All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to “Make cities inclusive, safe, resilient and sustainable”. In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992–2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities.

Using laboratory experimental data taken from a temperature-controlled water tank, the basic features of the circulation associated with an inland urban heat island (UHI) of diameter D and surface heating rate H0 and its interaction with a sea-breeze current were investigated. When the environment was stably stratified with a buoyancy frequency N (nocturnal UHIs) and the sea breeze was absent, the UHI circulation was mainly governed by the Froude number [Fr = U/(ND)], in agreement with theoretical predictions found in literature (U was the horizontal velocity scale of the flow based on H0 and D). Furthermore, the results were in agreement with other laboratory studies and with nighttime field observations conducted in large cities. It was found that U, when calculated with H0 + HL (where HL was the surface heat flux associated with the land temperature growth occurring after sunrise), could also be employed as the velocity scale for UHI circulations that develop in statically unstable environment... Abstract Using laboratory experimental data taken from a temperature-controlled water tank, the basic features of the circulation associated with an inland urban heat island (UHI) of diameter D and surface heating rate H0 and its interaction with a sea-breeze current were investigated. When the environment was stably stratified with a buoyancy frequency N (nocturnal UHIs) and the sea breeze was absent, the UHI circulation was mainly governed by the Froude number [Fr = U/(ND)], in agreement with theoretical predictions found in literature (U was the horizontal velocity scale of the flow based on H0 and D). Furthermore, the results were in agreement with other laboratory studies and with nighttime field observations conducted in large cities. It was found that U, when calculated with H0 + HL (where HL was the surface heat flux associated with the land temperature growth occurring after sunrise), could also be employed as the velocity scale for UHI circulations that develop in statically unstable environment...

The flux Richardson number R_f (also known as the mixing efficiency) for the stably stratified atmospheric boundary layer is investigated as a function of the gradient Richardson number Ri_g using data taken during two field studies: the Vertical Transport and Mixing Experiment (VTMX) in Salt Lake City, Utah (October 2000), and a long-term rural field data set from Technical Area 6 (TA-6) at Los Alamos National Laboratory, New Mexico. The results show the existence of a maximum R_f (0.4–0.5) at a gradient Richardson number of approximately unity. These large-Reynolds-number results agree well with recent laboratory stratified shear layer measurements, but are at odds with some commonly used R_f parameterizations, particularly under high-Ri_g conditions. The observed variations in buoyancy flux and turbulent kinetic energy production are consistent with the concept of global intermittency of the atmospheric stable boundary layer.

Ocean transport and dispersion processes are at the present time simulated using Lagrangian stochastic models coupled with Eulerian circulation models that are supplying analyses and forecasts of the ocean currents at unprecedented time and space resolution. Using the Lagrangian approach, each particle displacement is described by an average motion and a fluctuating part. The first one represents the advection associated with the Eulerian current field of the circulation models while the second one describes the sub-grid scale diffusion. The focus of this study is to quantify the sub-grid scale diffusion of the Lagrangian models written in terms of a horizontal eddy diffusivity. Using a large database of drifters released in different regions of the Mediterranean Sea, the Lagrangian sub-grid scale diffusion has been computed, by considering different regimes when averaging statistical quantities. In addition, the real drifters have been simulated using a trajectory model forced by OGCM currents, focusing on how the Lagrangian properties are reproduced by the simulated trajectories.