The energy sector is highly dependent on climate variability for electricity generation, maintenance activities and demand. In recent years, a few climate services have appeared that provide tailored ...information for the energy sector. In particular, seasonal climate predictions of wind speed have proven useful to the wind power industry. However, most of the service users are ultimately interested in forecasts of electricity generation instead of wind. Although power generation depends on many factors other than wind conditions, the capacity factor is a suitable indicator to quantify the impact of wind variability on production. In this paper a methodology to produce seasonal predictions of capacity factor for a range of turbine classes is proposed for the first time. The strengths and weaknesses of the method are discussed and the forecast quality is evaluated for an application example over Europe.
•A method to produce seasonal forecasts of renewable generation is presented.•A unified approach that fits the specific nature of any wind farm is employed.•Some limitations of seasonal prediction systems are identified and addressed.•The generation forecasts perform better than climatology in some European regions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
One of the key quality aspects in a probabilistic prediction is its reliability. However, this property is difficult to estimate in the case of seasonal forecasts due to the limited size of most of ...the hindcasts that are available nowadays. To shed light on this issue, this work presents a detailed analysis of how the ensemble size, the hindcast length and the number of points pooled together within a particular region affect the resulting reliability estimates. To do so, we build on 42 land reference regions recently defined for the IPCC‐AR6 and assess the reliability of global seasonal forecasts of temperature and precipitation from the European Center for Medium Weather Forecasts SEAS5 prediction system, which is compared against its predecessor, System4. Our results indicate that whereas longer hindcasts and larger ensembles lead to increased reliability estimates, the number of points that are pooled together within a homogeneous climate region is much less relevant.
Plain Language Summary
Seasonal climate forecasts provide information on the average conditions that can be expected for the next months (up to a year) and can help decision making in different socio‐economic sectors such as agriculture, energy and health (among others). However, predictability at this time‐scale is in general limited, so the actual usefulness of seasonal forecasts must be carefully evaluated before they are used in practical applications. In this aspect, reliability—which measures how well/bad the forecast probability for a particular event fits with its actual occurrence—is a key property. This work assesses the reliability of global seasonal forecasts of temperature and precipitation using the latest operational seasonal forecasting system from European Center for Medium Weather Forecasts. Our results show that reliability is generally better for temperature than for precipitation. Moreover, we demonstrate that reliability is sensitive to the number of retrospective forecasts (known as hindcast) and ensemble members (from which forecast probabilities are obtained) available. Finally, we also demonstrate that the new IPCC‐AR6 land reference regions are adequate for seasonal verification purposes. These findings are important for a fair interpretation of the reliability of seasonal forecasts which are obtained for specific regions/seasons/systems building on different experimental frameworks.
Key Points
KP1 The new SEAS5 from the European Center for Medium Weather Forecasts increases the reliability of the previous System4 for global seasonal predictions of temperature and precipitation
KP2 The reliability of probabilistic seasonal forecasts can vary substantially due to the ensemble size and the length of the available hindcast
KP3 The newly defined IPCC‐AR6 land reference regions are adequate for the verification of seasonal forecast reliability
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This study aimed to identify the helminth parasites of invasive anuran species in selected barangays in Butuan City, Philippines. In urbanized areas, invasive species dominate anuran diversity, and ...one of the primary threats they pose to native wildlife is the transmission of diseases and parasites. Out of the 91 collected individuals of invasive anuran species,
was the most abundant (88 %), followed by
(12 %) and
(3 %). The study identified five species of parasites, with
sp. being the most prevalent (17.58 %), followed by
sp. (16.5 %),
(14.3 %),
sp. (6.6 %), and
(3.30 %), respectively.
sp. also had the highest intensity (7.67), followed by
a sp. (5),
(3.33),
(3.30), and
a sp. (2.73). This parasitological survey revealed that
had the highest prevalence and infection of parasites, and residential areas had the highest parasite prevalence among the habitat types. Adult hosts were found to harbor a higher prevalence and intensity, and male hosts had a higher prevalence. The results highlight the high risk of parasite transmission from anurans to other animals and emphasize the need for the community to control the population of invasive anuran species for the safety of native anurans and to prevent zoonotic transmission to other animals and humans.
Dynamical forecast systems have low to moderate skill in continental winter predictions in the extratropics. Here we assess the multimodel predictive skill over Northern Hemisphere high latitudes and ...midlatitudes using four state‐of‐the‐art forecast systems. Our main goal was to quantify the impact of the Arctic sea ice state during November on the sea level pressure (SLP), surface temperature, and precipitation skill during the following winter. Interannual variability of the November Barents and Kara Sea ice is associated with an important fraction of December to February (DJF) prediction skill in regions of Eurasia. We further show that skill related to sea ice in these regions is accompanied with enhanced skill of DJF SLP in western Russia, established by a sea ice‐atmosphere teleconnection mechanism. The teleconnection is strongest when atmospheric blocking conditions in Scandinavia/western Russia in November reduce a systematic SLP bias that is present in all systems.
Plain Language Summary
There is a broad range of stakeholders that could benefit from Northern Hemisphere, midlatitude winter climate predictions from dynamical forecast systems. However, a widespread use is currently hindered by important forecast system limitations. The results from this study suggest that autumnal Arctic sea ice state may have an important impact on winter climate forecast capacity in parts of Eurasia. We further show that large ensembles of simulations can be further exploited, by identifying the members with a better representation of certain processes, in this case the teleconnection between Arctic sea ice and the atmospheric circulation, to enhance the prediction skill of temperature and precipitation over the continents. Exploring this approach for other regions and seasons can provide a possible pathway toward more human‐relevant seasonal climate predictions.
Key Points
Climate forecast systems have limited predictive capacity at seasonal scales in the Northern Hemisphere midlatitudes
Autumnal Barents and Kara Sea ice is likely a source of winter climate predictability in large regions of northern Eurasia
Analysis of multimodel initialized predictions suggests that winter predictability in Eurasia is enhanced by a sea ice‐atmosphere linkage
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Both renewable energy supply and electricity demand are strongly influenced by meteorological conditions and their evolution over time in terms of climate variability and climate change. However, ...knowledge of power output and demand forecasting beyond a few days remains poor. Current methodologies assume that long-term resource availability is constant, ignoring the fact that future wind resources could be significantly different from the past wind energy conditions. Such uncertainties create risks that affect investment in wind energy projects at the operational stage where energy yields affect cash flow and the balance of the grid. Here we assess whether sub-seasonal to seasonal climate predictions (S2S) can skilfully predict wind speed in Europe. To illustrate S2S potential applications, two periods with an unusual climate behaviour affecting the energy market will be presented. We find that wind speed forecasted using S2S exhibits predictability some weeks and months in advance in important regions for the energy sector such as the North Sea. If S2S are incorporated into planning activities for energy traders, energy producers, plant operators, plant investors, they could help improve management climate variability related risks.
An increasing number of researchers are working on Structural Health Monitoring (SHM)
applications. However, the high price of traditional commercial accelerometers is known to
be one of the ...significant drawbacks of SHM methods. On the one hand, to apply SHM
applications to structures with a lower budget dedicated for their health and safety
assessments, development of low-cost sensors can be an answer. On the other hand, low-cost
sensors are known to have lower accuracy and resolution compared with those of traditional
commercial accelerometers. For the first time in the literature, this paper represents a
methodology for improving the resolution and accuracy of low-cost, low-resolution
accelerometers. To do so, this paper proposes averaging the outputs of several aligned
synchronized low-cost accelerometers.
The validity of the proposed methodology has been examined through a series of laboratory
experiments. These experiments tested accelerometers made from one, two, three, four and
five combined MPU9250 chipsets on a shaking table. Moreover, two commercial accelerometers
(393A03 and 356B18) were used to validate the accuracy of the developed solutions.
Objectives
To review the impact of social isolation during COVID-19 pandemic on mental and physical health of older people and the recommendations for patients, caregivers and health professionals.
...Design
Narrative review.
Setting
Non-institutionalized community-living people.
Participants
20.069 individuals from ten descriptive cross-sectional papers.
Measurements
Articles since 2019 to 2020 published on Pubmed, Scielo and Google Scholar databases with the following MeSh terms (‘COVID-19’, ‘coronavirus’, ‘aging’, ‘older people’, ‘elderly’, ‘social isolation’ and ‘quarantine’) in English, Spanish or Portuguese were included. The studies not including people over 60 were excluded. Guidelines, recommendations, and update documents from different international organizations related to mental and physical activity were also analysed.
Results
41 documents have been included in this narrative review, involving a total of 20.069 individuals (58% women), from Asia, Europe and America. 31 articles included recommendations and 10 addressed the impact of social distancing on mental or physical health. The main outcomes reported were anxiety, depression, poor sleep quality and physical inactivity during the isolation period. Cognitive strategies and increasing physical activity levels using apps, online videos, telehealth, are the main international recommendations.
Conclusion
Mental and physical health in older people are negatively affected during the social distancing for COVID-19. Therefore, a multicomponent program with exercise and psychological strategies are highly recommended for this population during the confinement. Future investigations are necessary in this field.
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