G2.07

G2.07   Need for assimilation experiments using lidar measurements

Gap detailed description

Uncertainties  associated  with  aerosol  emissions,  both  in  terms  of  their  intensity and distribution pattern, atmospheric processes, and optical properties, represent a significant part of the uncertainty associated with the quantification of the impact of aerosols on climate and air quality in regional and global models. Data assimilation techniques are implemented to decrease these uncertainties, constraining models with available information from observations. Data assimilation is possible with horizontally sparse vertically dense data. In particular, lidar data can be effectively assimilated to greatly improve model skills.

The use of ground-based lidar data allows us to anchor the bias correction for satellite lidar data using a variational bias correction scheme, in line with the growing interest by the global NWP community in using high-accuracy data from ground-based networks to constrain satellite data biases.

Aerosol lidar data can also be used to constrain uncertain model processes in global aerosol-climate models. Satellite-borne lidar data can be effectively assimilated to improve model skills but, at the current stage, aerosol lidar data assimilation experiments are mainly limited to the assimilation of attenuated backscatter, which is a non-quantitative optical property of aerosol. Ground based lidar networks can instead provide quantitative measurements of aerosol backscatter and extinction coefficients. However, a limited number of aerosol lidar data assimilation experiments have been performed, preventing us from assessing the effective impact of assimilating continuous satellite lidar data and wether the current state of the lidar technology fulfils the modellers needs.

Activities within GAIA-CLIM related to this gap

GAIA-CLIM has no specific activities to help addressing this gap.

Gap remedy(s)

Remedy #1

Specific remedy proposed

ACTRIS-2 activities (ACTRIS-2 WP12) will develop a new solution for lidar data assimilation. In particular, the available lidar Near-Real time (NRT) data will be used for routine evaluation of operational models, while quality-checked (QC) and added-value (higher level data) products generated within ACTRIS networking activities will be used for the retrospective assessments of model simulations (reanalysis/reforecasts). The potential of ground-based measurements of ACTRIS-2 aerosol parameters for improvements in the regional prediction of aerosol distributions will also be explored through pilot studies addressing extreme events of public relevance, like volcanic eruptions, mineral dust storms and biomass burning events. Building on the growing interest from the global NWP community in using high accuracy data from ground-based networks to constrain satellite data biases, ACTRIS-2 will also test the use of ground-based lidar data to anchor the bias correction for satellite lidar data, using a variational bias correction scheme. The activity will overlap with the current challenges like those related to the observation density, the observation biases, and the need of models to be able to capture realistic correlations in the vertical for global forecasts.

Measurable outcome of success

Lidar data used in data assimilation techniques for appropriate model-based activities will provide a measurable outcome of success as will a few deliverables within the ACTRIS-2 project:

• D13.4    Initial report on assimilation activities expected on March 2017.
• D13.5    Report on value of measurements in the reduction in global       models expected on April 2018.
• D13.7 Final report on combined measurement/model activities expected on April 2019.

Achievable outcomes

Technological/organizational viability: High.  The current infrastructure already used by the ACTRIS-2 partners to assimilate CALIPSO lidar data allows for the possibility to extend their OSSE to the assimilation of other optical properties using ground-based lidar data (e.g. EARLINET). This excludes any technological challenge to remedying the reported gap.

Indicative cost estimate: low (<1 million).  Costs for future operational aerosol lidar data assimilation cannot be estimated at the current stage.

Relevance

The described remedy via the ACTRIS-2 project shows a promising perspective to start addressing these gaps, and to foster further long-term project and data assimilation experiments, also given the upcoming satellite missions operating a lidar technique on-board(i.e. ADM-Aeolus, EarthCARE).

Timebound

ACTRIS-2 deliverables relevant to this gap are expected within the period March 2017 to April 2019. This activity should continue over subsequent years with an effort that will be quantified by the Met services.

Gap risks to non-resolution