Copernicus

Atmosphere

Monitoring

Service

CAMS General Assembly, Athens, 14-16 June 2016

Rapporteur: Anna Agusti-Panareda (ECMWF)

Attendees from regional modelling, climate forcing,

anthropogenic emissions, global modelling, flux

inversions, validation

Report on “what are the

emerging research needs for

CAMS”

2

Outline

• Main development needs for CAMS

• Interaction with international model inter-comparison frameworks

• Interaction with science community

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CAMS developments

• Regional data assimilation aspects:

• Explore the use of hybrid methods without the need for linear and adjoin models.

• Assimilation of satellite data (Sentinel 4). Work needs to be done to get the models

to a state where they could assimilate the satellite data.

• Every model has different approach to do data assimilation increasing the effort for

further development. One of the problems is how to spread the increment in the

vertical using vertically integrated information from observations.

• Assimilation of in situ OBS: short-lived impact on the forecast (1-day only) which is

a problem if the in situ and analysis data is only available 1-day behind real time.

• Assimilating emissions to ensure longer lived impact of observations in the

forecast.

• Improvements in the way the models are combined in ensemble (now using median).

• Secondary organic aerosol formation affecting many models (low bias in PM forecast).

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CAMS developments

• Components to be considered in the assimilation of emissions: temporal profiles of

emissions. Example: adjusting emissions of NOx during rush hours (recalibrating

diurnal profile). It is very challenging because the parameters or temporal profiles vary

from country to country.

• How do we ensure consistency when we adapt emissions of certain species with

observations? The co-emitting species have to be considered by sector.

• Can we learn from ensemble spread ? No comprehensive analysis has been done on

where it comes from.

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Model development and validation

• A process oriented evaluation is important for model development. Example of why

scores are not enough: you can have a good agreement with observations for the wrong

reasons (i.e. due to error cancellation).

• Cross-checking features common to different compounds and separating processes

(transport, chemistry, deposition, etc)

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Identification of priorities to guide development

• This has been addressed to some extent in the ITTs (e.g. cross-validation of

regional and global models).

• We need to get guidance from users: e.g. what is preventing users from using

a product (e.g. PM bias, solar radiation output).

• Interaction of validation and users: to identify key inaccuracies, flagging of

products.

• It is important to have feedback from user requirement database to the

service providers in CAMS .

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How to estimate uncertainties in the CAMS products

• Estimating the different error sources: representation error using high

resolution models, observation error, analysis error using EDA.

• This can only be done in a comprehensive way using case studies when there

are few stations providing observations in NRT.

• Example: Assessment in errors associated with PM10 , PM2.5 in regional

models. Information of shortcoming of different models focus on specific

events

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International collaborations

• Use of open-IFS : start with a showcase with a specific partner.

• Collaborations of CAMS with scientific community: AEROCOM, ACME, TransCom,

HTAP, ICAP, WRF-Chem community, climate modelling community.

• There is a value in participating in model inter-comparison. It can be an essential

part of model validation/development and it can also answer specific question on

modelling aspects that are highly uncertain, increase visibility and credibility of the

models used in CAMS.

• CAMS focuses on operational production, whereas other scientific communities

focus on past simulations. Participating in such model inter-comparisons requires

a large investment which is not possible without extra funding. We need to find a

funding mechanism to support model inter-comparison activities in CAMS.

• Possibility of engaging other models to use similar configuration to CAMS and

using data assimilation.

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Collaboration with academic community

• Collaboration with universities. For example, PhD students making use of CAMS

models/products in their research. How can we interest universities? Impact

studies with CAMS users will appeal to universities and research councils.

Academic partnerships between operational centres (Met Office) and research

institutions only requires letters of support and small financial contribution.

• Copernicus-related research within Horizon-2020 is very limited. Should we

consider a discussion with member states about national funding opportunities

related to Copernicus longer-term developments?

• Show added value of CAMS collaboration by providing useful information for

research activities (e.g. running additional simulations, explaining what is already

available). This need to be balanced with operational workload.

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Priorities

• Interface between global and regional, cross-cutting aspects between different projects

• Develop emission adjustment in data assimilation as a way to bias correct model

• Assimilation of satellite data in regional systems. Added value is expected but large effort

required.

• Stratospheric chemistry with constraint from observations beyond ozone (e.g. NO2).

• Speciation of aerosols in data assimilation.

• Regional air quality: improved ensemble processing (beyond taking the median, use

information from tails of distribution) and exploration of post-processing bias corrections.

• Interaction of aerosol with radiative model and clouds in NWP. Improve stratospheric

aerosol modelling and impact of volcanic eruptions.

• Supporting remote sensing community to improve retrieval products (first validation,

monitoring, prior information).

• Dynamic modelling of emissions and natural biogenic emissions (land and ocean):

anthropogenic emissions, isoprene emissions, formation of secondary aerosols.

• Pollen forecasting using sectors to make information more relevant for users.

• Insect forecasting!