Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern US – Part 1: An overview
Authors: Huang Min , Crawford James H. , DiGangi Joshua P. , Carmichael Gregory R. , Bowman Kevin W. , Kumar Sujay V. , Zhan Xiwu 
Published: July 2021

Abstract. This study evaluates the impact of satellite soil moisture (SM)
data assimilation (DA) on regional weather and ozone (O3) modeling over
the southeastern US during the summer. Satellite SM data are assimilated
into the Noah land surface model using an ensemble Kalman filter approach
within National Aeronautics and Space Administration's Land Information
System framework, which is semicoupled with the Weather Research and
Forecasting model with online Chemistry (WRF-Chem; standard version
3.9.1.1). The DA impacts on the model performance of SM, weather states, and
energy fluxes show strong spatiotemporal variability. Dense vegetation and
water use from human activities unaccounted for in the modeling system are
among the factors impacting the effectiveness of the DA. The daytime surface
O3 responses to the DA can largely be explained by the
temperature-driven changes in biogenic emissions of volatile organic
compounds and soil nitric oxide, chemical reaction rates, and dry
deposition velocities. On a near-biweekly timescale, the DA modified the
mean daytime and daily maximum 8 h average surface O3 by up to 2–3 ppbv, with the maximum impacts occurring in areas where daytime surface air temperature most strongly (i.e., by ∼2 K) responded to the DA. The DA impacted WRF-Chem upper tropospheric O3 (e.g., for its daytime-mean, by up to 1–1.5 ppbv) partially via altering the transport of O3 and its precursors from other places as well as in situ chemical
production of O3 from lightning and other emissions. Case studies
during airborne field campaigns suggest that the DA improved the model
treatment of convective transport and/or lightning production. In the cases
that the DA improved the modeled SM, weather fields, and some O3-related
processes, its influences on the model's O3 performance at various
altitudes are not always as desirable. This is in part due to the
uncertainty in the model's key chemical inputs, such as anthropogenic
emissions, and the model representation of stratosphere–troposphere
exchanges. This can also be attributable to shortcomings in model
parameterizations (e.g., chemical mechanism, natural emission, photolysis
and deposition schemes), including those related to representing water
availability impacts. This study also shows that the WRF-Chem upper
tropospheric O3 response to the DA has comparable magnitudes with its response to the estimated US anthropogenic emission changes within 2 years. As reductions in anthropogenic emissions in North America would
benefit the mitigation of O3 pollution in its downwind regions, this
analysis highlights the important role of SM in quantifying air pollutants'
source–receptor relationships between the US and its downwind areas. It also
emphasizes that using up-to-date anthropogenic emissions is necessary for
accurately assessing the DA impacts on the model performance of O3 and other pollutants over a broad region. This work will be followed by a
Noah-Multiparameterization (with dynamic vegetation)-based study over the
southeastern US, in which selected processes including photosynthesis and
O3 dry deposition will be the foci.