Machine learning based bias correction for numerical 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 NO<sub>2</sub> 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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Budget of tropospheric ozone during TOPSE from two chemical transport models

Journal of Geophysical Research Atmospheres ◽

10.1029/2002jd002665 ◽

2003 ◽

Vol 108 (D8) ◽

Cited By ~ 48

Author(s):

L. K. Emmons

Keyword(s):

Tropospheric Ozone ◽

Chemical Transport ◽

Transport Models ◽

Chemical Transport Models

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Evaluation of the performance of different atmospheric chemical transport models and inter-comparison of nitrogen and sulphur deposition estimates for the UK

Atmospheric Environment ◽

10.1016/j.atmosenv.2015.08.008 ◽

2015 ◽

Vol 119 ◽

pp. 131-143 ◽

Cited By ~ 36

Author(s):

A.J. Dore ◽

D.C. Carslaw ◽

C. Braban ◽

M. Cain ◽

C. Chemel ◽

...

Keyword(s):

Chemical Transport ◽

Transport Models ◽

Sulphur Deposition ◽

Chemical Transport Models ◽

The Uk

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An improved lightning flash rate parameterization developed from Colorado DC3 thunderstorm data for use in cloud‐resolving chemical transport models

Journal of Geophysical Research Atmospheres ◽

10.1002/2015jd023470 ◽

2015 ◽

Vol 120 (18) ◽

pp. 9481-9499 ◽

Cited By ~ 19

Author(s):

B. M. Basarab ◽

S. A. Rutledge ◽

B. R. Fuchs

Keyword(s):

Chemical Transport ◽

Lightning Flash ◽

Transport Models ◽

Flash Rate ◽

Chemical Transport Models

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On the representation of IAGOS/MOZAIC vertical profiles in chemical transport models: contribution of different error sources in the example of carbon monoxide

Tellus B ◽

10.3402/tellusb.v67.28292 ◽

2015 ◽

Vol 67 (1) ◽

pp. 28292 ◽

Cited By ~ 4

Author(s):

Fabio Boschetti ◽

Huilin Chen ◽

Valerie Thouret ◽

Philippe Nedelec ◽

Greet Janssens-Maenhout ◽

...

Keyword(s):

Carbon Monoxide ◽

Chemical Transport ◽

Vertical Profiles ◽

Transport Models ◽

Error Sources ◽

Chemical Transport Models

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Model Performance Differences in Sulfate Aerosol in Winter over Japan Based on Regional Chemical Transport Models of CMAQ and CAMx

Atmosphere ◽

10.3390/atmos9120488 ◽

2018 ◽

Vol 9 (12) ◽

pp. 488 ◽

Cited By ~ 5

Author(s):

Syuichi Itahashi ◽

Kazuyo Yamaji ◽

Satoru Chatani ◽

Kunihiro Hisatsune ◽

Shinji Saito ◽

...

Keyword(s):

Air Quality ◽

Deposition Velocity ◽

Model Performance ◽

Chemical Transport ◽

Sulfate Aerosol ◽

Performance Criteria ◽

Second Phase ◽

Transport Models ◽

Chemical Transport Models ◽

So2 Concentration

Sulfate aerosol (SO42−) is a major component of particulate matter in Japan. The Japanese model intercomparison study, J-STREAM, found that although SO42− is well captured by models, it is underestimated during winter. In the first phase of J-STREAM, we refined the Fe- and Mn-catalyzed oxidation and partly improved the underestimation. The winter haze in December 2016 was a target period in the second phase. The results from the Community Multiscale Air Quality (CMAQ) and Comprehensive Air quality Model with eXtentions (CAMx) regional chemical transport models were compared with observations from the network over Japan and intensive observations at Nagoya and Tokyo. Statistical analysis showed both models satisfied the suggested model performance criteria. CMAQ sensitivity simulations explained the improvements in model performance. CMAQ modeled lower SO42− concentrations than CAMx, despite increased aqueous oxidation via the metal catalysis pathway and NO2 reaction in CMAQ. Deposition explained this difference. A scatter plot demonstrated that the lower SO42− concentration in CMAQ than in CAMx arose from the lower SO2 concentration and higher SO42− wet deposition in CMAQ. The dry deposition velocity caused the difference in SO2 concentration. These results suggest the importance of deposition in improving our understanding of ambient concentration behavior.

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Effects of aerosol dynamics and gas–particle conversion on dry deposition of inorganic reactive nitrogen in a temperate forest

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-4933-2020 ◽

2020 ◽

Vol 20 (8) ◽

pp. 4933-4949 ◽

Cited By ~ 1

Author(s):

Genki Katata ◽

Kazuhide Matsuda ◽

Atsuyuki Sorimachi ◽

Mizuo Kajino ◽

Kentaro Takagi

Keyword(s):

Dry Deposition ◽

Particle Deposition ◽

Chemical Transport ◽

Reactive Nitrogen ◽

Surface Model ◽

Deposition Flux ◽

Transport Models ◽

Inorganic Particles ◽

Aerosol Dynamics ◽

Chemical Transport Models

Abstract. Dry deposition has an impact on nitrogen status in forest environments. However, the mechanism for the high dry-deposition rates of fine nitrate particles (NO3-) observed in forests remains unknown and is thus a potential source of error in chemical transport models (CTMs). Here, we modified and applied a multilayer land surface model coupled with dry-deposition and aerosol dynamic processes for a temperate mixed forest in Japan. This represents the first application of such a model to ammonium nitrate (NH4NO3) gas–particle conversion (gpc) and the aerosol water uptake of reactive nitrogen compounds. Thermodynamics, kinetics, and dry deposition for mixed inorganic particles are modeled by a triple-moment modal method. Data for inorganic mass and size-resolved total number concentrations measured by a filter pack and electrical low-pressure impactor in autumn were used for model inputs and subsequent numerical analysis. The model successfully reproduces turbulent fluxes observed above the canopy and vertical micrometeorological profiles noted in our previous studies. The sensitivity tests with and without gpc demonstrated clear changes in the inorganic mass and size-resolved total number concentrations within the canopy. The results also revealed that within-canopy evaporation of NH4NO3 under dry conditions significantly enhances the deposition flux of fine-NO3- and fine-NH4+ particles, while reducing the deposition flux of nitric acid gas (HNO3). As a result of the evaporation of particulate NH4NO3, the calculated daytime mass flux of fine NO3- over the canopy was 15 times higher in the scenario of “gpc” than in the scenario of “no gpc”. This increase caused high contributions from particle deposition flux (NO3- and NH4+) to total nitrogen flux over the forest ecosystem (∼39 %), although the contribution of NH3 was still considerable. A dry-deposition scheme coupled with aerosol dynamics may be required to improve the predictive accuracy of chemical transport models for the surface concentration of inorganic reactive nitrogen.

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Modeling Emissions from Concentrated Sources into Large-Scale Models: Theory and apriori Testing

Atmosphere ◽

10.3390/atmos11080863 ◽

2020 ◽

Vol 11 (8) ◽

pp. 863

Author(s):

Roberto Paoli

Keyword(s):

Large Scale ◽

General Procedure ◽

Chemical Transport ◽

Reaction Rates ◽

Small Scale ◽

Background Concentrations ◽

Transport Models ◽

Chemical Transport Models ◽

Scale Models ◽

Effective Reaction

This paper presents a general procedure to incorporate the effects emissions from localized sources, such as aircraft or ship engines, into chemical transport models (CTM). In this procedure, the species concentrations in each grid box of a CTM are split into plume or small-scale concentrations and background concentrations, respectively, and the corresponding conservation equations are derived. The plume concentrations can be interpreted as subgrid contributions for the CTM grid-box averaged concentrations. The chemical reactions occurring inside the plume are parameterized by introducing suitable “effective” reaction rates rather than modifying the emission indices of the species inside the plume. Various methods for implementation into large-scale models are discussed that differ by the accuracy of the description of plume process. The mathematical consistency of the method is verified on simple idealized setting consisting of a reactive plume in homogeneous turbulence.

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