Measurement and knowledge of wind spatial and temporal characteristics at the regional scale is a relevant issue for several applications in meteorology. Wind is an important variable per se in applications such as wind energy potential assessment, atmospheric transport, air pollutant dispersion and civil engineering. It is also a fundamental driver of the surface energy balance by modulating the aerodynamic resistance of the Earth's surface, therefore directly controlling energy and momentum exchange between the surface and the atmosphere (Zhang and Lemeur, 1992). Wind close to the Earth's surface is the result of a complex interaction between mesoscale circulations and the local nature of the surface terrain (Petersen et al., 1998). Changes in land properties (Wieringa, 1992; Farrugia, 2003; Bañuelos-Ruedas et al., 2010) and topography (Ayotte, 2008; Mason et al., 2010) can drive wind patterns substantially.
Wind observations are typically provided by weather stations. However, such wind measurements are affected by some constraints and limitations (Al-Yahyai et al., 2010): (1) they are costly, as typically complete meteorological stations including additional sensors are installed; (2) they have a coarse resolution, resulting in sparse networks not capable of accurately assessing wind spatial variability that is needed to derive wind maps for large regions; (3) stations are installed at specific locations such as airports, ports and areas with high population density, or where relevant phenomena need to be monitored, rather than, e.g., in high elevated remote areas which are relevant for wind energy assessment; (4) measurements are made at standard levels only (typically, 10 or 2 m), not providing information at upper levels (above 50 m). Overall, several applications such as wind energy exploitation and infrastructure construction, need a detailed knowledge of wind fields both in space and time, and at relatively high levels from the ground. For such applications, tall towers are typically installed at precise candidate locations, providing point-level information. On the other hand, Numerical Weather Prediction (NWP) models are capable of overcoming part of these constraints. These prognostic models solve the dynamic primitive equations describing the atmospheric processes numerically, consisting of simplified models of the actual physical processes of the atmosphere (Sarrat et al., 2009). Recently, many meteorological departments have started running limited area models (LAMs, i.e., where primitive equations are solved only over a limited domain), to cover the domain of a single region and the surrounding areas. Different mesoscale LAMs are available for research and operational use, such as RAMS (Pielke et al., 1992), ETA (Black, 1994), MM5 (Grell et al., 1994), WRF (Skamarock et al., 2005), COSMO (Doms and Schattler, 2008), and HRM (Majewski, 2009). NWP models are generally capable of addressing: (1) cost issues, as many of them are based on freely available open source code; (2) resolution issues, as NWP models can be run in high resolution also due to steadily increasing computational power; (3) spatial coverage, since NWP models provide gridded data for the whole model domain, both horizontally and vertically. NWP models provide flexibility to simulate relatively long periods (such as one or more years) in relatively short times, with no data gap. However, they have limitations due to simplification in physics, and uncertainty in the initial state, lateral boundary conditions and surface characteristics (Al–Yahyai et al., 2010). While NWP models are operationally applied for weather forecasts, diagnostic wind models based on mass conservation still play an important role because of their fast computation and high accuracy in local areas (Wang et al., 2008), where they are capable of considering fine scale details such as, e.g., complex topography and land–water interfaces (Hu et al., 2010). Examples of diagnostic models are AERMET (US EPA, 2004), MCSCIPUF (Sykes et al., 1998), and CALMET (Scire et al., 1999). Moreover, diagnostic models may be run in combination with prognostic mesoscale models (Bellasio et al., 2005). Recently, several wind assessment studies were carried out using NWP-alone or NWP-diagnostic coupled models, typically implemented on two or more nested domains (Beaucage et al., 2012). Hiroyuki et al. (2006) run the RAMS model through 8 and 2 km resolution simulations to investigate wind energy potential over the area of Tokyo (Japan), and found good agreement with observations, with a 4.8% prediction error on annual mean wind speed. Rather good scores were also obtained by Shimada et al. (2009) in reproducing wind data for offshore wind resource assessment over Japan after applying MM5 and WRF models with 4.5 and 1.5 km resolutions. Guilherme et al. (2009) ran the WRF model to derive wind data for Portugal using 6 and 3 km resolution, obtaining model simulation winds slightly weaker (about 5%) than the measured data. Bellasio et al. (2005) used a coupled WRF-CALMET framework obtaining good representation of actual wind patterns, even in complex topography. Mari et al. (2011) used a similar model chain to create maps of wind speed to assess the large-scale wind resource potential of the Tuscany region (central Italy). The main limitation of using prognostic-diagnostic models to derive wind fields is often the lack of validation observations at high elevation, as well as at a sufficiently large number of points that allow the assessment of spatial patterns. Therefore, their accuracy and actual capability to reproduce complex dynamic phenomena still need to be assessed against adequate observational frameworks.
The main goal of the present work is to overpass such a limitation by introducing aircraft measurements to assess model performance. In fact, besides observations from anemometers installed on weather stations, winds can also be measured by aircraft. In recent years, a number of platforms have been deployed for the measurement of turbulence in the atmosphere, ranging from large research aircraft (Black et al., 2007), to small platforms such as the SkyArrow ERA (Environmental Research Aircraft) (Gioli et al., 2004), to un-manned aerial vehicles (Martin et al., 2011). Such platforms use pressure spheres to measure instantaneous angles of attack, and then retrieve actual wind components by means of calibrated motion measurements and upwash modelling (Vellinga et al., 2013). Aircraft measurements are per se sporadic, referring to specific experiments in specific conditions, thus are rarely capable of providing a comprehensive picture of wind fields across a region and across different time scales. This work is based on a previously published WRF-CALMET wind dataset calculated at 75 m altitude above the ground (Mari et al., 2011), that is assessed against an intensive regional observational dataset of aircraft wind measurements. The latter span a study area of about 100 × 120 km in Tuscany (Italy), from the coast to the inland areas, across different land use categories ranging from extensive forest to agriculture crops. Aircraft flights were scheduled as Intensive Operations Periods (IOP) in different seasons of the year, and different times of the day, aiming at maximizing the sampling of both spatial and temporal variability. Model runs were instead performed continuously for a 2 year period including all the flights. Overall, this study aims at using this innovative level of observations assessing the performance of a coupled model across spatial and temporal transects, at moderate altitude from the ground that is relevant for meteorological applications and where measurements are rarely deployed. Finally, the characteristics and the benefit of aircraft wind measurements are highlighted.