Prévision locale des faibles visibilités pour l'aéronautique

TOULOUSE3-BU Sciences (315552104) / SudocTOULOUSE-Observ. Midi Pyréné (315552299) / SudocSudocFranceF

Etude de l'optimisation d'un système d'observation adaptatif pour améliorer la prévision des dépressions météorologiques

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

On the Predictability of Radiation Fog Formation in a Mesoscale Model: A Case Study in Heterogeneous Terrain

This study evaluates the predictability of the formation phase of a radiation fog event observed during the night of 31 October 2015 to 01 November 2015 in the north-east of France at three sites managed by OPE (Observatoire Pérenne de l’Environnement). The fog layer shows significantly different behaviors at the three areas, which are located only a few kilometers apart. Three fog life cycles were observed: the formation of a dense adiabatic fog, the formation of a thin patchy fog, or no fog formation despite favorable conditions. This event was studied with the Meso-NH numerical mesoscale model at two horizontal resolutions, 500 m and 50 m. Simulations at 50 m allow estimation of the spread of the predicted parameters over the heterogeneous terrain studied. These numerical simulations strongly suggest that this event involved numerous interactions and complex circulations. The wind above the nocturnal boundary layer greatly affects the transition of shallow patchy fog into thick adiabatic fog. These numerical simulations also show that the occurrence and type of fog could be very different over a small but heterogeneous area. It is also interesting to note that the spread of the simulated parameters was very high during the transition from shallow fog to a deep fog layer. The spread was concentrated during the regime transition between the fog formation and its maturity. This appeared to be the result of the complex interplay of processes at numerous ranges of scale. A new concept called “pseudo-process diagram„ is presented. These pseudo-process diagrams are very good tools to analyze fog, and allow a good illustration of the spread of fog during this chaotic phase. This kind of concept seems a promising tool to analyze fog predictability in depth

Prévision d'ensemble locale des brouillards et nuages bas à l'aéroport International de Roissy Charles De Gaulle

TOULOUSE3-BU Sciences (315552104) / SudocTOULOUSE-Observ. Midi Pyréné (315552299) / SudocSudocFranceF

Observation, Simulation and Predictability of Fog: Review and Perspectives

Fog affects human activities in various ways, but the societal impact of fog has significantly increased during recent decades due to increasing air, marine and road traffic [...

Fog Decision Support Systems: A Review of the Current Perspectives

Accurate and timely fog forecasts are needed to support decision making for various activities which are critically affected by low visibility conditions [...

Analog Ensemble Forecasting System for Low-Visibility Conditions over the Main Airports of Morocco

Low-visibility conditions (LVC) are a common cause of air traffic, road, and sailing fatalities. Forecasting those conditions is an arduous challenge for weather forecasters all over the world. In this work, a new decision support system is developed based on an analog ensemble (AnEn) method to predict LVC over 15 airports of Morocco for 24 forecast hours. Hourly forecasts from the AROME model of eight predictors were used to select the skillful analogs from 2016 to 2018. The verified hourly observations were used as members of the ensemble. The developed ensemble prediction system (EPS) was assessed over 1 year (2019) as a single-value forecast and as a probabilistic forecast. Results analysis shows that AnEn outperforms persistence and its best performances are perceived generally during night and early-morning lead times. From continuous verification analysis, AnEn forecasting errors are found to be location- and lead-time-dependent and become higher for low-visibility cases. AnEn draws an averaged Centered Root Mean Square Error of about 1500 m for all visibilities, 2000 m for fog and 1500 m for mist. As an EPS, AnEn is under-dispersive for all lead times and draws a positive bias for fog and mist events. For probabilistic verification analysis, AnEn visibility forecasts are converted to binary occurrences depending on a set of thresholds from 200 m to 6000 m by a step of 200 m. It is found that the averaged Heidke Skill Score for AnEn is 0.65 for all thresholds. However, AnEn performance generally becomes weaker for fog or mist events prediction

A Single-Column Comparison of Model-Error Representations for Ensemble Prediction

International audienceVarious perturbation approaches have been proposed for representing model error in convection-permitting ensemble prediction. Their evaluation usually relies on time-averaged ensemble prediction statistics and on complex case studies. In this work, their detailed physical behaviour is studied in order to understand their differences, and to help their optimization. A process-level intercomparison framework is used to investigate the widely used SPPT (stochastic perturbations of physics tendencies), independent SPPT, and random-parameters model-perturbation approaches. Ensemble predictions with the single-column version of the Arome numerical-weather-prediction model are evaluated on three different boundary-layer regimes: cumulus convection, stratocumulus-topped boundary layer, and radiation fog. The independent SPPT approach is found to produce more dispersion than the SPPT approach, particularly when several physics parametrizations are in near equilibrium. It also appears to be more numerically stable near the surface. The random parameters approach perturbations are structurally very different from the other approaches, particularly regarding cloud structure. The independent SPPT and random parameters approaches have very different sensitivities to the atmospheric conditions, which suggests that intercomparisons of ensemble-model-error approaches should carefully account for situation dependency. Substantial forecast biases are produced by random parameters with respect to the unperturbed model. These results suggest that the independent SPPT approach can bring major improvements over the SPPT approach with minimal effort, that there is some complementarity between the independent SPPT and random parameters approaches, but that implementing random-parameters-type approaches in operational applications may require careful tuning to avoid creating forecast biases

Particulate contribution to extinction of visible radiation: Pollution, haze, and fog

International audienceA data set acquired by eight particle-dedicated instruments set up on the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique, which is French for Instrumented Site for Atmospheric Remote Sensing Research) during the ParisFog field campaign are exploited to document microphysical properties of particles contributing to extinction of visible radiation in variable situations. The study focuses on a 48-hour period when atmospheric conditions are highly variable: relative humidity changes between 50 and 100%, visibility ranges between 65 and 35 000 m, the site is either downwind the Paris area either under maritime influence. A dense and homogeneous fog formed during the night by radiative cooling. In 6 h, visibility decreased down from 30 000 m in the clear-sky regime to 65 m within the fog, because of advected urban pollution (factor 3 to 4 in visibility reduction), aerosol hydration (factor 20) and aerosol activation (factor 6). Computations of aerosol optical properties, based on Mie theory, show that extinction in clear-sky regime is due equally to the ultrafine modes and to the accumulation mode. Extinction by haze is due to hydrated aerosol particles distributed in the accumulation mode, defined by a geometric mean diameter of 0.6 μm and a geometric standard deviation of 1.4. These hydrated aerosol particles still contribute by 20 ± 10% to extinction in the fog. The complementary extinction is due to fog droplets distributed around the geometric mean diameter of 3.2 μm with a geometric standard deviation of 1.5 during the first fog development stage. The study also shows that the experimental set-up could not count all fog droplets during the second and third fog development stages

Casual Rerouting of AERONET Sun/Sky Photometers: Toward a New Network of Ground Measurements Dedicated to the Monitoring of Surface Properties?

International audienceThis paper presents an innovative method for observing vegetation health at a very high spatial resolution (~5 × 5 cm) and low cost by upgrading an existing Aerosol RObotic NETwork (AERONET) ground station dedicated to the observation of aerosols in the atmosphere. This study evaluates the capability of a sun/sky photometer to perform additional surface reflectance observations. The ground station of Toulouse, France, which belongs to the AERONET sun/sky photometer network, is used for this feasibility study. The experiment was conducted for a 5-year period (between 2016 and 2020). The sun/sky photometer was mounted on a metallic structure at a height of 2.5 m, and the acquisition software was adapted to add a periodical (every hour) ground-observation scenario with the sun/sky photometer observing the surface instead of being inactive. Evaluation is performed by using a classical metric characterizing the vegetation health: the normalized difference vegetation index (NDVI), using as reference the satellite NDVI derived from a Sentinel-2 (S2) sensor at 10 × 10 m resolution. Comparison for the 5-year period showed good agreement between the S2 and sun/sky photometer NDVIs (i.e., bias = 0.004, RMSD = 0.082, and R = 0.882 for a mean value of S2A NDVI around 0.6). Discrepancies could have been due to spatial-representativeness issues (of the ground measurement compared to S2), the differences between spectral bands, and the quality of the atmospheric correction applied on S2 data (accuracy of the sun/sky photometer instrument was better than 0.1%). However, the accuracy of the atmospheric correction applied on S2 data in this station appeared to be of good quality, and no dependence on the presence of aerosols was observed. This first analysis of the potential of the CIMEL CE318 sun/sky photometer to monitor the surface is encouraging. Further analyses need to be carried out to estimate the potential in different AERONET stations. The occasional rerouting of AERONET stations could lead to a complementary network of surface reflectance observations. This would require an update of the software, and eventual adaptations of the measurement platforms to the station environments. The additional cost, based on the existing AERONET network, would be quite limited. These new surface measurements would be interesting for measurements of vegetation health (monitoring of NDVI, and also of other vegetation indices such as the leaf area and chlorophyll indices), for validation and calibration exercise purposes, and possibly to refine various scientific algorithms (i.e., algorithms dedicated to cloud detection or the AERONET aerosol retrieval algorithm itself). CIMEL is ready to include the ground scenario used in this study in all new sun/sky photometers