The Netherlands is characterized by highly variable land use within a small area, and a strong influence of the North Sea on national climate. Devoid of significant topography, it is an excellent location for assessing the relative influence of various factors on fog occurrence in the absence of terrain effects. Using observations from a dense network of weather stations throughout the country, the climatology of fog in the Netherlands is assessed over a period of 45 years. On a national scale, interannual variability is linked to changes in synoptic pressure-gradient forcing. Within the country, a comprehensive in-depth analysis of regional differences between fog occurrence is made, together with an assessment of local physical factors which could bias fog formation in one location over another. Regional variability is shown to be strongly related to the mesoscale influences of urbanization and the North Sea. In fact, some locations experience over twice as much fog as others. From this finding, a simple index is presented, which combines the water and urban fraction surrounding a station. This “Regionally Weighted Index” (RWI) is able to accurately sort the stations according to their relative fogginess. Its practical use is encouraged for assessing a given site's climatological favourability, even when in situ meteorological observations are unavailable.@en

Nocturnal weather can have significant impact on society. Dangerous conditions can occur after clear sky nights with weak winds. The lack of turbulent mixing during these nights can cause the near-surface temperature to rapidly decrease. A single short-lived turbulent event can renew turbulent mixing and hamper/stop the formation of fog or severe frost, averting dangerous conditions for e.g. Short-lived turbulence has been studied using single tower observations, but the spatial scale of this phenomenon is not yet known. The goal of this study is to fill that gap and estimate the spatial scale of short-lived turbulent structures in the nocturnal boundary layer. Short-lived turbulent structures are studied using wind speed and near-surface temperature observations from existing observational networks in the Netherlands. Nights with short-lived turbulence are selected using an automated filter and manual selection. Visual analysis is first used to identify that the turbulent structures span multiple stations of the network. If so, the correlation between pairs of stations is quantified as a function of their spatial separation. Next, an exponentially decaying fit is applied to this data. The e-folding distance of this fit serves as the estimation of the spatial scale of the turbulent structure. A dense network is found by using the observational masts at and around Schiphol Airport. Visual analysis shows that this network is sufficiently dense for the turbulent structures to span multiple stations. Using the aforementioned methodology, the scales of the short-lived turbulent structures are estimated to range between 1.5 km and 24 km, with a median of 9 km. The spatial scale of short-lived turbulent structures is therefore estimates at 9 km. This study should be considered to be a first-order estimate. In fact, one of the weaknesses of the method used is that it only takes the distance between stations into account (without incorporating directional effects). As such, it assumes the turbulent event to be spatially isotropic. At present, limitations by the network do not allow for a clear assessment of the spatial structure of the events. To accurately study both the dynamics and the spatial coherence of short-lived turbulent structures, a dense and uniformly distributed network is recommended. Ideally, such set-up would consist of a nested fine-scale network within a courser network which needs to span a distance of 25 km minimum to capture turbulent events such as the ones studied here. In addition to turbulent events, regional temperature variations are found. Near-surface temperature differences up to 9 degrees over a distance of 40 km are observed during nighttime. The daytime temperature at these stations is nearly identical, stressing the differences in atmospheric physics during day and night. These local variations may result from different local (soil) conditions, local turbulence and clouds. Artificial turbulence, caused by large airplanes taking off, is observed close to the airport’s runways, increasing the local near-surface temperature up to 4 degrees. It will be interesting to also study the impact of artificial turbulence on the near-surface temperature as a follow up of the current work.

This poster will be presented at EMS 2019 in Copenhagen. Fog - in particular, the associated reduction is visibility - presents a hazard to airport operations. Although tech- nology has improved to allow greater safety during fog events, protocol still requires more time between aircraft movements, often resulting in significant delays and cancellations. Yet, observations at Amsterdam’s Schiphol International Airport in the Netherlands (one of Europe’s busiest airports) suggest that the airport buildings and aircraft operations themselves may help to alleviate some of the fog hazard. Meteorological data from from a network of weather stations at and around Schiphol airport are used to assess the occurrence and severity of fog events. Runways located closer to airport terminals are shown to experience both fewer and shorter fog events than those at greater distances from the main terminal complex. Further, large aircraft - in particular Boeing 747s - are observed to raise local runway temperature and wind speed by as much as a few degrees under nocturnal stable boundary layer conditions, which can have a significant impact on the formation of local fog. These find- ings present an interesting look at the local influence of airport construction and aircraft operations, suggesting that they may ultimately lead to "built-in" fog mitigation beyond the natural, undisturbed state. The benefit likely grows with the size of the airport. The larger and busier the airport (such as Heathrow, or Paris-Charles de Gaulle), for example, the more damaging a fog event can be, but the greater potential for disruption of fog formation due to greater number of aircraft movements.@en

Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called "indirect" damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates inter-model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.@en