Aircraft wind measurements to assess a coupled WRF‐CALMET mesoscale system

Abstract

Long term aircraft observations of wind magnitude along an ∼250 km flight track in central Italy, performed over 1.5 years, are compared with the output of an existing mesoscale prognostic-diagnostic (WRF-CALMET) model chain aimed at assessing wind potential maps at regional scale. Aircraft measurements are used to evaluate model performance along spatial and temporal transects at moderate altitude from the ground (∼75 m), where observational frameworks are rarely available. Spatial wind analysis was capable of assessing overall model performance, while highlighting some limitations: the implemented models have better performance in inland areas with respect to coastal areas, while they are capable of representing diurnal variability in all regions correctly. Overall agreement is within 3% in the cold season and 16% in the warm season, while the greatest differences, above 30%, are obtained in coastal areas in the summer. The hypothesis supporting these results is that summer sea breeze regimes that develop consistently from the coast through the interior land are not entirely resolved from mesoscale modelling. Finally, the model performance and limitations related to complex orography are highlighted. This study demonstrates the added value that may derive from aircraft wind measurements as an additional observational framework for applied meteorology studies.

Performance Analysis of Planetary Boundary Layer Parameterization Schemes in WRF Modeling Set Up over Southern Italy

“…As the aircraft observations include high frequency PBL turbulence fluctuations that are filtered out in the WRF model results as a subgrid scale turbulent component of meteorological variables, this reproduction is proving the model's ability to simulate the conditions on a smaller scale aptly. Overall, the findings of the present study are consistent with Gioli et al [65], indicating that the sea breeze transition is a critical phenomenon to be correctly simulated in time and space with a WRF-based modeling setup.…”

“…As evidenced by the discussion of WRF results at each measured profile location, the variability in model errors is more related to both the spatial and temporal variability of atmospheric properties than to the behavior of the different PBL schemes. Overall, our findings are consistent with Gioli et al [65], indicating that sea breeze transition is a critical phenomenon to be correctly simulated in time and space with a WRF-based modeling setup. However, these results allow the identification of a subset of PBL schemes that perform comparably, and constitute useful data to constrain the choice of the PBL scheme in the setup of a WRF modeling chain for meteorological or air quality predictions.…”

WRF wind field assessment under multiple forcings using spatialized aircraft data

“…These discrepancies may be ascribed to breeze regimes: the misrepresentation of wind speed magnitude during a sea breeze with the WRF was reported, among others, by Hernández-Ceballos et al (2013) while simulating breeze conditions in the Guadalquivir Valley. The aircraft data set presented here was used by Gioli et al (2014) to validate a modelling chain using the WRF (Non-Hydrostatic Mesoscale Model v.2.1 initialized with the NCEP-GFS) and CALMET (Scire et al, 2000), and a similar influence of the breeze regime was also reported. In that work, the largest differences were observed in coastal areas in summer where breeze regimes develop consistently.…”

“…The region's climate is typically Mediterranean, with mean annual rainfall between 700 and 1,000 mm and average annual temperature around 14-16 C. According to the Corine Land Cover 2006 classification (ISPRA, 2010) the flight area is dominated by forest and agricultural land. The orography is generally flat on the coast and becomes more variable moving from the southwest to the northeast inner area, reaching a peak altitude of 600 masl, while the final inland part is moderately hilly (Gioli et al, 2014). The experimental plan for the flights, originally designed for measuring carbon fluxes at a regional scale for the CARBIUS project (Maselli et al, 2010), was based on a set of IOPs in different seasons of the year, over a repetitive path and in different times of the day, therefore sampling both spatial and temporal variability.…”

Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling

“…Despite the potential improvement in spatial resolution and trajectory simulation, there are still biases in computed trajectories due to non-perfect matching between meteorological transport fields and real meteorological situations (type I error), as well as due to their limited spatio-temporal resolution (type II errors) (Bowman et al 2013 ). In this regard, Gioli et al ( 2014b ) performed a detailed comparison of the WRF/CALMET modelling chain against aircraft measurements, finding that model performance varied depending on season, land use and orography, with overall agreements ranging between 2% (inland, hilly areas) and 31% (coastal areas).…”