The overall depth of a fog layer is one of the important factors in determining the hazard that a fog event presents. With discrete observations and often coarse numerical grids, however, fog depth cannot always be accurately determined. To address this, we derive a simple analytical relation that describes the change in depth of a fog interface with time, which depends on the tendencies and vertical gradients of moisture. We also present a lengthscale estimate for the maximum depth over which mixing can occur in order for the fog layer to be sustained, assuming a uniform mixing of the vertical profiles of temperature and moisture. Even over several hours, and when coarse observational resolution is used, the analytical description is shown to accurately diagnose the depth of a fog layer when compared against observational data and the results of large-eddy simulations. Such an analytical description not only enables the estimation of sub-grid or inter-observation fog depth, but also provides a simple framework for interpreting the evolution of a fog layer in time.

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Near-surface wind speed is typically only measured by point observations. The actively heated fiber-optic (AHFO) technique, however, has the potential to provide high-resolution distributed observations of wind speeds, allowing for better spatial characterization of fine-scale processes. Before AHFO can be widely used, its performance needs to be tested in a range of settings. In this work, experimental results on this novel observational wind-probing technique are presented. We utilized a controlled wind tunnel setup to assess both the accuracy and the precision of AHFO under a range of operational conditions (wind speed, angles of attack and temperature difference). The technique allows for wind speed characterization with a spatial resolution of 0.3 m on a 1 s timescale. The flow in the wind tunnel was varied in a controlled manner such that the mean wind ranged between 1 and 17 m s^-1. The AHFO measurements are compared to sonic anemometer measurements and show a high coefficient of determination (0.92–0.96) for all individual angles, after correcting the AHFO measurements for the angle of attack. Both the precision and accuracy of the AHFO measurements were also greater than 95 % for all conditions. We conclude that AHFO has the potential to measure wind speed, and we present a method to help choose the heating settings of AHFO. AHFO allows for the characterization of spatially varying fields of mean wind. In the future, the technique could potentially be combined with conventional distributed temperature sensing (DTS) for sensible heat flux estimation in micrometeorological and hydrological applications.

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Fog is of critical importance to forecast accurately, not least because of the hazard it presents to human safety. Yet, while weather forecasts have improved significantly over recent decades—and continue to improve—fog remains a particularly challenging phenomenon to predict. The research presented within this thesis takes a step back from prediction, and aims to better understand the conditions under which fog forms and deepens. Topics investigated include the observational likelihood of fog, the near-surface conditions during the infancy of a fog layer, the spatial variability of fog (and the influences thereon), and the growth and evolution of a fog layer.@en

Conventional in situ observations of meteorological variables are restricted to a limited number of levels near the surface, with the lowest observation often made around 1-m height. This can result in missed observations of both shallow fog, and the initial growth stage of thicker fog layers. At the same time, numerical experiments have demonstrated the need for high vertical grid resolution in the near-surface layer to accurately simulate the onset of fog; this requires correspondingly high-resolution observational data for validation. A two-week field campaign was conducted in November 2017 at the Cabauw Experimental Site for Atmospheric Research (CESAR) in the Netherlands. The aim was to observe the growth of shallow fog layers and assess the possibility of obtaining very high-resolution observations near the surface during fog events. Temperature and relative humidity were measured at centimetre resolution in the lowest 7 m using distributed temperature sensing. Further, a novel approach was employed to estimate visibility in the lowest 2.5 m using a camera and an extended light source. These observations were supplemented by the existing conventional sensors at the site, including those along a 200-m tall tower. Comparison between the increased-resolution observations and their conventional counterparts show the errors to be small, giving confidence in the reliability of the techniques. The increased resolution of the observations subsequently allows for detailed investigations of fog growth and evolution. This includes the observation of large temperature inversions in the lowest metre (up to 5 K) and corresponding regions of (super)saturation where the fog formed. Throughout the two-week observation period, fog was observed twice at the conventional sensor height of 2.0 m. Two additional low-visibility events were observed in the lowest 0–0.5 m using the camera-based observations, but were missed by the conventional sensors. The camera observations also showed the growth of shallow radiation fog, forming in the lowest 0.5 m as early as two hours before it was observed at the conventional height of 2 m.@en

Conventional in situ observations of visibility and other meteorological variables are restricted to a limited number of heights near the surface, with the lowest observation often made above 1 m. This can result in missed observations of shallow fog as well as the initial growth stage of thicker fog layers. At the same time, numerical experiments have demonstrated the need for high vertical grid resolution in the near-surface layer to accurately simulate the onset of fog; this requires correspondingly high-resolution observational data for validation. In November 2017, a field experiment was conducted at the Cabauw Experimental Site for Atmospheric Research (CESAR) in the Netherlands with the aim of observing the growth of shallow fog from the ground up, assessing the applicability of emerging high-resolution methods for observing shallow fog. Two innovative, high-resolution techniques were employed: distributed temperature sensing (DTS), providing temperature and relative humidity observations at vertical resolutions as fine as 1 cm, and a novel camera-LED method to observe near-surface visibility below the conventional sensor height of 2.0 m. These observations were supplemented by the existing observations at the site, including those along a 200-m tall tower. Comparison between the high-resolution observations and their conventional counterparts shows the errors to be small, giving confidence to the reliability of the techniques. The high resolution of the observations subse- quently allows for detailed investigations of near surface processes. The growth of fog layers from the ground up was observed with very strong temperature inversions in the lowest metre (up to 5 K), and corresponding region of (super)saturation where the fog formed and grew. Throughout the two-week observation period, fog was observed twice at the conventional sensor height of 2.0 m, but up to four times in the lowest 0-0.5 m using the camera estimates, with the shallow fog also forming up to two hours before it was observed by the conventional sensor. The observations are supplemented by high-resolution numerical simulations of the experimental period, highlighting the sensitivity of the fog layer to surface properties and ambient conditions, providing greater insight into what drives the growth of a very shallow fog layer (i.e. < 1 m) into a deeper, and therefore more dangerous, layer.@en

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.

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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

The reduction in visibility that accompanies fog events presents a hazard to human safety and navigation. However, accurate fog prediction remains elusive, with numerical methods often unable to capture the conditions of fog formation, and observational methods having high false-alarm rates in order to obtain high hit rates of prediction. In this work, 5 years of observations from the Cabauw Experimental Site for Atmospheric Research are used to further investigate how false alarms may be reduced using the statistical method for diagnosing radiation-fog events from observations developed by Menut et al. (Boundary-Layer Meteorol 150:277–297, 2014). The method is assessed for forecast lead times of 1–6 h and implementing four optimization schemes to tune the prediction for different needs, compromising between confidence and risk. Prediction scores improve significantly with decreased lead time, with the possibility of achieving a hit rate of over 90% and a false-alarm rate of just 13%. In total, a further 31 combinations of predictive variables beyond the original combination are explored (including mostly, e.g., variables related to moisture and static stability of the boundary layer). Little change to the prediction scores indicates any appropriate combination of variables that measure saturation, turbulence, and near-surface cooling can be used. The remaining false-alarm periods are manually assessed, identifying the lack of spatio–temporal information (such as the temporal evolution of the local conditions and the advective history of the airmass) as the ultimate limiting factor in the methodology’s predictive capabilities. Future observational studies are recommended that investigate the near-surface evolution of fog and the role of non-local heterogeneity on fog formation.

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Rivers deliver large amounts of fresh water, nutrients, and other terrestrially derived materials to the coastal ocean. Where inputs accumulate on the shelf, harmful effects such as hypoxia and eutrophication can result. In contrast, where export to the open ocean is efficient riverine inputs contribute to global biogeochemical budgets. Assessing the fate of riverine inputs is difficult on a global scale. Global ocean models are generally too coarse to resolve the relatively small scale features of river plumes. High-resolution regional models have been developed for individual river plume systems, but it is impractical to apply this approach globally to all rivers. Recently, generalized parameterizations have been proposed to estimate the export of riverine fresh water to the open ocean (Izett & Fennel, 2018, https://doi.org/10.1002/2017GB005667; Sharples et al., 2017, https://doi.org/10.1002/2016GB005483). Here the relationships of Izett and Fennel (), https://doi.org/10.1002/2017GB005667 are used to derive global estimates of open-ocean export of fresh water and dissolved inorganic silicate, dissolved organic carbon, and dissolved organic and inorganic phosphorus and nitrogen. We estimate that only 15-53% of riverine fresh water reaches the open ocean directly in river plumes; nutrient export is even less efficient because of processing on continental shelves. Due to geographic differences in riverine nutrient delivery, dissolved silicate is the most efficiently exported to the open ocean (7-56.7%), while dissolved inorganic nitrogen is the least efficiently exported (2.8-44.3%). These results are consistent with previous estimates and provide a simple way to parameterize export to the open ocean in global models.

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Rivers deliver large amounts of terrestrially derived materials (such as nutrients, sediments, and pollutants) to the coastal ocean, but a global quantification of the fate of this delivery is lacking. Nutrients can accumulate on shelves, potentially driving high levels of primary production with negative consequences like hypoxia, or be exported across the shelf to the open ocean where impacts are minimized. Global biogeochemical models cannot resolve the relatively small-scale processes governing river plume dynamics and cross-shelf export; instead, river inputs are often parameterized assuming an "all or nothing" approach. Recently, Sharples et al. (2017), https://doi.org/10.1002/2016GB005483 proposed the S_P number-a dimensionless number relating the estimated size of a plume as a function of latitude to the local shelf width-as a simple estimator of cross-shelf export. We extend their work, which is solely based on theoretical and empirical scaling arguments, and address some of its limitations using a numerical model of an idealized river plume. In a large number of simulations, we test whether the S_P number can accurately describe export in unforced cases and with tidal and wind forcings imposed. Our numerical experiments confirm that the S_P number can be used to estimate export and enable refinement of the quantitative relationships proposed by Sharples et al. We show that, in general, external forcing has only a weak influence compared to latitude and derive empirical relationships from the results of the numerical experiments that can be used to estimate riverine freshwater export to the open ocean.

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Fog is a global phenomenon that presents a hazard to navigation and human safety, resulting in significant economic impacts for air and shipping industries as well as causing numerous road traffic accidents. Accurate prediction of fog events, however, remains elusive both in terms of timing and occurrence itself. Statistical methods based on set threshold criteria for key variables such as wind speed have been developed, but high rates of correct prediction of fog events still lead to similarly high “false alarms” when the conditions appear favourable, but no fog forms. Using data from the CESAR meteorological observatory in the Netherlands, we analyze specific cases and perform statistical analyses of event climatology, in order to identify the necessary conditions for correct prediction of fog. We also identify potential “missing ingredients” in current analysis that could help to reduce the number of false alarms. New variables considered include the indicators of boundary layer stability, as well as the presence of aerosols conducive to droplet formation. The poster presents initial findings of new research as well as plans for continued research. @en