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- Author or Editor: Samuel Rémy x
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Abstract
Because poor visibility conditions have a considerable influence on airport traffic, a need exists for accurate and updated fog and low-cloud forecasts. Couche Brouillard Eau Liquide (COBEL)-Interactions between Soil, Biosphere, and Atmosphere (ISBA), a boundary layer 1D numerical model, has been developed for the very short-term forecast of fog and low clouds. This forecast system assimilates local observations to produce initial profiles of temperature and specific humidity. The initial conditions have a great impact on the skill of the forecast.
In this work, the authors first estimated the background error statistics; they varied greatly with time, and cross correlations between temperature and humidity in the background were significant. This led to the implementation of an ensemble Kalman filter (EnKF) within COBEL-ISBA. The new assimilation system was evaluated with temperature and specific humidity scores, as well as in terms of its impact on the quality of fog forecasts. Simulated observations were used and focused on the modeling of the atmosphere before fog formation and also on the simulation of the life cycle of fog and low clouds. For both situations, the EnKF brought a significant improvement in the initial conditions and the forecasts. The forecast of the onset and burn-off times of fogs was also improved. The EnKF was also tested with real observations and gave good results. The size of the ensemble did not have much impact when simulated observations were used, thanks to an adaptive covariance inflation algorithm, but the impact was greater when real observations were used.
Abstract
Because poor visibility conditions have a considerable influence on airport traffic, a need exists for accurate and updated fog and low-cloud forecasts. Couche Brouillard Eau Liquide (COBEL)-Interactions between Soil, Biosphere, and Atmosphere (ISBA), a boundary layer 1D numerical model, has been developed for the very short-term forecast of fog and low clouds. This forecast system assimilates local observations to produce initial profiles of temperature and specific humidity. The initial conditions have a great impact on the skill of the forecast.
In this work, the authors first estimated the background error statistics; they varied greatly with time, and cross correlations between temperature and humidity in the background were significant. This led to the implementation of an ensemble Kalman filter (EnKF) within COBEL-ISBA. The new assimilation system was evaluated with temperature and specific humidity scores, as well as in terms of its impact on the quality of fog forecasts. Simulated observations were used and focused on the modeling of the atmosphere before fog formation and also on the simulation of the life cycle of fog and low clouds. For both situations, the EnKF brought a significant improvement in the initial conditions and the forecasts. The forecast of the onset and burn-off times of fogs was also improved. The EnKF was also tested with real observations and gave good results. The size of the ensemble did not have much impact when simulated observations were used, thanks to an adaptive covariance inflation algorithm, but the impact was greater when real observations were used.
Abstract
A specific event, called a low-visibility procedure (LVP), has been defined when visibility is under 600 m and/or the ceiling is under 60 m at Paris-Charles de Gaulle Airport, Paris, France, to ensure air traffic safety and to reduce the economic issues related to poor visibility conditions. The Local Ensemble Prediction System (LEPS) has been designed to estimate LVP likelihood in order to help forecasters in their tasks. This work evaluates the skill of LEPS for each type of LVP that takes place at the airport area during five winter seasons from 2002 to 2007. An event-based classification reveals that stratus base lowering, advection, and radiation fogs make up for 78% of the LVP cases that occurred near the airport during this period. This study also demonstrates that LEPS is skillful on these types of event for short-term forecasts. When the ensemble runs start with initialized LVP events, the prediction of advection fogs is as skillful as the prediction of radiation fog events and stratus base lowering. At 3 and 6 h before the runs where LVP events were initialized, LEPS still shows positive skill for radiation fog events and stratus base lowering cases.
Abstract
A specific event, called a low-visibility procedure (LVP), has been defined when visibility is under 600 m and/or the ceiling is under 60 m at Paris-Charles de Gaulle Airport, Paris, France, to ensure air traffic safety and to reduce the economic issues related to poor visibility conditions. The Local Ensemble Prediction System (LEPS) has been designed to estimate LVP likelihood in order to help forecasters in their tasks. This work evaluates the skill of LEPS for each type of LVP that takes place at the airport area during five winter seasons from 2002 to 2007. An event-based classification reveals that stratus base lowering, advection, and radiation fogs make up for 78% of the LVP cases that occurred near the airport during this period. This study also demonstrates that LEPS is skillful on these types of event for short-term forecasts. When the ensemble runs start with initialized LVP events, the prediction of advection fogs is as skillful as the prediction of radiation fog events and stratus base lowering. At 3 and 6 h before the runs where LVP events were initialized, LEPS still shows positive skill for radiation fog events and stratus base lowering cases.
Abstract
In the absence of a dynamical fire model that could link the emissions to the weather dynamics and the availability of fuel, atmospheric composition models, such as the European Copernicus Atmosphere Monitoring Services (CAMS), often assume persistence, meaning that constituents produced by the biomass burning process during the first day are assumed constant for the whole length of the forecast integration (5 days for CAMS). While this assumption is simple and practical, it can produce unrealistic predictions of aerosol concentration due to an excessive contribution from biomass burning. This paper introduces a time-dependent factor 
Abstract
In the absence of a dynamical fire model that could link the emissions to the weather dynamics and the availability of fuel, atmospheric composition models, such as the European Copernicus Atmosphere Monitoring Services (CAMS), often assume persistence, meaning that constituents produced by the biomass burning process during the first day are assumed constant for the whole length of the forecast integration (5 days for CAMS). While this assumption is simple and practical, it can produce unrealistic predictions of aerosol concentration due to an excessive contribution from biomass burning. This paper introduces a time-dependent factor
