scholarly journals Modeling Urban Precipitation Modification by Spatially Heterogeneous Aerosols

2017 ◽  
Vol 56 (8) ◽  
pp. 2141-2153 ◽  
Author(s):  
Paul E. Schmid ◽  
Dev Niyogi
Keyword(s):  
Land Surface ◽  
Surface Effect ◽  
Cloud Condensation Nuclei ◽  
Atmospheric Modeling ◽  
Specific Response ◽  
Air Quality Model ◽  
Quality Model ◽  
Model Framework ◽  
Weather Model ◽  
Simulated Precipitation

AbstractThis study introduces a methodology to simulate how spatially heterogeneous urban aerosols modify a precipitating thunderstorm in a numerical weather model. An air quality model (simple photochemical model) was coupled with a high-resolution mesoscale weather model (the Regional Atmospheric Modeling System) and generated variable urban cloud condensation nuclei values consistent with those measured in previous field studies. The coupled emission model was used to simulate the passage of a synoptic low pressure system with embedded thunderstorms over an idealized city using the real-atmosphere idealized land surface (RAIL) method. Experiments were conducted to calibrate the surface formation of cloud-nucleating aerosols in an urban environment and then to assess the specific response of different aerosol loads on simulated precipitation. The model response to aerosol heterogeneity reduced the total precipitation but significantly increased simulated rain rates. High-aerosol-loading scenarios produced a peak city-edge precipitation rate of over 100 mm h−1 greater than a control containing only a city land surface with no emissions. In comparing the control with a scenario with no city, it was seen that the land surface effect produced a rain rate increase of up to 20 mm h−1. Results indicate, within the limits of the model framework, that the urban rainfall modification is a combination of land heterogeneity causing the dynamical lifting of the air mass and aerosols, with rainfall enhancement being maintained and synergistically increased because of the aerosol indirect effects on cloud properties.

scholarly journals Intercomparison of Sonde, WRF/CAMx and Satellite Sounder Profile Data for the Paso Del Norte Region

2020 ◽  
Vol 4 (4) ◽  
pp. 277-292
Author(s):  
Suhail Mahmud ◽  
Nakul N. Karle ◽  
Rosa M. Fitzgerald ◽  
Duanjun Lu ◽  
Nicholas R. Nalli ◽  
...  
Keyword(s):  
El Paso ◽  
Forecast Model ◽  
Quality Data ◽  
Ciudad Juarez ◽  
Air Quality Model ◽  
Quality Model ◽  
Ozone Concentrations ◽  
Weather Model ◽  
Paso Del Norte ◽  
Meteorological Satellite

AbstractThe Paso Del Norte (PdN) region comprises the city of El Paso, TX, Ciudad Juarez, Mexico, and some neighboring cities in the state of New Mexico. Developing a regional weather model for this specific region has always been challenging due to its complex terrain. To obtain more accurate weather and pollution forecasting for the PdN region, the results of the downscaled WRF (Weather Research and Forecast) model were intercompared with meteorological satellite data, with ground and radiosonde dataset. In addition, it is critical to analyze the distributions of ozone concentrations to better understand atmospheric aerosol concentrations and predict them both more accurately. Hence, in this study the ozone results of CAMx (Comprehensive Air Quality Model with Extensions) were extensively intercompared with ozonesonde data. The radiosonde/ozonesonde data were obtained throughout a campaign conducted during the summer of 2017 in the PdN region. Different meteorological variables such as temperature, pressure, relative humidity, wind speed, and ozone concentrations were used for comparison at several locations in the PdN region. The TCEQ (Texas Commission of Environment Quality) data from different CAMS (Continuous Ambient Monitoring Stations) were used for ground data intercomparison with the WRF results. The meteorological satellite sounding data were retrieved using an in-house satellite antenna receiver. The results of this research paper will not only provide better pollution forecasting capability for the PdN region but also for other regions with similar topography and terrain.

scholarly journals Using SEVIRI fire observations to drive smoke plumes in the CMAQ air quality model: a case study over Antalya in 2008

2015 ◽  
Vol 15 (14) ◽  
pp. 8539-8558 ◽  
Author(s):  
G. Baldassarre ◽  
L. Pozzoli ◽  
C. C. Schmidt ◽  
A. Unal ◽  
T. Kindap ◽  
...  
Keyword(s):  
Air Quality ◽  
Land Surface ◽  
Wrf Model ◽  
Accurate Information ◽  
Spatial And Temporal Variability ◽  
Air Quality Model ◽  
Quality Model ◽  
Diurnal Variability ◽  
Fire Emissions ◽  
Smoke Plumes

Abstract. Among the atmospheric emission sources, wildfires are episodic events characterized by large spatial and temporal variability. Therefore, accurate information on gaseous and aerosol emissions from fires for specific regions and seasons is critical for air quality forecasts. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) in geostationary orbit provides fire observations over Africa and the Mediterranean with a temporal resolution of 15 min. It thus resolves the complete fire life cycle and captures the fires' peak intensities, which is not possible in Moderate Resolution Imaging Spectroradiometer (MODIS) fire emission inventories like the Global Fire Assimilation System (GFAS). We evaluate two different operational fire radiative power (FRP) products derived from SEVIRI, by studying a large forest fire in Antalya, Turkey, in July–August 2008. The EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF) has higher FRP values during the fire episode than the Wildfire Automated Biomass Burning Algorithm (WF_ABBA). It is also in better agreement with the co-located, gridded MODIS FRP. Both products miss small fires that frequently occur in the region and are detected by MODIS. Emissions are derived from the FRP products. They are used along-side GFAS emissions in smoke plume simulations with the Weather Research and Forecasting (WRF) model and the Community Multiscale Air Quality (CMAQ) model. In comparisons with MODIS aerosol optical thickness (AOT) and Infrared Atmospheric Sounding Interferometer (IASI), CO and NH3 observations show that including the diurnal variability of fire emissions improves the spatial distribution and peak concentrations of the simulated smoke plumes associated with this large fire. They also show a large discrepancy between the currently available operational FRP products, with the LSA SAF being the most appropriate.

Use of a Dynamic Air Quality Model to Reduce the Uncertainties of Seasonal and Annual Atmospheric Depositions for Mercury TMDLs

2005 ◽  
Vol 2005 (3) ◽  
pp. 1393-1414
Author(s):  
Kuo-Liang Lai ◽  
Janet Kremer ◽  
Susan Sciarratta ◽  
R. Dwight Atkinson ◽  
Tom Myers
Keyword(s):  
Air Quality ◽  
Air Quality Model ◽  
Quality Model ◽  
Atmospheric Depositions

scholarly journals Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

2021 ◽  
Vol 13 (11) ◽  
pp. 2121
Author(s):  
Changsuk Lee ◽  
Kyunghwa Lee ◽  
Sangmin Kim ◽  
Jinhyeok Yu ◽  
Seungtaek Jeong ◽  
...  
Keyword(s):  
Cross Validation ◽  
Ground Level ◽  
Reanalysis Data ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

scholarly journals Assessment of Emission Reduction and Meteorological Change in PM2.5 and Transport Flux in Typical Cities Cluster during 2013–2017

2021 ◽  
Vol 13 (10) ◽  
pp. 5685
Author(s):  
Panbo Guan ◽  
Hanyu Zhang ◽  
Zhida Zhang ◽  
Haoyuan Chen ◽  
Weichao Bai ◽  
...  
Keyword(s):  
Air Quality ◽  
Action Plan ◽  
Influence Factors ◽  
Pollution Prevention ◽  
Weather Conditions ◽  
Yangtze River Delta ◽  
Control Measures ◽  
Joint Control ◽  
Air Quality Model ◽  
Quality Model

Under the Air Pollution Prevention and Control Action Plan (APPCAP) implemented, China has witnessed an air quality change during the past five years, yet the main influence factors remain relatively unexplored. Taking the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions as typical cluster cities, the Weather Research Forecasting (WRF) and Comprehensive Air Quality Model with Extension (CAMx) were introduced to demonstrate the meteorological and emission contribution and PM2.5 flux distribution. The results showed that the PM2.5 concentration in BTH and YRD significantly declined with a descend ratio of −39.6% and −28.1%, respectively. For the meteorological contribution, those regions had a similar tendency with unfavorable conditions in 2013–2015 (contribution concentration 1.6–3.8 μg/m3 and 1.1–3.6 μg/m3) and favorable in 2016 (contribution concentration −1.5 μg/m3 and −0.2 μg/m3). Further, the absolute value of the net flux’s intensity was positively correlated with the degree of the favorable/unfavorable weather conditions. When it came to emission intensity, the total net inflow flux increased, and the outflow flux decreased significantly across the border with the emission increasing. In short: the aforementioned results confirmed the effectiveness of the regional joint emission control and provided scientific support for the proposed effective joint control measures.

scholarly journals Comparison of the RADM2 and RACM chemical mechanisms in O3 simulations: effect of the photolysis rate constant

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chien-Hung Chen ◽  
Tu-Fu Chen ◽  
Shang-Ping Huang ◽  
Ken-Hui Chang
Keyword(s):  
East Asia ◽  
Rate Constants ◽  
Daily Maximum ◽  
Bias Error ◽  
Air Quality Model ◽  
Quality Model ◽  
Yield Data ◽  
Emission Data ◽  
Photolysis Rate ◽  
Photolysis Rates

AbstractSince the photolysis rate plays an important role in any photoreaction leading to compound sink and radical formation/destruction and eventually O3 formation, its impact on the simulated O3 concentration was evaluated in the present study. Both RADM2 and RACM were adopted with and without updated photolysis rate constants. The newly developed photolysis rates were determined based on two major absorption cross-section and quantum yield data sources. CMAQ in conjunction with meteorological MM5 and emission data retrieved from Taiwan and East Asia were employed to provide spatial and temporal O3 predictions over a one-week period in a three-level nested domain [from 81 km × 81 km in Domain 1 (East Asia) to 9 km × 9 km in Domain 3 (Taiwan)]. Four cases were analyzed, namely, RADM2, with the original photolysis rates applied in Case 1 as a reference case, RADM2, with the updated photolysis rates applied in Case 2, and RACM, with and without the updated photolysis rates applied in Cases 3 and 4, respectively. A comparison of the simulation and observed results indicates that both the application of updated photolysis rate constants and RACM instead of RADM2 enhanced all three error analysis indicators (unpaired peak prediction accuracy, mean normalized bias error and mean absolute normalized gross error). Specifically, RADM2 with the updated photolysis rates resulted in an increase of 12 ppb (10%) in the daily maximum O3 concentration in southwestern Taiwan, while RACM without the updated photolysis rates resulted in an increase of 20 ppb (17%) in the daily maximum O3 concentration in the same area. When RACM with the updated photolysis rate constants was applied in the air quality model, the difference in the daily maximum O3 concentration reached up to 30 ppb (25%). The implication of Case 4 (RACM with the updated photolysis rates) for the formation and degradation of α-pinene and d-limonene was examined.

A contribution to the air quality impact assessment of a roadway in Lisbon. The air quality model APOLO

1993 ◽  
Vol 134 (1-3) ◽  
pp. 1-7 ◽  
Author(s):  
Ana Isabel A. Miranda ◽  
Miguel S. Conceição ◽  
Carlos S. Borrego
Keyword(s):  
Air Quality ◽  
Impact Assessment ◽  
Air Quality Model ◽  
Quality Model

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

scholarly journals Impact of a new condensed toluene mechanism on air quality model predictions in the US

2010 ◽  
Vol 3 (4) ◽  
pp. 2291-2314
Author(s):  
G. Sarwar ◽  
K. W. Appel ◽  
A. G. Carlton ◽  
R. Mathur ◽  
K. Schere ◽  
...  
Keyword(s):  
Air Quality ◽  
Los Angeles ◽  
Production Efficiency ◽  
Sensitivity Study ◽  
Chemical Mechanism ◽  
Ozone Production ◽  
Daily Maximum ◽  
Air Quality Model ◽  
Quality Model ◽  
Mixing Ratios

Abstract. A new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system. Model simulations are performed using the CB05 chemical mechanism containing the existing (base) and the new toluene mechanism for the western and eastern US for a summer month. With current estimates of tropospheric emission burden, the new toluene mechanism increases monthly mean daily maximum 8-h ozone by 1.0–3.0 ppbv in Los Angeles, Portland, Seattle, Chicago, Cleveland, northeastern US, and Detroit compared to that with the base toluene chemistry. It reduces model mean bias for ozone at elevated observed ozone mixing ratios. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. Sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While changes in total fine particulate mass are small, predictions of in-cloud SOA increase substantially.

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