This study evaluates the impact of satellite soil moisture (SM)
data assimilation (DA) on regional weather and ozone (
Tropospheric ozone (
Ozone aloft is more conducive to rapid long-range transport to influence
surface air quality in downwind regions (e.g., Zhang et al., 2008; Fiore et
al., 2009; Hemispheric Transport of Air Pollution, HTAP, 2010, and references therein; Huang et al., 2010, 2013, 2017a; Doherty, 2015). In the
upper troposphere–lower stratosphere regions,
On a wide range of spatial and temporal scales, atmospheric weather and composition interact with land surface conditions (e.g., soil and vegetation states, topography, and land use and land cover, LULC), which can be altered by various human activities and/or natural disturbances such as urbanization, deforestation, irrigation, and natural disasters (e.g., Betts et al., 1996; Kelly and Mapes, 2010; Taylor et al., 2012; Collow et al., 2014; Guillod et al., 2015; Tuttle and Salvucci, 2016; Cioni and Hohenegger, 2017; Fast et al., 2019; Schneider et al., 2019). As a key land variable, soil moisture (SM) influences the atmosphere via evapotranspiration, including evaporation from bare soil and plant transpiration. The SM–atmosphere coupling strengths are overall strong over transitional climate zones (i.e., the regions between humid and arid climates) where evapotranspiration is moderately high and constrained by SM (e.g., Koster et al., 2004, 2006; Seneviratne et al., 2010; Dirmeyer, 2011; Miralles et al., 2012; Gevaert et al., 2018). The southeastern US includes large areas of transitional climate zones, whose geographical boundaries vary temporally (e.g., Guo and Dirmeyer, 2013; Dirmeyer et al., 2013). Soil moisture and other land variables are currently measurable from space. It has been shown in a number of scientific and operational applications that satellite SM data assimilation (DA) impacts model skill of atmospheric weather states and energy fluxes (e.g., Mahfouf, 2010; de Rosnay et al., 2013; Santanello et al., 2016; Yin and Zhan, 2018). An effort began recently to evaluate the impacts of satellite SM DA on short-term regional-scale air quality modeling. Based on case studies in East Asia, such effects are shown to vary in space and time, partially dependent on surface properties (e.g., vegetation density and terrain) and synoptic weather patterns. Also, the SM DA impacts on model performance can be complicated by other sources of model error, such as the uncertainty of the models' chemical inputs including emissions and chemical initial and lateral boundary conditions (Huang et al., 2018).
This study extends the work by Huang et al. (2018) to the southeastern US
during intensive field campaign periods in the summer convective season.
Modified from the approach used in Huang et al. (2018), we assimilate
satellite SM into the Noah land surface model (LSM) within National
Aeronautics and Space Administration (NASA)'s Land Information System (LIS),
which is semicoupled with the Weather Research and Forecasting model with
online Chemistry (WRF-Chem). The term “semicoupled” indicates that the SM
DA within LIS influences WRF-Chem's land initial conditions. Atmospheric
states and energy fluxes from the no-DA and DA cases are compared with
surface, aircraft, and satellite observations during selected field campaign
periods. The WRF-Chem results are also compared with the chemical fields of
the Copernicus Atmosphere Monitoring Service (CAMS), which serves as the
chemical initial/lateral boundary condition model of WRF-Chem. Other sources
of errors in WRF-Chem simulated
This study focuses on a summer southeastern US deployment (16–28 August
2016) of the Atmospheric Carbon and Transport (ACT)-America campaign
(
Summary of WRF-Chem simulations conducted in this study.
Acronyms are given as follows. ACT: Atmospheric Carbon and Transport; ESA CCI: European Space Agency
Climate Change Initiative; NEI: National Emission Inventory; SEAC
Version 3.6 of the widely used, four-soil-layer Noah LSM (Chen and Dudhia, 2001) within LIS (Kumar et al., 2006) version 7.1rp8 served as the land component of the modeling/DA system used. An offline Noah simulation was performed within LIS prior to all WRF-Chem simulations for equilibrated land conditions (details in Sect. S1 in the Supplement). Consistent model grids and geographical inputs of the Noah LSM were used in the offline LIS and all WRF-Chem simulations. Specifically, topography, time-varying green vegetation fraction, LULC type, and soil texture type inputs were based on the Shuttle Radar Topography Mission Global Coverage-30 version 2.0, Copernicus Global Land Service, the International Geosphere-Biosphere Programme-modified Moderate Resolution Imaging Spectroradiometer (Fig. 1a–c), and the State Soil Geographic (Fig. S1, upper, in the Supplement; Miller and White, 1998) datasets, respectively.
Successful, valid retrievals of morning-time SM (version 2 of the 9 km
enhanced product, generated using baseline retrieval algorithm) from NASA's
Soil Moisture Active Passive (SMAP; Entekhabi et al., 2010) L-band
polarimetric radiometer were assimilated into Noah within LIS. SMAP provides
global coverage of surface (i.e., the top 5 cm of the soil column) SM within
2–3 d along its morning orbit (
All WRF-Chem cases, except the minus001 case, were started on 13 August
2016. Atmospheric meteorological initial and lateral boundary conditions were
downscaled from the 3-hourly, 32 km North American Regional Reanalysis
(NARR). Consistent with NARR, the WRF-Chem model top was set at 100 hPa,
slightly above the climatological tropopause heights for the study
region and month. The
In all WRF-Chem simulations, key physics options applied include the local
Mellor–Yamada–Nakanishi–Niino planetary boundary layer (PBL) scheme along
with its matching surface layer scheme (Nakanishi and Niino, 2009), the
Rapid Radiative Transfer Model short- and longwave radiation schemes (Iacono et
al., 2008), the Morrison double-moment microphysics, which predicts the mass
and number concentrations of hydrometeor species (Morrison et al., 2009),
and the Grell–Freitas scale-aware cumulus scheme (Grell and Freitas, 2014),
which has also been implemented in the GMAO GEOS-Forward Processing system
(
Daily biomass burning emissions came from the Quick Fire Emissions Dataset
(Darmenov and da Silva, 2015) version 2.5r1, and plume rise with a recent
bug fix (suggested by Ravan Ahmadov, NOAA/ESRL, in August 2019) was applied.
Emissions of biogenic VOCs and soil NO were computed online (i.e., driven by
the WRF meteorology) using the Model of Emissions of Gases and Aerosols from
Nature (MEGAN; Guenther et al., 2006). It has been shown that MEGAN may
overpredict biogenic VOC emissions over the study regions and tends to
underpredict soil NO emissions, especially in high-temperature (i.e.,
Chemical loss via dry deposition (i.e., dry deposition velocity
This paper also briefly discusses in Sect. 3.4 some results from two
WRF-Chem simulations (i.e., “SEACf” and “SEACa” in Table 1) during the
2013 Studies of Emissions and Atmospheric Composition, Clouds and Climate
Coupling by Regional Surveys (SEAC
The model horizontal resolutions of 12 and 25 km were set to be close to the assimilated satellite SM products to minimize the horizontal representation errors. At these resolutions, land surface heterogeneity and fine-scale processes (e.g., cloud formation and turbulent mixing) may not be realistically represented. Cloud-top-height-based lightning emissions and SM–precipitation feedbacks can be highly dependent on convective parameterizations (e.g., Hohenegger et al., 2009; Wong et al., 2013; Taylor et al., 2013). Addressing shortcomings of convective parameterizations in simulations at these scales is still in strong need. Performing convection-permitting simulations with assimilation of downscaled microwave SM or/and high-resolution thermal-infrared-based SM (e.g., 2–8 km from the Geostationary Operational Environmental Satellite) for cloudless conditions should also be experimented on in the future.
During the 2016 ACT-America deployment, the NASA B-200 aircraft took
meteorological and trace gas measurements in the southeastern US from the
surface to
Aircraft (NASA DC-8) in situ measurements of CO,
WRF-Chem results were evaluated by various surface meteorological and
chemical observations. These include the following: (1) SM values observed at
The WRF-Chem precipitation fields were also qualitatively compared with two
precipitation data products: (1) the 4 km, hourly NCEP Stage IV Quantitative
Precipitation Estimates product (Lin and Mitchell, 2005), which is a widely used,
national radar- and rain-gauge-based analysis product mosaicked from 12 River
Forecast Centers over the contiguous US, and of which quality partially depends
on the manual quality control done at the River Forecast Centers; and (2) the
In August 2016, several states in the southern US experienced
moderately to extremely moist conditions according to major drought indexes
such as the Palmer Hydrological Drought Index (Fig. S2, left). These were
largely due to the influences of passing cold fronts and tropical systems
from the Gulf of Mexico (
The anomalies in synoptic patterns and drought conditions in August 2016 and 2013, as well as the day-to-day weather changes, can partially explain
the regional
Studies have shown that the variations in land–atmosphere coupling strength are connected with SM interannual variability and the local spatiotemporal evolution of hydrologic regime (e.g., Guo and Dirmeyer, 2013; Tuttle and Salvucci, 2016). Therefore, over the Southern Great Plain and Atlantic regions, SM–atmosphere coupling strengths in August 2016 and August 2013 may have diverged from the climatology in opposite directions. For example, in August 2016, the overall potential impacts of SM on surface water and energy fluxes and atmospheric states may be higher than normal over the Atlantic regions and below the average in the Southern Great Plain. In August 2013, the land–atmosphere coupling may be stronger than normal and abnormally weak over the Southern Great Plain and the Atlantic regions, respectively.
Land and surface weather states as well as energy fluxes from the WRF-Chem base simulation, together with the SMAP DA impacts on these variables, are illustrated in Fig. 2 (for SM), Figs. 3–4 (for T2, RH, WS, and PBL height, PBLH), Fig. 5 (for precipitation), and Figs. 6, S3, and S4 (for energy fluxes and their partitioning) for the 16–28 August 2016 period.
Period-mean (16–28 August 2016)
Period-mean (16–28 August 2016) WRF-Chem base case daytime
The
Period-mean (16–28 August 2016)
Period-mean (16–28 August 2016) daily evaporative fraction, defined as daily latent heat
The highest daytime (13:00–24:00 UTC, local time
The dry and wet anomalies in the southeastern US based on the modeled SM (Fig. 2a) are shown to be consistent with ground-based SM measurements (e.g., Fig. 2c), as well as weekly (not shown in figures) and monthly drought indexes (e.g., Fig. S2, left). The modeled SM values in various soil layers are near the model-based soil wilting points and field capacities (Fig. S1, middle and lower) over drought-influenced and wetter-than-normal regions, respectively. The WRF-Chem base simulation overall captured the observed patterns of T2, RH, and WS across the domain, with its daytime PBLH spatially correlated with the T2 patterns (Table 2 and Figs. 3a–b; d; i–j; k–l). Referring to the Stage IV and GPM rainfall data, the WRF-Chem base case also reproduced the diurnal cycles of rainfall during the study period fairly well overall, but the rainfall hotspots simulated by the model appear west to those in the Stage IV and GPM products (Fig. 5c). Dirmeyer et al. (2012) found that models' rainfall performance more strongly depended on the distinctive treatment of the model physics than on the model resolution. Our WRF-Chem performance for rainfall diurnal cycle in this region is similar to previous convection-permitting WRF-Chem simulations (e.g., Barth et al., 2012). Additionally, the WRF-Chem predicted mean rainfall rates over low-precipitation regions (e.g., several Atlantic states) are higher than those based on the Stage IV and GPM rainfall products, which tend to overestimate precipitation at the low end (e.g., Nelson et al., 2016; Tan et al., 2016). Such positive model biases for low-precipitation regions have also been reported in Barth et al. (2012).
The SMAP DA impacts on modeled surface meteorological and
Acronyms: AQS: Air Quality System; CASTNET: Clean Air Status and Trends Network; MDA8: daily maximum 8 h average; RMSE: root-mean-square error; SMAP: Soil Moisture Active Passive.
Surface SM at the model initial times (i.e., 00:00 UTC each day) was broadly
reduced by the SMAP DA, except parts of coastal Texas, Ohio, and Florida
(Fig. 2d). Such changes in the modeled SM fields are consistent with the
modeled daytime specific humidity (not shown in figures) and RH responses
(Fig. 3m). They are anti-correlated with the model responses in the
averaged daytime T2 and PBLH fields (Fig. 3e; h) as well as their daily
amplitudes (not shown in figures). The daytime T2 and RH responses to the
SMAP DA are statistically significant in
It is indicated by Fig. 2b and e that, during the study period, the SMAP DA
successfully reduced the discrepancies between SMAP and Noah-calculated
surface SM across the model domain. The modeled surface SM was also
cross-validated with ground-based SM measurements at dozens of SCAN sites,
using the root-mean-square error (RMSE) metric. Figure 2f–g show the
results based on a comparison of the modeled surface SM with
The spatial patterns of evaporative fraction (defined as latent
heat
The WRF-Chem-modeled weather states were also evaluated with ACT-America
aircraft observations at various altitudes. Along the flight paths, the
observed air temperature and water vapor mixing ratios decrease with
altitude, which were captured by WRF-Chem fairly well (Figs. 7a–b; e–f and
8a; c). The modeled air temperature and humidity as well as their responses
to the SMAP DA vary in space and time. In general, these responses are
particularly strong near the surface, where the majority of the samples were
collected. Under stormy weather conditions on 16, 20, and 21 August 2016, the
maximum changes in air temperature and humidity in the free troposphere
exceed 2.3 K and 2 g kg
Vertical profiles of
Evaluation of WRF-Chem results with the B-200 aircraft observations during the ACT-America 2016 campaign:
The changes in the above-discussed meteorological variables (e.g., air
temperature, humidity, WS, PBLH) due to the SMAP DA alter various
atmospheric processes which can have mixed impacts on surface
Period-mean (16–28 August 2016) WRF-Chem base case daytime
biogenic emissions of
Figure 10a–b compare the observed and WRF-Chem base case daytime surface
Period-mean (16–28 August 2016) daytime surface
Box-and-whisker plots of WRF-Chem daytime
Period-mean (16–28 August 2016) daily maximum 8 h average (MDA8) surface
The overall enhanced biogenic emissions of VOCs and soil NO (Figs. 9a–b; e–f and S5, first two rows) belong to the major causes of the changes
in the surface daytime-average and MDA8
The overall accelerated chemical reactions, including those strongly
controlling the lifetime of peroxyacetyl nitrate, are also highly
responsible for the above-mentioned changes in surface daytime-average and
MDA8
The
The SMAP DA improved surface MDA8 at 42 % and 51 % of the model grids
where AQS and/or CASTNET observations are available, respectively. It
increased the domain-wide mean MDA8 RMSEs by 0.057 and 0.007 ppbv, referring to the gridded AQS and CASTNET
The number of model grids with surface MDA8
Acronyms: AQS: Air Quality System; CASTNET: Clean Air Status and Trends Network; MDA8: daily maximum 8 h average.
It is noticed that daytime surface
The SMAP DA impacts on WRF-Chem-modeled chemical fields are also
investigated at a wide range of altitudes. Figure 7i–p compare the observed
and WRF-Chem base case CO and
To help better understand SM controls on upper tropospheric
Period-mean (16–28 August 2016) daytime
Similar to the
To help interpret the SMAP DA impacts on various atmospheric processes such
as vertical transport and lightning associated with convection and other
phenomena, model results from the base and minus001 cases during two
ACT-America flights were compared (Fig. S8). In the afternoon of 20 August
2016, the B-200 flew at
It is also noticed that the daytime
We compared CO,
This study focused on evaluating SMAP SM DA impacts on coupled WRF-Chem weather and air quality modeling over the southeastern US during the ACT-America campaign in August 2016. The impacts of SMAP DA on WRF-Chem-modeled daytime RH as well as evaporative fraction were qualitatively consistent with the changes in the model's initial SM states, which were anti-correlated with the modeled daytime surface T2 and PBLH changes. The DA impacts on the model performance of SM, weather states, and energy fluxes showed strong spatiotemporal variability. Many factors may have impacted the effectiveness of the DA, including missing processes such as water use from human activities (e.g., irrigation), as well as dense vegetation and complex terrain, as also discussed in detail in our previous SMAP DA study. Referring to the gridded NCEP surface observations, the domain-wide mean RMSEs of modeled T2, RH, and WS were changed by the DA by
The SMAP DA impact on WRF-Chem surface daytime-average and MDA8
We showed that at
This study is a critical first step towards using satellite SM products to
help improve the simulated weather and chemistry fields in models that are
widely used for air quality research and forecasting, as well as
policy-relevant assessments. It was demonstrated that, via changing the
model's weather fields that drove its chemistry calculations online, the SM
DA influenced various
The stand-alone LIS is accessible at
The supplement related to this article is available online at:
MH led the design and execution of the study, as well as the writing of the paper. JHC, JPD, GRC, and KWB contributed to the field campaign data collection and/or analysis. GRC, KWB, SVK, and XZ contributed to the modeling and/or DA work. All authors helped finalize the paper.
The authors declare that they have no conflict of interest.
We thank the ACT-America flight, instrument, and data management teams and
the principal investigator of ACT-America, Kenneth Davis (Penn State), for
designing and conducting the NASA B-200 flights, as well as helping with the
analysis. We also thank the SEAC
This research has been supported by NASA’s Earth Science Division, through the Science Utilization of SMAP program (grant no. NNX16AN39G) and the Earth Venture Suborbital-2 ACT-America project (grant no. NNX15AG76G to Penn State).
This paper was edited by Rolf Müller and reviewed by two anonymous referees.