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wind

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  • A seasonal forecast dataset of several atmospheric and oceanic variables is here presented for climate studies and comparisons. Forecasts are performed four times a year, with start dates on Feb 1st, May 1st, Aug 1st, Nov 1st, and integrated for six months. Each forecast is constituted of nine ensemble members to sample the uncertainty on the initial conditions. The sampled variables are: - Specific Humidity (Q) - Relative Humidity (RH) - Temperature (T) - Zonal Wind (U) - Meridional Wind (V) - Surface temperature (surf_temp) - Zonal Wind at 10m (u10) - Meridional Wind at 10m (v10). The seasonal forecast datasets are available for download on demand only. Please contact Stefano Materia at [email protected] to place your request. This data set has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 2 (WP2). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

  • A Decadal prediction data set of several atmospheric and oceanic variables is here presented for climate studies and comparisons. Predictions are performed one time every 5 years, with start date on Nov 1st, and integrated for twenty years. Each prediction is constituted of three ensemble members to have a minimal sample of the uncertainty on the initial conditions. The sampled variables are: The sampled variables are: - Specific Humidity (Q) - Relative Humidity (RH) - Temperature (T) - Zonal Wind (U) - Meridional Wind (V) - Surface temperature (surf_temp) - Zonal Wind at 10m (u10) - Meridional Wind at 10m (v10). The decadal predictions data are available for download on demand only. Please contact Andrea Borrelli at [email protected] to place your request. This data set has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 2 (WP2). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

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    The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the repository of information associated with tropical cyclones that is the most up to date. Topography was taken from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world. To account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). This was considered a good proxy of the spatial variation of surface roughness. A digital bathymetry model is employed with a spatial resolution of 30 arc-seconds, taken from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009). Bathymetry is the information about the underwater floor of the ocean having direct influence on the formation of the storm surge. More information about the cyclone wind and strom surge hazard can be found in CIMNE et al., 2015a. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014). The vulnerability models used in the risk calculation for GAR correlate loss to the wind speed for 3-seconds gusts. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.