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Project overview

AIRSENSE’s main objective is to enhance the understanding of aerosol and aerosol-cloud interactions. This activity is part of Atmosphere Science Cluster of ESA’s EO Science for Society programme, an element of the ESA FutureEO programme, which aims at boosting Europe’s excellence in EO science and its applications.

One of the goals of this programme is to establish a strong coordinated scientific effort in Europe on Aerosol and Aerosol/Cloud interaction research by promoting a cooperation between activities launched by ESA and the European Commission (EC), in particular with CLEANCLOUD and CERTAINTY projects that were selected under the EC Horizon Europe Call “Improved knowledge in cloud-aerosol interaction” (HORIZON-CL5-2023-D1-01-04).

AIRSENSE objectives

  1. Support algorithms development for multi-mission approach promoting synergies between different space-borne instruments to compensate for individual weaknesses allowing the creation of long Aerosol Optical properties (e.g., AOD, AE) time series (i.e., Aerosol_CCI) combining mid resolution satellite such as Sentinel5-p and CO2M with high resolution sensors such as PRISMA and Sentinel-3.
  2. Explore the capability of new aerosol and cloud products from existing (e.g. POLDER) or upcoming (CO2M, PACE, MAIA) Multi Angle Polarimeters (MAP) to infer aerosol characterization and absorption properties developing products such as Angstrom Exponent (AE), Single Scattering Albedo (SSA), Absorbing Aerosol Optical Depth (AAOD) and Fine mode Aerosol Optical Depth (AODF) but also Cloud Condensation Nuclei (CCN) and their role in climate and radiative forcing of the Earth system.
  3. Maximize the scientific impact of EarthCARE (in combination with additional EO missions and ground observations) in terms of novel observations and enhance scientific understanding of cloud, aerosol properties and their interactions: e.g., Long-term assessments in combination with Aeolus and CALIPSO data; aerosol-cloud interactions from the synergy between space- and ground-based instruments, study of precipitation initiation processes, characterisation of convection with synergistic GEO and LEO satellite observations, light precipitation and low-level oceanic clouds, global estimates of hydrometeors sedimentation rates, etc.
  4. Study the effects of cloud screening and aerosol retrievals in partly cloudy scenes, develop and improve the capabilities to detect aerosol above clouds and over challenging scenarios such as snow, ice shelves and in low illumination conditions such as the Arctic.
  5. Study cloud height, aerosol-cloud interactions and chemistry to understand the processes that can lead to cloud formation and to infer radiative properties of different cloud and aerosol types.
  6. Improve the quantification of the impact of 3D cloud shape and cloud shadow on cloud retrievals and for the impact of 3D cloud effects and apply this to aerosol retrievals close to clouds edges.
  7. Investigate the aerosol influence on the hydrological cycle fostering the use of aerosols products in combination with water vapour and water vapour isotopologues satellite observations. 
  8. Investigate aerosol and cloud observations from Aeolus and extend this into the use of ATLID on EarthCARE with respect to humidity-growth effects in different areas of the world and for different aerosol types by comparing them with ground-based measurements during nearby overpasses; Make use of multiwavelength polarization Raman lidars that comprise also water-vapour channels are best suited for the detection of changes in scattering properties at high relative humidity.
  9. Improve the capabilities to detect stratospheric aerosol with a classification scheme allowing their separation by sources. Build on existing work and enhance the generation of stratospheric aerosol CDRs
  10. Advance the retrieval of aerosol vertical profiles fostering the simultaneous use of active and passive satellite instruments considering lessons learnt from Aeolus, but mainly novel EarthCARE products and validation activities
  11. Support (coordinated) activities on the radiative forcing due to aerosol-cloud interactions and the anthropogenic contribution on it considering lessons learnt from Aeolus and the future availability of EarthCARE mission products that will provide vertical information about aerosol particles and clouds (e.g., shape, size, type, amount).
  12. Support (coordinated) activities on the effect of climate and air quality on cloud properties, relevance for extreme events such as heavy rainfall, hailstorm, etc.
  13. Support (coordinated) activities to quantify the improvement of the numerical weather predictions (NWP), Earth System Models (ESM), and for the understanding of atmospheric dynamics and its interaction with the water cycle related to the development of novel aerosol products.
  14. Capitalise on novel EO-based capabilities, in particular EarthCARE observations, to advance our understanding and characterisation, including uncertainty reduction, of radiative forcing due to aerosol-cloud interactions and the anthropogenic contribution on it considering lessons learnt from Aeolus and the future availability of EarthCARE mission products that will provide vertical information about aerosol particles and clouds.
  15. Capitalise on novel EO-based capabilities, in particular EarthCARE observations, to advance understanding of atmospheric dynamics and its interaction with the water cycle related to the development of novel aerosol products and potentially numerical weather predictions (NWP).

Figure 1: Illustration of the AIRSENSE project approach


AIRSENSE sensors


Instrument name

 Spectral coveragePolarization capability ResolutionSwath/


Polar orbiting passive instruments
Sentinel-3A,-3B/ OLCIVIS, NIRno~300 m~1270 km2016-present
S5p/TROPOMIUV, VIS, NIR, SWIR (hyperspectral)no~7×3.5 km~2600 km2017-present


no~20 mTarget selected2019-present
Sentinel-2A,-2BVIS, NIR, SWIRno~10 m290 km2015-present
POLDERVIS, NIRMAP~6 km1600 km2005–2013
SPEXone/PACE, HARP-2/PACEUV, VIS, NIRMAP~ 5.2 km~100 km – 2200 km2024-
GAPMAPVIS, NIRMAP~ 1-6 kmTarget selected2023-present


VIS, NIR, SWIR, TIRno~500 m~150 km2024-
PACE/OCIVIS, NIR, SWIRno~1 km2700 km2024-
Geostationary passive instruments

(10 min)

Full disk, 140.7°E2014-present
MTG-I/FCIVIS, NIR, SWIRno500 m-2 km

(10 min)

Full disk,


Polar-orbiting active instruments
EarthCARE/ATLIDUV (HSRL)depolarisation103 m


Aeolus/ALADINUV (HSRL)no500-2000 m


CALIPSO/CALIOPVIS, IRdepolarisation60m


Ground-based instruments and networks
PollyNET/ACTRIS/EARLINETUV,VIS,IRdepolarisation>30 m




AERONETUV,VIS,NIR,SWIRMAPevery 15 minWorldwide1993-present
Field campaigns
Variety of available and future field campaigns*UV, VIS, NIR, SWIRMAP, depolarisation>30 m


<60m MAP

Target selectedTarget selected



Amiridis, V. et al.: LIVAS: a 3-D multi-wavelength aerosol/cloud database based on CALIPSO and EARLINET, Atmos. Chem. Phys., 15, 7127–7153,, 2015.
Chen, C. et al.: Properties of aerosol and surface derived from OLCI/Sentinel-3A using GRASP approach: Retrieval development and preliminary validation, Remote Sens. Environ., 280, 113142,, 2022a.
Chen, C. et al.: Multi-angular polarimetric remote sensing to pinpoint global aerosol absorption and direct radiative forcing. Nat. Commun., 13, 7459,, 2022b.
Chen, C. et al.: Aerosol and Surface Retrieval from S5P/TROPOMI with GRASP Algorithm. Part II: Global Validation and Intercomparison, Remote Sens. Environ., submitted, 2023.
Dubovik, O. et al.: Synergy of PARASOL and CALIOP observations using GRASP algorithm for enhanced aerosol characterisation. In AGU Fall Meeting Abstracts (Vol. 2019, pp. A23B-05), 2019.
Dubovik, O. et al.: A comprehensive description of multi-term LSM for applying multiple a priori constraints in problems of atmospheric remote sensing: GRASP algorithm, concept, and applications, Front. Remote Sens., 2, 23,, 2021a.
Dubovik, O. et al.: Grand challenges in satellite remote sensing, Front. Remote Sens., 2, 619818,, 2021b.
Hasekamp, O. P. et al.: Aerosol measurements by SPEXone on the NASA PACE mission: expected retrieval capabilities, J. Quant. Spectrosc. Radiat. Transf., 227, 170–184,, 2019.
Litvinov, P. et al.: New Possibilities For Air Quality Monitoring From Space-Borne Remote Sensing: Application Of GRASP Algorithm To S5p/TROPOMI and PRISMA Measurements, ATMOS-2021, 2021.
Litvinov, P. et al.: Surface Validation Dataset in Worldwide Locations Based on the Synergetic Retrieval from Satellite and Ground Based Measurements, AGU Fall Meeting 2022, Chicago, 12-16 December 2022.
Litvinov, P. et al: Multi-instrument synergetic retrieval for aerosol/surface characterization and validation with GRASP algorithm. APOLO, May, 2023a.
Lopatin, A. et al.: Synergy processing of diverse ground-based remote sensing and in situ data using the GRASP algorithm: applications to radiometer, lidar and radiosonde observations, Atmos. Meas. Tech., 14, 2575–2614,, 2021.
Wandinger, U., et al.: HETEAC – the Hybrid End-To-End Aerosol Classification model for EarthCARE, Atmos. Meas. Tech., 16, 2485–2510,, 2023a.
Wandinger, U. et al.: Cloud top heights and aerosol layer properties from EarthCARE lidar observations: the A-CTH and A-ALD products, EGUsphere [preprint],, 2023.





Prime contractor
  • KNMI (NL)
  • Leibniz-Institute for Tropospheric (DE)
  • Netherlands Institute for Space Research (NWO-I) (NL)