scholarly journals Pattern-Based Evaluation of Coupled Meteorological and Air Quality Models

2010 ◽  
Vol 49 (10) ◽  
pp. 2077-2091 ◽  
Author(s):  
Scott Beaver ◽  
Saffet Tanrikulu ◽  
Ahmet Palazoglu ◽  
Angadh Singh ◽  
Su-Tzai Soong ◽  
...  
Keyword(s):  
Air Quality ◽  
San Francisco ◽  
Model Evaluation ◽  
Clustering Algorithm ◽  
Surface Wind ◽  
Major Advance ◽  
Evaluation Technique ◽  
Meteorological Model ◽  
Quality Models ◽  
Weather Patterns

Abstract A novel pattern-based model evaluation technique is proposed and demonstrated for air quality models (AQMs) driven by meteorological model (MM) output. The evaluation technique is applied directly to the MM output; however, it is ultimately used to gauge the performance of the driven AQM. This evaluation of AQM performance based on MM performance is a major advance over traditional evaluation methods. First, meteorological cluster analysis is used to assign the days of a historical measurement period among a small number of weather patterns having distinct air quality characteristics. The clustering algorithm groups days sharing similar empirical orthogonal function (EOF) representations of their measurements. In this study, EOF analysis is used to extract space–time patterns in the surface wind field reflecting both synoptic and mesoscale influences. Second, simulated wind fields are classified among the determined weather patterns using the measurement-derived EOFs. For a given period, the level of agreement between the observation-based clustering labels and the simulation-based classification labels is used to assess the validity of the simulation results. Mismatches occurring between the two sets of labels for a given period imply inaccurately simulated conditions. Moreover, the specific nature of a mismatch can help to diagnose the downstream effects of improperly simulated meteorological fields on AQM performance. This pattern-based model evaluation technique was applied to extended simulations of fine particulate matter (PM2.5) covering two winter seasons for the San Francisco Bay Area of California.

Overview of the atmospheric model evaluation tool (AMET) v1.1 for evaluating meteorological and air quality models

2011 ◽  
Vol 26 (4) ◽  
pp. 434-443 ◽  
Author(s):  
K. Wyat Appel ◽  
Robert C. Gilliam ◽  
Neil Davis ◽  
Alexis Zubrow ◽  
Steven C. Howard
Keyword(s):  
Air Quality ◽  
Model Evaluation ◽  
Atmospheric Model ◽  
Evaluation Tool ◽  
Quality Models

scholarly journals Technical Note – AQMEII4 Activity 1: Evaluation of Wet and Dry Deposition Schemes as an Integral Part of Regional-Scale Air Quality Models

2021 ◽  
Author(s):  
Stefano Galmarini ◽  
Paul Makar ◽  
Olivia Clifton ◽  
Christian Hogrefe ◽  
Jesse Bash ◽  
...  
Keyword(s):  
Air Quality ◽  
Dry Deposition ◽  
Model Evaluation ◽  
Regional Scale ◽  
Technical Note ◽  
Air Pollutant ◽  
Evaluation Activity ◽  
Quality Models ◽  
Wet And Dry Deposition ◽  
Regional Air Quality

Abstract. We present in this technical note the research protocol for Phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided in two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reducing input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (Part 2). The scope of these papers is to present the scientific protocols for AQMEII4, as well to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving land use/land cover (LULC)-specific diagnostics and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air-quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

scholarly journals Comparison of the influence of two types of cold surge on haze dispersion in eastern China

2021 ◽  
Vol 21 (19) ◽  
pp. 15185-15197
Author(s):  
Shiyue Zhang ◽  
Gang Zeng ◽  
Xiaoye Yang ◽  
Ruixi Wu ◽  
Zhicong Yin
Keyword(s):  
Air Quality ◽  
Clustering Algorithm ◽  
Wave Train ◽  
Eastern China ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Horizontal Surface ◽  
Cold Surge ◽  
Thermal Inversion ◽  
Favorable Weather

Abstract. Cold surge (CS) is considered a favorable weather process to improve air quality and is widely recognized. However, there is no detailed study on the differences in the dispersion ability of different types of CSs in relation to haze days in eastern China (HDEC). This paper uses the hierarchical clustering algorithm to classify the cool-season (November to February of the following year) CSs across eastern China into blocking CSs and wave-train CSs and compares their influences on the number of HDEC from 1980 to 2017. Results show that the wave-train CSs can significantly improve the visibility in eastern China and generally improve air quality for about 2 d longer than the blocking CSs, which indicates that the blocking CSs have a weaker ability to dissipate HDEC compared with the wave-train CSs. The CSs affect the HDEC by changing meteorological elements like thermal inversion potential, horizontal surface wind, sea level pressure (SLP), and surface air temperature (SAT). A period of 4 d after the outbreak of CSs, the variations of thermal inversion potential and horizontal surface wind of two types of CSs tend to be consistent. However, the negative SAT anomalies and the positive SLP anomalies caused by the blocking CSs lasted shorter than those caused by the wave-train CSs, forming favorable conditions for the rapid growth of HDEC. Furthermore, results show that in recent years, especially after the 1990s, the frequency of wave-train CSs has decreased significantly, while the frequency of blocking CSs has slightly increased, indicating that the overall ability of CSs to dissipate HDEC has weakened in general. This work may provide reference for the future formulation of haze control policies in East Asia.

scholarly journals A Weather-Pattern-Based Approach to Evaluate the Antarctic Mesoscale Prediction System (AMPS) Forecasts: Comparison to Automatic Weather Station Observations

2011 ◽  
Vol 26 (2) ◽  
pp. 184-198 ◽  
Author(s):  
Melissa A. Nigro ◽  
John J. Cassano ◽  
Mark W. Seefeldt
Keyword(s):  
Model Evaluation ◽  
Single Case ◽  
Evaluation Process ◽  
Synoptic Climatology ◽  
Prediction System ◽  
Evaluation Technique ◽  
Model Errors ◽  
Weather Patterns ◽  
Weather Pattern ◽  
The Antarctic

Abstract Typical model evaluation strategies evaluate models over large periods of time (months, seasons, years, etc.) or for single case studies such as severe storms or other events of interest. The weather-pattern-based model evaluation technique described in this paper uses self-organizing maps to create a synoptic climatology of the weather patterns present over a region of interest, the Ross Ice Shelf for this analysis. Using the synoptic climatology, the performance of the model, the Weather Research and Forecasting Model run within the Antarctic Mesoscale Prediction System, is evaluated for each of the objectively identified weather patterns. The evaluation process involves classifying each model forecast as matching one of the weather patterns from the climatology. Subsequently, statistics such as model bias, root-mean-square error, and correlation are calculated for each weather pattern. This allows for the determination of model errors as a function of weather pattern and can highlight if certain errors occur under some weather regimes and not others. The results presented in this paper highlight the potential benefits of this new weather-pattern-based model evaluation technique.

scholarly journals Comparison of influence between two types of cold surge on haze dispersion in Eastern China

2021 ◽  
Author(s):  
Shiyue Zhang ◽  
Gang Zeng ◽  
Xiaoye Yang ◽  
Ruixi Wu ◽  
Zhicong Yin
Keyword(s):  
Air Quality ◽  
Clustering Algorithm ◽  
Wave Train ◽  
Eastern China ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Horizontal Surface ◽  
Cold Surge ◽  
Thermal Inversion ◽  
Favorable Weather

Abstract. Cold surge (CS) is considered as a favorable weather process to improve air quality and is widely recognized. However, there is no detailed study on the differences in the dispersion ability of different types of CSs to haze days in eastern China (HDEC). This paper uses the hierarchical clustering algorithm to classify the cool season (November to February of the following year) CSs across eastern China into blocking and wave-train CSs and compares their influences on the number of HDEC from 1980 to 2017. Results show that the wave-train CS can significantly improve the visibility in eastern China and generally make the high air quality last for about 2 days longer than the blocking CS, which indicates that the blocking CS has a weaker ability to dissipate HDEC compared with the wave-train CS. The CSs affect the HDEC by changing these meteorological elements like thermal inversion potential, horizontal surface wind, sea level pressure (SLP), and surface air temperature (SAT). 4 days after the CSs outbreak, the variations of thermal inversion potential and horizontal surface wind of two types of CSs tend to be consistent. However, the negative SAT anomalies, and the positive SLP anomalies caused by the blocking CSs lasted shorter than those caused by the wave-train CSs, which forms favorable conditions for the rapid growth of HDEC. Furthermore, results show that in recent years, especially after the 1990s, the frequency of wave-train CSs has decreased significantly, while the frequency of blocking CSs has slightly increased, indicating that the overall ability of CSs to dissipate HDEC has weakened in general.

scholarly journals Improving Cloud Simulation for Air Quality Studies through Assimilation of Geostationary Satellite Observations in Retrospective Meteorological Modeling

2017 ◽  
Vol 146 (1) ◽  
pp. 29-48 ◽  
Author(s):  
Andrew T. White ◽  
Arastoo Pour-Biazar ◽  
Kevin Doty ◽  
Bright Dornblaser ◽  
Richard T. McNider
Keyword(s):  
United States ◽  
Air Quality ◽  
Surface Wind ◽  
Model Performance ◽  
Surface Wind Speed ◽  
Shortwave Radiation ◽  
Percentage Increase ◽  
Meteorological Model ◽  
Average Percentage ◽  
Space And Time

Abstract Development of clouds in space and time within numerical meteorological models as observed in nature is essential for producing an accurate representation of the physical atmosphere for input into air quality models. In this study, a new technique was developed to assimilate Geostationary Operational Environmental Satellite (GOES)-derived cloud fields into the Weather Research and Forecasting (WRF) meteorological model to improve the placement of clouds in space and time within the model. The simulations were performed on 36-, 12-, and 4-km grid-size domains covering the contiguous United States, the south-southeastern United States, and eastern Texas, respectively. The technique was tested over the month of August 2006. The results indicate that the assimilation technique significantly improves the agreement between the model-predicted and GOES-derived cloud fields. The daily average percentage increase in the cloud agreement was determined to be 14.02%, 11.29%, and 4.96% for the 36-, 12-, and 4-km domains, respectively. This was accomplished without degrading the model performance with respect to surface wind speed, temperature, and mixing ratio, which are important parameters for air quality applications; in some cases these variables were even slightly improved. The assimilation technique also produced improvements in the model-predicted precipitation and predicted downwelling shortwave radiation reaching the surface.

scholarly journals Four-dimensional evaluation of regional air quality models

2013 ◽  
Vol 6 (1) ◽  
pp. 521-584
Author(s):  
E. Solazzo ◽  
R. Bianconi ◽  
G. Pirovano ◽  
M. D. Moran ◽  
R. Vautard ◽  
...  
Keyword(s):  
Air Quality ◽  
Model Evaluation ◽  
Regional Scale ◽  
Measurement Data ◽  
High Sensitivity ◽  
Air Quality Model ◽  
Quality Model ◽  
Quality Models ◽  
Time Space ◽  
Regional Air Quality

Abstract. The evaluation of regional air quality models is a challenging task, not only for the intrinsic complexity of the topic but also in view of the difficulties in finding sufficiently abundant, harmonized and time/space-well-distributed measurement data. This study, conducted in the framework of AQMEII (Air Quality Model Evaluation International Initiative), evaluates 4-D model predictions obtained from 15 modelling groups and relating to the air quality of the full year of 2006 over the North American and European continents. The modelled variables are ozone, CO, wind speed and direction, temperature, and relative humidity. Model evaluation is supported by the high quality in-flight measurements collected by instrumented commercial aircrafts in the context of the MOZAIC programme. The models are evaluated at five selected domains positioned around major airports, four in North America (Portland, Philadelphia, Atlanta, Dallas) and one in Europe (Frankfurt). Due to the extraordinary scale of the exercise (number of models and variables, spatial and temporal extent), this study is primarily aimed at illustrating the potential for using MOZAIC data for regional-scale evaluation and the capabilities of models to simulate concentration and meteorological fields in the vertical rather than just at the ground. We apply various approaches, metrics, and methods to analyze this complex dataset. Results of the investigation indicate that, while the observed meteorological fields are modelled with some success, modelling CO in and above the boundary layer remains a challenge and modelling ozone also has room for significant improvement. We note, however, that the high sensitivity of models to height, season, location, and metric makes the results rather difficult to interpret and to generalize. With this work, though, we set the stage for future process-oriented and in-depth diagnostic analyses.

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