A simple energy balance model which simulates the thermal regime of urban and rural surfaces under calm, cloudless conditions at night is used to assess the relative importance of the commonly stated causes of urban heat islands. Results show that the effects of street canyon geometry on radiation and of thermal properties on heat storage release, are the primary and almost equal causes on most occasions. In very cold conditions, space heating of buildings can become a dominant cause but this depends on wall insulation. The effects of the urban 'greenhouse' and surface emissivity are relatively minor. The model confirms the importance of local control especially the relation between street geometry and the heat island and highlights the importance of rural thermal properties and their ability to produce seasonal variation in the heat island. A possible explanation for the small heat islands observed in some tropical and Asian settlements is proposed.

A new method is presented for the extraction of mixed layer depth and entrainment zone thickness from lidar, backscatter ratio profiles. The method is based on fitting a four parameter, idealized profile to observed profiles. Optimization of the fit yields values for mixed layer depth and entrainment zone thickness. Since the fitting procedure is based on the entire measured profile, it has a robustness not found in methods based on critical backscatter or backscatter gradient. The method is tested by application to four measured profiles and three synthetic profiles. The sets of profiles include some that are very demanding because of small mixed layer to upper layer backscatter ratio contrasts, or have plumes of high backscatter imbedded in mixed and upper layers. It is shown that the method is robust and simple to implement, even for a sequence of independent profiles.

This paper discusses the need for critically evaluating regional-scale (∼200-2,000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development, and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.

Hourly wind speed and direction data from 12 coastline stations on Sardinia, Italy, are analysed in order to characterize sea breezes in the region. A set of criteria based on the diurnal reversal of wind direction, and the thermal gradient necessary to drive the circulation, is used to identify sea breeze days. Statistics are presented that describe the occurrence, duration, and strength of the sea breezes. On a stationwide basis, sea breezes are most frequent in the summer months (May-August), when they appear on more than one-third of the days. Sea breeze occurrence and duration are the greatest for the stations on the east coast of the island. The all-station average sea breeze duration reaches a maximum of about 9 h in June. The strength of the sea breezes is roughly 3 m s −1 during summer months on average over all stations in the sample.An analysis of mean daily hodographs for the stations in the sample shows clearly the onshore-offshore nature of the sea breeze circulation, and the response of the sea breezes to the local coastline. Sea breezes are shown to develop simultaneously on all coasts of the island under appropriate synoptic conditions.

New radio (MeerKAT and Parkes) and X-ray (XMM-Newton, Swift, Chandra, and NuSTAR) observations of PSR J1622–4950 indicate that the magnetar, in a quiescent state since at least early 2015, reactivated between 2017 March 19 and April 5. The radio flux density, while variable, is approximately 100× larger than during its dormant state. The X-ray flux one month after reactivation was at least 800× larger than during quiescence, and has been decaying exponentially on a 111 ± 19 day timescale. This high-flux state, together with a radio-derived rotational ephemeris, enabled for the first time the detection of X-ray pulsations for this magnetar. At 5%, the 0.3–6 keV pulsed fraction is comparable to the smallest observed for magnetars. The overall pulsar geometry inferred from polarized radio emission appears to be broadly consistent with that determined 6–8 years earlier. However, rotating vector model fits suggest that we are now seeing radio emission from a different location in the magnetosphere than previously. This indicates a novel way in which radio emission from magnetars can differ from that of ordinary pulsars. The torque on the neutron star is varying rapidly and unsteadily, as is common for magnetars following outburst, having changed by a factor of 7 within six months of reactivation.