Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Estimation of Cloud Condensation Nuclei (CCN) from SPEXone on PACE using a neural network retrieval algorithm: Comparison to AERONET and ATLID/EarthCARE

View through CrossRef
Cloud condensation nuclei (CCN) play a critical role in aerosol–cloud interactions (ACI).  It has been shown that the column number of aerosol particles exceeding a predetermined threshold radius (NCCN) is a suitable CCN proxy. Previously this CCN proxy has been estimated from PARASOL using Level-2 aerosol microphysical and/or optical property retrievals. With the launch of SPEXone on Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, further improvements in NCCN retrievals can be achieved. In particular, retrieved refractive index can enable estimation of the volume fraction of aerosol-water, facilitating the derivation of dry aerosol size distibution and correspondingly dry CCN. In addition, retrieved aerosol layer height (ALH) can be used to estimate the boundary layer (BL) contribution of NCCN (NCCN(BL)).We developed a deep neural network (NN) algorithm as an extension of  the Remote sensing of Trace gas and Aerosol Products (RemoTAP)-NN framework to directly retrieve dry NCCN, and NCCN(BL) from SPEXone measurements. The algorithm was trained on synthetic SPEXone measurements generated from a three-mode aerosol representation including fine mode, insoluble coarse/dust mode, and soluble coarse mode. Initial validation was performed using  independent synthetic measurements, based on the ECHAM-HAM global aerosol-climate model.For validating NCCN from real SPEXone observations, we use collocated AERONET data, for which both dry and ambient NCCN are computed. On the log base 10 scale, the NN algorithm achieved RMSDs of 0.33 (dry) and 0.21 (ambient) over land, and 0.21 (dry) and 0.20 (ambient) over ocean. The slightly higher RMSD for dry NCCN is attributed to the cases in which the AERONET derived refractive index reaches its upper limit of 1.6. In comparison, CCN proxies derived using the classical RemoTAP algorithm exhibited RMSDs approximately 20% higher. Ongoing work focuses on validating the retrieved fraction of aerosols within the boundary layer using EarthCARE ATLID Level-2 observations.
Title: Estimation of Cloud Condensation Nuclei (CCN) from SPEXone on PACE using a neural network retrieval algorithm: Comparison to AERONET and ATLID/EarthCARE
Description:
Cloud condensation nuclei (CCN) play a critical role in aerosol–cloud interactions (ACI).
 It has been shown that the column number of aerosol particles exceeding a predetermined threshold radius (NCCN) is a suitable CCN proxy.
Previously this CCN proxy has been estimated from PARASOL using Level-2 aerosol microphysical and/or optical property retrievals.
With the launch of SPEXone on Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, further improvements in NCCN retrievals can be achieved.
In particular, retrieved refractive index can enable estimation of the volume fraction of aerosol-water, facilitating the derivation of dry aerosol size distibution and correspondingly dry CCN.
In addition, retrieved aerosol layer height (ALH) can be used to estimate the boundary layer (BL) contribution of NCCN (NCCN(BL)).
We developed a deep neural network (NN) algorithm as an extension of  the Remote sensing of Trace gas and Aerosol Products (RemoTAP)-NN framework to directly retrieve dry NCCN, and NCCN(BL) from SPEXone measurements.
The algorithm was trained on synthetic SPEXone measurements generated from a three-mode aerosol representation including fine mode, insoluble coarse/dust mode, and soluble coarse mode.
Initial validation was performed using  independent synthetic measurements, based on the ECHAM-HAM global aerosol-climate model.
For validating NCCN from real SPEXone observations, we use collocated AERONET data, for which both dry and ambient NCCN are computed.
On the log base 10 scale, the NN algorithm achieved RMSDs of 0.
33 (dry) and 0.
21 (ambient) over land, and 0.
21 (dry) and 0.
20 (ambient) over ocean.
The slightly higher RMSD for dry NCCN is attributed to the cases in which the AERONET derived refractive index reaches its upper limit of 1.
6.
In comparison, CCN proxies derived using the classical RemoTAP algorithm exhibited RMSDs approximately 20% higher.
 Ongoing work focuses on validating the retrieved fraction of aerosols within the boundary layer using EarthCARE ATLID Level-2 observations.

Related Results

ATLID Cloud Climate Product
ATLID Cloud Climate Product
Abstract. Despite significant advances in atmospheric measurements and modeling, clouds response to human-induced climate warming remains the largest source of uncertainty in model...
First Retrievals of Cloud Condensation 1 Nuclei (CCN) Proxies from PACE/SPEXone: Validation and Global Distribution
First Retrievals of Cloud Condensation 1 Nuclei (CCN) Proxies from PACE/SPEXone: Validation and Global Distribution
Cloud condensation nuclei (CCN) are critical for understanding aerosol-cloud interactions (ACI), yet the scarcity of direct CCN measurements limits global characterization. This st...
Evaluation of new polarimetric products
Evaluation of new polarimetric products
The new satellite missions including active sensors (e.g. EarthCare), passive multi-angular polarimeters (e.g. PACE/SPEXone, PACE/HARP-2) and single-viewing instruments (e.g. OLCI)...
Estimation of Cloud Condensation Nuclei (CCN) from SPEXone on PACE using a deep neural network retrieval algorithm 
Estimation of Cloud Condensation Nuclei (CCN) from SPEXone on PACE using a deep neural network retrieval algorithm 
Proper proxies for CCN are vital to provide accurate constraints for Aerosol-Cloud Interactions (ACI) in climate models. An effective proxy for CCN is the column number of aerosol ...
Polarimetric Remote Sensing of atmospheric aerosols: The first year of SPEXone PACE
Polarimetric Remote Sensing of atmospheric aerosols: The first year of SPEXone PACE
On February 8, 2024 the NASA Plankton, Aerosol, Cloud & ocean Ecosystem (PACE) mission has been launched with onboard the SPEXone Multi-Angle Polarimeter. SPEXone is design...
Regime-dependent Impacts of CCN and Cloud Glaciation on Global Lightning Activity 
Regime-dependent Impacts of CCN and Cloud Glaciation on Global Lightning Activity 
Lightning activity is influenced by both aerosols and cloud microphysics, particularly ice formation and charge separation. While aerosols can greatly modify microphysical processe...
Cloud Condensation Nuclei (CCN) and Ice Nucleating Particles (INP) conversion factors based on Thessaloniki AERONET station
Cloud Condensation Nuclei (CCN) and Ice Nucleating Particles (INP) conversion factors based on Thessaloniki AERONET station
Several studies [1,2] have shown the potential of polarization lidar to provide vertical profiles of aerosol parameters from which cloud condensation nuclei (CCN) and ice-nucleatin...
Cloud condensation nuclei over the Southern Ocean: wind dependence and seasonal cycles
Cloud condensation nuclei over the Southern Ocean: wind dependence and seasonal cycles
Abstract. Multi-decadal observations of aerosol microphysical properties from regionally representative sites can be used to challenge regional or global numerical models that simu...

Back to Top