Climate data. Information about climate data is important to calculate the performance of several energy conversion units (e.g. internal combustion engine, gas turbine) and renewable energy systems (e.g. photovoltaic, wind turbine). Depending on which conversion units are chosen for the development of the energy system superstructure, different climate parameters are necessary. For this case the mandatory climate data are: ▪ Dry-bulb temperature ▪ Global Horizontal Irradiance
Climate data. Our historical climatic data comes from the Global Land Assimilation System (GLDAS v2.1), this is a re-analysed gridded climatic dataset, with 0.25° x 0.25° spatial and 3-hourly temporal resolution. We begin with the daily temperature, precipitation, humidity data and compute the various aggregated indicators at the country and NUTS-2 level. ● ptotal - total amount of precipitation over a given period; ● psummer - total amount of precipitation for summer season; ● tmax - maximum temperature for a region (or country) over a given period; ● tmin - minimum temperature for a region (or country) over a given period; ● tmean - average temperature for a region (or country) over a given period. Based on daily climate data we construct different climate extremity indices in order to check the effect of extremity events on tourism demand.55 ● HImax - the maximum of daily Heat Index over the period. ● THImax - the maximum of daily Temperature Humidity Index (Sometimes they call it Discomfort Index (DI)) over the period. ● THImin - the minimum of daily Temperature Humidity Index over the period. (Sometimes they call it the Discomfort Index (DI). ● WSDI - Warm spell duration index is defined as an annual or seasonal count of days with at least 6 consecutive days when the daily maximum T exceeds the 90th 55 For detailed description of the indexes, see, for instance, xxxxx://xx.xxxxxxxxx.xxx/wiki/Heat_index, xxxxx://xxx.xxxxxxxxxx.xxx/omkarjoshi31521/why-so-discomfort-discomfort-index-47964323, xxxxx://xxx.xxxxxxxxxx.xxx/omkarjoshi31521/why-so-discomfort-discomfort-index-47964323. percentile in the calendar 5-day window for the base period 1979-2009. (Data is provided only for NUTS2 annual level). ● HI_Caution - number of days where 27℃ < HI < 32.5℃. ● HI_Ext_Caution - number of days where 32.5℃ <= HI < 39.5℃. Other weather-related indexes tested in our modelling are: trange - mean of daily (Tmax - Tmin) over the period; summerdays - number of days where Tmax > 25℃ over the period; tropnights - number of days where Tmin > 20℃ over the period; stdmax - std of Tmax over the period; stdmean - std of Tmin over the period; HI_Danger - number of days where 39.5℃ <= HI < 51.5℃; HI_Ext_Danger - number of days where HI >= 51.5℃; DI_uncomf_exist - number of days where THI <=14.9℃ or THI > 26.5℃; DI_uncomf_prop - number of days where THI <=14.9℃ or THI > 26.5℃; DI_uncomf_exist_neg - number of days where THI <=14.9℃; DI_uncomf_exist_posit - number of days where THI >=26.5℃; DI_uncom...
Climate data. Climate data have been developed within WP2 by the CMCC (Euro‐Mediterranean Centre for Climate Change) as it was described in WP2 Final Report and D 2.1.4.. The data have been derived from the ENSEMBLES project (EC‐FP6‐ENV) RCM simulations driven by the General Circulation Model ECHAM5. The data refer to A1B SRES scenario. RACMO2 was selected as the most suitable precipitation model for the Mediterranean region and Case Study (CS) areas. In order to estimate uncertainties of model results, the mean and the two “top” and “bottom” cycles of the ENSEMBLES models driven by ECHAM5 have been done to represent the inter‐modal spread. Data representing the mean values of simulations done by different models were used which ensure the robustness of the results over the whole Mediterranean area. Spatial resolution of data is approximately 0.25° x 0.25°. The climatic databases for the CS areas considered only the grid of points corresponding geographically to the areas of study and/or those being close to that area. For each of 5 CS areas have been done a separated analysis and elaboration of the climatic data as presented and described in D 4.4.1. The following climatic variables were available on a monthly basis: average temperature at 2m height (°C); maximum temperature at 2m height; minimum temperature at 2m height; precipitation (mm/month); average relative humidity (%); maximum relative humidity (%); minimum relative humidity (%); solar (incoming) radiation (W/m2); sunshine duration (hours); wind speed at 10m (m/s), converted to a reference 2m height as described by Xxxxx et al. (1998). The generation of daily weather data from the available monthly database was necessary for the completion of soil water balance and irrigation scheduling at different locations. The linear regression was used to generate daily temperature, humidity, solar radiation and wind speed data from available monthly climatic variables. Daily precipitations were generated with a simplified stochastic approach. The number of rainy days was generated as the integer of (P/10+1) where P is monthly precipitation. Maximum number of rainy days was fixed to 28 and adopted for all months when P was greater than 280mm. The rainy days are selected randomly in each month. Consequently, the rainfall amounts on wet days were fixed for each specific month depending on the monthly precipitation and on the number of rainy days. The climate scenarios considered refers to two time periods: i) “present”, als...
