Assimilating Compact Phase Space Retrievals (CPSRs): Joint Assimilation of MOPITT and IASI CO as CPSRs and MODIS AOD as Raw Retrievals with Constrained Emissions

Abstract for oral presentation at the 21th Conference on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface (IOAS-AOLS at the 97th AMS Annual Meeting, January 22-26, 2017, Seattle, WA 

Assimilating Compact Phase Space Retrievals (CPSRs): Joint Assimilation of

MOPITT CO and IASI CO as CPSRs and MODIS AOD as Raw Retrievals

with Constrained Emissions 

Arthur P. Mizzi^*, Xueling Liu⁺, Avelino F. Arellano^%, Jianyu Liang^&, Ronald C. Cohen⁺, Yongshen Chen^&, David P. Edwards^*, and Jeffery Anderson^#

^*Atmospheric Chemistry Division

National Center for Atmospheric Research

Boulder, CO 80307 

⁺Department of Chemistry and

Department of Earth and Planetary Science

University of California

Berkeley, CA 94720-1460 

^%Department of Atmospheric Science

University of Arizona

Tucson, AZ 85721

^&Department of Earth and Atmospheric Science

York University

Toronto, ON, Canada 

^#Institute for Mathematics Applied to Geosciences

National Center for Atmospheric Research

Boulder, CO 80307 

mizzi@ucar.edu

303-497-8987 

Compact phase space retrievals (CPSRs) address the computational challenges associated with assimilating atmospheric composition retrievals by projecting the retrieval into a phase space based on the linearly independent modes of the averaging kernel.  Assimilation results show that CPSRs perform as well or better than raw or quasi-optimal retrievals (reductions of ~60% in root-mean square error (RMSE) and bias when compared to assimilating raw retrievals) at reduced computational cost (reductions of ~35% for MOPITT CO and ~50% for IASI CO).  Those improvements were found when compared against assimilated observations (MOPITT CO) and independent observations (MOZAIC in situ observations and IASI CO retrievals).  CPSRs offer great promise because they can be used to assimilate atmospheric composition retrievals obtained from any optimal estimation method.

Different satellite instruments can observe: (i) different atmospheric constituents, and/or (ii) the same atmospheric constituent with different vertical sensitivities.  Joint assimilation of those observations is necessary to robustly characterize the atmospheric composition.  However, another source of uncertainty is the emissions.  Emissions are reported to have errors ranging up to 100s of percent.  Thus, constraining the emissions with atmospheric composition observations can be an important tool for: (i) characterizing atmospheric composition, and (ii) modeling its evolution.

In this talk, we study those issues in the context of CPSRs by: (i) jointly assimilating MOPITT CO CPSRs, IASI CO CPSRs, and MODIS AOD raw retrievals (RAWRs), and (ii) constraining the associated/related emissions with the state augmentation method.  We show that: (i) joint assimilation of MOPITT and IASI CO RAWRs more closely resembles the assimilation of IASI CO RAWRs (than MOPITT CO RAWRs), (ii) joint assimilation of MOPITT and IASI CO CPSRs more closely resembles the assimilation of MOPITT CO CPSRs (than IASI CO RAWRs), and (iii) constraining the emissions with RAWRs has greater impacts than constraining them with CPSRs.  We suspect that results (i) and (ii) occur because the CPSR transforms impact the observation errors, and result (iii) occurs because the relationship between raw retrievals and the emissions is stronger than that between the phase space retrievals (the mode amplitudes) and the emissions.  We conclude that CPSRs can be: (i) jointly assimilated with CPSRs and RAWRs, and (ii) used to constrain emissions.