scholarly journals Effects of model resolution on the interpretation of satellite NO<sub>2</sub> observations

2011 ◽  
Vol 11 (22) ◽  
pp. 11647-11655 ◽  
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.

scholarly journals Effects of model spatial resolution on the interpretation of satellite NO<sub>2</sub> observations

2011 ◽  
Vol 11 (7) ◽  
pp. 20245-20265 ◽  
Author(s):  
L. C. Valin ◽  
A. R. Russell ◽  
R. C. Hudman ◽  
R. C. Cohen
Keyword(s):  
New Mexico ◽  
Los Angeles ◽  
Power Plants ◽  
Chemical Transport ◽  
Linear Process ◽  
Ozone Production ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Four Corners ◽  
Non Linear

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.

Airborne Lidar Plume and Haze Analyzer (ALPHA-1)

1980 ◽  
Vol 61 (9) ◽  
pp. 1035-1043 ◽  
Author(s):  
Edward E. Uthe ◽  
Norman B. Nielsen ◽  
Walter L. Jimison
Keyword(s):  
Boundary Layer ◽  
Los Angeles ◽  
Power Plants ◽  
Particle Concentration ◽  
Layer Structure ◽  
Airborne Lidar ◽  
Test Program ◽  
Lidar System ◽  
Four Corners ◽  
Terrain Features

A new two-wavelength airborne lidar system has been constructed and field-tested. The system was designed to observe the distribution of particle concentrations over large regional areas. During a one-week field-test program, the system was used to observe boundary layer structure over the Los Angeles area and the downwind structure of particulate plumes from the Navajo (Page, Ariz.) and Four Corners (Farmington, N.Mex.) power plants. Data examples presented show the importance of terrain features in influencing particle concentration distributions over regional areas.

scholarly journals Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis

2019 ◽  
Author(s):  
Kazuyuki Miyazaki ◽  
Kevin W. Bowman ◽  
Keiya Yumimoto ◽  
Thomas Walker ◽  
Kengo Sudo
Keyword(s):  
Data Assimilation ◽  
Model Errors ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Tropospheric No2 ◽  
Satellite Sensors ◽  
Regional Emissions ◽  
Multiple Species ◽  
Data Assimilation System ◽  
Fast Chemical

Abstract. We introduce a Multi-mOdel Multi-cOnstituent Chemical data assimilation (MOMO-Chem) framework that directly accounts for model error in transport and chemistry by integrating a portfolio of forward chemical transport models (GEOS-Chem, AGCM-CHASER, MIROC-Chem, MIROC-Chem-H) into a state-of-the-art ensemble Kalman filter data assimilation system that simultaneously optimizes both concentrations and emissions of multiple species through ingestion of a suite of measurements (ozone, NO2, CO, HNO3) from multiple satellite sensors. In spite of substantial model differences, the observational density and accuracy was sufficient for the assimilation to reduce the multi-model spread by 20–85 % for ozone, and annual mean bias by 39–97 % for ozone in the middle troposphere, while simultaneously reducing the tropospheric NO2 column biases by more than 40 %, and the negative biases of surface CO in the Northern Hemisphere by 41–94 %. For tropospheric mean OH, the multi-model mean meridional hemispheric gradient was reduced from 1.32 ± 0.03 to 1.19 ± 0.03, while the multi-model spread was reduced by 24–58 % over polluted areas. These improvements extended to emissions where uncertainty ranges in the a posteriori emissions due to model errors were quantified in 4–31 % for NOx and 13–35 % for CO regional emissions. Harnessing assimilation increments in both NOx and ozone, we show that the sensitivity of ozone and NO2 surface concentrations to NOx emissions varied by a factor of 2 for end-member models revealing fundamental differences in the representation of fast chemical and dynamical processes. Consequently, diagnostic information readily available from MOMO-Chem has the potential to improve chemical predictions through relationships such as emergent constraints.

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

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

&lt;p&gt;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&amp;#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 (PM&lt;sub&gt;1&lt;/sub&gt;) 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 NH&lt;sub&gt;4&lt;/sub&gt; / (2&amp;#215;mol SO&lt;sub&gt;4&lt;/sub&gt; + mol NO&lt;sub&gt;3&lt;/sub&gt;)) rapidly decreases from ~1 at the highest inorganic PM&lt;sub&gt;1&lt;/sub&gt; concentration to 0 at the lowest inorganic PM&lt;sub&gt;1&lt;/sub&gt;. The data indicate a robust trend for ammonium balance vs inorganic PM&lt;sub&gt;1&lt;/sub&gt; at all altitude levels in the troposphere, suggesting that NH&lt;sub&gt;3&lt;/sub&gt; emissions and subsequent neutralization of H&lt;sub&gt;2&lt;/sub&gt;SO&lt;sub&gt;4&lt;/sub&gt; becomes negligible in the most remote (lowest inorganic PM&lt;sub&gt;1&lt;/sub&gt;) regions. Further, a robust trend for PM&lt;sub&gt;1&lt;/sub&gt; pH (calculated with E-AIM) vs inorganic PM&lt;sub&gt;1&lt;/sub&gt; is observed at all levels for these campaigns, as well, decreasing from a pH of ~3 to a pH of ~ &amp;#8211;1 from the highest to lowest inorganic PM&lt;sub&gt;1&lt;/sub&gt;. The data overall implies very low NH&lt;sub&gt;3&lt;/sub&gt; (and NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;) 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 PM&lt;sub&gt;1&lt;/sub&gt; 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&lt;sub&gt;&amp;#173;1&lt;/sub&gt;, compared to observations. Generally, we find a high bias in the ammonium balance and pH, likely due to too much NH&lt;sub&gt;&amp;#173;3&lt;/sub&gt; in model (possibly too high NH&lt;sub&gt;3&lt;/sub&gt; 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.&lt;/p&gt;

scholarly journals A new stratospheric and tropospheric NO<sub>2</sub> retrieval algorithm for nadir-viewing satellite instruments: applications to OMI

2013 ◽  
Vol 6 (1) ◽  
pp. 1361-1407 ◽  
Author(s):  
E. J. Bucsela ◽  
N. A. Krotkov ◽  
E. A. Celarier ◽  
L. N. Lamsal ◽  
W. H. Swartz ◽  
...  
Keyword(s):  
Wave Pattern ◽  
Retrieval Algorithm ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Standard Product ◽  
Tropospheric No2 ◽  
Novel Method ◽  
Made In ◽  
Satellite Instruments

Abstract. We describe a new algorithm for the retrieval of nitrogen dioxide (NO2) vertical columns from nadir-viewing satellite instruments. This algorithm (SP2) is the basis for the Version 2.1 OMI NO2 Standard Product and features a novel method for separating the stratospheric and tropospheric columns. The approach estimates the stratospheric NO2 directly from satellite data without using stratospheric chemical transport models or assuming any global zonal wave pattern. Tropospheric NO2 columns are retrieved using air mass factors derived from high-resolution radiative transfer calculations and a monthly climatology of NO2 profile shapes. We also present details of how uncertainties in the retrieved columns are estimated. The sensitivity of the retrieval to assumptions made in the stratosphere-troposphere separation is discussed and shown to be small, in an absolute sense, for most regions. We compare daily and monthly mean global OMI NO2 retrievals using the SP2 algorithm with those of the original Version 1 Standard Product (SP1) and the Dutch DOMINO product. The SP2 retrievals yield significantly smaller summertime tropospheric columns than SP1 and are relatively free of modeling artifacts and negative tropospheric NO2 values. In a re-analysis of an INTEX-B validation study, we show that SP2 largely eliminates a ∼20% discrepancy that existed between OMI and independent in situ springtime NO2 SP1 measurements.

Air quality forecasting system based on chemical transport models

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

scholarly journals Effect of Environmental Pollutants PM2.5, CO, NO2, and O3 on the Incidence and Mortality of SARS-CoV-2 Infection in Five Regions of the USA

2021 ◽  
Vol 18 (15) ◽  
pp. 7810
Author(s):  
Sultan Ayoub Meo ◽  
Abdulelah Adnan Abukhalaf ◽  
Omar Mohammed Alessa ◽  
Abdulrahman Saad Alarifi ◽  
Waqas Sami ◽  
...  
Keyword(s):  
New York ◽  
New Mexico ◽  
Los Angeles ◽  
Environmental Pollution ◽  
Environmental Pollutants ◽  
Daily Basis ◽  
First Case ◽  
Incidence And Mortality ◽  
Unit Increase ◽  
The Usa

In recent decades, environmental pollution has become a significant international public problem in developing and developed nations. Various regions of the USA are experiencing illnesses related to environmental pollution. This study aims to investigate the association of four environmental pollutants, including particulate matter (PM2.5), carbon monoxide (CO), Nitrogen dioxide (NO2), and Ozone (O3), with daily cases and deaths resulting from SARS-CoV-2 infection in five regions of the USA, Los Angeles, New Mexico, New York, Ohio, and Florida. The daily basis concentrations of PM2.5, CO, NO2, and O3 were documented from two metrological websites. Data were obtained from the date of the appearance of the first case of (SARS-CoV-2) in the five regions of the USA from 13 March to 31 December 2020. Regionally (Los Angeles, New Mexico, New York, Ohio, and Florida), the number of cases and deaths increased significantly along with increasing levels of PM2.5, CO, NO2 and O3 (p < 0.05), respectively. The Poisson regression results further depicted that, for each 1 unit increase in PM2.5, CO, NO2 and O3 levels, the number of SARS-CoV-2 infections significantly increased by 0.1%, 14.8%, 1.1%, and 0.1%, respectively; for each 1 unit increase in CO, NO2, and O3 levels, the number of deaths significantly increased by 4.2%, 3.4%, and 1.5%, respectively. These empirical estimates demonstrate an association between the environmental pollutants PM2.5, CO, NO2, and O3 and SARS-CoV-2 infections, showing that they contribute to the incidence of daily cases and daily deaths in the five different regions of the USA. These findings can inform health policy decisions about combatting the COVID-19 pandemic outbreak in these USA regions and internationally by supporting a reduction in environmental pollution.

Machine learning based bias correction for numerical chemical transport models

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

scholarly journals Nonlinear effects in 4D-Var

2018 ◽  
Vol 25 (3) ◽  
pp. 713-729 ◽  
Author(s):  
Massimo Bonavita ◽  
Peter Lean ◽  
Elias Holm
Keyword(s):  
Data Assimilation ◽  
Prognostic Model ◽  
Nonlinear Effects ◽  
Model Resolution ◽  
Test Models ◽  
Incremental Formulation ◽  
The Cost ◽  
The Relationship ◽  
Data Assimilation System ◽  
Global Data

Abstract. The ability of a data assimilation system to deal effectively with nonlinearities arising from the prognostic model or the relationship between the control variables and the available observations has received a lot of attention in theoretical studies based on very simplified test models. Less work has been done to quantify the importance of nonlinearities in operational, state-of-the-art global data assimilation systems. In this paper we analyse the nonlinear effects present in ECMWF 4D-Var and evaluate the ability of the incremental formulation to solve the nonlinear assimilation problem in a realistic NWP environment. We find that nonlinearities have increased over the years due to a combination of increased model resolution and the ever-growing importance of observations that are nonlinearly related to the state. Incremental 4D-Var is well suited for dealing with these nonlinear effects, but at the cost of increasing the number of outer loop relinearisations. We then discuss strategies for accommodating the increasing number of sequential outer loops in the tight schedules of operational global NWP.

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