Speaker
Q. Laffineur
(KMI-RMI)
Description
The Eyjafjallajökull and the Grimsvötn volcano's in Iceland erupted in April 2010 and in May 2011 respectively causing massive disruption to the European air traffic. These eruptions highlighted the need for automatic LIDAR-ceilometer (ALC) monitoring stations capable of routinely estimating the vertical profile of aerosols. The ALC primarily designed for cloud base height detection has greatly improved over the last years, and now offers the opportunity to monitor the vertical profile of aerosols on a continuous temporal scale.
In recent years, several Meteorogical services in Europe replaced or improved their traditional cloud-base ceilometer network to be able to monitor aerosol plumes. To coordinate and to make available the ALC measurements of each national network to the European meteorological community in near real time, two major European projects supported by EUMETNET (E-PROFILE) and by COST Action (TOPROF) have been established in 2013. An ALC network can monitor not only ash plumes but also others type of aerosol plumes (dust, smokes...) less innocuous for the aviation but whose monitoring enables to validate and to improve the dispersion models. An intercontinental wildfire smoke transport event observed over United Kingdom and Belgium by ALC in summer 2013 will be presented. This case study illustrates clearly the ambiguity that can happen if one type of remote sensor instrument is used to monitor aerosol plumes.
ALC measurements also offer the opportunity to analyse the backscatter signal in the boundary layer that potentially contains major information to predict radiation fog formation or not. Fog is the most frequent cause of surface visibility below 1 km, and is one of the most common and persistent weather hazards encountered in aviation and to nearly all forms of surface transport. The financial and human losses related to fog became comparable to the losses from other hazardous weather events e.g., tornadoes or, in some situations, even hurricanes. Forecasting fog can be difficult, a number of approaches have been used to integrate the satellite data, numerical modeling, and standard surface observations. Successful numerical modeling and forecasting of fog depends on the fog type that has to be predicted. Radiation fog events is a typical example of fog that is particularly difficult to predict by comparison with advection fog or orographic fog.
The Royal Meteorological Institute of Belgium (RMI) and The Pierre-Simon Laplace Institute (IPSL, SIRTA) developed a forward stepwise screening algorithm to help prediction of radiation fog formation based on the hygroscopic growth function of aerosol scattering coefficient deduced from LIDAR-ceilometer measurements. This algorithm will be tested in October 2015 at Paris-Charles de Gaulle airport in real-time in parallel with the usual fog prediction methods. A description of this new fog prediction algorithm will be also presented.
Primary author
Q. Laffineur
(KMI-IRM)
Co-authors
A. Delcloo
(KMI-IRM)
H. De Backer
(KMI-IRM)
