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3 results for “Extreme event forecasting”

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dryad32/100

Data from: A risk-based forecast of extreme mortality events in small cetaceans: using stranding data to inform conservation practice

Effective conservation requires monitoring and pro-active risk assessments. We studied the effects of at-sea mortality events (ASMEs) in marine mammals over two decades (1990-2012) and built a risk- based indicator for the European Union's Marine Strategy Framework Directive (MSFD). Strandings of harbour porpoises (Phocoena phocoena), short-beaked common dolphins (Delphinus delphis), and striped dolphins (Stenella coeruleoalba) along French coastlines were analysed using Extreme Value Theory (EVT). EVT operationalises what is an extreme ASME, and allows the probabilistic forecasting of the expected maximum number of dead animals assuming constant pressures. For the period 2013-2018, we forecast the strandings of 80 harbour porpoises, 860 common dolphins, and 57 striped dolphins in extreme ASMEs. Comparison of these forecasts with observed strandings informs whether pressures are increasing, decreasing or stable. Applying probabilistic methods to stranding data facilitates the building of risk-based indicators, required under the MSFD, to monitor the effect of pressures on marine mammals.

opencc-zeroDec 2018View details →
zenodo32/100

Extreme Weather Event Real-time Attribution Machine (EWERAM) forecasts for Cyclone Gabrielle

<p>These files contain the hourly precipitation, wind, humidity, and pressure data as well as the land-sea mask, orography, and regional council data supporting the investigation into the human role in Cyclone Gabrielle performed by the EWERAM (Extreme Weather Event Real-time Attribution Machine) consortium.&nbsp; The EWERAM experiment design is outline by Tradowsky and co-authors (2023, 10.1088/2752-5295/acf4b4).</p> <p>Directory and data format follows the conventions of the Climate of the 20th Century Plus Detection and Attribution (C20C+ D&amp;A) Project.&nbsp; Details are provided at https://portal.nersc.gov/c20c/experiment.html and in Stone and co-authors (2019, 10.1016/j.wace.2019.100206).</p>

opencc-by-nc-sa-4.0Apr 2024View details →
dryad32/100

Data from: A risk-based forecast of extreme mortality events in small cetaceans: using stranding data to inform conservation practice

Open the record for dataset details and reuse information.

publicMar 2019View details →

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