Articles | Volume 23, issue 22
https://doi.org/10.5194/acp-23-14451-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-14451-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Nov 2023
Research article |
| 23 Nov 2023
| 23 Nov 2023
Investigating multiscale meteorological controls and impact of soil moisture heterogeneity on radiation fog in complex terrain using semi-idealised simulations
Te Kura Matū / School of Physical and Chemical Sciences, University of Canterbury, Ōtautahi / Christchurch, New Zealand
Te Kura Aronukurangi / School of Earth and Environment, University of Canterbury, Ōtautahi / Christchurch, New Zealand
Marwan Katurji
Te Kura Aronukurangi / School of Earth and Environment, University of Canterbury, Ōtautahi / Christchurch, New Zealand
Laura E. Revell
Te Kura Matū / School of Physical and Chemical Sciences, University of Canterbury, Ōtautahi / Christchurch, New Zealand
Basit Khan
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), 82467 Garmisch-Partenkirchen, Germany
now at: Arabian Center for Climate and Environmental Sciences (ACCESS), New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
Andrew Sturman
Te Kura Aronukurangi / School of Earth and Environment, University of Canterbury, Ōtautahi / Christchurch, New Zealand
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Cited articles
Ackerman, A. S., VanZanten, M. C., Stevens, B., Savic-Jovcic, V., Bretherton, C. S., Chlond, A., Golaz, J.-C., Jiang, H., Khairoutdinov, M., Krueger, S. K., Lewellen, D. C., Lock, A., Moeng, C.-H., Nakamura, K., Petters, M. D., Snider, J. R., Weinbrecht, S., and Zulauf, M.: Large-eddy simulations of a drizzling, stratocumulus-topped marine boundary layer, Mon. Weather Rev., 137, 1083–1110, https://doi.org/10.1175/2008MWR2582.1, 2009. a
Avdan, U. and Jovanovska, G.: Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data, J. Sensors, 2016, 1480307, https://doi.org/10.1155/2016/1480307, 2016. a
Baker, R., Cramer, J., and Peters, J.: Radiation fog: UPS Airlines conceptual models and forecast methods, in: Proc. 10th Conf. on Aviation, Range and Aerospace Meteorology, 12–16 May 2002, Portland, Oregon, USA, 154–159, 2002. a
Bari, D., Bergot, T., and El Khlifi, M.: Numerical study of a coastal fog event over Casablanca, Morocco, Q. J. Roy. Meteor. Soc., 141, 1894–1905, https://doi.org/10.1002/qj.2494, 2015. a
Bari, D., Bergot, T., and El Khlifi, M.: Local meteorological and large-scale weather characteristics of fog over the Grand Casablanca region, Morocco, J. Appl. Meteorol. Clim., 55, 1731–1745, https://doi.org/10.1175/JAMC-D-15-0314.1, 2016. a
Belorid, M., Lee, C. B., Kim, J.-C., and Cheon, T.-H.: Distribution and long-term trends in various fog types over South Korea, Theor. Appl. Climatol., 122, 699–710, https://doi.org/10.1007/s00704-014-1321-x, 2015. a
Bergot, T. and Guedalia, D.: Numerical forecasting of radiation fog. Part I: Numerical model and sensitivity tests, Mon. Weather Rev., 122, 1218–1230, https://doi.org/10.1175/1520-0493(1994)122<1218:NFORFP>2.0.CO;2, 1994. a
Bergot, T., Escobar, J., and Masson, V.: Effect of small-scale surface heterogeneities and buildings on radiation fog: Large-eddy simulation study at Paris-Charles de Gaulle airport, Q. J. Roy. Meteor. Soc., 141, 285–298, https://doi.org/10.1002/qj.2358, 2015. a, b
Bou-Zeid, E., Meneveau, C., and Parlange, M. B.: Large-eddy simulation of neutral atmospheric boundary layer flow over heterogeneous surfaces: Blending height and effective surface roughness, Water Resour. Res., 40, w02505, https://doi.org/10.1029/2003WR002475, 2004. a
Brown, R. and Roach, W. T.: The physics of radiation fog: II–a numerical study, Q. J. Roy. Meteor. Soc., 102, 335–354, https://doi.org/10.1002/qj.49710243205, 1976. a
Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J., Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative transfer modeling: a summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005. a
Corsmeier, U., Kossmann, M., Kalthoff, N., and Sturman, A.: Temporal evolution of winter smog within a nocturnal boundary layer at Christchurch, New Zealand, Meteorology and Atmospheric Physics, 91, 129–148, https://doi.org/10.1007/s00703-005-0111-5, 2006. a, b
Courault, D., Drobinski, P., Brunet, Y., Lacarrere, P., and Talbot, C.: Impact of surface heterogeneity on a buoyancy-driven convective boundary layer in light winds, Bound.-Lay. Meteorol., 124, 383–403, https://doi.org/10.1007/s10546-007-9172-y, 2007. a
Cuxart, J.: When Can a High-Resolution Simulation Over Complex Terrain be Called LES?, Frontiers in Earth Science, 3, 87, https://doi.org/10.3389/feart.2015.00087, 2015. a, b
Dale, E. R., Katurji, M., McDonald, A. J., Voss, P., Rack, W., and Seto, D.: A comparison of AMPS forecasts near the Ross Sea polynya with Controlled Meteorological balloon observations, J. Geophys. Res.-Atmos., 125, e2019JD030591, https://doi.org/10.1029/2019JD030591, 2020. a
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, https://doi.org/10.1007/BF00119502, 1980. a
Ducongé, L., Lac, C., Vié, B., Bergot, T., and Price, J. D.: Fog in heterogeneous environments: the relative importance of local and non-local processes on radiative-advective fog formation, Q. J. Roy. Meteor. Soc., 146, 2522–2546, https://doi.org/10.1002/qj.3783, 2020. a, b
Duynkerke, P. G.: Radiation Fog: A Comparison of Model Simulation with Detailed Observations, Mon. Weather Rev., 119, 324–341, https://doi.org/10.1175/1520-0493(1991)119<0324:RFACOM>2.0.CO;2, 1991. a, b, c
Environment Canterbury Regional Council: Christchurch and Ashley River, Canterbury, New Zealand 2018, OpenTopography, https://doi.org/10.5069/G91J97WQ, 2020. a, b, c
Gehrke, K. F., Sühring, M., and Maronga, B.: Modeling of land–surface interactions in the PALM model system 6.0: land surface model description, first evaluation, and sensitivity to model parameters, Geosci. Model Dev., 14, 5307–5329, https://doi.org/10.5194/gmd-14-5307-2021, 2021. a
Gultepe, I., Müller, M. D., and Boybeyi, Z.: A new visibility parameterization for warm-fog applications in numerical weather prediction models, J. Appl. Meteorol. Clim., 45, 1469–1480, https://doi.org/10.1175/JAM2423.1, 2006. a
Gultepe, I., Tardif, R., Michaelides, S. C., Cermak, J., Bott, A., Bendix, J., Müller, M. D., Pagowski, M., Hansen, B., Ellrod, G., Jacobs, W., Toth, G., and Cober, S. G.: Fog Research: A Review of Past Achievements and Future Perspectives, Pure Appl. Geophys., 164, 1121–1159, https://doi.org/10.1007/s00024-007-0211-x, 2007. a, b, c
Haeffelin, M., Bergot, T., Elias, T., Tardif, R., Carrer, D., Chazette, P., Colomb, M., Drobinski, P., Dupont, E., Dupont, J.-C., Gomes, L., Musson-Genon, L., Pietras, C., Plana-Fattori, A., Protat, A., Rangognio, J., Raut, J.-C., Rémy, S., Richard, D., Sciare, J., and Zhang, X.: Parisfog: Shedding new Light on Fog Physical Processes, B. Am. Meteorol. Soc., 91, 767–783, https://doi.org/10.1175/2009BAMS2671.1, 2010. a
Heldens, W., Burmeister, C., Kanani-Sühring, F., Maronga, B., Pavlik, D., Sühring, M., Zeidler, J., and Esch, T.: Geospatial input data for the PALM model system 6.0: model requirements, data sources and processing, Geosci. Model Dev., 13, 5833–5873, https://doi.org/10.5194/gmd-13-5833-2020, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Horányi, A., Sabater, J. M., Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.: Global reanalysis: goodbye ERA-Interim, hello ERA5, ECMWF newsletter, 159, ECMWF, 17–24, https://doi.org/10.21957/vf291hehd7, 2019. a, b
Honnert, R., Efstathiou, G. A., Beare, R. J., Ito, J., Lock, A., Neggers, R., Plant, R. S., Shin, H. H., Tomassini, L., and Zhou, B.: The atmospheric boundary layer and the “gray zone” of turbulence: A critical review, J. Geophys. Res.-Atmos., 125, e2019JD030317, https://doi.org/10.1029/2019JD030317, 2020. a
Huang, H.-Y. and Margulis, S. A.: Impact of soil moisture heterogeneity length scale and gradients on daytime coupled land-cloudy boundary layer interactions, Hydrol. Process., 27, 1988–2003, https://doi.org/10.1002/hyp.9351, 2013. a
Hume, T.: Fog at New Zealand Airports: A Thesis Submitted to the Victoria University of Wellington in Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Geophysics, PhD thesis, Victoria University of Wellington, 1999. a
Hunt, E. D., Hubbard, K. G., Wilhite, D. A., Arkebauer, T. J., and Dutcher, A. L.: The development and evaluation of a soil moisture index, Int. J. Climatol., 29, 747–759, https://doi.org/10.1002/joc.1749, 2009. a
Jakub, F. and Mayer, B.: A three-dimensional parallel radiative transfer model for atmospheric heating rates for use in cloud resolving models–The TenStream solver, J. Quant. Spectrosc. Ra., 163, 63–71, https://doi.org/10.1016/j.jqsrt.2015.05.003, 2015. a
Jakub, F. and Mayer, B.: 3-D radiative transfer in large-eddy simulations – experiences coupling the TenStream solver to the UCLA-LES, Geosci. Model Dev., 9, 1413–1422, https://doi.org/10.5194/gmd-9-1413-2016, 2016. a
Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., Roy, D. P., and Morisette, J. T.: An overview of MODIS Land data processing and product status, Remote Sens. Environ., 83, 3–15, https://doi.org/10.1016/S0034-4257(02)00084-6, 2002. a
Kadasch, E., Sühring, M., Gronemeier, T., and Raasch, S.: Mesoscale nesting interface of the PALM model system 6.0, Geosci. Model Dev., 14, 5435–5465, https://doi.org/10.5194/gmd-14-5435-2021, 2021. a
Kessler, E.: On the distribution and continuity of water substance in atmospheric circulations, Springer, https://doi.org/10.1007/978-1-935704-36-2_1, 1–84, 1969. a
Krč, P., Resler, J., Sühring, M., Schubert, S., Salim, M. H., and Fuka, V.: Radiative Transfer Model 3.0 integrated into the PALM model system 6.0, Geosci. Model Dev., 14, 3095–3120, https://doi.org/10.5194/gmd-14-3095-2021, 2021. a, b, c
Landcare Research: NZDEM South Island 25 metre, the Land Resource Information Systems Portal, New Zealand, https://doi.org/10.7931/L1R94, 2018. a, b
Landcare Research: LCDB v5.0 – Land Cover Database version 5.0, Mainland New Zealand, the Land Resource Information Systems Portal, New Zealand, https://doi.org/10.26060/W5B4-WK93, 2020. a, b
Lin, D.: PALM model system 6.0 source code, revision r4828 [Software], Zenodo [code], https://doi.org/10.5281/zenodo.10158001, 2023. a
Lin, D., Khan, B., Katurji, M., Bird, L., Faria, R., and Revell, L. E.: WRF4PALM v1.0: a mesoscale dynamical driver for the microscale PALM model system 6.0, Geosci. Model Dev., 14, 2503–2524, https://doi.org/10.5194/gmd-14-2503-2021, 2021. a, b, c, d
Lin, D., Zhang, J., Khan, B., Katurji, M., and Revell, L. E.: GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-150, in review, 2023b. a
Macara, G. R.: The climate and weather of Canterbury, NIWA, Taihoro Nukurangi, 2016. a
Maronga, B., Hartogensis, O. K., Raasch, S., and Beyrich, F.: The effect of surface heterogeneity on the structure parameters of temperature and specific humidity: A large-eddy simulation case study for the LITFASS-2003 experiment, Bound.-Lay. Meteorol., 153, 441–470, https://doi.org/10.1007/s10546-014-9955-x, 2014. a, b
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015, 2015. a, b, c
Maronga, B., Banzhaf, S., Burmeister, C., Esch, T., Forkel, R., Fröhlich, D., Fuka, V., Gehrke, K. F., Geletič, J., Giersch, S., Gronemeier, T., Groß, G., Heldens, W., Hellsten, A., Hoffmann, F., Inagaki, A., Kadasch, E., Kanani-Sühring, F., Ketelsen, K., Khan, B. A., Knigge, C., Knoop, H., Krč, P., Kurppa, M., Maamari, H., Matzarakis, A., Mauder, M., Pallasch, M., Pavlik, D., Pfafferott, J., Resler, J., Rissmann, S., Russo, E., Salim, M., Schrempf, M., Schwenkel, J., Seckmeyer, G., Schubert, S., Sühring, M., von Tils, R., Vollmer, L., Ward, S., Witha, B., Wurps, H., Zeidler, J., and Raasch, S.: Overview of the PALM model system 6.0, Geosci. Model Dev., 13, 1335–1372, https://doi.org/10.5194/gmd-13-1335-2020, 2020. a, b, c
Mason, J.: The Physics of Radiation Fog, J. Meteorol. Soc. Jpn. Ser. II, 60, 486–499, https://doi.org/10.2151/jmsj1965.60.1_486, 1982. a
Mazoyer, M., Lac, C., Thouron, O., Bergot, T., Masson, V., and Musson-Genon, L.: Large eddy simulation of radiation fog: impact of dynamics on the fog life cycle, Atmos. Chem. Phys., 17, 13017–13035, https://doi.org/10.5194/acp-17-13017-2017, 2017. a
Meng, L. and Quiring, S. M.: A Comparison of Soil Moisture Models Using Soil Climate Analysis Network Observations, J. Hydrometeorol., 9, 641–659, https://doi.org/10.1175/2008JHM916.1, 2008. a
Moeng, C.-H. and Wyngaard, J. C.: Spectral analysis of large-eddy simulations of the convective boundary layer, J. Atmos. Sci., 45, 3573–3587, https://doi.org/10.1175/1520-0469(1988)045<3573:SAOLES>2.0.CO;2, 1988. a
New Zealand Meteorological Service: The Climatology of Christchurch International Airport, Tech. rep., The Service, Wellington, NZ, volume: 171 (3) Report, 1982. a
Osborne, N.: An Investigation into the Clearing Time of Stratus or Fog at Christchurch International Airport, with the Use of an Acoustic Sodar, A Thesis Submitted to the Victoria University of Wellington in Fulfilment of the Requirements for the Diploma of Applied Science (Meteorology), Victoria University of Wellington, New Zealand, 2002. a, b
Potić, I., Bugarski, M., and Matić-Varenica, J.: Soil moisture determination using remote sensing data for the property protection and increase of agriculture production, in: Worldbank conference on land and poverty, The World Bank, Washington, DC, https://doi.org/10.13140/RG.2.2.30426.59845, 2017. a
Rémy, S. and Bergot, T.: Assessing the impact of observations on a local numerical fog prediction system, Q. J. Roy. Meteor. Soc., 135, 1248–1265, https://doi.org/10.1002/qj.448, 2009. a
Resler, J., Krč, P., Belda, M., Juruš, P., Benešová, N., Lopata, J., Vlček, O., Damašková, D., Eben, K., Derbek, P., Maronga, B., and Kanani-Sühring, F.: PALM-USM v1.0: A new urban surface model integrated into the PALM large-eddy simulation model, Geosci. Model Dev., 10, 3635–3659, https://doi.org/10.5194/gmd-10-3635-2017, 2017. a
Rihani, J. F., Chow, F. K., and Maxwell, R. M.: Isolating effects of terrain and soil moisture heterogeneity on the atmospheric boundary layer: Idealized simulations to diagnose land-atmosphere feedbacks, J. Adv. Model. Earth Sy., 7, 915–937, https://doi.org/10.1002/2014MS000371, 2015. a
Roux, B., Potts, R., Siems, S., and Manton, M.: Towards a better understanding of fog at Perth Airport, J. Hydrol., 600, 126516, https://doi.org/10.1016/j.jhydrol.2021.126516, 2021. a
Roy, D. P., Wulder, M. A., Loveland, T. R., C.e., W., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F., Belward, A. S., Bindschadler, R., Cohen, W. B., Gao, F., Hipple, J. D., Hostert, P., Huntington, J., Justice, C. O., Kilic, A., Kovalskyy, V., Lee, Z. P., Lymburner, L., Masek, J. G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R. H., and Zhu, Z.: Landsat-8: Science and product vision for terrestrial global change research, Remote Sens. Environ., 145, 154–172, https://doi.org/10.1016/j.rse.2014.02.001, 2014. a
Saiki, E. M., Moeng, C.-H., and Sullivan, P. P.: Large-eddy simulation of the stably stratified planetary boundary layer, Bound.-Lay. Meteorol., 95, 1–30, https://doi.org/10.1023/A:1002428223156, 2000. a
Salim, M. H., Schubert, S., Resler, J., Krč, P., Maronga, B., Kanani-Sühring, F., Sühring, M., and Schneider, C.: Importance of radiative transfer processes in urban climate models: a study based on the PALM 6.0 model system, Geosci. Model Dev., 15, 145–171, https://doi.org/10.5194/gmd-15-145-2022, 2022. a, b
Schwenkel, J. and Maronga, B.: Large-eddy simulation of radiation fog with comprehensive two-moment bulk microphysics: impact of different aerosol activation and condensation parameterizations, Atmos. Chem. Phys., 19, 7165–7181, https://doi.org/10.5194/acp-19-7165-2019, 2019. a, b, c, d
Shao, Y., Liu, S., Schween, J. H., and Crewell, S.: Large-Eddy Atmosphere–Land-Surface Modelling over Heterogeneous Surfaces: Model Development and Comparison with Measurements, Bound.-Lay. Meteorol., 148, 333–356, https://doi.org/10.1007/s10546-013-9823-0, 2013. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A description of the advanced research WRF model version 4.3, National Center for Atmospheric Research: Boulder, CO, USA, p. 165, https://doi.org/10.5065/1dfh-6p97, 2021. a
Smith, D. K., Renfrew, I. A., Dorling, S. R., Price, J. D., and Boutle, I. A.: Sub-km scale numerical weather prediction model simulations of radiation fog, Q. J. Roy. Meteor. Soc., 147, 746–763, https://doi.org/10.1002/qj.3943, 2020. a, b
Sohrabinia, M., Rack, W., and Zawar-Reza, P.: Soil moisture derived using two apparent thermal inertia functions over Canterbury, New Zealand, J. Appl. Remote Sens., 8, 083624, https://doi.org/10.1117/1.JRS.8.083624, 2014. a
Srivastava, A., Kumari, N., and Maza, M.: Hydrological Response to Agricultural Land Use Heterogeneity Using Variable Infiltration Capacity Model, Water Resour. Manag., 34, 3779–3794, https://doi.org/10.1007/s11269-020-02630-4, 2020. a
Steeneveld, G.-J. and de Bode, M.: Unravelling the relative roles of physical processes in modelling the life cycle of a warm radiation fog, Q. J. Roy. Meteor. Soc., 144, 1539–1554, https://doi.org/10.1002/qj.3300, 2018. a, b
Steeneveld, G. J., Ronda, R. J., and Holtslag, A. A. M.: The challenge of forecasting the onset and development of radiation fog using mesoscale atmospheric models, Bound.-Lay. Meteorol., 154, 265–289, https://doi.org/10.1007/s10546-014-9973-8, 2015. a
Sturman, A. P. and Tapper, N. J.: The weather and climate of Australia and New Zealand, Oxford University Press, USA, ISBN 0-19-58466-X, 2006. a
Tardif, R.: The impact of vertical resolution in the explicit numerical forecasting of radiation fog: A case study, Pure Appl. Geophys., 164, 1221–1240, https://doi.org/10.1007/s00024-007-0216-5, 2007. a
Tardif, R. and Rasmussen, R. M.: Event-based climatology and typology of fog in the New York City region, J. Appl. Meteorol. Clim., 46, 1141–1168, https://doi.org/10.1175/JAM2516.1, 2007. a
Uni Hannover: The PALM model system, http://palm-model.org (last access: 20 November 2023), 2023. a
Van Schalkwyk, L. and Dyson, L. L.: Climatological characteristics of fog at Cape Town International airport, Weather Forecast., 28, 631–646, https://doi.org/10.1175/WAF-D-12-00028.1, 2013. a
Vosper, S., Carter, E., Lean, H., Lock, A., Clark, P., and Webster, S.: High resolution modelling of valley cold pools, Atmos. Sci. Lett., 14, 193–199, https://doi.org/10.1002/asl2.439, 2013. a
Vosper, S. B., Hughes, J. K., Lock, A. P., Sheridan, P. F., Ross, A. N., Jemmett-Smith, B., and Brown, A. R.: Cold-pool formation in a narrow valley, Q. J. Roy. Meteor. Soc., 140, 699–714, https://doi.org/10.1002/qj.2160, 2014. a
Werner, K.: WRF Version 4.2, GitHub [code], https://github.com/wrf-model/WRF/releases/tag/v4.2 (last access: 20 November 2023), 2020. a
WMO: International meteorological vocabulary, 1992, Tech. rep., WMO/OMM/IMGW, 182, Geneva, 1992. a
Zeng, Y., Feng, Z., and Xiang, N.: Assessment of soil moisture using Landsat ETM+ temperature/vegetation index in semiarid environment, in: IGARSS 2004, 2004 IEEE International Geoscience and Remote Sensing Symposium, vol. 6, 20–24 September 2004, Anchorage, Alaska, USA, 4306–4309, https://doi.org/10.1109/IGARSS.2004.1370089, 2004. a
Short summary
Accurate fog forecasting is difficult in a complex environment. Spatial variations in soil moisture could impact fog. Here, we carried out fog simulations with spatially different soil moisture in complex topography. The soil moisture was calculated using satellite observations. The results show that the spatial variations in soil moisture do not have a significant impact on where fog occurs but do impact how long fog lasts. This finding could improve fog forecasts in the future.
Accurate fog forecasting is difficult in a complex environment. Spatial variations in soil...
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