Remedy 1: Extension of the GAIA-CLIM data assimilation approach to aerosol lidars

Primary gap remedy type: 
Proposed remedy description: 

New solutions for assessing and enhancing the value of lidar data assimilation must be developed. This requires efforts in two complementary areas:

  1. Aerosol lidar networks must strongly work on their capability to provide NRT data, through the implementation of automatic processing calculus chains and to adopt shared/common metadata international standards in order to facilitate the data usage and manipulation.
  2. Modellers must develop methodologies to use the available lidar Near-Real time (NRT) data for routine evaluation of operational models or data assimilation, through the development of improved forward operators, while quality-checked (QC) and added-value (higher level data) products must be used for the retrospective assessments of model simulations (reanalysis/reforecast).

Building on the growing interest from the global NWP community in using high accuracy data from ground-based networks to constrain satellite data biases, ground-based lidar data could be used by modellers also to anchor the bias correction for satellite lidar data, using a variational bias correction scheme.

However, further work must be implemented aimed at improving model skill, i.e models are better at predicting horizontal transport than vertical distributions. Formulation of a specified work plan should take into account that:

  • Collaboration with data providers is paramount;
  • NRT data delivery from all lidar satellite missions is important;
  • With respect to other lidar measurements of atmospheric composition, the community is largely ready to use lidar data to improve aerosol predictions;
  • Wind data will also improve atmospheric composition prediction by improving the model wind fields.



Aerosol is one the key factors in the determination of the radiative balance with its direct and indirect effect. An appropriate and successful assimilation within numerical models may strongly improve our climate knowledge as well as the prediction of severe weather events. This value is enhanced by the multitude of data which will be available at global scale – with the advent of upcoming satellite missions with a lidar technique on-board including the ESA missions, ADM-Aeolus and EarthCARE.

Measurable outcome of success: 

A number of initiatives are currently ongoing and their outcome will give us within a few years a quantitative idea of the importance of using lidar measurements in data assimilation

Expected viability for the outcome of success: 
  • High
Scale of work: 
  • Consortium
Time bound to remedy: 
  • Less than 5 years
Indicative cost estimate (investment): 
  • High cost (> 5 million)
Indicative cost estimate (exploitation): 
  • Yes
Potential actors: 
  • National Meteorological Services
  • Academia, individual research institutes