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<p>Wind assessment studies call for accurate and consistent datasets to evaluate the wind resource potential in the long term. Satellite-derived wind speed estimates have been widely employed in wind energy applications [1–3] due to their high spatial resolution. Synthetic Aperture Radar (SAR) sensors, in particular, provide image snapshots of wind fields on a (sub-) kilometer scale, although at irregular temporal intervals. Moreover, the scenes acquired are often tilted due to satellite’s orbit. The formed wind speed image time-series is, therefore, both spatially and temporally incomplete.</p> <p>This study attempts to reconstruct Sentinel-1 A&B OCN Level-2 wind speed image time-series by employing a data-driven framework and using reanalysis as auxiliary data. More precisely, the methodology resembles what is generally called analog forecasting in climate studies, where past climate conditions are used to predict current weather state [4]. Although the analog method has been long used for empirical-statistical downscaling of Global Circulation Models (GCMs) [5,6], few studies address the problem of gap-filling record observations/estimates [7,8]. In the same context, Empirical Orthogonal Functions (EOF) are used in this work to classify (decompose) the data sets into classes of similar weather states and use this classification to reconstruct the missing information based on the co-registered climate variables. Once physically consistent patterns (analogs) are identified in the historical image record, synthetic wind speed images are generated to fill the data gaps.</p> <p>The method is benchmarked in the offshore area around Cyprus against the probabilistic framework of Multiple-Point Statistics (MPS). Image cross-validation, in combination with statistical metrics, is used to evaluate the method’s performance. Results show that the proposed methodology can furnish a reliable framework for wind speed spatiotemporal variability reconstruction in an offshore wind resource assessment context. An illustration of the method in terms of wind power density estimation is provided over an annual period.</p> <p>References</p> <ul> <li>Nielsen, M.; Astrup, P.; Hasager, C.B.; Barthelmie, R.; Pryor, S. Satellite Information for Wind Energy Applications. <strong>2004</strong>, <em>1479</em>.</li> <li>Medina-Lopez, E.; McMillan, D.; Lazic, J.; Hart, E.; Zen, S.; Angeloudis, A.; Bannon, E.; Browell, J.; Dorling, S.; Dorrell, R.M.; et al. Satellite Data for the Offshore Renewable Energy Sector: Synergies and Innovation Opportunities. <em>Remote Sens. Environ.</em> <strong>2021</strong>, <em>264</em>, 112588, doi:10.1016/j.rse.2021.112588.</li> <li>Edwards, M.R.; Holloway, T.; Pierce, R.B.; Blank, L.; Broddle, M.; Choi, E.; Duncan, B.N.; Esparza, Á.; Falchetta, G.; Fritz, M.; et al. Satellite Data Applications for Sustainable Energy Transitions. <em>Front. Sustain.</em> <strong>2022</strong>, <em>3</em>, 64, doi:10.3389/frsus.2022.910924.</li> <li>Dutton, J. What Is Analog Forecasting? - World Climate Service Available online: https://www.worldclimateservice.com/2021/09/02/what-is-analog-forecasting/ (accessed on 9 January 2023).</li> <li>Bettolli, M.L. Analog Models for Empirical-Statistical Downscaling. <em>Oxford Res. Encycl. Clim. Sci.</em> <strong>2021</strong>, doi:10.1093/acrefore/9780190228620.013.738.</li> <li>Zorita, E.; Storch, H. von The Analog Method as a Simple Statistical Downscaling Technique: Comparison with More Complicated Methods in: Journal of Climate Volume 12 Issue 8 (1999) Available online: https://journals.ametsoc.org/view/journals/clim/12/8/1520-0442_1999_012_2474_tamaas_2.0.co_2.xml (accessed on 9 January 2023).</li> <li>Hoeltgebaum, L.E.B.; Dias, N.L.; Costa, M.A. An Analog Period Method for Gap-Filling of Latent Heat Flux Measurements. <em>Hydrol. Process.</em> <strong>2021</strong>, <em>35</em>, doi:10.1002/hyp.14105.</li> <li>Henn, B.; Raleigh, M.S.; Fisher, A.; Lundquist, J.D. A Comparison of Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data. <em>J. Hydrometeorol.</em> <strong>2013</strong>, <em>14</em>, 929–945, doi:10.1175/JHM-D-12-027.1.</li> </ul>

Offshore wind is expected to play a key role in future energy systems. Wind energy resource studies often call for long-term and spatially consistent datasets to assess the wind potential. Despite the vast amount of available data sources, no current means can provide relevant sub-daily information at a fine spatial scale (~1 km). Synthetic aperture radar (SAR) delivers wind field estimates over the ocean at fine spatial resolution but suffers from partial coverage and irregular revisit times. Physical model outputs, which are the basis of reanalysis products, can be queried at any time step but lack fine-scale spatial variability. To combine the advantages of both, we use the framework of multiple-point geostatistics to realistically reconstruct wind speed patterns at time instances for which satellite information is absent. Synthetic fine-resolution wind speed images are generated conditioned to coregistered regional reanalysis information at a coarser scale. Available simultaneous data sources are used as training data to generate the synthetic image time series. The latter are then evaluated via cross validation and statistical comparison against reference satellite data. Multiple realizations are also generated to assess the uncertainty associated with the simulation outputs. Results show that the proposed methodology can realistically reproduce fine-scale spatiotemporal variability while honoring the wind speed patterns at the coarse scale and thus filling the satellite information gaps in space and time.

Abstract. Geography has long sought to explain spatial relationships between social and physical processes, including the spread of infectious diseases, within the context of modelling human-environment interactions. The spread of the recent COVID-19 pandemic, and its devastating effects on human activity and welfare, represent but examples of such complex human-environment interactions. In this paper, we discuss the value of agent-based models for simulating the spread of the COVID-19 virus to support decision-making with regards to non-pharmaceutical interventions, e.g., lock-down. We also develop a prototype agent-based model using a minimal set of rules regarding patterns of human mobility within a hypothetical town, and couple that with an epidemiological model of infectious disease spread. The coupled model is used to: (a) create synthetic trajectories corresponding to daily and weekly activities postulated between a set of predefined points of interest (e.g., home, work), and (b) simulate new infections at contact points and their subsequent effects on the spread of the disease. We finally use the model simulations as a means of evaluating decisions regarding the number and type of activities to be limited during a planned lockdown in a COVID-19 pandemic context.

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