1. Report on “what are the emerging research needs for CAMS”
Rapporteur: Anna Agusti-Panareda (ECMWF)
Attendees from regional modelling, climate forcing, anthropogenic emissions, global modelling, flux inversions, validation

2. Outline Main development needs for CAMS
Interaction with international model inter-comparison frameworks
Interaction with science community

3. 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).

4. 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.

5. 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)

6. 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 .

7. 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

8. 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.

9. 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.

10. 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!