Effects of model spatial resolution on the interpretation of satellite NO&lt;sub&gt;2&lt;/sub&gt; observations

Abstract. Prediction of ozone production, a photochemically non-linear process, is known to be biased in coarse-resolution chemical transport models (CTM). Other species subject to non-linear sources or sinks are also susceptible. Here we compute the resolution-dependent bias in predicted NO2 column, a quantity relevant to the interpretation of space-based observations. We use 1-D and 2-D models to illustrate the mechanisms responsible for these biases over a range of NO2 concentrations and model resolutions. We find that the behavior of calculated biases in NO2 depends on the magnitude and spatial extent of the NO2 source. We use WRF-CHEM to determine the resolution necessary to predict column NO2 to 10 % and 25 % over the Four Corners power plants in NW New Mexico, Los Angeles, and the San Joaquin Valley in California.

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Effects of model resolution on the interpretation of satellite NO2 observations

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-11647-2011 ◽

2011 ◽

Vol 11 (22) ◽

pp. 11647-11655 ◽

Cited By ~ 94

Author(s):

L. C. Valin ◽

A. R. Russell ◽

R. C. Hudman ◽

R. C. Cohen

Keyword(s):

New Mexico ◽

Los Angeles ◽

Power Plants ◽

Loss Rate ◽

Nonlinear Effects ◽

Model Resolution ◽

Transport Models ◽

Chemical Transport Models ◽

Four Corners ◽

Tropospheric No2

Abstract. Inference of NOx emissions (NO+NO2) from satellite observations of tropospheric NO2 column requires knowledge of NOx lifetime, usually provided by chemical transport models (CTMs). However, it is known that species subject to non-linear sources or sinks, such as ozone, are susceptible to biases in coarse-resolution CTMs. Here we compute the resolution-dependent bias in predicted NO2 column, a quantity relevant to the interpretation of space-based observations. We use 1-D and 2-D models to illustrate the mechanisms responsible for these biases over a range of NO2 concentrations and model resolutions. We find that predicted biases are largest at coarsest model resolutions with negative biases predicted over large sources and positive biases predicted over small sources. As an example, we use WRF-CHEM to illustrate the resolution necessary to predict 10 AM and 1 PM NO2 column to 10 and 25% accuracy over three large sources, the Four Corners power plants in NW New Mexico, Los Angeles, and the San Joaquin Valley in California for a week-long simulation in July 2006. We find that resolution in the range of 4–12 km is sufficient to accurately model nonlinear effects in the NO2 loss rate.

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Global Survey of Aerosol Acidity from Polluted to Remote Locations: Measurements and Comparisons with Global Models

10.5194/egusphere-egu2020-11366 ◽

2020 ◽

Author(s):

Benjamin A Nault ◽

Pedro Campuzano-Jost ◽

Duseong Jo ◽

Doug Day ◽

Roya Bahreini ◽

...

Keyword(s):

Los Angeles ◽

Major Ions ◽

Water Concentration ◽

Chemical Transport ◽

Aerosol Composition ◽

Transport Models ◽

Large Variability ◽

Cloud Properties ◽

Aerosol Acidity ◽

Chemical Transport Models

The inorganic composition of aerosol impacts numerous chemical and physical processes and properties. However, many chemical transport models show large variability in both the concentration of the inorganic aerosols and their precursors (up to 3 orders of magnitude differences) and the inorganic aerosol composition. Different models predict very different properties (e.g., aerosol liquid water concentration and aerosol acidity) and outcomes (e.g., heterogeneous uptake of gases or aerosols&#8217; direct and indirect impacts on climate). Here, we use airborne observations from campaigns conducted around the world to investigate how the inorganic fine aerosol (PM1) composition, and one of its key parameters, aerosol acidity, changes from polluted regions (Mexico City, Los Angeles, Northeastern US, and Seoul) to remote ocean basins (the Atmospheric Tomography campaigns 1 and 2) in order to provide constraints for the chemical transport models. I find that the empirical ammonium balance with major ions (ammonium balance = mol NH4 / (2&#215;mol SO4 + mol NO3)) rapidly decreases from ~1 at the highest inorganic PM1 concentration to 0 at the lowest inorganic PM1. The data indicate a robust trend for ammonium balance vs inorganic PM1 at all altitude levels in the troposphere, suggesting that NH3 emissions and subsequent neutralization of H2SO4 becomes negligible in the most remote (lowest inorganic PM1) regions. Further, a robust trend for PM1 pH (calculated with E-AIM) vs inorganic PM1 is observed at all levels for these campaigns, as well, decreasing from a pH of ~3 to a pH of ~ &#8211;1 from the highest to lowest inorganic PM1. The data overall implies very low NH3 (and NH4+) throughout most of the atmosphere, contrary to predictions of some models, implying different physical properties than predicted in models. We compare these trends of ammonium balance and pH vs inorganic PM1 against 9 chemical transport models (CTMs), and we find that the CTMs show large variability for both the ammonium balance and pH vs inorganic PM&#173;1, compared to observations. Generally, we find a high bias in the ammonium balance and pH, likely due to too much NH&#173;3 in model (possibly too high NH3 emissions over oceans or too long lifetime) and inclusion of externally mixed seasalt into the submicron pH calculation. These results overall would imply different aerosol properties in the models than observed, impacting the chemistry, optical properties, and cloud properties.

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Air quality forecasting system based on chemical transport models

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2019-4-203-218 ◽

2019 ◽

Vol 4 ◽

pp. 203-218

Author(s):

I.N. Kusnetsova ◽

◽

I.U. Shalygina ◽

M.I. Nahaev ◽

U.V. Tkacheva ◽

...

Keyword(s):

Air Quality ◽

Chemical Transport ◽

Transport Models ◽

Chemical Transport Models ◽

Forecasting System ◽

Air Quality Forecasting

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Machine learning based bias correction for numerical chemical transport models

Atmospheric Environment ◽

10.1016/j.atmosenv.2020.118022 ◽

2021 ◽

Vol 248 ◽

pp. 118022

Author(s):

Min Xu ◽

Jianbing Jin ◽

Guoqiang Wang ◽

Arjo Segers ◽

Tuo Deng ◽

...

Keyword(s):

Machine Learning ◽

Bias Correction ◽

Chemical Transport ◽

Transport Models ◽

Chemical Transport Models

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Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

10.20944/preprints201811.0607.v1 ◽

2018 ◽

Author(s):

Scott D. Chambers ◽

Elise-Andree Guérette ◽

Khalia Monk ◽

Alan D. Griffiths ◽

Yang Zhang ◽

...

Keyword(s):

Air Quality ◽

Diurnal Cycle ◽

Transport Model ◽

Chemical Transport ◽

Chemical Transport Model ◽

Transport Models ◽

Potential Health ◽

Meteorological Model ◽

Model Skill ◽

Chemical Transport Models

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

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The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO2 from nadir-viewing satellites by weighted convolution

Atmospheric Measurement Techniques ◽

10.5194/amt-9-2753-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2753-2779 ◽

Cited By ~ 13

Author(s):

Steffen Beirle ◽

Christoph Hörmann ◽

Patrick Jöckel ◽

Song Liu ◽

Marloes Penning de Vries ◽

...

Keyword(s):

Input Data ◽

Polar Vortex ◽

Chemical Transport ◽

Estimation Algorithm ◽

Satellite Observations ◽

Satellite Measurements ◽

Transport Models ◽

Verification Algorithm ◽

Chemical Transport Models ◽

Total Column

Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

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