SOuth west FOGs 3D experiment for processes study
|Partenariat||CNRM, LMD, LATMOS, MET OFFICE, IRSN, INRA/AGIR, the Köln University, the CNR-IMAA, MeteoSwiss|
The primary objective of SOFOG3D is to advance our understanding of fog processes at the smallest scale to improve forecasts of fog events by numerical weather prediction (NWP) models. Specifically, SOFOG3D conducts process studies on very well documented situations, using synergy between 3D high-resolution Large Eddy Simulation (LES) and unprecedented detailed observations. A field campaign specifically designed to explore both horizontal and vertical variability of fog layers will be conducted with innovative sensors including in situ and remote sensing networks and Unmanned Aerial Vehicle (UAV) fleet.
The main objective of SOFOG3D is to advance our understanding of fog processes by conducting process studies on very well documented situations, using synergy between 3D high-resolution LES and unprecedented detailed observations.
To this end, the proposed project is divided into five general tasks (Figure 5) : observations collected during the field campaign feed all other tasks (thin black arrows) : they are processed in task 1 and 2 for in-situ and remote sensing measurements, respectively, used to validate 3D LES case studies in task 3, analysed in synergy with LES results in task 4 to conduct process studies and develop conceptual models, and finally assimilated and used to validate operational forecasts in task 5.
Improvement of our understanding of fog physics will result from reinforced interactions between measurements analysis, 3D LES and process studies (thick purple arrows).
Ultimately derived refinement of processes parametrizations and assimilation strategies will contribute to improve forecasts of fog events by NWP models.
Task1: Field campaign and in situ data analysis (CNRM)
Task2: Fog retrievals based on remote sensing measurements (LATMOS)
Task3 : 3D LES simulation and impact of heterogeneities (CNRM)
Task4 : Advanced process studies based on highly documented cases (LMD,CNRM)
Task5 : Data Assimilation and Forecast (CNRM)
D1.2.1 Conduct the six month field campaign with continuous monitoring and IOP operations
D1.2.2 Database integrated in the AERIS web site at the end of the project
D1.3.1 Analysis of energy budget closure and impact of heterogeneities on the residual.
D1.3.2 Analysis of turbulence anisotropy parameter
D1.3.3 Characterization of CCN activation spectra to prescribe CCN parameterization
D1.3.4 Aerosol absorption properties within fog
D1.3.5 Vertical profile of fog microphysics (droplet size distribution and LWC)
D1.3.6 Analysis of entrainment-mixing processes at fog top
D2.1.1 LWC profiles depending on different constraints from dedicated variational method
D2.1.2 Dynamics of the fog layer from velocity azimuth display technique
D2.2.1 Evaluation of radar LWC retrievial vs in-situ measurements
D2.2.2 Improve radar forward model thanks to calibrated metallic targets
D2.3.1 Improved MWR temperature and humidity profiles retrieved with cloud radar LWC
D2.3.2 Feasibility study of cloud radar LWC assimilation within the MWR 1D-Var framework
D2.4.1 Time series of 2-D maps of cloud classes using a classification adapted for fog and low stratus evolution tracking (e.g. separating core fog, dissipation fog, formation fog pixels)
D2.4.2 Time series of fog evolution indicators, such as distance to fog boundaries, cloud albedo and evolution of brightness temperature of the different cloud classes.
D3.1 : LES simulations of observed fog cases and validation of advances in physical parametrizations
D3.2 : Report analyzing the impact of surface heterogeneities during the fog life cycle
D3.3 : Report analyzing the impact of orography on the formation and evolution of fog
D4.1 Analysis of the transition thin/thick fog and fog-top entrainment
D4.2 Thesis on the stratus-to-fog transition
D4.3.1 LWP budget for each documented fog case
D4.3.2 Sensitivity of fog life cycle to key variables driving major processes
D5.1 Data assimilation trials with different settings and configurations (observations, background-errors, vertical and temporal resolutions)
D5.2 Data assimilation strategy for an optimal use of «fog sensitive» observations to improve NWP fog forecasts
D5.3 Quantitative evaluation of the benefit on the fog forecast using in-situ observations
The project involves 3 French partners (CNRM, LMD/IPSL, LATMOS/IPSL) whose expertise encompasses all the required skills to tackle the challenges raised in that project. They have already collaborated in fog related projects (ParisFOG experiments since 2006 funded by LEFE, PreViBOSS 2010-2013 funded by DGA.)
|the Köln University|
View online : Conférence IFDA2019