Evaluation and development of regional air quality modelling and data assimilation aspects - Highlights from CAMS-61
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Evaluation and development of
regional air quality modelling and
data assimilation aspects -
Highlights from CAMS-61
Renske Timmermans – TNO, on behalf of CAMS 61 team
Atmosphere Monitoring
CAMS general assembly 8-10 June 2021
CAMS_61 project (January 2020 – June 2021)
Atmosphere
Monitoring
Improve the quality of CAMS regional air quality service
Through: provision of development plans, guidelines, working
examples and tools for the continuous upgrade of the service. It
includes
(i) a in depth assessment of the CAMS regional forecasts and a
prioritized list of proposed model developments
(ii) best practices for coupling forecasts to analyses, and
(iii) model-agnostic tools for the data assimilation of Sentinel-4 and
5p observations.
+ efforts from regional air
quality modeling teams
In-depth assessment of the CAMS Regional Systems
Atmosphere
Monitoring
WP 6110 In-depth assessment of the CAMS Regional Systems to identify future
development needs (lead: Met Norway)
• Phase 1: Evaluation of the operational CAMS regional
forecast data (2018-2019)
- PM10, PM2.5, NO2, O3
- Where do all models go wrong? Where do we see large
spread or outliers?
- Using screened EEA AQ e-Reporting/EBAS/WMO-GAW
NRT observational data (GHOST)
• Phase 2: Diagnostic evaluation based on dedicated
model runs (model re-runs 2018)
• Speciated PM, Deposition, PBL, meteorology
• Phase 3: Sensitivity studies
• Role of boundary conditions versus inner domain
production of dust and sea salt
• Sensitivity to BVOC emissions
• Sensitivity to BLH
AEROCOM interface for evaluation in all three phases
Atmosphere
Monitoring
• https://aerocom-evaluation.met.no/
Some results….
Atmosphere Evaluation of O3 performance around Mediterranean
Impact of boundary conditions and within-
Monitoring
domain production for dust and sea salt
Model A
P95
With bc
Without bc
obs
Analysis of temporal origin of ozone error
Model B MSE O3 summer MSE O3 winter
obs
Seasonal
Synoptic
Diurnal
ABCDEFGHI ABCDEFGHI models
More detailed presentations on the in-depth evaluation
Atmosphere
Monitoring
Wednesday afternoon during the parallel session in room
B: “Chemistry/aerosol modelling: what are the next key challenges
(regionalglobal)?”
– Hilde Fagerli (MetNorway) -'Evaluation of PM and its
chemical components modelled by regional models in
CAMS'
– Dene Bowdalo (BSC) - “An investigation of the
Mediterranean surface ozone bias in CAMS regional
models”
Work package 6120 - Coupling of regional forecasts and analyses (lead: FMI)
Atmosphere
Monitoring • Analysis of existing material on analysis usage for air quality models initialization
• Multi-model (3 models) assessment of efficiency of analysis-based initialization +
Analysis of duration of forecast improvement for different assimilation strategies
- Testruns with forecast initiated by the 00h, 06h, 12h and 18h analysis, for different species separately and
together, for different seasons
O3 bias summer
The benefits of initial state assimilation disappeares within a few hours (short lived
species) up to 24h for longer lived species in winter
Longer lasting adjustment in some cases also leads to problems when the model errors show substantial
diurnal and day-to day variation
Work package 6120 - Coupling of regional forecasts and analyses (lead: FMI)
Atmosphere
Monitoring • Potential of data assimilation adjusted emissions (EnKF), SILAM and LOTOS-EUROS
Initial state data assimilation Forecast with DA – adjusted emissions
PM2.5 bias
SILAM
Can these updates be used in other models without
active data assimilation methods?
How does this compare to bias correction methods?
NO2 bias How does this compare to improvements from model
LOTOS-EUROS (input) updates?
Work package 6120 - Coupling of regional forecasts and analyses (lead: FMI)
Atmosphere
Monitoring
Wednesday afternoon parallel session in Room A:
“Observations based emissions/fluxes, including upcoming satellite
missions”
Presentation Mikhail Sofiev: Recommendations for estimation of
emissions parameters and use of Sentinel-4
WP6130 Towards assimilation Sentinel 4
Atmosphere
Monitoring WP 6130 Towards assimilation of observations from geostationary
satellite sensors (Sentinel-4) to constrain concentrations and
emissions of main pollutants (lead: TNO)
• CSO tool - Generic observation operator for satellite data
- Pre-processor satellite data (download, selection, inspection and
conversion)
- Observation operator for simulating S4/S5p(or other) retrievals
from model state
- Support for "local airmass correction" in NO2; replace air mass
factors by AMF from own local model
- GitLab repository to distribute codesWP6130 description
Atmosphere
Monitoring WP 6130 Towards assimilation of observations from geostationary
satellite sensors (Sentinel-4) to constrain concentrations and emissions
of main pollutants (lead: TNO)
• Experiment with synthetic S4 data
• Tool tested with different assimilation systems -Synthetic Sentinel 4 data set produced with
for simulation of retrievals but also for CSO tool in MONARCH model and machine
assimilation of Sentinel-5p observations learning algorithm to generate retrieval
- Generic observation operator tested on S5p data errors, kernels, …
(NO2, SO2, HCHO) by 3 models trained with S5p data
- Set of recommendations for other models (e.g. on
filtering of the S5p data) If you would liketo get the
- tool andCSO tool with synthetic S4 data
Testing
test it, please send us an email
ongoing
NO2 retrieval error S5p
10 day average NO2 in EMEP model product, versus prediction
using machine learning
algorithmWP6130 description
Atmosphere
Monitoring WP 6130 Towards assimilation of observations from geostationary
satellite sensors (Sentinel-4) to constrain concentrations and
emissions of main pollutants (lead: TNO)
• Research and Development plan for emission estimates
- Focus on potential of using observations from geostationary satellites (specifically Sentinel-4
data) for emission parameter estimation as part of the data assimilation process.
Wednesday afternoon parallel session in Room A:
“Observations based emissions/fluxes, including upcoming satellite missions”
Presentation by Mikhail Sofiev:
Observation based emissions – relevant work and outcomes from CAMS-61Thank you for your attention
Questions? comments?
Atmosphere Monitoring
renske.timmermans@tno.nlYou can also read
