Characterization of errors in satellite-based HCHO ∕ NO2 tropospheric column ratios with respect to chemistry, column-to-PBL translation, spatial representation, and retrieval uncertainties
Authors: Souri Amir H. , Johnson Matthew S. , Wolfe Glenn M. , Crawford James H. , Fried Alan , Wisthaler Armin , Brune William H. , Blake Donald R. , Weinheimer Andrew J. , Verhoelst Tijl , Compernolle Steven , Pinardi Gaia , Vigouroux Corinne , Langerock Bavo , Choi Sungyeon , Lamsal Lok , Zhu Lei , Sun Shuai , Cohen Ronald C. , Min Kyung-Eun , Cho Changmin , Philip Sajeev , Liu Xiong , Chance Kelly 
Published: February 2023

Abstract. The availability of formaldehyde (HCHO) (a proxy for volatile organic
compound reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen
oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the
near-surface ozone sensitivity. Strong emphasis has been placed on the
challenges that come with transforming what is being observed in the
tropospheric column to what is actually in the planetary boundary layer
(PBL) and near the surface; however, little attention has been paid to other
sources of error such as chemistry, spatial representation, and retrieval
uncertainties. Here we leverage a wide spectrum of tools and data to
quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained
by more than 500 h of aircraft data from NASA's air quality campaigns is
used to simulate the ratio of the chemical loss of HO2 + RO2
(LROx) to the chemical loss of NOx (LNOx). Subsequently, we challenge
the predictive power of HCHO/NO2 ratios (FNRs), which are commonly
applied in current research, in detecting the underlying ozone regimes by comparing them to LROx/LNOx.
FNRs show a strongly linear (R2=0.94)
relationship with LROx/LNOx, but only on the logarithmic scale. Following the baseline (i.e., ln(LROx/LNOx) = −1.0 ± 0.2) with the model and
mechanism (CB06, r2) used for segregating NOx-sensitive from VOC-sensitive
regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The
transitioning ratios strictly follow a Gaussian distribution with a mean and
standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately
describing the ROx–HOx cycle. We calculate high ozone production rates (PO3) dominated by large HCHO × NO2 concentration levels,
a new proxy for the abundance of ozone precursors. The relationship between
PO3 and HCHO × NO2 becomes more pronounced when moving
towards NOx-sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO2
metric that has not been utilized in ozone studies. The vast amount of
vertical information on HCHO and NO2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a
second-order rational function permitting an analytical solution for an
altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation
error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial
information at its native resolution with respect to a high-resolution
sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km2). A pixel with a grid size of 216 km2 fails at capturing ∼ 65 % of the spatial information in FNRs at a
50 km length scale comparable to the size of a large urban center (e.g., Los
Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI
and OMI tropospheric NO2 and HCHO columns. At a 68 % confidence
interval (1σ), errors pertaining to daily TROPOMI observations, either
HCHO or tropospheric NO2 columns, should be above 1.2–1.5 × 1016 molec. cm−2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total
error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total
relative error (< 50 %) tends to be mild, whereas areas with low
vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing
development in the retrieval algorithm and sensor design and calibration is
essential to be able to advance the application of FNRs beyond a qualitative
metric.