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•Models based on kriging with external drift on elevation were the optimal choices.•Additional RMI stations improved the accuracy but not the robustness of the models.•Meteorological ...reanalysis used as predictor did not improve spatial interpolation.•Spatial interpolation is a promising option to improve current agricultural DSSs.•Agromet.be is an application for spatial interpolation of weather data in Wallonia.
Food production will have to increase in the future to face the growing world population. Agricultural decision support systems (DSSs) are part of the solution since they aim at protecting crops against fungal diseases, a significant contributor to yield losses, while minimising pesticide use. DSSs are mainly driven by weather data which, currently, are usually obtained from the nearest available weather station. Since the latter is sometimes located far away from a farmer’s field, this can lead to inaccurate recommendations. In order to provide better local weather data, spatial interpolation is a solution. However, since it must be delivered in near real-time, integrating a spatial interpolation process into an operational application necessitates addressing four constraints: Accuracy, Robustness, Reliability and Latency.
This study aimed at developing an operational application for a near real-time spatial interpolation of air temperature and relative humidity at hourly and daily timescales. The first objective was to select the best spatial interpolation models among five algorithms: nearest neighbour, inverse distance weighting, multiple linear regression, ordinary kriging and kriging with external drift. The best models were based on kriging with elevation as external drift. They largely reduced the mean absolute error (MAE) compared to using the nearest station: for hourly air temperature MAE dropped from 0.93 °C to 0.59 °C. It performed also better than multiple linear regression (MAE = 0.68 °C).
The second objective was to evaluate the impact of increasing station density by adding stations from the Belgian synoptic network. Additional stations improved Accuracy (MAE = 0.57 °C) but to a lesser extent than expected and had no clear impact on Robustness. The third objective was to assess the interest of using reanalyses (i.e. climate model outputs) as dynamic predictor variables. Reanalyses did not improve Accuracy (MAE = 0.62 °C) because, compared to elevation, they did not provide useful additional information that can be leveraged by the interpolation models. Using such a dynamical input also impacted Reliability negatively due to potential availability issues. Kriging models presented the highest computing times. However, Latency caused by the interpolation process itself was very small compared to the entirety of data processing.
The selected models were implemented on an online application “Agromet.be”. Near real-time dissemination of interpolated weather data enables to produce local warnings helping farmers to take better decisions about spraying schedules. As a future improvement of spatial interpolation, integrating numerous personal weather stations owned by farmers seems promising.
Output from meteorological reanalyses are used extensively in both academia and industry for modelling wind power. Recently, the first batch of the new ERA5 reanalysis was released. The main purpose ...of this paper is to compare the performance of ERA5 and MERRA-2 (a commonly used reanalysis today) in terms of modelling i) the aggregated wind generation in five different countries and ii) the generation for 1051 individual wind turbines in Sweden. The modelled wind power generation was compared to measurements. In conclusion, ERA5 performs better than MERRA-2 in all analysed aspects; correlations are higher, mean absolute and root mean square errors are in average around 20% lower and distributions of both hourly data and changes in hourly data are more similar to those for measurements. It is also shown that the uncertainty related to long-term correction (using one year of measurements and reanalysis data to predict the energy production during the remaining 1–5 years) is 20% lower for ERA5. In fact, using one year sample data and ERA5 gives slightly more accurate estimates than using two years of sample data and MERRA-2. Additionally, a new metric for quantifying the system size and dispersion of wind farms is proposed.
•The performances of ERA5 and MERRA-2 for modelling wind power are compared.•Errors in country-wise output are around 20% lower for ERA5.•ERA5 also performs considerably better for 1051 individual wind turbines.•The lower uncertainty translates directly to higher project values.•A new metric for quantifying system size is proposed.
The elusive nature of the post‐2004 upper ocean warming has exposed uncertainties in the ocean's role in the Earth's energy budget and transient climate sensitivity. Here we present the time ...evolution of the global ocean heat content for 1958 through 2009 from a new observation‐based reanalysis of the ocean. Volcanic eruptions and El Niño events are identified as sharp cooling events punctuating a long‐term ocean warming trend, while heating continues during the recent upper‐ocean‐warming hiatus, but the heat is absorbed in the deeper ocean. In the last decade, about 30% of the warming has occurred below 700 m, contributing significantly to an acceleration of the warming trend. The warming below 700 m remains even when the Argo observing system is withdrawn although the trends are reduced. Sensitivity experiments illustrate that surface wind variability is largely responsible for the changing ocean heat vertical distribution.
Key Points
Absence of recent global warming hiatus when depths below 700m are considered
Deep ocean heat uptake is linked to wind variability
Total ocean heat content affected by ENSO and volcanic eruptions
Plastic pollution is widespread in the global oceans, but at the same time several other types of hydrophobic pollutants contaminate the marine environment. As more and more evidence highlights, ...microplastics and polluting chemicals are intertwined via adsorption/desorption processes. A thorough assessment of their total impact on marine ecosystems thus requires that these two kinds of pollution are not considered separately. Here we compare the outcomes of two complementary, data-driven modelling approaches for microplastic dispersal and for Plastic-Related Organic Pollutants (PROPs) in the marine environment. Focusing on the Mediterranean Sea, we simulate two years of Lagrangian particle tracking to map microplastic dispersion from the most impacting sources of pollution (i.e. coastal areas, the watersheds of major rivers, and fishing activities). Our particle sources are data-informed by national census data, hydrological regimes, and vessel tracking data to account for spatial and temporal variability of mismanaged plastic waste generation. These particle-based simulations are complemented with a simulation of the dynamics of primary pollutants in the sea, obtained via an advection-diffusion Eulerian model. While providing further understanding of the spatiotemporal distribution of microplastics and the dynamics of PROPs at a Mediterranean-wide scale, our results call for the development of novel integrated modelling approaches aimed at coupling the dynamics of microplastics with the chemical exchanges occurring through them, thus promoting a holistic description of marine plastic pollution.
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•Dynamics at sea of Plastic-Related Organic Pollutants (PROPs) modelled for the first time.•Data-informed particle sources affect modelled patterns of marine microplastics.•Distributions of microplastics and PROPs are different when modelled separately.•Future models should couple the dynamics of microplastics and PROPs at sea.
Here we evaluate five atmospheric reanalyses in an Arctic gateway during late summer. The reanalyses include ERA5, ERA‐Interim, Japanese 55 year Re‐Analysis (JRA‐55), Climate Forecasting System ...Reanalysis‐version 2 (CFSv2), and Modern Era Retrospective analysis for Research and Applications‐version 2 (MERRA‐2). We use observations from 50 radiosondes launched in the Fram Strait around 79‐80°N, between 25 August and 11 September 2017. Crucially, data from 27 radiosondes were not transmitted to the Global Telecommunications System and therefore not assimilated into any reanalysis. In most reanalyses, the magnitude of wind speed and humidity errors is similar for profiles with and without data assimilation. In cases without data assimilation, correlation coefficients (R) exceed 0.88 for temperature, wind speed, and specific humidity, in all reanalyses. Overall, the newly released ERA5 has higher correlation coefficients than any other reanalyses as well as smaller biases and root‐mean‐square errors, for all three variables. The largest improvements identified in ERA5 are in its representation of the wind field, and temperature profiles over warm water.
Plain Language Summary
The Arctic is undergoing rapid and ongoing changes. However, due to the harsh environment, there are relatively few observations from this region. To understand the drivers of these changes, we rely heavily on atmospheric reanalyses. Reanalyses are our best guess at the state of the atmosphere at a given time. Reanalyses are generated by assimilating all available atmospheric observations into a weather forecast model. A key question within the scientific community is how accurate reanalyses are in the Arctic. One problem with answering this question is that most observations used to test the performance of reanalyses were ingested in to the model and are therefore not an independent data set. Here we present a new set of balloon‐borne atmospheric observations from the Fram Strait, between Svalbard and Greenland. Many of these data were not assimilated in to any reanalyses, providing a rare opportunity to evaluate their performance in this important Arctic gateway. We test five products, including the newly released ERA5 from the European Centre for Medium Ranged Weather Forecasting. All products simulate the temperature, humidity, and wind fields well, even without data assimilation. Overall, the newly released ERA5 performs best, with the largest improvements in the wind and temperature fields.
Key Points
In situ Arctic observations: 27 atmospheric profiles from radiosondes in Fram Strait (August–Septmber 2017) were not transmitted to GTS
ERA5 simulates observed atmospheric profiles more accurately than ERA‐Interim, JRA‐55, CFSv2, and MERRA‐2
Largest improvements are found in ERA5 for wind and temperature profiles over warmer eastern Fram Strait
Spurious heritability of ability tilts Sorjonen, Kimmo; Melin, Bo; Nilsonne, Gustav
Personality and individual differences,
02/2024, Volume:
217
Journal Article
Peer reviewed
Open access
Ability tilts refer to within-individual differences between two abilities, e.g. math ability - verbal ability. Coyle et al. (2023) found ability tilts to be genetically heritable and concluded that ...ability tilts are genuine and, presumably, genetically coded individual characteristics. Moreover, Coyle et al. found a large portion of variance in ability tilts to be attributable to non-shared environmental factors (i.e. environmentability), which they interpreted to indicate that ability tilts are potentially generated by niche-picking. However, through simulations we show that heritability and environmentability of X-Y tilts are spurious consequences of heritability and environmentability of the constituent variables X and Y. Furthermore, we reanalyzed data used by Coyle et al. and show that the logic of their arguments would lead to the conclusions, for example, that the human genome codes for a difference between head circumference and verbal ability and that some individuals have picked a niche that includes a long nose at the expense of spatial ability. We do not find these conclusions tenable and propose, instead, that heritability and environmentability of tilts are spurious consequences of heritability and environmentability of the constituent variables.
•Simulated winds and wind energy estimates forced by different reanalysis were evaluated in Portugal.•ERA-Interim reanalysis is the one that likely provides the most realistic initial and boundary ...data.•NCEP-FNL and NCEP-GFS analyses showed better results than the other reanalyses datasets tested.•New generation reanalysis provide considerable improvement in the near surface wind simulation.•NCEP-FNL and NCEP-GFS analyses are the best alternatives to ERA-Interim.
The performance of the WRF mesoscale model in the wind simulation and wind energy estimates was assessed and evaluated under different initial and boundary forcing conditions. Due to the continuous evolution and progress in the development of reanalyses datasets, this work aims to compare an older, yet widely used, reanalysis (the NCEP-R2) with three recently released reanalyses datasets that represent the new generation of this type of data (ERA-Interim, NASA-MERRA and NCEP-CFSR). Due to its intensive use in wind energy assessment studies, the NCEP-GFS and NCEP-FNL analysis were also used to drive WRF and its results compared to those of the simulations driven by reanalyses.
Six different WRF simulations were conducted and their results compared to measured wind data collected at thirteen wind measuring stations located in Portugal in areas of high wind energy potential. Based on the analysis and results presented in this work, it can be concluded that the new generation reanalyses are able to provide a considerable improvement in wind simulation when compared to the older reanalyses. Among all the initial and boundary conditions datasets tested here, ERA-Interim reanalysis is the one that likely provides the most realistic initial and boundary data, providing the best estimates of the local wind regimes and potential wind energy production. The NCEP-GFS and NCEP-FNL analyses seem to be the best alternatives to ERA-Interim, showing better results than all the other reanalyses datasets here tested, and can therefore be considered as valid alternatives to ERA-Interim, in particular for cases where reliable forcing data is needed for real-time applications due to its fast availability.
Global and regional ocean and sea ice reanalysis products (ORAs) are increasingly used in polar research, but their quality remains to be systematically assessed. To address this, the Polar ORA ...Intercomparison Project (Polar ORA-IP) has been established following on from the ORA-IP project. Several aspects of ten selected ORAs in the Arctic and Antarctic were addressed by concentrating on comparing their mean states in terms of snow, sea ice, ocean transports and hydrography. Most polar diagnostics were carried out for the first time in such an extensive set of ORAs. For the multi-ORA mean state, we found that deviations from observations were typically smaller than individual ORA anomalies, often attributed to offsetting biases of individual ORAs. The ORA ensemble mean therefore appears to be a useful product and while knowing its main deficiencies and recognising its restrictions, it can be used to gain useful information on the physical state of the polar marine environment.
The correct strategy for monitoring and assessing marine Renewable Energy Sources (RESs) is of great importance for local/national sustainable development. To achieve this goal, it is necessary to ...measure in the most precise possible manner the local/regional RESs potential. This is especially true for Offshore Wind (OW) energy potential, since the most precise techniques are long and expensive, and are not able to assess the RESs potential of large areas. Today, Remote Sensing (RS) satellites can be considered the most important land and marine observation tools. The RS tools can be used to identify the interested areas for future OW energy converters installations in large and small-scale areas. In this study, the OW energy potential has been analysed by means of a 40 years wind speed data from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset of the Samothraki island surrounding area in the Mediterranean Sea. The OW speed potential has been analysed by means of monthly data from ECMWF Interim reanalysis (ERA-Interim) datasets using the Network Common Data Form (NetCDF) format. Automatically, analyses have been carried out using the Region Of Interest (ROI) tool and Geographical Information System (GIS) software in order to extract information about the OW speed assessment of the Samothraki island area. The primary results of this study show that the southwest area of Samothraki island has good potential for future OW farms installation (bottom fixed and floating version) in near and offshore areas. This study shows the OW energy potential per location, as well as the trend of OW speed, which has changed over the past 40 years in the Mediterranean Sea.
•Wind speed retrieval from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis data.•Wind speed mapping around the island through the Geographical Information System (GIS) software.•A novel methodology for primary wind farm site assessment.•Wind farm site assessment using time series data Analysis.
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