Dynamic Meteorology-Induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis
Abstract. The main focus of this study is to develop a dynamic-coupling “inline” air quality modeling system for the meteorology-induced emissions with simulated meteorological data. To improve the spatiotemporal representations and accuracy of onroad vehicle emissions, which are largely senstivie to local meteorology, we developed the “inline” coupler module called “MetEmis” for Meteorology-Induced Emission sources within the Community Multiscale Air Quality (CMAQ) version 5.3.2 modeling system. It can dynamically estimate meteorology-induced hourly gridded emissions within the CMAQ modeling system using modeled meteorology. The CMAQ air quality modeling system is applied over the continental U.S. for two months (January and July 2019) for two emissions scenarios: a) current “offline” based onroad vehicle emissions, and b) “inline” CMAQ-MetEmis onroad vehicle emissions. Overall, the “MetEmis” coupler allows us to dynamically simulate onroad vehicle emissions from the MOVES onroad emission model for CMAQ with a better spatio-temporal representation compared to the “offline” scenario based on static temporal profiles. With an instance interpolation calculation approach, the new “inline” approach significantly enhances the computational efficiency and accuracy of estimating mobile source emissions, compared to the existing “offline” approach that yields almost identical hourly emission estimation. The domain total of daily VOC emissions from the “inline” scenario shows the largest impacts from the local meteorology, which is approximately 10 % lower than the ones from the “offline” scenario. Especially, the major difference of VOC estimates was shown over the California region. These local meteorology impacts on onroad vehicle emissions via CMAQ-MetEmis revealed an improvement in hourly NO2, daily maximum ozone, and daily average PM2.5 patterns with a higher agreement and correlation with daily ground observations.
Bok H. Baek et al.
Status: final response (author comments only)
- RC1: 'Comment on gmd-2022-253', Anonymous Referee #1, 22 Feb 2023
- RC2: 'Comment on gmd-2022-253', Anonymous Referee #2, 12 Apr 2023
- AC1: 'Comment on gmd-2022-253', Jung-Hun Woo, 03 May 2023
Bok H. Baek et al.
Bok H. Baek et al.
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This manuscript describes the development of a new module which realizes online consideration of influences of meteorological conditions on emissions used in air quality simulations. An application of this module on vehicle emissions and air quality simulations is also described.
I can understand importance of considering influences of meteorological conditions on emissions. However, the descriptions are very confusing for me. I have difficulties in understanding what was done in this study and what is a scientific significance of the module developed in this study.
MOVES is a model to estimate vehicle emissions as described in the first paragraph in Section 2.3. Its computational requirements are prohibitive in real-time air quality forecasting applications. Therefore, SMOKE-MOVES tool was developed to overcome the issues. It runs SMOKE to estimate air quality model-ready emissions using the MOVES EF LUTs with hourly meteorological inputs as described in the second paragraph in Section 2.3. However, MOVES EF LUT files require significant computational resources, such as memory and storage spaces as described in the third paragraph.
According to Figure 2, MOVES EF LUTs are the starting points in SMOKE-MetEmis and CMAQ-MetEmis. Therefore, I thought that SMOKE-MOVES is inevitable even if MOVES EF LUT files require significant computational resources.
However, the fourth paragraph of Section 3.1 says that the SMOKE-MetEmis can generate a single MetEmis_TBL emissions input file that can represent the 334 MOVES LUTs files and its size is significantly smaller than the size of the 334 MOVES LUTs files.
Does it mean the new module can generate a single MetEmis_TBL emissions input file which can be used instead of 334 MOVES LUTs files? But I cannot find how the new module can generate a single MetEmis_TBL emissions input file without using SMOKE-MOVES and MOVES LUTs files.
The third paragraph of Section 3.1 says that CMAQ-ready gridded daily emissions require approximately 1.9 hours per day while the CMAQ-MetEmis “inline” approach does not cause much computational processing time. Is the emission used in “Base” the one generated by SMOKE-MOVES taking 1.9 hours, and is the emission used in “MetEmis” the one generated by CMAQ-MetEmis taking only 1 minute? Does it mean the emission used in “Base” is less accurate and more time consuming than the emission used in “MetEmis”?
It is important to consider influences of meteorological conditions on emissions. However, if their influences are poorly represented, the model performance could be also poorer when they are considered. Therefore, accuracies of influences of meteorological conditions are critical in term of this study. However, as described in Line 171-175, the dependency of mobile emissions on local meteorology can vary by vehicle types, fuel types, road types, processes, vehicle speed for onroad vehicles, hour of day for off-network vehicles, as well as by pollutants. Uncertainties in them could be quite large. Nevertheless, this manuscript just believed the dependence of vehicle emissions on meteorological conditions represented in MOVES. Their uncertainties should be discussed.
Figure 4 (b) indicates that TOG emissions estimated in “Base” is mostly higher than “MetEmis”. This is not due to an issue of “online” or “offline”. The profile used in “Base” may not be just representative. The performance could become higher if a more representative meteorological profile is provided.
Then, improvement in model performance with the new module is quite small as shown in Table 3. Differences between “Base” and “MetEmis” are within uncertainties in concentrations simulated by chemical transport models.
Large differences can be seen only in the selected episodes. In case of the episode (July 24th, 2019) over San Jose, CA, the maximum VOC concentration is 1263 ppbC as shown in Table 6. This unrealistic high value could be originated in errors in the meteorological profiles used in “Base”.
Due to these issues, I cannot recommend accepting this manuscript in the current form.