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Atmósfera

Print version ISSN 0187-6236

Atmósfera vol.30 n.3 México Jul. 2017

http://dx.doi.org/10.20937/atm.2017.30.03.06 

Short communications

Implementation of the Unified Post Processor (UPP) and the Model Evaluation Tools (MET) for WRF-chem evaluation performance

Agustín García-Reynoso1  * 

Marco A. Mora-Ramírez1 

1Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Circuito de la Investigación Científica s/n, Ciudad Universitaria, 04510 Ciudad de México

ABSTRACT

This study focuses on a detailed description of several modifications made in both the Unified Post-Processor (UPP) and the Model Evaluation Tools (MET) release packages, which are necessary in order to incorporate relevant chemical species and meteorological parameters into the verification process. The changes made in UPP and MET are illustrated with a high ozone concentration episode, comparing the Weather Research and Forecasting-chemistry (WRF-chem) simulations against observational data from the Red Automática de Monitoreo Atmosférico (Automatic Atmospheric Monitoring Network) during a weekend in the Mexico City Metropolitan Area. National Emission Inventory (2006) formatted data was supplied to the WRF-chem model. Examples of statistical results and plots contemplating the new chemical species added to the verification process are given, with the aim to illustrate the kind of verification measurements and plots that MET could provide now. Finally, The modifications made over different files in UPP and MET packages could be of particular interest for users and developers of the WRF-chem model concerned about the forecast of the analysis episodes related with poor urban air quality.

Keywords: WRF-chem; evaluation; UPP; MET

RESUMEN

Este trabajo se centra en la descripción detallada de varias modificaciones realizadas a las versiones más recientes de los paquetes informáticos Unified Post Processor (UPP) y Model Evaluation Tools (MET), las cuales son necesarias para incorporar especies químicas importantes y parámetros meteorológicos al proceso de verificación. Los cambios realizados en ambos programas se ejemplifican con un episodio de altas concentraciones de ozono, comparando simulaciones del modelo Weather Research and Forecasting-chemistry (WRF-chem) con datos observacionales de la Red Automática de Monitoreo Atmosférico correspondientes a un fin de semana en la Zona Metropolitana de la Ciudad de México. El modelo WRF-chem se alimentó con datos formateados del Inventario Nacional de Emisiones (2006). Se proporcionan ejemplos de resultados y gráficas que contemplan la adición de nuevas especies químicas al proceso de verificación, con el objeto de explicar el tipo de mediciones de verificación y gráficas que el MET podría aportar en la actualidad. Por último, las modificaciones realizadas a diferentes archivos de los paquetes UPP y MET podrían ser de interés en particular para los usuarios y desarrolladores del modelo WRF-chem preocupados por el pronóstico o la investigación de episodios con mala calidad del aire urbano.

1. Introduction

Improving air quality in many large cities requires a better understanding of the sources and transformation of pollutants in the atmosphere. The air quality models constitute a major tool to carry out this task. However, the process of verifying the model results can be even more important.

The Weather Research Forecast (WRF) model was developed at the National Center for Atmospheric Research (NCAR). Grell et al. (2005) and Fast et al. (2006) updates incorporated into the WRF the chemical transformations, complex gas-phase chemistry, photolysis, and aerosols, creating in this way the WRF-chem model. In order to work with the WRF-chem model outputs, there are several computing packages: NCL (NCAR, 2015); GrADS (COLA, 2015); NetCDF (UNIDATA, 2015); and the Unified Post-Processor (UPP), developed at NOAA (DTC, 2015). All of them are very useful to visualize and extract information. Also, there are statistical tools that serve to evaluate the performance of model simulations, in some cases comparing the simulation results against observations.

Recently, the Model Evaluation Tools (MET) (Gotway et al., 2014), a state-of-the-art suite of verification tools, was released by the Developmental Testbed Center (DTC) (http://www.dtcenter.org/met/users/index.php). It can perform a set of standard verification scores by comparing gridded model data with point-base observations and with gridded observations, among others. This kind of software provides useful information to the model users in order to improve the model performance by testing different model configuration setups, to improve the forecast and the decision making, and to identify forecast weakness and strengths. MET reads the output from UPP. In turn, the UPP code take in WRF output files (wrfout*) in NetCDF format. The original configuration of UPP can read several fields (eg, U, V, T, albedo) (see Baldwin et al., 2012, chapter 7, table 2). However, as far as we have seen, the chemical species oriented to the study of air quality are not included in these fields. Therefore, changes were made in the UPP source code and the MET configuration file to add new fields for air quality modeling evaluation.

In this document, a detailed description of modifications made in both UPP and MET is provided. These changes must be included to incorporate relevant chemical species (NO, NO2, SO2, CO, particulate matter, O3) and meteorological parameters into the verification process. These modifications have been tested in the UPPV3.1 (http://www.dtcenter.org/upp/users/) and METv5.2 releases, under a Linux 86-64 cluster with the corresponding Fortran, C and C++ Intel compilers. A script is given in Appendix B that allows controlling the desired flow through MET. However, the process to perform the evaluation is similar to the procedure described in the WRF-NMM users page for UPP and MET.

A specific episode of high weekend ozone concentration to illustrate the verification process is considered, where MET is used to verify agreement between simulated species concentrations and data form the Red Automática de Monitoreo Atmosférico (Automatic Atmospheric Monitoring Network, RAMA) in Mexico City. The episode corresponds to the “ozone weekend effect” reported in Stephens et al. (2008), which occurs when vehicular traffic emissions decrease during the weekend; the amount of ozone measured in the monitoring stations remains approximately the same or higher that during the weekdays.

2. UPP modifications

Modifications are needed in order to incorporate new chemical species in the post-processing data from the WRF-chem model. It is essential to modify the following files:

DEALLOCATE.f, INITPOST.F , MDLFLD.f, ALLOCATE_ALL.f, RQSTLD.f , VRBLS3D_mod.f and wrf cntrl.parm. Specific line codes for each file are presented in Appendix A.

To place data in a standard grib format, run the UPP tool unipost.exe provided script in UPP (run_unipost). The WRF-Chem considers the ARW core therefore the utility copygb was not used.

3. MET modifications

MET reads gridded forecast data for both gridded and point observations. The tools interpolate gridded fields to a point observation using user specified options. Point observations may be supplied in PREPBUFR or ASCII format. In our case the ASCII observation files are re-formatted by the ascii2nc tool to create an intermediate NetCDF file for point statistics evaluation by using the Point-Stat tool. The output NetCDF file can contain meteorological and chemical variables.

Example of variables that can be incorporated in the input file for ascii2nc:

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 11 776. -9999. 1 284.150

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 33 776. -9999. 1 -0.618

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 34 776. -9999. 1 0.329

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 52 776. -9999. 1 58.000

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 180 776. -9999. 1 2000.000

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 148 776. -9999. 1 1500.000

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 232 776. -9999. 1 6000.000

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 141 776. -9999. 1 23000.000

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 142 776. -9999. 1 30000.000

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 156 776. -9999. 1 127.000

ADPSFC 76201 20130101_000000 19.635 -98.912 2240. 157 776. -9999. 1 -9999.000

The names particle matter (coarse) and particle matter (fine) are used for PM10 and PM2.5, respectively. The chemical variables are set up in different parameter table versions (ON388, 2013) as shown in Table I. For the chemical variables CO, NO2, NO and SO2 the table version 141 has to be explicit. In order to do the comparison between model and observations in the configuration file (PointStatConfig) the following lines has to be modified. For meteorological variables temperature, u wind, v wind and relative humidity there is no need to set the grib table:

Table I Chemical species description and grib table version and code 

Note: μl m-3 = ppb.

field = [

{

name = “TMP”;

level = [ “L1” ];

cat_thresh = [ >273, >288, >293 ];

},

{

name = “UGRD”;

level = [ “L1” ];

cat_thresh = [ >=2 ];

},

{

name = “VGRD”;

level = [ “L1” ];

cat_thresh = [ >=2 ];

},

{

name = “RH”;

level = [ “L1” ];

cat_thresh = [ >=30 ];

},

for chemical compounds and particles, O3, NO, NO2, CO, SO2, PMTC and PMTF :

{

name = “OZCON”;

level = [ “L1” ];

cat_thresh = [ >=50, >=110 ];

GRIB1_ptv = 129;

},

{

name = “NO”;

level = [ “L1” ];

cat_thresh = [ >=105, >=210 ];

GRIB1_ptv = 141;

},

{

name = “NO2”;

level = [ “L1” ];

cat_thresh = [ >=105, >=210 ];

GRIB1_ptv = 141;

},

{

name = “CO”;

level = [ “L1” ];

cat_thresh = [ >=5500, >=11000 ];

GRIB1_ptv = 141;

},

{

name = “SO2”;

level = [ “L1” ];

cat_thresh = [ >=65, >=130 ];

GRIB1_ptv = 141;

},

{

name =”PMTF”;

level = [ “L1” ];

cat_thresh = [ >=65, >=130 ];

GRIB1_ptv = 129;

},

{

name =”PMTC”;

level = [ “L1” ];

cat_thresh = [ >=65, >=130 ];

GRIB1_ptv = 129;

{

];

For a demonstrative purpose, gas pollutants categorical threshold values were set on the 1-h average air quality standard (upper value) and half of the standard (lower value).

4. Results

A high weekend ozone episode was considered to show a comparison between model and observed data by using MET and UPP modified codes. The episode took place in Mexico City from 06:00 LT on April 13, 2007 through 03:00 LT on Apr 15, 2007. During that period, measurements of criteria pollutants were made by RAMA. Although it is possible to extract the model formaldehyde concentrations during this episode, this compound was not measured.

WRF-chem was configured to use the chemical mechanism RADM2 as the chemical module (Stockwell et al., 1990). The emissions inventory was gridded based on the National Emission Inventory for the Mexico City Metropolitan Area (MCMA) for the year 2006 (SMA, 2015). These emissions were updated to fill in a 3-km spatial resolution and simulations were carried out for a 40-h time period. Finally, the observational data, required by MET, is provided by the MCMA monitoring stations (RAMA). Figures 1 and 2 show examples where simulations are compared against monitoring stations data.

Fig. 1 Simulated and observed fields in ground based stations in Mexico City, from April 13, 2007 at 6:00 LT to April 14 at 03:00 LT. Filled circles correspond to measurements from the ground-based Air Quality Monitoring Network (RAMA) in the MCMA and lines (filled squares) are the simulations made with WRF-chem model 

Fig. 2 Simulated and observed averaged fields, from April 13, 2007 at 6:00 LT to April 14 at 03:00 LT, in Mexico City. Continuous line represents the average field calculated with the WRF-chem model, and filled circles represent the average measured field from RAMA in the MCMA. 

The Point-Stat tool computes several statistics to evaluate the forecast performance in monitoring stations. Figure 1 shows the simulated and observed concentrations of O3 and CO in ground-base stations. To the left, the ozone concentration in the Cerro de la Estrella (CES) monitoring station is shown. The simulations (continuous line with fill squares) fit well with the observation data (filled circles) in most of the time domains, except for the high ozone concentration time interval, where the simulations underestimate the ozone peak (Apr 14, 15:00 LT). Conversely, illustrated on the right side of Figure 1, the simulated CO concentrations at San Agustín (SAG) monitoring station are well fitted in the high measured ozone concentrations. The CO simulated concentrations systematically underestimate the measurements, but almost follow the same observed data pattern, which could indicate that the emission inventory should be modified in that case. Also it is possible that MET computes the grid average of fields; the Stat-Analysis tool provides verification statistics for a matched forecast and observation grid.

Figure 2 shows the point averages of the observations and simulations, the simulated variables (continuous line with fill squares) are compared against the average of all RAMA stations (filled circle). On the left in figure 2, the average ozone concentration against RAMA data is shown. The maximum ozone concentration occurred at 15:00h Apr 14 (Saturday) and is pretty close to 140 μL m-3 (ppbv), this value is bigger than the previous day maximum Apr 13 (Ozone weekend effect). The ozone numerical simulation concentrations underestimate the second ozone peak; the model concentration value is approximately 42% of the measured value. This pattern is presented in other monitoring stations indicating that the model underestimates the ozone concentration. In figure 2 (right panel) the average SO2 concentration for model and observations is shown. This chemical species is well reproduced by the model especially in the first hours of the high concentration episode.

Ambient concentrations depend on the emissions and weather conditions. In the studied episode, primary pollutants (CO and SO2) concentrations have a better agreement with the measurements than secondary pollutants (O3). The O3 concentration depends on primary pollutants like NO2 and Volatile Organic Compounds (VOC), and the highest ambient concentration is reached downwind from its precursor emissions. Because CO and SO2 concentrations from the model are in agreement with measurements, and they are primary pollutants that depend on local emissions, the high O3 concentrations on April 14 suggest that this pollutant was transported from elsewhere.

Additionally, Table II shows examples of several verification measurements for SO2, including the normal and bootstrap lower and upper confidence limits (NCL, BCL, NCU and BCU). In this case the threshold concentration were 130 ppb in order to compute the categorical statistics. The first parameter shown on this table correspond to the accuracy (ACC) contingency parameter. In our simulation ACC = 0.883, meaning the fraction of forecast that was correct, ACC ranges from 0 to 1, perfect forecast has an ACC value of 1.

Table II Statistical analysis results for SO2 evaluation. Accuracy (ACC), Hansen-Kuipers Discriminant (HK) and Heidke Skill Score (HSS) for 662 total observations. 

The two other parameters are the Hanssen-Kuipers Discriminant (HK) and the Heidke Skill. Its values range from -1 to 1. A perfect forecast has HK = 1, and a value of 0 indicates no skill. HSS is a skill score based on accuracy with values ranging from −∞ to 1. A perfect forecast will have an HSS = 1. For a more comprehensive description of these and other verification measurements, see Appendix C, Verification measures on the MET Users Guide v5.2.

5. Conclusions

The modifications made in the UPP code can provide files that include pollutant concentration variables (i.e., O3, CO, SO2, PM10 and PM2.5, among others); these files can be used by MET in order to evaluate objectively the model performance with a set of statistical parameters. A demonstration of the functionality of the code modifications was presented through its application in the case study, which showed that CO and SO2 model concentrations have a better agreement than O3 when compared to measured values. In the case of O3, the first day presents a good agreement, but for the second day its concentrations could indicate that it comes from elsewhere.

These code additions can reduce the time for data analysis and standardize the evaluation procedure for chemical and meteorological variables by using a state-of-the-art suite of verification tools.

Acknowledgment

The authors want to tanks Paul Oldenburg and John Halley Gotway from Meted-UCAR for their support.

References

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Appendix B.

Scripts to run MET

Received: October 14, 2016; Accepted: March 17, 2017

*Corresponding author; email: agustin@atmosfera.unam.mx

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