Sensitivity of AERMOD to AERMINUTE- Generated Meteorology

Transcription

1 Sensitivity of AERMOD to AERMINUTE- Generated Meteorology Paper No 22-A-42-AWMA Prepared By: George J. Schewe, CCM, QEP Principal Consultant Abhishek Bhat, PhD Consultant TRINITY CONSULTANTS 77 Dixie Highway Suite 9 Covington, KY 4 (859) 34-8 trinityconsultants.com June 9, 22 Environmental solutions delivered uncommonly well

2 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-99 s. In the coding used to report surface observations beginning July 996, a calm wind is defined as a wind speed less than 2 knots and is assigned a value of knots. The ASOS system also truncates values from 2. 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 -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 325). 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 Model was introduced to the regulatory dispersion modeling community in the late 99s. 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 b of the Guideline on Air Quality Models (GAQM), Appendix W, 4 CFR Part 5 2 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. AERSURFACE 3 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. AERMET 4 is the meteorological 2

3 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 995, surface data measured and archived by the NWS used a threshold velocity for the wind instruments of 2 knots (about 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 5-2% 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 AERMINUTE 5 program reprocesses these -minute readings to a lower threshold and does not truncate the values. These -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) TD355 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 AERMINUTE/AERMET will refer to data sets based on using -minute running 2- minute average winds from NOAA at ftp://ftp.ncdc.noaa.gov/pub/data/asosonemin/ along with NWS ISHD (integrated surface hourly data format) TD355 data for the surface meteorology as available from NCDC along with upper air soundings in the FSL format as obtained from the NOAA website METHODOLOGY Study locations were defined in several areas of the U.S. including Gainseville, Florida, Orangeburg, South Carolina, Harrisburg, Pennsylvania,, 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. 3

4 The year of data selected was 26 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. Table. 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 47, ISHD August 22, 28 KIAD, NWS 93734, FSL irardeau, Springfield KCGI, NWS 3935, 328VB December 6, 26 KSGF, NWS 3995, 62FB, Aberdeen KFAR, NWS 2753, September 26, 26 KABR, NWS 4929, FSL CD44 Orangeburg, Charleston KOGB, NWS 53854, ISHD April 29, 29 KCHS, NWS 388, FSL Gainesville, Jacksonville KGNV, NWS 286, ISHD March 9, 27 KJAX, NWS 3889, 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 CD44 and 32VB while the remainder was in the ISHD format (TD355-Full). Upper air data was primarily in the FSL format with one file in the 62FB format. AERMET can and does read and process any of the above formats. The latest version (59) 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 325) was used to process the -minute data as input to AERMET for each of the meteorological stations processed for use in AERMOD. The -minute wind data was obtained from the NCDC s online ftp directory (website above) in the TD645 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 -minute wind data consists of running sequential 2-minute average winds that are reported every minute at each ASOS station. The archived -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 6 -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 26. The NWS website 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 -minute winds from the - 4

5 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 2 standard 3 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 -. Table 2 presents the comparison of average wind speeds and number of calms. Figure. KCGI AERMET 5

6 Figure 2. KCGI AERMET/AERMINUTE Figure 5. KFAR AERMET Figure 3. KGNV AERMET Figure 4. KGNV AERMINUTE/AERMET 6

7 Figure 6. KFAR AERMINUTE/AERMET Figure 9. KMDT AERMET Figure 7. KORG AERMET Figure 8. KORG AERMINUTE/AERMET 7

8 Figure. 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/AER MET Calms, % AERMINUTE/AER MET Average Wind Speed, m/s Harrisburg, Dulles 23.5% % 3.4 irardeau, Springfield 22.8% % 3.43, Aberdeen 5.72% % 5.3 Orangeburg, Charleston Gainesville, Jacksonville 23.8% % % % 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 -minute data is considered. Also Table 2 shows that the number of calms decreases when considering the -minute data, sometimes in dramatic fashion as in the case of irardeau where the number of calms dropped from 22.8% to 2.48%. Interestingly, wind speeds increased slightly at each NWS site when considering the -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 -minute data in AERMET has is on the calculations that take place in AERMOD. To determine if any differences in concentration estimates 8

9 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 26 found at website ( Table 3. Source Types and Parameters Source Type Height, m Diameter,m Temp, K Velocity, m/s Emissions, g/s Stack Stack Stack Stack Area 2 = Length 2 = Width -. AreaCircle Volume 2 = zo 2 = yo -. These sources were modeled using AERMOD (Version 26) for averaging periods of 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 75m from the center at about a 65m spacing. All other receptors were set up in a Cartesian grid at spacings of 5m out to 5m, m out to 5m, and 25m out to 35m. 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. indicate an expected increase or and ratios greater than. indicate an expected decrease in concentration, while a ratio equal to. indicates no expected change in concentration. Similarly, ratios of 2. and.5 indicate a halving or doubling of the concentrations, respectively. Figures though 6 present comparisons for the various source types and averaging periods. As can be seen from Figures -7, 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 -minute winds (ratios less than.). 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 met data sets as that was the only station to have sonic anemometers installed in a portion of the 26 data set). The combination of these two factors results in many hours originally reported as calm being replaced with non-calm 9

10 wind data, and occasionally with wind speeds that are less than 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. All Sites, 65 m Stack OrangeB hr 3-hr 8-hr 24-hr Annual Figure 2. All Sites, 35 m Stack OrangeB hr 3-hr 8-hr 24-hr Annual

11 Figure 3. All Sites, 2 m Stack Figure 4. All Sites, m Stack CapeG OrangeB Orange B.2.2 hr 3-hr 8-hr 24-hr Annual hr 3-hr 8-hr 24-hr Annual Figure 5. All Sites, Area Figure 6. All Sites, AreaCircle.9 AREA.4 AreaCircle OrangeB OrangeB..2 hr 3-hr 8-hr 24-hr Annual hr 3-hr 8-hr 24-hr Annual Figure 7. All Sites, Volume VOLUME CapeG Orange B.2 hr 3-hr 8-hr 24-hr Annual

12 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, 2m and m but rather consistently giving higher concentrations using the -minute data. Another comparison of the results is shown in Figures 8 and 9 for just the two extremes of averaging time, namely, -hour and annual. In these figures the sources are compared to one another to discern the affect of the -minute data by source type. As can be seen in Figure 8, the Area source was affected the most with concentrations ranging from about.2 to.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.6-., with the best agreement between data sets in. This was expected given the higher average wind speed in (5.3 m/s) than other sites and the low frequency of calms before the -minute data was even considered. Figure 8. All Sites, All Sources, -Hr Figure 9. All Sites, All Sources, Annual.2 -hr by Source Type.4 Annual by Source Type OrangeB Orange B AREA CIRC VOL AREA CIRC VOL 2

13 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 -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. User s Guide for the AMS/EPA Regulatory Model - AERMOD. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Revised September Guideline on Air Quality Models. Appendix W to 4 CFR Parts 5 and 52. FederalRegister, November 9, 25. pp AERSURFACE Users Guide, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, User s guide for the AERMOD Meteorological Preprocessor (AERMET), EPA-454/B-3-2, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Under Revision, November AERMINUTE User s Instructions, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Date given on File December 2, 2. KEYWORDS AERMOD, AERMINUTE, dispersion, meteorology, calms 3