Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 3
Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1821-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1821-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 3
Development and technical paper
 | 
01 Apr 2021
Development and technical paper |  | 01 Apr 2021

Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0

Dana L. McGuffin, Yuanlong Huang, Richard C. Flagan, Tuukka Petäjä, B. Erik Ydstie, and Peter J. Adams

Supplement

https://doi.org/10.5194/gmd-14-1821-2021-supplement

Data sets

Finland - Hyytiälä (FI0050R) - dmps - particle_number_size_distribution - aerosol P. Aalto and M. Kulmala http://ebas.nilu.no/DataSets.aspx?stations=FI0050R&InstrumentTypes=dmps&components=particle_number_size_distribution&fromDate=1970-01-01&toDate=2021-12-31

Germany - Melpitz (DE0044R) - dmps - particle_number_size_distribution - aerosol A. Sonntag, T. Tuch, B. Wehner, and A. Wiedensohler http://ebas.nilu.no/DataSets.aspx?stations=DE0044R&InstrumentTypes=dmps&components=particle_number_size_distribution&fromDate=2006-01-01&toDate=2007-12-31

MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4 (M2T1NXSLV) Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/VJAFPLI1CSIV

MERRA-2 tavg3_3d_asm_Nv: 3d,3-Hourly,Time-Averaged,Model-Level,Assimilation,Assimilated Meteorological Fields V5.12.4 (M2T3NVASM) Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/SUOQESM06LPK

Interactive computing environment

TOMAS-InverseModel: First release used in GMD paper D. L. McGuffin https://doi.org/10.5281/ZENODO.4010877

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Short summary
Atmospheric particle formation, emissions, and growth process rates are significant sources of uncertainty in predicting climate change. We aim to reduce that uncertainty by using measurements from several ground-based sites across Europe. We developed an estimation technique to adapt the governing process rates so model–measurement bias decays. The estimation framework developed has potential to improve model predictions while providing insight into the underlying atmospheric particle physics.
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