Modelling and data assimilation of hazardous volcanic ash plumes in the chemical-transport model MOCAGE

Transcription

1 Modelling and data assimilation of hazardous volcanic ash plumes in the chemical-transport model MOCAGE Bojan Sic, Laaziz El Amraoui, Matthieu Plu CNRM/Météo-France

2 2 Introduction Model MOCAGE of Météo-France The state-of-the-art chemical transport model (CTM) MOCAGE, used as well for aerosol studies and predictions Importance of volcanic aerosols in the context of the CTM MOCAGE: Aerosols play an important role in problems connected with air pollution and linked health effects EUNADICS-AV project integration of a system for the observation analysis relevant to the aerosol (and nuclear) related aviation hazards Economic and safety impact Toulouse Volcanic Ash Advisory Center (VAAC) responsibility over Africa and the big part of Eurasia Need for a good representation of the volcanic aerosols in the model Reuters

3 3 CTM MOCAGE CTM MOCAGE is a chemical transport model of Météo-France covers both troposphere and stratosphere with gases and aerosols: 47 vertical levels in sigma-pressure hybrid coordinates from the ground up to 5 mbar (resolution from ~40m next to the surface till ~800m in the stratosphere) Transport: semi-lagrangian advection, Louis (1979) diffusion, Bechtold et al. (2001) convection Meteorological forcing from ARPEGE or IFS analyses Embedded (nested) domains with horizontal resolution: Global: from 2 x2 to 0.5 x 0.5 Regional: from 0.5 x 0.5 to 0.1 x 0.1 Over France (or elsewhere): 0.1 x 0.1 to x 0.025

4 4 Aerosols in the CTM MOCAGE 9 types of aerosols available in the model Desert Dust Dynamical emission Sea salt Dynamical emission Black Carbon emission inventories (AEROCOM,GEIA,ACCMIP,GFAS) Organic carbon emission inventories Secondary inorganic aerosols (module ISORROPIA) Sulphates Nitrates Amonnium Pollen (3 types) Volcanic ash aerosols

5 5 Aerosols in the CTM MOCAGE Sectional representation of aerosols (6 bins) Size bins are the same for all species to enable transfer of mass between species and bins Bins size range [m] Pollen species and volcanic ash aerosols have a variable size bins For volcanic ash, the model bins cover only a fine-ash component that can be transported on a long distance

6 6 Volcanic ash in the CTM MOCAGE MOCAGE (MOCAGE-Accident) is also operational fast-response dispersion model To model a plume it is necessary to know a source term : Height of the eruption column Mass eruption rate Particle size distribution Duration of eruption ( F(t) ) Due to complex processes and difficult-to-make observations, different parameterizations are developed Connection: plume height - mass eruption rate (Mastins et al. (2009.)) H = 2.0 V0.241 But they do not answer on many questions they do not reflect complexity of processes (physics, meteorology) uncertainty of data on which are based unconsidered parameters, like size distribution (initial, final)

7 7 Volcanic aerosols in the CTM MOCAGE FPLUME model (Folch et. al. 2015) is introduced in MOCAGE in order to improve the representation of the volcanic plumes in the model steady-state 1-D model with an averaged eruption column based on the buoyant plume theory takes into account the effects of meteorological conditions and of important physical processes like wet aggregation, air and particle entrainment, water phase change, sedimentation, etc. Differences in the initialized volcanic plume between the current module in MOCAGE-Accident and FPLUME: The current algorithm : - estimates the erupted mass from the observed plume height via the Mastins et al. (2009) empirical relation - presumed the uniform vertical distribution - presumed the final bin size distribution - takes into account the wind influence on the plume shape - initialized in a cube 4x4 to avoid too strong gradients FPLUME : - calculates the erupted mass from the observed plume height and inital size distribution by solving the governing equations - calculates the plume vertical distribution - calculates the height dependent size distribution - takes into account the wind influence on the plume shape and height, as well as the influence of important physical processes

8 8 Volcanic aerosols in the CTM MOCAGE FPLUME model (Folch et. al. 2015) is introduced in MOCAGE in order to improve the representation of the volcanic plumes in the model steady-state 1-D model with an averaged eruption column based on the buoyant plume theory takes into account the effects of meteorological conditions and of important physical processes like wet aggregation, air and particle entrainment, water phase change, sedimentation, etc. Differences in the initialized volcanic plume between the current module in MOCAGE-Accident and FPLUME: FPLUME : - calculates the erupted mass from the observed plume height and inital size distribution by solving the governing equations - calculates the plume vertical distribution - calculated the height dependent size distribution - takes into account the wind influence on the plume shape and height, as well as the influence of important physical processes Marti et al. 2016

9 9 Volcanic plume rise model in MOCAGE The plume profile figure shows the uniform vertical distribution in the current module profile The FPLUME profile has initialized a significant mass only from the neutral buoyancy level till the plume top The Mastins et al. (2009) relation initializes significantly more particles compared to the plume rise model Only a part of the mass will be longdistance transported

10 10 Volcanic plume rise model in MOCAGE Vertical slices of the plume at the vent longitude Plume in AOD

11 11 Volcanic plume rise model in MOCAGE The bin size distribution in the current module is an empirical distribution that tries to reflect also the distribution transformation in the plume (losing of large particles in the initial distribution due to sedimentation) and it is estimated as an unimodal distribution. The FPLUME evolved/transformed the initial size distribution (at the vent) into a bimodal final distribution (in the figure the height dependent distribution is averaged). The size distribution transformation in the model particularly depends on sedimentation and wet aggregation.

12 12 Volcanic plume rise model in MOCAGE Size distribution in mainly controlled by style of eruption and physical processes inside the plume Wet aggregation occurs in presence of ice and liquid water and it can have a very strong effect on transport and sedimentation of ash particles. It is still a major source of uncertainty, but already detailed physical schemes do exist (Costa et al. 2010) It can significantly promote fall-out of particles and change the size distribution as shown. It can alter modes of the initial distribution Sedimentation of particles is twice stronger with aggregation in our test case

13 13 Aerosol data assimilation in MOCAGE Variational data assimilation system used for the assimilation of gases and aerosols in MOCAGE Assimilation of aerosols in the system : Aerosol optical depth Lidar profiles The control variable is a 3D total concentrations Mass fractions between species/bins considered constant when applying the increment

14 14 AOD assimilation of a volcanic plume Background Analysis Assimilated observations Assimilation efficient in correcting the extent and intensity of the plume Important to have modelled aerosols at the observation locations in the model

15 15 Lidar assimilation of a volcanic plume CALIOP inverted backscattering coefficient Background Analysis CALIOP observations and assimilation of volcanic ash plume Assimilation efficient in correcting the extent and intensity of the plume

16 16 EUNADICS-AV project This work is done in the framework of the EUNADICS-AV project The goal of the project is to develop the data platform that will make the connection between observations and the information availability for airborne hazards Develop an observation infrastructure to combine, harmonize or develop existing and tailored (groundbased and satellite) products Integrate this data into state-of-the-art modelling and data assimilation systems (source inversion, initial state correction, improved forecasts) Integrate model outputs into a system for the consistent data analysis Poster A27 : M.Plu et al. - A multi-model system to estimate volcanic, aerosols and nuclear hazards to aviation (EUNADICS-AV) Poster B17 : G. Wotawa et al. - European Natural Disaster Coordination and Information System for Aviation (EUNADICS-AV)

17 17 Further work and developments In the framework of the EUNADICS-AV project, we continue to study different past real test cases in MOCAGE (Etna, Grimsvotn eruptions ) using the source terms developed in the project Besides the improvements of the volcanic plume characterization in the model MOCAGE itself (the FPLUME volcanic plume model), we showed that a significant improvements of the characterization can come from the data assimilation. Further assessment of the data assimilation added value using different available observations Aerosol optical depth and ash total column from satellites Lidar and celiometer profile observations from the ground and satellites Assimilation of SO2 products can give additional information Total column observations Averaging kernel profiles/partial columns The final goal is to develop an assimilation demonstrator system that will show the capacity of the model and the data assimilation algorithm for the fast-response alerting and predicting of dangerous situations for the aviation using the near real-time observations.

18 18 Conclusion In the framework of the EUNADICS-AV project, we made modelling and assimilation efforts to improve results of the volcanic plume predictions Implementation of the plume rise model FPLUME in the system Assimilation of AOD and lidar data Our results confirm the significant uncertainties related to ash predictions We show that the combined approach of modelling and data assimilation is a necessary strategy in order to adequately improve the forecasts of hazards that can impact the aviation safety. Our goal is to develop an assimilation demonstrator system that will show the capacity of the model and the data assimilation for predicting hazardous situations for aviation