sensitivity of aermod to aerminute-generated meteorology

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Sensitivity of AERMOD to AERMINUTE- Generated Meteorology

Paper No 2012-A-421-AWMA

Prepared By:

George J. Schewe, CCM, QEP – Principal Consultant Abhishek Bhat, PhD – Consultant

TRINITY CONSULTANTS

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trinityconsultants.com

June 19, 2012

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ABSTRACT

Recent modeling studies in support of regulatory permitting, SO2 nonattainment modeling, and other AERMOD Model applications have switched to the use of AERMINUTE-enhanced meteorological data sets. Surface meteorological data collected by the National Weather Service (NWS) are often used as the source of meteorological data for AERMOD. Over the past several years the use of NWS data resulted in a high incidence of calms and variable wind conditions as reported for the Automated Surface Observing Stations (ASOS) now in use at most NWS stations since the mid-1990’s. In the coding used to report surface observations beginning July 1996, a calm wind is defined as a wind speed less than 2 knots and is assigned a value of 0 knots. The ASOS system also truncates values from 2.1 to 2.9 knots to 2 knots and thus, all values 2.9 or lower are set to a calm. AERMOD currently cannot simulate dispersion under calm or missing wind conditions. To reduce the number of calms and missing winds in the surface data, archived 1-minute winds for the ASOS stations may be used to calculate hourly average wind speed and directions. EPA released a processor due perform these calculations, namely, AERMINUTE (latest version is 11325). These AERMINUTE generated wind speeds and wind directions may be used to supplement the standard NWS archive of hourly observed winds processed in AERMET. This paper presents the results of applying meteorological data sets with and without the supplemental AERMINUTE data. Air concentrations for various source types are modeled representative if an industrial facility point, area, and volume sources representing stacks, transfer points, storage piles and roads. Meteorology from various U.S. regions is used along with the land use characteristics for each airport. Comparisons of the concentrations for various source types and meteorological data sets are made. INTRODUCTION The AERMOD Model1 was introduced to the regulatory dispersion modeling community in the late 1990s. AERMOD was developed specifically by the AMS/EPA Regulatory Model Improvement Committee (AERMIC) to employ best state-of-practice parameterizations for characterizing the meteorological influences on dispersion. Section 4.2.2.b of the Guideline on Air Quality Models (GAQM), Appendix W, 40 CFR Part 512 states that AERMOD is the recommended model for “a wide range of regulatory applications in all types of terrain” thus, AERMOD is the primary refined analytical technique for modeling traditional stationary sources. Provided along with the AERMOD Model are a number of preprocessors for preparing data sets applicable to running the AERMOD algorithms for transport, dispersion, convective boundary layer turbulence, stable boundary layer, terrain influences, building downwash, and land use. These are AERMAP, AERSURFACE, and AERMET. AERMAP is used to process elevation data from digitized data sets to generate elevations of receptors, sources, and structures as well the critical height for each receptor. AERSURFACE3 uses land use land cover (LULC) data to calculate albedo (reflectivity of the earth’s surface), Bowen Ratio (ratio of sensible to latent heat), and the surface roughness parameter (related to the height of obstructions but more of a measure of the height above ground where the wind speed approaches zero) which can vary on an annual, seasonal, or monthly basis for one or up to twelve sectors around a site. AERMET4 is the meteorological

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data processor that uses a combination of surface observation data from the National Weather Service (NWS), upper air data from NWS, onsite data if available and meeting prescribed collection and quality assurance criteria, and albedo, Bowen Ratio, and surface roughness parameters from AERSURFACE.

Prior to about 1995, surface data measured and archived by the NWS used a threshold velocity for the wind instruments of 2 knots (about 1 m/s). EPA adjusted for this lower limit of wind speed by making any value below 2 knots equal to a calm in AERMET. The NWS still uses 2 knots as the threshold velocity but also truncates up to 2.9 knots to 2 thus, making a wider range of calms. This has been noted in recent data sets where periods of calm up to 15-20% are common. Thus, true low wind speeds are not being considered and the number of hours in the data set is much reduced. Rather than accepting the archived NWS wind speed and direction as the best representation of each hour, the AERMINUTE5 program reprocesses these 1-minute readings to a lower threshold and does not truncate the values. These 1-minute values are then averaged for all values that are considered valid readings resulting in a one-hour wind speed and wind direction. While it is certainly beneficial to have these values filled in and have fewer calm periods, it is also of concern that potentially low wind speeds will be used in AERMOD. Of concern is the fact that AERMOD has not been shown to perform well in low wind speed conditions and the number of these conditions has increased. This may become more apparent in terms of the ambient air concentrations generated with AERMINUTE data sets and compared to AERMET without AERMINUTE. For the remainder of this paper the following will apply:

AERMET will refer to data sets based on using straight NWS ISHD (integrated surface hourly data format) TD3505 data for the surface meteorology as available from the National Climatic Data Center (NCDC) along with upper air soundings in the FSL format as obtained from the National Oceanic and Atmospheric Administration website http://www.esrl.noaa.gov/raobs/. AERMINUTE/AERMET will refer to data sets based on using 1-minute running 2-minute average winds from NOAA at ftp://ftp.ncdc.noaa.gov/pub/data/asos-onemin/ along with NWS ISHD (integrated surface hourly data format) TD3505 data for the surface meteorology as available from NCDC along with upper air soundings in the FSL format as obtained from the NOAA website http://www.esrl.noaa.gov/raobs/.

METHODOLOGY Study locations were defined in several areas of the U.S. including Gainseville, Florida, Orangeburg, South Carolina, Harrisburg, Pennsylvania, Fargo, North Dakota, and Cape Girardeau, Missouri. The diversity of these locations insured that no one location with its specific climatological characteristics would dominate the analysis or influence the results.

http://www.esrl.noaa.gov/raobs/�

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The year of data selected was 2006 which gave a mix of whether the ice free winds (IFW) instruments were being used or not (different sites have changed over to sonic anemometers at different times). The specific sites used in the comparisons in AERMET are shown in Table 1. Table 1. Sites Used AERMET Vs. AERMINUTE/AERMET Comparisons

Surface/Upper Air Sites Surface Data Ice Free Winds Start Date Upper Air Data

Harrisburg, Dulles KMDT, NWS 14711, ISHD August 22, 2008 KIAD, NWS 93734, FSL Cape Girardeau, Springfield KCGI, NWS 03935,

3280VB December 16, 2006 KSGF, NWS 13995,

6201FB Fargo, Aberdeen KFAR, NWS 27530,

CD144 September 26, 2006 KABR, NWS 14929, FSL

Orangeburg, Charleston KOGB, NWS 53854, ISHD April 29, 2009 KCHS, NWS 13880, FSL Gainesville, Jacksonville KGNV, NWS 12816, ISHD March 9, 2007 KJAX, NWS 13889, FSL After these sites were selected, both the standard NWS suface data and upper air files were obtained. Some surface data sets were based on older formats CD144 and 3201VB while the remainder was in the ISHD format (TD3505-Full). Upper air data was primarily in the FSL format with one file in the 6201FB format. AERMET can and does read and process any of the above formats. The latest version (11059) of AERMET was used to process these data sets into AERMET output format suitable for processing in AERMOD. For the AERMINUTE/AERMET processing the latest version of AERMINUTE (dated 11325) was used to process the 1-minute data as input to AERMET for each of the meteorological stations processed for use in AERMOD. The 1-minute wind data was obtained from the NCDC’s online ftp directory (website above) in the TD6405 format which is compatible with the AERMINUTE program. The downloaded data consists of text files; each text file contains data for one station-month. The 1-minute wind data consists of running sequential 2-minute average winds that are reported every minute at each ASOS station. The archived 1-minute winds contained in the downloaded text files were used by AERMINUTE to calculate hourly average wind speed and direction which was then used to supplement the standard archive of hourly observed winds in the surface data. Because each hour could have up to 60 1-minute winds, these could be averaged to determine the winds and thereby reducing the number of calms, variable winds and missing data. The AERMINUTE preprocessor requires the start and end month and year of the data being processed as well as whether or not the station is part of the Ice Free Winds (IFW) group. The IFW group date refers to start of use at the ASOS site of sonic anemometers instead of cup and vane anemometers (which may have icing problems) to measure winds. If the station is part of the IFW group during the data period being processed by AERMINUTE, then the IFW installation date must be entered into the program. In this analysis each IFW date was entered into AERMINUTE to consitently report this date even if not applicable in 2006. The NWS website http://www.weather.gov/ops2/Surface/documents/IFW_stat.pdf was used to determine if the stations were part of the IFW group and their respective installation dates. AERMINUTE gives an option to include data files of standard NWS observations in order to compare the non-quality controlled 1-minute winds from the 1-

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minute data files against the quality controlled standard observations. This comparison was performed only for the Harrisburg, Orangeburg, and Gainesville sites which had ISHD data. The combination of the data sets described above was processed by AERMINUTE to produce the necessary hourly wind speed and direction file for merging with the NWS surface and upper air data. All other inputs to AERMET were set using regulatory default options (like random winds), airport specific coordinates and time, dates of processing, and airport specific roughness parameters, albedo, and Bowen ratio as generated by AERSURFACE (using the 12 standard 30 degree sectors). Ten sets of meteorological data were processed including five sets through AERMET and five sets through AERMINUTE/AERMET. Comparisons of the wind roses and the information pertaining to calms and average wind speeds is presented in Figures 1-10. Table 2 presents the comparison of average wind speeds and number of calms.

Figure 1. KCGI AERMET

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Figure 2. KCGI AERMET/AERMINUTE

Figure 3. KGNV AERMET

Figure 5. KFAR AERMET

Figure 4. KGNV AERMINUTE/AERMET

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Figure 6. KFAR AERMINUTE/AERMET

Figure 7. KORG AERMET

Figure 9. KMDT AERMET

Figure 8. KORG AERMINUTE/AERMET

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Figure 10. KMDT

AERMINUTE/AERMET

Table 2. Comparison of AERMET vs. AERMINUTE/AERMET Wind Speeds Surface/Upper Air

Sites AERMET Calms, % AERMET Average Wind speed, m/s

AERMINUTE/AERMET Calms, %

AERMINUTE/AERMET Average Wind

Speed, m/s

Harrisburg, Dulles 23.5% 3.33 10.89% 3.41

Cape Girardeau, Springfield 22.8% 3.28 2.48% 3.43

Fargo, Aberdeen 5.72% 4.79 1.18% 5.03

Orangeburg, Charleston 23.18% 2.81 12.1% 2.90

Gainesville, Jacksonville 23.63% 2.85 12.56% 2.93

As can be seen in making a qualitative comparison between the AERMET and AERMINUTE/AERMET data sets, more lower wind speeds occur when the 1-minute data is considered. Also Table 2 shows that the number of calms decreases when considering the 1-minute data, sometimes in dramatic fashion as in the case of Cape Girardeau where the number of calms dropped from 22.8% to 2.48%. Interestingly, wind speeds increased slightly at each NWS site when considering the 1-minute data. Thus, on one hand the number of low wind speeds increased (which means more low wind speeds) while overall the average wind speed increased. Of considerable interest that this use of 1-minute data in AERMET has is on the calculations that take place in AERMOD. To determine if any differences in concentration estimates

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results, a number of sources ranging from tall stacks to short stacks and area and volume sources were examined. Table 3 presents the source types reviewed in this analysis. Included with each stack configuration was an influencing building that could cause downwash. Most of these scenarios were derived from EPA test files for the new AERMOD, Version 12060 found at website (http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#aermod).

Table 3. Source Types and Parameters Source Type Height, m Diameter,m Temp, K Velocity, m/s Emissions, g/s

Stack 65 5.0 425 15.0 500 Stack 35 2.4 432 11.7 100 Stack 20 1.5 350 7.5 10 Stack 10 0.5 325 4.0 2.5 Area 1 20 = Length 20 = Width - 0.001

AreaCircle 10 40 - - 0.1

Volume 1 20 = zo 20 = yo - 0.1

These sources were modeled using AERMOD (Version 12060) for averaging periods of 1 hour, 3 hours, 8 hours, 24 hours, and annual for each NWS site and for each AERMET and AERMINUTE/AERMET set. A receptor grid was set such that fence line receptors were positioned around the sources at 175m from the center at about a 65m spacing. All other receptors were set up in a Cartesian grid at spacings of 50m out to 500m, 100m out to 1500m, and 250m out to 3500m. All regulatory default options were exercised in AERMOD except that flat terrain was used. RESULTS Comparisons of the ambient concentrations between averaging times and source types were made to facilitate the determination of the differences that the meteorological data sets could have on an analysis. The concentrations estimated using the straight AERMET meteorological data were divided by the concentrations estimated using the AERMET plus AERMINUTE meteorological data to determine the ratio of the difference in concentration caused by the change. Ratios less than 1.0 indicate an expected increase or and ratios greater than 1.0 indicate an expected decrease in concentration, while a ratio equal to 1.0 indicates no expected change in concentration. Similarly, ratios of 2.0 and 0.5 indicate a halving or doubling of the concentrations, respectively. Figures 11 though 16 present comparisons for the various source types and averaging periods. As can be seen from Figures 11-17, the model results for the short term concentrations using the AERMINUTE/AERMET data sets are greater than for those just using the AERMET data with no 1-minute winds (ratios less than 1.0). This is expected given that a greater number of hourly values will be available for AERMOD processing and winds may be lower because of the increased sensitivity of sonic anemometers (but this would only affect Fargo met data sets as that was the only station to have sonic anemometers installed in a portion of the 2006 data set). The combination of these two factors results in many hours originally reported as calm being replaced with non-calm

http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#aermod�

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wind data, and occasionally with wind speeds that are less than 1 m/s. This is apparent in terms of the number of calms shown in Table 2. Also shown is that the AERMINUTE/AERMET data sets give somewhat lower concentrations for longer averaging periods.

Figure 11. All Sites, 65 m Stack


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Figure 15. All Sites, Area

Figure 17. All Sites, Volume


Figure 16. All Sites, AreaCircle

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While for the taller stacks, 65m and 35m the results across all sites is similar, more disparity is shown in the ratios of the concentrations across the averaging periods for the shorter stacks, 20m and 10m but rather consistently giving higher concentrations using the 1-minute data. Another comparison of the results is shown in Figures 18 and 19 for just the two extremes of averaging time, namely, 1-hour and annual. In these figures the sources are compared to one another to discern the affect of the 1-minute data by source type. As can be seen in Figure 18, the Area source was affected the most with concentrations ranging from about 0.2 to 0.7 for the AERMET concentrations verssu the AERMINUTE/AERMET concentrations. The same infrmation for the circular area source, CIRC was not as dramatic but still in the range of 0.6-1.0, with the best agreement between data sets in Fargo. This was expected given the higher average wind speed in Fargo (5.03 m/s) than other sites and the low frequency of calms before the 1-minute data was even considered.

Figure 18. All Sites, All Sources, 1-Hr

Figure 19. All Sites, All Sources, Annual

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FargoAnnvilleCape GOrange BGainsville

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CONCLUSIONS While it is not the interest of these authors to make such comparisons as above in the interest of saying one data set is better, or one data set is less or more conservative. Each data set has its merits with that of AERMINUTE to help define hourly winds for hours that in the past may have had a calm or no calculation performed. The possible downside to using the 1-minute data is exacerbating the known AERMOD problem in calculating concentrations for low wind speed conditions. Thus, the main conclusion to this analysis is that the user should be aware of the possible differences in concentrations for various source types and for various averaging periods.

REFERENCES

1. User’s Guide for the AMS/EPA Regulatory Model - AERMOD. U.S. Environmental

Protection Agency, Research Triangle Park, North Carolina. Revised September 2004.

2. Guideline on Air Quality Models. Appendix W to 40 CFR Parts 51 and 52. FederalRegister, November 9, 2005. pp. 68217-68261. 2005.

3. AERSURFACE Users Guide, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 2008.

4. User’s guide for the AERMOD Meteorological Preprocessor (AERMET), EPA-454/B-03-002, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Under Revision, November 2004.

5. AERMINUTE User’s Instructions, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Date given on File December 20, 2011.

KEYWORDS AERMOD, AERMINUTE, dispersion, meteorology, calms