The Influence of Drop Size Distributions on the Relationship between Liquid Water Content and Radar Reflectivity in Radiation Fogs

Thies, Boris; Egli, Sebastian; Bendix, Jörg

doi:10.3390/atmos8080142

Cited by 15 publications

(13 citation statements)

References 42 publications

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“…The variability of the size distribution during the fog life cycle therefore has an important impact on the Z-LWC [16]. In [26], using in situ measurements at 2 m, authors found important variability in the size distribution both between fog events and during the lifecycle of each event. Therefore, they recommended measurement of the size distribution continuously near the surface as well as vertical profiles in order For a low wind speed of about 1 m/s and an inlet oriented to the vertical (AIV = 0 • , blue curve), the droplet settling velocity drives the aspiration efficiency reaching 350% for particle size of 300 µm.…”

Section: Variability Of Droplet-size Distributionmentioning

confidence: 98%

“…All the key microphysical parameters are affected by DSD when fog develops [16]. Fog drop-size distributions pertain to various types with spatial and temporal variations [26], and even at different heights of fog layers, fog drop size distributions vary due to inhomogeneous microphysical structures in fog [28]. The variability of fog drop-size distributions is incurred by the combined effects of microphysical processes, such as nucleation, activation, growth, gravitational setting, turbulent mixing, and macro conditions such as wind and radiative fluxes [17].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Fog and Low Stratus Cloud Microphysical Properties Derived from In Situ Sensor, Cloud Radar and SYRSOC Algorithm

et al. 2018

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The microphysical properties of low stratus and fog are analyzed here based on simultaneous measurement of an in situ sensor installed on board a tethered balloon and active remote-sensing instruments deployed at the Instrumented Site for Atmospheric Remote Sensing Research (SIRTA) observatory (south of Paris, France). The study focuses on the analysis of 3 case studies where the tethered balloon is deployed for several hours in order to derive the relationship between liquid water content (LWC), effective radius (Re) and cloud droplet number concentration (CDNC) measured by a light optical aerosol counter (LOAC) in situ granulometer and Bistatic Radar System for Atmospheric Studies (BASTA) cloud radar reflectivity. The well-known relationship Z = α × (LWC) β has been optimized with α [0.02, 0.097] and β [1.91, 2.51]. Similar analysis is done to optimize the relationship Re = f(Z) and CDNC = f(Z). Two methodologies have been applied to normalize the particle-size distribution measured by the LOAC granulometer with a visible extinction closure (R 2 [0.73, 0.93]) and to validate the LWC profile with a liquid water closure using the Humidity and Temperature Profiler (HATPRO) microwave radiometer (R 2 [0.83, 0.91]). In a second step, these relationships are used to derive spatial and temporal variability of the vertical profile of LWC, Re and CDNC starting from BASTA measurement. Finally, the synergistic remote sensing of clouds (SYRSOC) algorithm has been tested on three tethered balloon flights. Generally, SYRSOC CDNC and Re profiles agreed well with LOAC in situ and BASTA profiles for the studied fog layers. A systematic overestimation of LWC by SYRSOC in the top half of the fog layer was found due to fog processes that are not accounted for in the cloud algorithm SYRSOC.

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Section: Variability Of Droplet-size Distributionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Evaluation of Fog and Low Stratus Cloud Microphysical Properties Derived from In Situ Sensor, Cloud Radar and SYRSOC Algorithm

et al. 2018

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“…The evolution from fog to dense fog is very rapid and difficult to predict, the sudden low visibility phenomenon is very easy to lead to sudden safety accidents [1]. Fog droplet microphysical parameters, such as number concentration (N), liquid water content (L), diameter (D), fog droplet size distribution and atmospheric fine particle concentration directly affect visibility (Vis) in fog [2] and determine the formation and dissipation of fog. Clarifying the distribution characteristics and the rules of microphysical parameter changes at various stages of the fog life cycle can help improve forecasts of dense fog and strong dense fog [3], and provide a theoretical basis for fog modification [4].…”

Section: Introductionmentioning

confidence: 99%

Fog Droplet Size Distribution and the Interaction between Fog Droplets and Fine Particles during Dense Fog in Tianjin, China

Liu

Wang

et al. 2020

Atmosphere

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From November 2016 to January 2017, there were large-scale dense fog processes in Tianjin area on the west coast of Bohai Bay, China, even strong dense fog with visibility less than 50 m occurred. Based on the observation data of fog droplet spectrum monitor, visibility sensor, environmental particle monitoring equipment and meteorological automatic station, the characteristics of fog droplet size distribution and the interaction between the fog droplets and fine particles during dense fog events were analyzed. The results show following characteristics: (1) The average concentration of fog droplets (Na), the average liquid water content (La) and the maximum liquid water content (Lmax) in the strong dense fog process are larger than those in the dense fog. The average spectrum of fog droplet size distribution conforms to Junge distribution, and they are all broad-spectrum fog with a spectrum width of about 45 μm. The average spectrum is similar to the dense fog of heavily industrialized inland in the world. (2) The maximum of fog droplet diameter during the formation stage have a good indication for the outbreak of strong dense fog. (3) The mass concentration of PM2.5 (CPM2.5) is ranged from 121–375 μg/m3, and the interaction between fog droplets and fine particles is analyzed. During the formation, development and maturity stages, fog process can scavenge atmospheric fine particles, and the scavenging efficiency of PM2.5 is more remarkable than PM10. When CPM2.5 does not exceed 350 μg/m3, the increase in the concentration of fine particles is conducive to the rapid growth of fog droplets and the sharp drop of visibility. However, when CPM2.5 exceeds the critical value, the increase has a negative feedback effect on the development of the fog process. More investigations and cases are necessary to fully assess the mechanisms related to the dense fog events in Tianjin area and further analysis will be done.

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“…In recent years, several studies have focused on microphysical processes in fog [3,[5][6][7][8][9][10][11][12][13][14]. Since the supersaturation in fog is low and the mean radius of fog droplets is rather small, collision and coalescence is a second order process, but the diffusional growth of the droplet population plays an important role.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the number of droplets the fog layer will deepen or decay as, e.g., an increased optical thickness leads to a reduced visibility, an increased longwave cooling rate at the fog top, and a decreased sedimentation rate due to smaller droplet radii. However, as observations show that neither the number of fog droplets nor the spectral shape remains constant during the different fog stages [3,7,8,11]. To represent these processes in numerical models accurately, fundamental properties of single particles (e.g., aerosol mass and chemical composition, radius of droplets) must be considered.…”

Section: Introductionmentioning

confidence: 99%

Towards a Better Representation of Fog Microphysics in Large-Eddy Simulations Based on an Embedded Lagrangian Cloud Model

Schwenkel

Maronga

2020

Atmosphere

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The development of radiation fog is influenced by multiple physical processes such as radiative cooling and heating, turbulent mixing, and microphysics, which interact on different spatial and temporal scales with one another. Once a fog layer has formed, the number of fog droplets and their size distribution have a particularly large impact on the development of the fog layer due to their feedback on gravitational settling and radiative cooling at the fog top, which are key processes for fog. However, most models do not represent microphysical processes explicitly, or parameterize them rather crudely. In this study we simulate a deep radiation fog case with a coupled large-eddy simulation (LES)–Lagrangian cloud model (LCM) approach for the first time. By simulating several hundred million fog droplets as Lagrangian particles explicitly (using the so-called superdroplet approach), we include a size-resolved diffusional growth including Köhler theory and gravitational sedimentation representation. The results are compared against simulations using a state of the art bulk microphysics model (BCM). We simulate two different aerosol backgrounds (pristine and polluted) with each microphysics scheme. The simulations show that both schemes generally capture the key features of the deep fog event, but also that there are significant differences: the drop size distribution produced by the LCM is broader during the formation and dissipation phase than in the BCM. The LCM simulations suggest that its spectral shape, which is fixed in BCMs, exhibits distinct changes during the fog life cycle, which cannot be taken into account in BCMs. The picture of the overall fog droplet number concentration is twofold: For both aerosol environments, the LCM shows lower concentrations of larger fog droplets, while we observe a higher number of small droplets and swollen aerosols reducing the visibility earlier than in the BCM. As a result of the different model formulation we observe higher sedimentation rates and lower liquid water paths for the LCM. The present work demonstrates that it is possible to simulate fog with the computational demanding approach of LCMs to assess the advantages of high-resolution cloud models and further to estimate errors of traditional parameterizations.

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The Influence of Drop Size Distributions on the Relationship between Liquid Water Content and Radar Reflectivity in Radiation Fogs

Cited by 15 publications

References 42 publications

Evaluation of Fog and Low Stratus Cloud Microphysical Properties Derived from In Situ Sensor, Cloud Radar and SYRSOC Algorithm

Evaluation of Fog and Low Stratus Cloud Microphysical Properties Derived from In Situ Sensor, Cloud Radar and SYRSOC Algorithm

Fog Droplet Size Distribution and the Interaction between Fog Droplets and Fine Particles during Dense Fog in Tianjin, China

Towards a Better Representation of Fog Microphysics in Large-Eddy Simulations Based on an Embedded Lagrangian Cloud Model

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