southern America, as well as Europe. Full article
(This article belongs to the Special Issue Seasonal Forecasting Climate Services for the Energy Industry)
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14 pages, 3754 KiB  
Article
Scale Dependence of Errors in Snow Water Equivalent Simulations Using ERA5 Reanalysis over Alpine Basins
by Susen Shrestha, Mattia Zaramella, Mattia Callegari, Felix Greifeneder and Marco Borga
Climate 2023, 11(7), 154; https://doi.org/10.3390/cli11070154 - 21 Jul 2023
Cited by 1 | Viewed by 1715
Abstract
This study aims to evaluate the potential of ERA5 precipitation and temperature reanalysis for snow water equivalent (SWE) simulation by considering the role of catchment spatial scale in controlling the errors obtained by comparison with corresponding SWE simulations from ground stations. This is [...] Read more.
This study aims to evaluate the potential of ERA5 precipitation and temperature reanalysis for snow water equivalent (SWE) simulation by considering the role of catchment spatial scale in controlling the errors obtained by comparison with corresponding SWE simulations from ground stations. This is obtained by exploiting a semi-distributed snowpack model (TOPMELT) implemented over the upper Adige River basin in the Eastern Italian Alps, where 16 sub-catchments of varying sizes are considered. The comparison is carried out from 1992 to 2019. The findings show that ERA5 precipitation overestimated low-intensity rainfall (drizzle problem) and underestimated high-intensity rainfall, while ERA5 temperature underestimated observations. The overestimation of low-intensity rainfall created fictitious low-intensity snowfall events, which, when combined with colder ERA5 temperature, resulted in delayed snowmelt and increased fictitious snow-cover days over the study area. The quantile mapping (QM) technique was used to remove errors in ERA5 variables. It was shown that ERA5 could struggle to resolve the orographic enhancement in precipitation, which may be particularly important during high-SWE years. This reduces the positive precipitation bias during those years, thus reducing comparatively the ability of the quantile mapping technique to correct for bias homogeneously during all years. This study highlighted the importance of temperature correction over precipitation correction in SWE simulation, particularly for smaller basins. Full article
(This article belongs to the Special Issue Seasonal Forecasting Climate Services for the Energy Industry)
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17 pages, 5536 KiB  
Article
A Novel Bias Correction Method for Extreme Events
by Laura Trentini, Sara Dal Gesso, Marco Venturini, Federica Guerrini, Sandro Calmanti and Marcello Petitta
Climate 2023, 11(1), 3; https://doi.org/10.3390/cli11010003 - 23 Dec 2022
Cited by 4 | Viewed by 3204
Abstract
When one is using climate simulation outputs, one critical issue to consider is the systematic bias affecting the modelled data. The bias correction of modelled data is often used when one is using impact models to assess the effect of climate events on [...] Read more.
When one is using climate simulation outputs, one critical issue to consider is the systematic bias affecting the modelled data. The bias correction of modelled data is often used when one is using impact models to assess the effect of climate events on human activities. However, the efficacy of most of the currently available methods is reduced in the case of extreme events because of the limited number of data for these low probability and high impact events. In this study, a novel bias correction methodology is proposed, which corrects the bias of extreme events. To do so, we extended one of the most popular bias correction techniques, i.e., quantile mapping (QM), by improving the description of extremes through a generalised extreme value distribution (GEV) fitting. The technique was applied to the daily mean temperature and total precipitation data from three seasonal forecasting systems: SEAS5, System7 and GCFS2.1. The bias correction efficiency was tested over the Southern African Development Community (SADC) region, which includes 15 Southern African countries. The performance was verified by comparing each of the three models with a reference dataset, the ECMWF reanalysis ERA5. The results reveal that this novel technique significantly reduces the systematic biases in the forecasting models, yielding further improvements over the classic QM. For both the mean temperature and total precipitation, the bias correction produces a decrease in the Root Mean Squared Error (RMSE) and in the bias between the simulated and the reference data. After bias correcting the data, the ensemble forecasts members that correctly predict the temperature extreme increases. On the other hand, the number of members identifying precipitation extremes decreases after the bias correction. Full article
(This article belongs to the Special Issue Seasonal Forecasting Climate Services for the Energy Industry)
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21 pages, 1842 KiB  
Article
The Value-Add of Tailored Seasonal Forecast Information for Industry Decision Making
by Clare Mary Goodess, Alberto Troccoli, Nicholas Vasilakos, Stephen Dorling, Edward Steele, Jessica D. Amies, Hannah Brown, Katie Chowienczyk, Emma Dyer, Marco Formenton, Antonio M. Nicolosi, Elena Calcagni, Valentina Cavedon, Victor Estella Perez, Gertie Geertsema, Folmer Krikken, Kristian Lautrup Nielsen, Marcello Petitta, José Vidal, Martijn De Ruiter, Ian Savage and Jon Uptonadd Show full author list remove Hide full author list
Climate 2022, 10(10), 152; https://doi.org/10.3390/cli10100152 - 16 Oct 2022
Cited by 2 | Viewed by 2339
Abstract
There is a growing need for more systematic, robust, and comprehensive information on the value-add of climate services from both the demand and supply sides. There is a shortage of published value-add assessments that focus on the decision-making context, involve participatory or co-evaluation [...] Read more.
There is a growing need for more systematic, robust, and comprehensive information on the value-add of climate services from both the demand and supply sides. There is a shortage of published value-add assessments that focus on the decision-making context, involve participatory or co-evaluation approaches, avoid over-simplification, and address both the quantitative (e.g., economic) and qualitative (e.g., social) values of climate services. The 12 case studies that formed the basis of the European Union-funded SECLI-FIRM project were co-designed by industrial and research partners in order to address these gaps while focusing on the use of tailored sub-seasonal and seasonal forecasts in the energy and water industries. For eight of these case studies, it was possible to apply quantitative economic valuation methods: econometric modelling was used in five case studies while three case studies used a cost/loss (relative economic value) analysis and avoided costs. The case studies illustrated the challenges in attempting to produce quantitative estimates of the economic value-add of these forecasts. At the same time, many of them highlighted how practical value for users—transcending the actual economic value—can be enhanced; for example, through the provision of climate services as an extension to their current use of weather forecasts and with the visualisation tailored towards the user. Full article
(This article belongs to the Special Issue Seasonal Forecasting Climate Services for the Energy Industry)
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17 pages, 21188 KiB  
Article
Verification and Bias Adjustment of ECMWF SEAS5 Seasonal Forecasts over Europe for Climate Service Applications
by Alice Crespi, Marcello Petitta, Paola Marson, Christian Viel and Lucas Grigis
Climate 2021, 9(12), 181; https://doi.org/10.3390/cli9120181 - 10 Dec 2021
Cited by 8 | Viewed by 3610
Abstract
This work discusses the ability of a bias-adjustment method using empirical quantile mapping to improve the skills of seasonal forecasts over Europe for three key climate variables, i.e., temperature, precipitation and wind speed. In particular, the suitability of the approach to be integrated [...] Read more.
This work discusses the ability of a bias-adjustment method using empirical quantile mapping to improve the skills of seasonal forecasts over Europe for three key climate variables, i.e., temperature, precipitation and wind speed. In particular, the suitability of the approach to be integrated in climate services and to provide tailored predictions for local applications was evaluated. The workflow was defined in order to allow a flexible implementation and applicability while providing accurate results. The scheme adjusted monthly quantities from the seasonal forecasting system SEAS5 of the European Centre for Medium-Range Forecasts (ECMWF) by using ERA5 reanalysis as reference. Raw and adjusted forecasts were verified through several metrics analyzing different aspects of forecast skills. The applied method reduced model biases for all variables and seasons even though more limited improvements were obtained for precipitation. In order to further assess the benefits and limitations of the procedure, the results were compared with those obtained by the ADAMONT method, which calibrates daily quantities by empirical quantile mapping conditioned by weather regimes. The comparable performances demonstrated the overall suitability of the proposed method to provide end users with calibrated predictions of monthly and seasonal quantities. Full article
(This article belongs to the Special Issue Seasonal Forecasting Climate Services for the Energy Industry)
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