scholarly journals Evaluation of modeled cloud properties against aircraft observations for air quality applications

2007 ◽  
Vol 112 (D10) ◽  
Author(s):  
Junhua Zhang ◽  
Wanmin Gong ◽  
W. Richard Leaitch ◽  
J. Walter Strapp
Keyword(s):  
Air Quality ◽  
Cloud Properties ◽  
Aircraft Observations

scholarly journals Modeling the Diurnal Variability of Agricultural Ammonia in Bakersfield, California during CalNex

2016 ◽  
Author(s):  
C. R. Lonsdale ◽  
J. D. Hegarty ◽  
K. Cady-Pereira ◽  
M. J. Alvarado ◽  
D. K. Henze ◽  
...  
Keyword(s):  
Air Quality ◽  
Diurnal Cycle ◽  
Vertical Mixing ◽  
Model Performance ◽  
Model Bias ◽  
Night Time ◽  
Diurnal Variability ◽  
Aircraft Observations ◽  
Diurnal Profile ◽  
Surface Observations

Abstract. NH3 retrievals from the NASA Tropospheric Emission Spectrometer (TES), as well as surface and aircraft observations of NH3(g) and submicron NH4(p), are to used to evaluate modelled concentrations of NH3(g) and NH4(p) from the Community Multiscale Air Quality (CMAQ) model in the San Joaquin Valley (SJV) during the California Research at the Nexus of Air Quality and Climate Change (CalNex) campaign. We find that simulations of NH3 driven with the California Air Resources Board (CARB) CalNex emission inventory are qualitatively and spatially consistent with TES satellite observations, with a correlation coefficient (r2) of 0.54. However, the surface observations at Bakersfield indicate a missing diurnal cycle in the model bias, with CMAQ overestimating surface NH3 at night and underestimating it during the day. The surface, satellite, and aircraft observations all suggest that the afternoon NH3 emissions in the CARB inventory are underestimated by at least a factor of two, while the night-time overestimate of NH3(g) is likely due to a combination of overestimated NH3 emissions, underestimated deposition, and insufficient vertical mixing in the WRF meteorological fields used to drive CMAQ. We used the surface observations at Bakersfield to derive an empirical diurnal cycle of NH3 emissions in the SJV, in which night-time and midday emissions differed by about a factor of 4.5. Adding this diurnal profile to the CMAQ simulations while keeping the daily NH3 emissions constant at the CARB values significantly improved the model performance at night, but sizable errors (up to 15 ppbv) in night-time NH3 remain, likely due to remaining errors in vertical mixing at night. The model performance is slightly degraded during the afternoon when the diurnal cycle is adjusted, but this may reflect relatively small (~ 20 %) errors in the total NH3 emissions rather than remaining errors in the diurnal cycle. Running CMAQv5.0.2 with bi-directional NH3 flux also improves model performance on a similar scale, while combining bi-directional NH3 fluxes and adjusted emissions significantly reduces the model bias at night.

Reactive nitrogen in the global upper troposphere from NASA DC8 and MOZAIC aircraft campaigns

2021 ◽  
Author(s):  
Nana wei ◽  
eloise a. marais ◽  
paul o. wennberg ◽  
hannah m. allen ◽  
john d. crounse ◽  
...  
Keyword(s):  
Nitric Acid ◽  
Air Quality ◽  
Global Climate ◽  
Reactive Nitrogen ◽  
Commercial Aircraft ◽  
Upper Troposphere ◽  
Initial Analysis ◽  
Aircraft Observations ◽  
Pernitric Acid

<p>Reactive nitrogen in the upper troposphere (~8-12 km) impacts global climate, air quality and the oxidizing capacity of the whole troposphere. Here we use aircraft observations from instruments onboard the NASA DC8 aircraft for campaigns from 1997 (SONEX) to the recent ATom campaign (2016-2018) and the MOZAIC commercial aircraft campaign (2003-2005) to address uncertainties in the dynamics of reactive nitrogen (NO<sub>y</sub> = NO<sub>x</sub> + NO<sub>x</sub> reservoir compounds) in the global upper troposphere (UT). Our initial analysis of the DC8 aircraft observations is consistent with previous work in that PAN is the dominant NO<sub>y</sub> component (average: 43%; range: 40-60%), followed by NO<sub>x </sub>(on average, 21%), with smaller contributions (on average, 3.5-12.5%) from pernitric acid (HNO<sub>4</sub>), organonitrate (RONO<sub>2</sub>) and nitric acid (HNO<sub>3</sub>). We go on to compare multiyear mean NO<sub>y</sub> from MOZAIC to the combination of all NASA DC8 campaigns to determine whether we can build a near-global climatology of NO<sub>y</sub> and its components to compare to GEOS-Chem to assess our understanding of these very important atmospheric components.</p>

Aerosol and cloud properties over a coastal area from aircraft observations in Zhejiang, China

2021 ◽  
Vol 267 ◽  
pp. 118771
Author(s):  
Yunfei Che ◽  
Jing Zhang ◽  
Chungang Fang ◽  
Xu Zhou ◽  
Wenhao Xue ◽  
...  
Keyword(s):  
Coastal Area ◽  
Cloud Properties ◽  
Aircraft Observations

scholarly journals Assessing Measurements of Pollution in the Troposphere (MOPITT) carbon monoxide retrievals over urban versus non-urban regions

2020 ◽  
Vol 13 (3) ◽  
pp. 1337-1356 ◽  
Author(s):  
Wenfu Tang ◽  
Helen M. Worden ◽  
Merritt N. Deeter ◽  
David P. Edwards ◽  
Louisa K. Emmons ◽  
...  
Keyword(s):  
Air Quality ◽  
Correlation Coefficients ◽  
Atmospheric Composition ◽  
Aircraft Measurements ◽  
Urban Regions ◽  
Model Based ◽  
Chemistry Research ◽  
Aircraft Observations ◽  
Co Concentrations

Abstract. The Measurements of Pollution in the Troposphere (MOPITT) retrievals over urban regions have not been validated systematically, even though MOPITT observations are widely used to study CO over urban regions. Here we compare MOPITT products over urban and non-urban regions with aircraft measurements from the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ – 2011–2014), Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS – 2013), Air Chemistry Research In Asia (ARIAs – 2016), A-FORCE (2009, 2013), and Korea United States Air Quality (KORUS-AQ – 2016) campaigns. In general, MOPITT agrees reasonably well with the in situ profiles, over both urban and non-urban regions. Version 8 multispectral product (V8J) biases vary from −0.7 % to 0.0 % and version 8 thermal-infrared product (TIR) biases vary from 2.0 % to 3.5 %. The evaluation statistics of MOPITT V8J and V8T over non-urban regions are better than those over urban regions with smaller biases and higher correlation coefficients. We find that the agreement of MOPITT V8J and V8T with aircraft measurements at high CO concentrations is not as good as that at low CO concentrations, although CO variability may tend to exaggerate retrieval biases in heavily polluted scenes. We test the sensitivities of the agreements between MOPITT and in situ profiles to assumptions and data filters applied during the comparisons of MOPITT retrievals and in situ profiles. The results at the surface layer are insensitive to the model-based profile extension (required due to aircraft altitude limitations), whereas the results at levels with limited aircraft observations (e.g., the 600 hPa layer) are more sensitive to the model-based profile extension. The results are insensitive to the maximum allowed time difference criterion for co-location (12, 6, 3, and 1 h) and are generally insensitive to the radius for co-location, except for the case where the radius is small (25 km), and hence few MOPITT retrievals are included in the comparison. Daytime MOPITT products have smaller overall biases than nighttime MOPITT products when comparing both MOPITT daytime and nighttime retrievals to the daytime aircraft observations. However, it would be premature to draw conclusions on the performance of MOPITT nighttime retrievals without nighttime aircraft observations. Applying signal-to-noise ratio (SNR) filters does not necessarily improve the overall agreement between MOPITT retrievals and in situ profiles, likely due to the reduced number of MOPITT retrievals for comparison. Comparisons of MOPITT retrievals and in situ profiles over complex urban or polluted regimes are inherently challenging due to spatial and temporal variabilities of CO within MOPITT retrieval pixels (i.e., footprints). We demonstrate that some of the errors are due to CO representativeness with these sensitivity tests, but further quantification of representativeness errors due to CO variability within the MOPITT footprint will require future work.

scholarly journals Biomass burning nitrogen dioxide emissions derived from space with TROPOMI: methodology and validation

2021 ◽  
Vol 14 (12) ◽  
pp. 7929-7957
Author(s):  
Debora Griffin ◽  
Chris A. McLinden ◽  
Enrico Dammers ◽  
Cristen Adams ◽  
Chelsea E. Stockwell ◽  
...  
Keyword(s):  
Air Quality ◽  
Biomass Burning ◽  
Transport Model ◽  
Synthetic Data ◽  
Estimation Methods ◽  
Nox Emissions ◽  
Wind Fields ◽  
Top Down ◽  
Vertical Column ◽  
Aircraft Observations

Abstract. Smoke from wildfires is a significant source of air pollution, which can adversely impact air quality and ecosystems downwind. With the recently increasing intensity and severity of wildfires, the threat to air quality is expected to increase. Satellite-derived biomass burning emissions can fill in gaps in the absence of aircraft or ground-based measurement campaigns and can help improve the online calculation of biomass burning emissions as well as the biomass burning emissions inventories that feed air quality models. This study focuses on satellite-derived NOx emissions using the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI) NO2 dataset. Advancements and improvements to the satellite-based determination of forest fire NOx emissions are discussed, including information on plume height and effects of aerosol scattering and absorption on the satellite-retrieved vertical column densities. Two common top-down emission estimation methods, (1) an exponentially modified Gaussian (EMG) and (2) a flux method, are applied to synthetic data to determine the accuracy and the sensitivity to different parameters, including wind fields, satellite sampling, noise, lifetime, and plume spread. These tests show that emissions can be accurately estimated from single TROPOMI overpasses. The effect of smoke aerosols on TROPOMI NO2 columns (via air mass factors, AMFs) is estimated, and these satellite columns and emission estimates are compared to aircraft observations from four different aircraft campaigns measuring biomass burning plumes in 2018 and 2019 in North America. Our results indicate that applying an explicit aerosol correction to the TROPOMI NO2 columns improves the agreement with the aircraft observations (by about 10 %–25 %). The aircraft- and satellite-derived emissions are in good agreement within the uncertainties. Both top-down emissions methods work well; however, the EMG method seems to output more consistent results and has better agreement with the aircraft-derived emissions. Assuming a Gaussian plume shape for various biomass burning plumes, we estimate an average NOx e-folding time of 2 ±1 h from TROPOMI observations. Based on chemistry transport model simulations and aircraft observations, the net emissions of NOx are 1.3 to 1.5 times greater than the satellite-derived NO2 emissions. A correction factor of 1.3 to 1.5 should thus be used to infer net NOx emissions from the satellite retrievals of NO2.

scholarly journals Modeling the diurnal variability of agricultural ammonia in Bakersfield, California, during the CalNex campaign

2017 ◽  
Vol 17 (4) ◽  
pp. 2721-2739 ◽  
Author(s):  
Chantelle R. Lonsdale ◽  
Jennifer D. Hegarty ◽  
Karen E. Cady-Pereira ◽  
Matthew J. Alvarado ◽  
Daven K. Henze ◽  
...  
Keyword(s):  
Air Quality ◽  
Diurnal Cycle ◽  
Management Practices ◽  
Fertilizer Application ◽  
Satellite Observations ◽  
Model Errors ◽  
Model Bias ◽  
Diurnal Variability ◽  
Aircraft Observations ◽  
Surface Observations

Abstract. NH3 retrievals from the NASA Tropospheric Emission Spectrometer (TES), as well as surface and aircraft observations of NH3(g) and submicron NH4(p), are used to evaluate modeled concentrations of NH3(g) and NH4(p) from the Community Multiscale Air Quality (CMAQ) model in the San Joaquin Valley (SJV) during the California Research at the Nexus of Air Quality and Climate Change (CalNex) campaign. We find that simulations of NH3 driven with the California Air Resources Board (CARB) emission inventory are qualitatively and spatially consistent with TES satellite observations, with a correlation coefficient (r2) of 0.64. However, the surface observations at Bakersfield indicate a diurnal cycle in the model bias, with CMAQ overestimating surface NH3 at night and underestimating it during the day. The surface, satellite, and aircraft observations all suggest that daytime NH3 emissions in the CARB inventory are underestimated by at least a factor of 2, while the nighttime overestimate of NH3(g) is likely due to a combination of overestimated NH3 emissions and underestimated deposition.Running CMAQ v5.0.2 with the bi-directional NH3 scheme reduces NH3 concentrations at night and increases them during the day. This reduces the model bias when compared to the surface and satellite observations, but the increased concentrations aloft significantly increase the bias relative to the aircraft observations. We attempt to further reduce model bias by using the surface observations at Bakersfield to derive an empirical diurnal cycle of NH3 emissions in the SJV, in which nighttime and midday emissions differ by about a factor of 4.5. Running CMAQv5.0.2 with a bi-directional NH3 scheme together with this emissions diurnal profile further reduces model bias relative to the surface observations. Comparison of these simulations with the vertical profile retrieved by TES shows little bias except for the lowest retrieved level, but the model bias relative to flight data aloft increases slightly. Our results indicate that both diurnally varying emissions and a bi-directional NH3 scheme should be applied when modeling NH3(g) and NH4(p) in this region. The remaining model errors suggest that the bi-directional NH3 scheme in CMAQ v5.0.2 needs further improvements to shift the peak NH3 land–atmosphere flux to earlier in the day. We recommend that future work include updates to the current CARB NH3 inventory to account for NH3 from fertilizer application, livestock, and other farming practices separately; adding revised information on crop management practices specific to the SJV region to the bi-directional NH3 scheme; and top-down studies focused on determining the diurnally varying biases in the canopy compensation point that determines the net land–atmosphere NH3 fluxes.

scholarly journals Evaluations of NO<sub>x</sub> and highly reactive VOC emission inventories in Texas and their implications for ozone plume simulations during the Texas Air Quality Study 2006

2011 ◽  
Vol 11 (22) ◽  
pp. 11361-11386 ◽  
Author(s):  
S.-W. Kim ◽  
S. A. McKeen ◽  
G. J. Frost ◽  
S.-H. Lee ◽  
M. Trainer ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Point Sources ◽  
Formation Mechanisms ◽  
Nox Emissions ◽  
Large Power ◽  
Voc Emissions ◽  
Aircraft Observations ◽  
Quality Study ◽  
Houston Ship Channel

Abstract. Satellite and aircraft observations made during the 2006 Texas Air Quality Study (TexAQS) detected strong urban, industrial and power plant plumes in Texas. We simulated these plumes using the Weather Research and Forecasting-Chemistry (WRF-Chem) model with input from the US EPA's 2005 National Emission Inventory (NEI-2005), in order to evaluate emissions of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOCs) in the cities of Houston and Dallas-Fort Worth. We compared the model results with satellite retrievals of tropospheric nitrogen dioxide (NO2) columns and airborne in-situ observations of several trace gases including NOx and a number of VOCs. The model and satellite NO2 columns agree well for regions with large power plants and for urban areas that are dominated by mobile sources, such as Dallas. However, in Houston, where significant mobile, industrial, and in-port marine vessel sources contribute to NOx emissions, the model NO2 columns are approximately 50%–70% higher than the satellite columns. Similar conclusions are drawn from comparisons of the model results with the TexAQS 2006 aircraft observations in Dallas and Houston. For Dallas plumes, the model-simulated NO2 showed good agreement with the aircraft observations. In contrast, the model-simulated NO2 is ~60% higher than the aircraft observations in the Houston plumes. Further analysis indicates that the NEI-2005 NOx emissions over the Houston Ship Channel area are overestimated while the urban Houston NOx emissions are reasonably represented. The comparisons of model and aircraft observations confirm that highly reactive VOC emissions originating from industrial sources in Houston are underestimated in NEI-2005. The update of VOC emissions based on Solar Occultation Flux measurements during the field campaign leads to improved model simulations of ethylene, propylene, and formaldehyde. Reducing NOx emissions in the Houston Ship Channel and increasing highly reactive VOC emissions from the point sources in Houston improve the model's capability of simulating ozone (O3) plumes observed by the NOAA WP-3D aircraft, although the deficiencies in the model O3 simulations indicate that many challenges remain for a full understanding of the O3 formation mechanisms in Houston.

Breaking the temporal barrier in air quality monitoring over Europe with Sentinel-4

2020 ◽  
Author(s):  
Diego Loyola ◽  
Michael Aspetsberger ◽  
Oleg Dubovik ◽  
Daniele Fantin ◽  
Yves Govaerts ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Earth Orbit ◽  
Trace Gas ◽  
Cloud Properties ◽  
Geo Satellite ◽  
Early Afternoon ◽  
Leo Satellites ◽  
Geo Satellites ◽  
Level 2 ◽  
Algorithm Verification

&lt;p&gt;European UVN satellite missions deliver global measurements for air quality and climate applications from Low Earth Orbit (LEO) satellites since over two decades. Currently we have in the morning data from GOME-2 on the three MetOp satellites and in the early afternoon data from OMI/Aura and TROPOMI/Sentinel-5 Precursor.&lt;/p&gt;&lt;p&gt;The temporal barrier imposed by LEO satellites, providing only one daily observation, can be broken using Geostationary Equatorial Orbit (GEO) satellites. The Sentinel-4 (S4) mission on-board the MTG-S GEO satellite will focus on monitoring of trace gas column densities and aerosols over Europe with an hourly revisit time, thereby covering the diurnal variation of atmospheric constituents.&lt;/p&gt;&lt;p&gt;We present the algorithm, verification, and processor work being performed as part of the ESA Sentinel-4 Level 2 (S4-L2) project responsible for developing the operational S4-L2 products: O&lt;sub&gt;3&lt;/sub&gt; total and tropospheric column, NO&lt;sub&gt;2&lt;/sub&gt; total and tropospheric column, SO&lt;sub&gt;2&lt;/sub&gt;, HCHO, CHOCHO columns, aerosol and cloud properties as well as surface reflectance.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Systematic bias in evaluating chemical transport models with maximum daily 8-hour average (MDA8) surface ozone for air quality applications

2019 ◽  
Author(s):  
Katherine R. Travis ◽  
Daniel J. Jacob
Keyword(s):  
Air Quality ◽  
Diurnal Cycle ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Systematic Bias ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Aircraft Observations ◽  
Southeast Us ◽  
Quality Policy

Abstract. Chemical transport models typically evaluate their simulation of surface ozone with observations of the maximum daily 8-hour average (MDA8) concentration, which is the standard air quality policy metric. This requires successful simulation of the surface ozone diurnal cycle including nighttime depletion, but models are generally biased high at night because of difficulty in resolving the stratified conditions near the surface. We quantify the problem with the GEOS-Chem model for the Southeast US during the NASA SEAC4RS aircraft campaign in August–September 2013. The model is unbiased relative to the daytime mixed layer aircraft observations but has a +5 ppb bias relative to MDA8 surface ozone observations. The model also does not capture observed occurrences of

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