A persistent wintertime fog episode at Lisbon airport (Portugal): performance of <scp>ECMWF</scp> and <scp>AROME</scp> models

Belo-Pereira, Margarida; Santos, João A.

doi:10.1002/met.1560

Cited by 15 publications

(7 citation statements)

References 43 publications

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“…The calculated SAR of water was high (35.4) but tolerated by halophyte species (Haro et al, 1993;Flowers and Colmer, 2008; Table 2). The maritime fog mostly observed from November to February each year in the Lisbon area (Belo-Pereira and Santos, 2016), is deposited as occult precipitation by condensing or sublimating directly onto plant surfaces (Karavoltsos et al, 2017), probably contributing to the water balance of plants in these saline habitats. After plant growth (T3), the soils irrigated with brackish water show no change in pH values, however the EC values of FLU (5.6 dS/m) and TEC (5.9 dS/m) had a significant (p < 1 × 10 −7 ) increase (34.03; 32.48 dS/m, respectively) due to the high salinity of estuarine water.…”

Section: Characterization Of Soils Amendments and Irrigation Watermentioning

confidence: 99%

Conservation of a Critically Endangered Endemic Halophyte of West Portugal: A Microcosm Assay to Assess the Potential of Soil Technology for Species Reintroduction

et al. 2021

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The soil system has been frequently overlooked during plant reintroduction planning and practice since working with soils and plant roots can be difficult, particularly in saline environments. Coastal saline environments are major contributors to regional and global biodiversity and an important source of endemic species. However, various species are in decline or considered threatened, particularly halophytes (salt tolerant) due to negative anthropic impacts. The Lusitanian endemic halophyte Limonium daveaui formerly had a large distribution range along the west coast of Portugal but currently it shows a restricted distribution in the Tagus estuary. Field surveys revealed that this critically endangered species forms few local populations with small size invaded by exotic species. In this study, we investigated the potential utilization of Technosols, an innovative sustainable, ecological engineering method combined with brackish water irrigation for potential L. daveaui reintroduction in native habitats. Seed germination percentages were evaluated in different environmental conditions. Through a microcosm assay, a Technosol was constructed using a saline Fluvisol with a mixture of low value inorganic and organic wastes, which were chemically characterized. Plants were cultivated in the Fluvisol and Technosol and irrigated with brackish water collected in the nearby area. To assess plant growth, morphometric parameters and the plants’ physiological status were assessed and the fresh and dry biomass determined. Results showed that seed germination was higher on moist filter paper with distilled water than in Fluvisol or Technosol. Plants grown in Technosol had a greater development, with higher values of photosynthetic indexes and biomass production than in Fluvisol. Our findings provide a basis for future in situ conservation studies and support the idea that eco-friendly soil technology approaches are beneficial to conserve rare halophyte species.

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Section: Characterization Of Soils Amendments and Irrigation Watermentioning

confidence: 99%

Conservation of a Critically Endangered Endemic Halophyte of West Portugal: A Microcosm Assay to Assess the Potential of Soil Technology for Species Reintroduction

et al. 2021

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“…The scientific community has developed various approaches for fog forecasting. Numerical weather prediction (NWP) models are widely used for fog prediction, but accuracy is still unsatisfactory (Belo-Pereira and Santos [2]). The adequate representation of the atmosphere physics in the lower part of the troposphere and the interaction with the land surface remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

Sofia Airport Visibility Estimation with Two Machine-Learning Techniques

Penov,

Guerova

2023

Remote Sensing

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Fog is a weather phenomenon with visibility below 1 km. Fog heavily influences ground and air traffic, leading to accidents and delays. The main goal of this study is to use two machine-learning (ML) techniques—the random forest (RF) and long short-term memory (LSTM) models—to estimate visibility using 11 meteorological parameters. Several meteorological elements related to fog are investigated, including pressure, temperature, wind speed, and direction. The seasonal cycle shows that fog in Sofia has a peak in winter, but a small secondary peak in spring was found in this study. Fog occurrence has a tendency to decrease during the studied period, with the peak of fog observations being shifted towards the higher visibility range. The input parameters in the models are day of year, hour, wind speed, wind direction, first-cloud-layer coverage, first-cloud-layer base height, temperature, dew point, dew-point deficit, pressure, and fog stability index (FSI). The FSI and dew-point deficit are evaluated as the most important input parameters by the RF model. Post-processing was performed with double linear regression for the correction of the predictions by the models, which led to a significant improvement in performance. Both models were found to describe the complexity of fog well.

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“…Among the weather phenomena that affect the visibility range near the Earth's surface, fog is the one that constrains human activities with most economic impact, and sometimes even jeopardizes human lives. Due to its great incidence on aviation, climatological studies and field campaigns have been focused on busy airports around the world [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…The World Meteorological Organization defines fog as the visibility restriction due to water droplets in the lower atmosphere that reduces the horizontal visibility to less than 1 km near to the ground [8]. The knowledge of local weather conditions that culminate in fog plays a major role in operational forecast [1]. A study carried out by Policarpo et al [9] has shown that a large artificial lake of 250 km 2 and its irrigated area become a local important source of moisture, that increases the availability of water vapour, favouring the formation of fog over the lake and its surroundings.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis and Assessment of Pre-Fog in the Mainland Portuguese International Airports: Statistical and Neural Network Models Comparison

Guerreiro

Cruz

2021

The 4th International Electronic Conference on Atmospheric Sciences

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The prediction of fog is a challenging task in operational weather forecast. Due to its dependency on small-scale processes, numerical weather models struggle to deal with under scale features, resulting in uncertainties in the fog forecast. Unawareness of the onset time and the duration of fog leads to disproportionate impact on open-air activities, especially in aviation. Nevertheless, in a small sized country such as Portugal mainland, the fog varies greatly. The traffic of the two busiest Portuguese international airports of Porto and Lisbon is affected by the occurrence of fog at different times of the year. The fog occurrence at Porto is a predominant winter phenomenon and a summer one at Lisbon. Observational variables and their trend are local indicators of favouring conditions to the fog’s onset, such as cooling, water vapour saturation and turbulent mixing. A dataset corresponding to 17 years of half-hourly METAR from the airports of Porto and Lisbon is used to diagnose the pre-fog conditioning. Two diagnostic models are proposed to assess pre-fog conditions. The first model is adapted from the statistical method proposed by Menut et al. (2014), which performs a diagnosis from key variables trend, such as temperature, wind speed and relative humidity. Thresholds are defined from the METAR samples in the 6 h period prior to the formation of fog. Due to the local character of fog, the presented thresholds are the most appropriate ones for each airport. The predictability of fog is then assessed using observations. The second approach consists of neural networks such as a fully connected (FC) network and a recurrent neural network (RNN), which are especially well suited for time series. By experimenting with different types of neural networks (NN), we will try to capture the connection between the temporal evolution of measured variables in the dataset and the fog onset. These experiments will include different time windows to measure its influence on prediction performance.

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A persistent wintertime fog episode at Lisbon airport (Portugal): performance of ECMWF and AROME models

Cited by 15 publications

References 43 publications

Conservation of a Critically Endangered Endemic Halophyte of West Portugal: A Microcosm Assay to Assess the Potential of Soil Technology for Species Reintroduction

Conservation of a Critically Endangered Endemic Halophyte of West Portugal: A Microcosm Assay to Assess the Potential of Soil Technology for Species Reintroduction

Sofia Airport Visibility Estimation with Two Machine-Learning Techniques

Diagnosis and Assessment of Pre-Fog in the Mainland Portuguese International Airports: Statistical and Neural Network Models Comparison

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