Meteorological inputs are of great importance when implementing an air quality prediction system. In this contribution, the Weather Research and Forecast (WRF-ARW) model was used to compare the performance of the different cumulus, microphysics and Planet Boundary Layer parameterizations over Bogotá, Colombia. Surface observations were used for comparison and the evaluated meteorological variables include temperature, wind speed and direction and relative humidity. Differences between parameterizations were observed in meteorological variables and Betts-Miller-Janjic, Morrison 2-moment and BouLac schemes proved to be the best parameterizations for cumulus, microphysics and PBL, respectively. As a complement to this study, a WRF-Large Eddy Simulation was conducted in order to evaluate model results with finer horizontal resolution for air quality purposes.
Air quality is one of the main issues that are concerned by current atmospheric research. Global air pollution has an impact on human health, climate change and on the physics and chemistry of the atmosphere [
Air quality modelling has become a useful tool for administrations since it provides them a method to deal with human resources, production, emergency proceedings or to improve existing air quality plans and test abatement strategies [
Mesoscale meteorological models allow us to study and simulate meteorological variables. These models have a wide range of physical options to set up. It is a fundamental factor when configuring a model the selection of the physical parameterizations that are used to simplify somehow unresolved processes applying diverse approximations, the determination of the suitable model setup is one of the challenges when establishing a mesoscale model in a new region. Apart from the existence of a large array of available options, the best combination for one region is not necessarily applicable to another [
In this paper, we focus our attention on the meteorological modelling system. Exploring its sensitivity to variation in its configuration options, it is an important model evaluation exercise [
Description of the studied area is presented in Section 2.1, as well as simulation domains and selected episodes. A characterization of the model and the methodology to evaluate it is presented in Sections 2.2 and 2.3, respectively. Detailed analysis of the experiments developed is presented in Section 2.4 and results obtained are presented in Section 3. Finally, some conclusions are reported in Section 4.
In the following sections we show a more detailed description of the studied area features, the simulation domains and periods analyzed as well as a more comprehensive explanation of the modelling approach and model evaluation.
Following the aim of implementing an air quality modelling system in Colombia, Bogotá was chosen to perform WRF model sensitivity.
Bogotá is the capital of Colombia, the fourth biggest country in South America. It is divided into 32 departments and one capital district (
In
Simulations were conducted in 16 specific days of the year 2010 (1-2/01/2010; 5-6/01/2010; 13-14/02/2010; 27-28/02/2010; 21-22/08/2010; 11-12/09/2010; 1-2/04/2010, 11-12/12/2010). These days present ozone concentrations above 60 ppb as a maximum running average over eight hours according to air pollution records supplied by the Red de Monitoreo y Calidad del Aire de Bogotá (RMCAB).
Domain | Description | Resolution | Grid points | Domain Size |
---|---|---|---|---|
D01 | Northern South America & Central America | 27 × 27 km2 | 187 × 157 | 5049 × 4239 km2 |
D02 | North-western South America | 9 × 9 km2 | 223 × 238 | 2007 × 2142 km2 |
D03 | Cundinamarca | 3 × 3 km2 | 79 × 85 | 237 × 255 km2 |
D04 | Bogotá | 1 × 1 km2 | 55 × 55 | 55 × 55 km2 |
D05 | Bogotá-Centre | 333 × 333 m2 | 55 × 55 | 18,315 × 18,315 m2 |
The Advanced Research WRF (WRF-ARWv3.5.1) mesoscale model developed by the National Center for Atmospheric Research (NCAR), USA, was the model chosen to conduct the simulations. It is a universally used community mesoscale model and a state-of-the-art atmospheric modelling system that is applicable for both meteorological research and numerical weather prediction. Different physical options that WRF offers can be combined in many different ways. Further details and description on this model appear in [
The initial and boundary conditions for domain D01 were supplied by the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Climate Forecast System Reanalysis (v1), with 0.5˚ (~55 km × 55 km) of spatial resolution and 6h of temporal sampling. Numerical simulations are executed for 48 hours corresponding on every day selected, taking the first 24 hours as spin-up time to minimize the effects of initial conditions and in order to represent a complete diurnal cycle. This is a common practice in meteorological modelling for air quality applications [
Two-way nesting was used for the three external domains (D01, D02 and D03) and one-way nesting for D04 and D05. The vertical structure of the model includes 32 vertical layers covering the whole troposphere and a resolution decreasing slowly with height in order to allow low-level flow details to be captured. The first 20 levels are inside atmospheric boundary layer (below 1500 m), with the first level at approximately 16 meters, and the domain top is about 100 hPa. The higher resolution close to the surface is a common practice in air quality studies in order to better represent the physical-chemical processes within de Atmospheric Boundary Layer [
The evaluation performed is focused on the innermost domains, D04 and D05, since the final aim of this study is to find the best model setup for high resolution simulations. Meteorological observations were provided by 10 air quality stations that belong to the Red de Monitoreo y Calidad del Aire de Bogotá (RMCAB).
There are several methodologies for model evaluation that all together complement themselves [
The circular nature of wind direction makes that statistical parameters should be carefully considered. Then, for the wind direction evaluation:
D represents the minimum difference between modelled values and observed ones and it is always between ‒180˚ and +180˚ and N is the total number of measurements for all the days considered.
if
if
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Many different physics options in WRF are available for microphysics, radiation, surface layer, land surface, Planet Boundary Layer (PBL) and cumulus. Physics options (schemes) considered in our study are listed in
A total of 14 experiments have been evaluated progressively, as
Cumulus parameterization is used to predict the collective effects of convective clouds at smaller scales as a function of larger-scale processes and conditions. First, three configurations, i.e. Default, C1 and C2, were analyzed to take out the best cumulus parameterization between Kain-Fritsch (KF) scheme [
Meteorological parameter (reference height) | Statistic | Benchmark |
---|---|---|
Temperature (2 m) | MB | <±0.50 K |
MAGE | <2.00 K | |
IOA | ≥0.80 | |
Wind speed (10 m) | MB | ±0.50 m∙s−1 |
RMSE | <2.00 m∙s−1 | |
Wind direction (10 m) | MB | <±10.00˚ |
MAGE | <30.00˚ | |
Relative humidity (2 m) | MB | <10.00% |
MAGE | <20.00% | |
IOA | ≥0.60 |
Parameter | Schemes | |
---|---|---|
Cumulus | ・ Kain-Fritsch (KF) ・ Betts-Miller-Janjic (BMJ) ・ Grell-Freitas (GF) (new in v.3.5.1) | |
Microphysics | ・ WSM3 ・ Stony Brook University (SBU-YLin) ・ Morrison double-moment (Morrison 2-mom) | |
Planet Boundary Layer (PBL) scheme―Surface Layer scheme | ・ Yonsei University (YSU)―Similarity theory MM5 (MM5 similarity) ・ Mellor-Yamada-Janjic (MYJ)―Eta similarity ・ Quasi-Normal Scale Elimination PBL (QNSE)―Similarity theory MM5 (MM5 similarity) ・ Assymetric Convective Model (ACM2) ・ Mellor-Yamada Nakanishi and Niino Level 3 (MYNN3)―Mellor-Yamada Nakanishi and Niino (MYNN) ・ Grenier-Bretherton-McCaa (GBM) (new in v.3.5.1) ―Similarity theory MM5 (MM5 similarity) ・ BouLac PBL (BouLac)―Similarity theory MM5 (MM5 similarity) ・ UW-Similarity theory MM5 (MM5 similarity) ・ TEMF-TEMF surface layer | |
Radiation | Longwave | ・ Rapid Radiative Transfer model (RRTM) |
Shortwave | ・ Dudhia | |
Land surface | ・ Noah |
Once cumulus option was selected, experiments M1 and M2 were evaluated together with the previous “best cumulus case” and with different Microphysics options. Microphysics parameterizations resolve water vapour, cloud and precipitation processes and that is the reason why they play such a significant role on air pollution levels [
Several authors have recently shown the impact of PBL parameterizations on air quality modelling applications. Some of these examples would be the [
Configuration | Microphysics | Longwave radiation | Shortwave radiation | Surface Layer | Land Surface | PBL | Cumulus |
---|---|---|---|---|---|---|---|
Default | WSM3 | RRTM | Dudhia | MM5 similarity | Noah | YSU | KF |
C1 | WSM3 | RRTM | Dudhia | MM5 similarity | Noah | YSU | BMJ |
C2 | WSM3 | RRTM | Dudhia | MM5 similarity | Noah | YSU | GF |
M1 | SBU-YLin | RRTM | Dudhia | MM5 similarity | Noah | YSU | Best cumulus configuration selected |
M2 | Morrisson 2-mom | RRTM | Dudhia | MM5 similarity | Noah | YSU | Best cumulus configuration selected |
P1 | Best microphysics configuration selected | RRTM | Dudhia | Eta similarity | Noah | MYJ | Best cumulus configuration selected |
P2 | Best microphysics configuration selected | RRTM | Dudhia | MM5 similarity | Noah | ACM2 | Best cumulus configuration selected |
P3 | Best microphysics configuration selected | RRTM | Dudhia | QNSE | Noah | QNSE | Best cumulus configuration selected |
P4 | Best microphysics configuration selected | RRTM | Dudhia | MYNN | Noah | MYNN3 | Best cumulus configuration selected |
P5 | Best microphysics configuration selected | RRTM | Dudhia | MM5 similarity | Noah | GBM | Best cumulus configuration selected |
P6 | Best microphysics configuration selected | RRTM | Dudhia | MM5 similarity | Noah | BouLac | Best cumulus configuration selected |
P7 | Best microphysics configuration selected | RRTM | Dudhia | MM5 similarity | Noah | UW | Best cumulus configuration selected |
P8 | Best microphysics configuration selected | RRTM | Dudhia | TEMF | Noah | TEMF | Best cumulus configuration selected |
Best configuration | Best microphysics configuration selected | RRTM | Dudhia | SL selected associated to the best PBL conf. | Noah | Best PBL configuration selected | Best cumulus configuration selected |
M2-LES | Morrisson 2 moment | RRTM | Dudhia | MM5 similarity | Noah | LES | Best cumulus configuration selected |
Elimination (QNSE) PBL [
As a result of the experiments evaluation and comparison, a model setup was chosen for prospective air quality applications in Bogotá. Additionally, we have included into the analysis, a modelling experiment with finer horizontal resolution (333 m) over Bogotá centre (D05). meteorological maximum horizontal resolution places a restriction on the maximum horizontal of coupled air quality modelling systems. In order to couple the different meteorological scales and to deal with the step from regional to local scale are a state-of-art topic in the atmospheric modelling science [
Results of the comparison of every configuration are presented below using the proposed statistics. They have been compared for each meteorological parameter; temperature, wind speed, wind direction and relative humidity, and the one that showed best results for the maximum meteorological parameters was selected as “best case”. It is necessary to clarify that in the event of a “tie” or not conclusive differences, wind direction will carry the most sway when selecting “best case” due to the importance of this variable in air quality modelling.
The first schemes analyzed have been cumulus. Wind direction errors are not within the benchmark for any of the simulations ran. Terrain complexity has a considerable influence on wind direction errors and the values found are substantially above the MB and MAGE benchmarks. However, these values were found in similar studies [
The three schemes produced similar results for temperature, with all values within the benchmarks and slightly overpredicting it. The C2 configuration produced the lowest MB for temperature (0.07 K) while the lowest MAGE (1.67 K) and highest IOA (0.91) corresponded to Default configuration, even though no significant differences are observed between them, as can be seen in
Meteorological parameter | Statistic and benchmark | WRF Configuration | ||
---|---|---|---|---|
Default | C1 | C2 | ||
Temperature | MB (K) <± 0.50 | 0.29 | 0.13 | 0.07 |
MAGE (K) < 2.00 | 1.67 | 1.68 | 1.68 | |
IOA ≥ 0.80 | 0.91 | 0.90 | 0.90 | |
Wind speed | MB (m∙s−1) < ±0.50 | 0.16 | 0.35 | 0.37 |
RMSE (m∙s−1) < 2.00 | 1.90 | 2.17 | 2.15 | |
Wind direction | MB (˚) <±10.00 | −13.27 | −9.30 | −13.08 |
MAGE (˚) < 30.00 | 70.79 | 66.43 | 73.45 | |
Relative humidity | MB (%) < ±10.00 | −0.62 | 1.00 | 0.31 |
MAGE (%) < 20.00 | 10.81 | 10.45 | 11.02 | |
IOA ≥ 0.60 | 0.79 | 0.80 | 0.79 |
wind speed RMSE (1.90 m∙s−1). Nevertheless, it is C1 configuration which produced the lowest MB (−9.30˚) and MAGE (66.43˚) for wind direction, and the lowest MAGE (10.45%) and highest IOA (0.80) for relative humidity. According to the results shown in
Graphics in
tion (b) and the mean daily relative humidity evolution (c) for Default, C1 and C2 configurations comparing with the same observed parameters. C1 and C2 show a good prediction for maximum temperature while Default overestimates it. As it can be seen in
Once BMJ cumulus parameterization was selected, three configurations were compared with this setting and by varying microphysics schemes: previous C1 “cumulus best case” using WSM3 microphysics scheme, M1 configuration with SBU-YLin and M2 using Morrison 2-moment.
Results for the three configurations with different microphysics schemes tested are shown in
Graphics in
The last evaluation of WRF physics options involves PBL parameterizations. Once Morrison-2moment microphysics parameterization was set for the next configurations as a result of the C1, M1 and M2 experiments, other nine configurations were compared with this microphysics scheme and by varying PBL parameterizations as
P2 (ACM2 PBL scheme), P4 (MYNN3 PBL scheme), P6 (BouLac PBL scheme) and P8 (TEMF scheme) configurations turned to be computationally more expensive than the others (about 30% - 40%) and P7 (UW scheme) up to 120%. In the later case, this can be explained by a reduction of the time step from 60 s to 40 s due to computational errors.
Meteorological parameter | Statistic and benchmark | WRF Configuration | ||
---|---|---|---|---|
C1 | M1 | M2 | ||
Temperature | MB (K) < ±0.50 | 0.13 | 0.41 | 0.18 |
MAGE (K) < 2.00 | 1.68 | 1.69 | 1.64 | |
IOA ≥ 0.80 | 0.90 | 0.90 | 0.90 | |
Wind speed | MB (m∙s−1) < ±0.50 | 0.35 | 0.08 | 0.22 |
RMSE (m∙s−1) < 2.00 | 2.17 | 1.84 | 2.01 | |
Wind direction | MB (˚) < ±10.00 | −9.30 | −12.54 | −10.01 |
MAGE (˚) < 30.00 | 66.43 | 66.76 | 66.32 | |
Relative humidity | MB (%) < ±10.00 | 1.00 | −0.74 | −0.79 |
MAGE (%) < 20.00 | 10.45 | 10.74 | 10.34 | |
IOA ≥ 0.60 | 0.80 | 0.78 | 0.80 |
Meteorological parameter | Statistic and benchmark | WRF Configuration | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
M2 | P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | ||
Temperature | MB (K) < ±0.50 | 0.18 | −0.25 | 0.30 | −0.46 | −0.32 | 0.06 | 0.02 | −0.05 | −0.97 |
MAGE (K) < 2.00 | 1.64 | 1.71 | 1.69 | 1.77 | 1.74 | 1.68 | 1.73 | 1.69 | 2.20 | |
IOA ≥ 0.80 | 0.90 | 0.89 | 0.90 | 0.90 | 0.89 | 0.90 | 0.89 | 0.90 | 0.81 | |
Wind speed | MB (m∙s−1) < ±0.50 | 0.22 | 1.01 | 0.68 | 1.13 | 0.07 | 0.41 | 0.41 | 0.25 | 0.48 |
RMSE (m∙s−1) < 2.00 | 2.01 | 2.40 | 2.26 | 2.53 | 1.73 | 1.95 | 2.27 | 1.89 | 2.06 | |
Wind direction | MB (˚) < ±10.00 | −10.01 | −1.78 | −13.40 | −10.97 | −10.66 | −12.12 | −7.06 | −6.14 | 0.60 |
MAGE (˚) < 30.00 | 66.32 | 65.39 | 62.62 | 70.63 | 64.02 | 61.48 | 57.24 | 64.74 | 75.97 | |
Relative humidity | MB (%) < ±10.00 | −0.79 | 4.22 | −1.42 | 3.11 | 2.80 | 0.61 | 0.26 | 1.59 | 9.06 |
MAGE (%) < 20.00 | 10.34 | 10.51 | 10.24 | 10.71 | 10.17 | 9.48 | 9.30 | 9.96 | 15.09 | |
IOA ≥ 0.60 | 0.80 | 0.78 | 0.81 | 0.80 | 0.81 | 0.82 | 0.82 | 0.80 | 0.65 |
A finer-grid LES covering a smaller horizontal domain (D05) is nested inside a coarser-grid covering a larger horizontal domain (D04). This last contribution aims to validate the model results by increasing the resolution so that a future coupling of the meteorological model and the photochemichal model would be interesting in terms of air quality applications. M2 configuration was selected to run LES simulation because cumulus and microphysics parameterizations were already evaluated obtaining the best results. M2-LES is compared with M2 for a smaller domain (D05) so that validation is consistent including the same stations.
Comparisons between M2-LES configuration and M2 configuration within the D05 are shown in
Meteorological parameter | Statistic and benchmark | WRF Configuration | |
---|---|---|---|
M2?LES (D05) | M2 (D05) | ||
Temperature | MB (K) < ±0.50 | 0.65 | 0.36 |
MAGE (K) < 2.00 | 1.43 | 1.32 | |
IOA ≥ 0.80 | 0.93 | 0.94 | |
Wind speed | MB (m∙s−1) < ±0.50 | −0.60 | −0.26 |
RMSE (m∙s−1) < 2.00 | 1.58 | 1.61 | |
Wind direction | MB (˚) < ±10.00 | 0.39 | −6.98 |
MAGE (˚) < 30.00 | 65.24 | 66.61 | |
Relative humidity | MB (%) < ±10.00 | −0.30 | −0.73 |
MAGE (%) < 20.00 | 9.91 | 9.87 | |
IOA ≥ 0.60 | 0.81 | 0.82 |
lution with LES approach, we find similar wind direction patterns and lower wind speed values for the same area at a higher resolution.
A total of thirteen WRF sensitivity experiments were conducted over Bogotá by varying cumulus, microphysics and Planet Boundary layer schemes during high air pollution episodes of 2010 and aiming to find the optimal setup of the model over this region. This work has focused most part of the configurations on PBL parameterizations due to its relevance on air quality modelling. We evaluate the differences in meteorological parameters of temperature, wind and relative humidity compared with observations in the innermost domain following a statistical analysis and the results show that no significant differences were found for temperature and relative humidity predictions depending on microphysics and cumulus parameterizations and no configuration perfectly works for all the variables. Among all the configurations analyzed, the best for the maximum meteorological parameters and selected as “best case” for cumulus, microphysics and PBL, proved to be P6, which improves the results for wind direction MAGE (57.24˚) and relative humidity MB (0.26%), MAGE (9.30%) and IOA (0.82). P6 has Betts-Miller-Janjic as cumulus scheme, the popular cumulus parameterization for tropical systems, Morrison 2-moment as microphysics scheme and Bougeault-Lacarrère (BouLac) as PBL scheme, a parameter-
zation of orography-induced turbulence.
The model replicated temperature observations with a global index of agreement of 0.90. Not so precisely wind direction was predicted, but uncertainty of the prediction associated to this variable plays an important role.
Finally, a WRF-Large Eddy Simulation was included into the analysis, a modelling experiment with finer horizontal resolution (333 m) over Bogotá centre (D05). This experiment was compared with M2 configuration and meteorological evaluation found that although the latter improved most metrics for all the meteorological parameters, there were not conclusive differences between them. These findings will allow us to couple WRF- LES with the emission and photochemical models at a higher resolution as an area of work for the future. However, default WRF physiographic data sets (topography and land uses) were used for 333 m resolution simulations. This analysis may be extended in the future by including higher resolution data sets so that we can accurately evaluate model performance of the LES approach. To achieve conclusive results, both in WRF and WRF-LES simulations, it will be useful to extend this study to a large period.
This work has been developed within the framework of Santander Universities SME Internship Program and it was partly funded by the Spanish Government through PTQ-12-05244. The observations used in the study were obtained from the open access Red de Monitoreo y Calidad del Aire de Bogotá that belongs to the Secretaría Distrital de Ambiente de Bogotá.