This work presents a new dataset for recent climate developed within the Highlander project by dynamically downscaling ERA5 reanalysis, originally available at ≃31 km horizontal resolution, to ≃2.2 km resolution (i.e., convection permitting scale). Dynamical downscaling was conducted through the COSMO Regional Climate Model (RCM). The temporal resolution of output is hourly (like for ERA5). Runs cover the whole Italian territory (and neighboring areas according to the necessary computation boundary) to provide a very detailed (in terms of space–time resolution) and comprehensive (in terms of meteorological fields) dataset of climatological data for at least the last 30 years (01/1989-12/2020). These types of datasets can be used for (applied) research and downstream services (e.g., for decision support systems).

This paper introduces VHR-PRO_IT (Very High-Resolution PROjections for ITaly), an open access hourly climate projection with a resolution of ≃2.2 km (i.e., Convection Permitting Scale) up to 2050, covering the Italian peninsula and some neighbouring areas. VHR-PRO_IT is produced within the Highlander project (https://highlanderproject.eu/) by dynamically downscaling the Italy8km-CM climate projection (spatial resolution ≃8 km; output frequency = 6 h; driven CMIP5 GCM = CMCC-CM) with the Regional Climate Model COSMO-CLM under the IPCC RCP4.5 and RCP8.5 scenarios. It covers the 60-year period 1989–2050. VHR-PRO_IT is intended for research purposes in the field of climate studies. For example, it may be included in the ongoing activities to clarify the added value of running climate simulation at the convection-permitting scale.

The flow of a viscous fluid in a plane channel is simulated numerically following the DNS approach, and using a computational code for the numerical integration of the Navier-Stokes equations implemented on a hybrid CPU/GPU computing architecture (for the meaning of symbols and acronyms used, one can refer to the Nomenclature). Three turbulent-flow databases, each representing the turbulent statistically-steady state of the flow at three different values of the Reynolds number, are built up, and a number of statistical moments of the fluctuating velocity field are computed. For turbulent-flow-structure investigation, the vortex-detection technique of the imaginary part of the complex eigenvalue pair in the velocity-gradient tensor is applied to the fluctuating-velocity fields. As a result, and among other types, hairpin vortical structures are unveiled. The processes of evolution that characterize the hairpin vortices in the near-wall region of the turbulent channel are investigated, in particular at one of the three Reynolds numbers tested, with specific attention given to the relationship that exists between the dynamics of the vortical structures and the occurrence of ejection and sweep quadrant events. Interestingly, it is found that the latter events play a preminent role in the way in which the morphological evolution of a hairpin vortex develops over time, as related in particular to the establishment of symmetric and persistent hairpins. The present results have been obtained from a database that incorporates genuine DNS solutions of the Navier-Stokes equations, without superposition of any synthetic structures in the form of initial and/or boundary conditions for the simulations.

A new multiscale Standardized Precipitation Evapotranspiration Index (SPEI) dataset is provided for a reference period (1960–1999) and two future time horizons (2040–2079) and (2060–2099). The historical forcing is based on combined climate observations and reanalysis (WATer and global CHange Forcing Dataset), and the future projections are fed by the Fast Track experiment of the Inter-Sectoral Impact Model Intercomparison Project under representative concentration pathways (RCPs) 4.5 and 8.5 and by an additional Earth system model (CMCC-CESM) forced by RCP 8.5. To calculate the potential evapotranspiration (PET) input to the SPEI, the Hargreaves–Samani and Thornthwaite equations were adopted. This ensemble considers uncertainty due to different climate models, development pathways, and input formulations. The SPEI is provided for accumulation periods of potential moisture deficit from 1 to 18 months starting in each month of the year, with a focus on the within-period variability, excluding long-term warming effects on PET. In addition to supporting drought analyses, this dataset is also useful for assessing wetter-than-normal conditions spanning one or more months. The SPEI was calculated using the SPEIbase package.