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Potential Benefits And Implementation of MM5/RUC2 Data with

the CALPUFF Air Modeling System

Paper No. 04-A-314-AWMA

Prepared By:

Jeff A. DeToro - Project Manager

TRINITY CONSULTANTS 12770 Merit Dr.

Suite 900 Dallas, TX 75251

+1 (972) 661-8881 trinityconsultants.com

Potential Benefits and Implementation of MM5/RUC2 Data with the CALPUFF Air Modeling System ABSTRACT

In April 2003, U.S. EPA officially revised the federal guidelines for air quality modeling (40 CFR Part 51, Appendix W) to incorporate CALPUFF as the preferred dispersion model to study long-range pollutant transport for regulatory purposes. At the absolute minimum, CALMET (CALPUFF’s meteorological preprocessor) requires hourly measurements of surface meteorological data and twice-daily upper air data soundings (along with terrain and land-use characteristics) as input to generate three-dimensional gridded fields of meteorology to be used in CALPUFF. These measurements come from a variety of sources, some of which are traditional/well-known, while others are new and emerging. During the past decade, a new source of meteorological data suitable for dispersion modeling applications has resulted from the emergence of next-generation weather forecasting models. Meteorological output from prognostic mesoscale forecast systems, such as the Penn State/NCAR Fifth-Generation Mesoscale Model (MM5) and Rapid Update Cycle Model Version 2 (RUC2), provides superior input data for long-range pollutant transport studies. This is due to the increased spatial and temporal resolution of atmospheric data fields. Prognostic mesoscale forecast systems operate by assimilating all available data and by using meteorological models to result in a consistent analysis of the current atmospheric conditions. This initial analysis is used as the starting point for numerical forecasting of conditions at some hourly intervals in the future. It is the analysis of the current conditions that is the desired information to use as input to the air quality models. Meteorological model analysis data allow CALMET to derive the meteorological input fields for CALPUFF much more accurately than would be possible from traditional National Weather Service data sets. As a result, the analysis data enable CALPUFF to provide greatly improved modeling results. CALPUFF modeling that uses meteorological model analysis data meets the requirements of the revised federal modeling guidelines; therefore, associated modeling results would be acceptable to regulatory agencies.

INTRODUCTION CALPUFF Modeling System In April 2003, U.S. EPA officially revised the federal guidelines for air quality modeling (40 CFR Part 51, Appendix W) to incorporate CALPUFF as the preferred dispersion model to study long-range pollutant transport for regulatory purposes.1 CALPUFF is a non steady-state, Lagrangian dispersion model that provides significant improvement in atmospheric motion simulation, especially across large horizontal distances (i.e., 50 kilometers or more) when compared to the traditional steady-state, Gaussian dispersion models. Although CALPUFF’s main regulatory use thus far has been limited mostly to the analysis of pollutant impacts on federal Class I areas, advances in computer hard drive storage capacity and processor speed now also allow the air quality community to utilize CALPUFF for short-range modeling studies. In fact, it is likely that CALPUFF, or another as-yet-to-be developed Langrangian model, will eventually replace Gaussian models altogether as the preferred tool for all regulatory dispersion modeling endeavors. CALMET is the preprocessor component of CALPUFF that generates the necessary three-dimensional meteorological data fields for the main model. At a minimum, CALMET requires hourly observations of surface meteorological conditions and twice-daily observations of upper air conditions, both of which are collected by the National Weather Service (NWS). This type of meteorological data is commonly referred to as “diagnostic” since the observations describe, or “diagnose”, the current meteorological conditions. These observational meteorological data are combined with geophysical data inputs that characterize terrain and land-use for input to CALMET. The primary shortcoming associated with meeting only the minimum data requirements of CALMET is that the NWS network of meteorological data collection lacks sufficient horizontal, vertical, and temporal resolution to accurately depict atmospheric motions in a typical CALPUFF modeling domain, which may be hundreds of kilometers in length and width. When data resolution is lacking, CALMET must rely more heavily on interpolation techniques to estimate wind, temperature, and turbulence fields throughout the domain, which reduces the overall accuracy of the modeling meteorology and, subsequently, the modeling results. Meteorological Model Analysis Data A second type of meteorological data that is available to the air quality community is generated by “prognostic”, or predictive, meteorological forecast models. These models use actual observations in conjunction with basic equations of atmospheric physics to predict a wide variety of additional meteorological parameters that are not routinely measured. These parameters are determined at many vertical levels over a uniformly spaced horizontal grid that covers a wide geographic area (i.e., on a continental scale). Prognostic mesoscale forecast systems operate by assimilating all available data and by using meteorological models to result in a consistent analysis of the current atmospheric conditions. This initial analysis is used as the starting point for numerical forecasting of conditions at some hourly intervals in the future. It is the analysis of the current conditions that is the desired information to use as input to the air quality models

(referred to henceforth as “meteorological model analysis data”). As might be expected, the number of calculations needed to generate such data sets requires significant computing resources; thus, this activity is normally accomplished in a government or university setting. Two of the more well-known prognostic meteorological data models are the Penn State/National Center for Atmospheric Research (NCAR) Fifth-Generation Mesoscale Model (MM5) and the Rapid Update Cycle Model Version 2 (RUC2). MM5 is the latest version of a forecast modeling system that was developed at Penn State University in the early 1970s to primarily predict mesoscale atmospheric phenomena (i.e., those that occur on the order of tens of kilometers).2 The model is currently supported by NCAR and has been widely available for use by air quality modelers over the past decade. MM5 can be initialized with forecast output data from any one of a number of different weather prediction models (including RUC2, which is described below). Many regulatory CALPUFF analyses have been performed using MM5 data in recent years. In fact, a few regulatory agencies have become comfortable enough working with the data set to archive entire years of MM5 meteorological data specifically for CALPUFF modeling in their respective regions. RUC2 (released in 1998) is the latest version of a newer forecast model prediction system that was developed to support users who require short-range weather forecasts.3 RUC2 is operated under the supervision of the National Centers for Environmental Prediction (NCEP). As the name indicates, this model is rapidly updated with the latest observations from a number of sources, including weather satellites, upper air balloons, NWS surface stations, wind profilers, Next-Generation Weather Radar (NEXRAD), commercial aircraft, and buoys/ships. RUC2 data can be utilized as a stand-alone data source for atmospheric dispersion modeling, or as an initialization database for MM5 forecast output. Because it is relatively new and has significant data-handling requirements (extremely large files), RUC2 has not yet gained widespread use in the air quality community. The following sections describe the main benefits of using MM5/RUC2 meteorological data sets in a dispersion modeling context and outline certain issues that have been identified regarding their practical implementation. This paper is not intended to be an exhaustive analysis of specific data set features, nor an in-depth technical discussion on appropriate CALMET settings when processing these data sets. Rather, the author provides basic information and awareness about MM5/RUC2 to interested parties. MM5 BENEFITS When compared to traditional meteorological data, MM5 offers many benefits to the air quality modeler, especially greatly increased horizontal resolution of meteorological data points. For example, North Dakota has 10 NWS surface weather-observing stations that collect meteorological data suitable for dispersion modeling and only one NWS upper-air observing station. In traditional modeling efforts, data from these stations would be used in an attempt to characterize atmospheric conditions over the entire 69,000 square-mile area of the state. Obviously, there would be many important weather phenomena that would be “missed” by this sparse set of observations. Because MM5 is designed to

simulate mesoscale meteorological phenomena, users have the ability to generate meteorological data from MM5 for input to CALMET at gridpoints spaced only 36 kilometers (km) apart, and as small as 1.33 km apart over smaller areas. This would allow one to set up a modeling domain over North Dakota with more than 100 surface meteorological data points (with 36-km spacing) covering the study area. In this example, MM5 produces at least a ten-fold increase in resolution over NWS data for CALMET to derive the necessary meteorological data fields. Increased resolution produces better air quality analyses. A second consideration is vertical resolution. While NWS balloon soundings record basic meteorological variables (i.e., temperature, pressure, winds, and humidity) at approximately 20 vertical levels above ground with every sounding, each MM5 data point can include over 50 vertical levels of meteorological data, which is more than double the number of readings from the soundings. This increased vertical resolution allows for much better characterization of the atmosphere’s vertical structure at any given time, especially in the boundary layer, which is where the majority of pollutant dispersion occurs. The MM5 data sets also allow modelers to overcome a significant shortcoming that has been associated with NWS soundings for many years, which is the forced extrapolation of hourly vertical conditions from soundings that are only conducted twice a day (usually near sunrise and near sunset when the atmosphere is transitioning the most). Because MM5 data contain vertical profiles for each hour, the simulation of the atmosphere during the hours that occur in-between NWS soundings can be greatly improved when utilizing this data source in CALMET. Another factor is the breadth of meteorological parameters available to the air quality community. Traditional meteorological observations are somewhat limited with respect to winds, temperature, turbulence, and moisture measurements. Since weather forecast models are run by supercomputing systems, the algorithms contained within the models can be significantly more complex and robust than those used in typical dispersion models. These complex algorithms require a wide range of weather parameters as inputs, many of which are incorporated into MM5 data sets, including (but not limited to):

• Vertical Heat Fluxes • Precipitation Types/Amounts • Available Moisture • Wind Vector Components • Atmospheric Instability Parameters

It is important to note that the MM5 data also include all of the parameters collected by NWS stations. To summarize, it should be again noted that the MM5 system was originally designed with an emphasis on mesoscale atmospheric simulation. As such, the output from this model is uniquely suited to characterizing pollutant dispersion in air quality studies. All of the benefits of MM5 outlined above would primarily allow CALPUFF to greatly

reduce the amount of interpolation required when deriving meteorological fields, thus minimizing errors in the calculations.4 MM5 IMPLEMENTATION As a practical matter, MM5 data sets are currently being utilized in a number of regulatory modeling studies with CALPUFF. Some regulatory agencies (in the Pacific Northwest, for example) even offer MM5 data sets to the air quality community, which is a step that had been taken earlier with meteorological data files for older models (such as ISCST3). Modelers can easily customize the MM5 data set (i.e., use data pertaining to a specific forecast model initialization and a specific modeling domain region) depending on the requirements of a particular application. Other than the time investment needed to become familiar with MM5 data and learn how to properly manipulate the data for input to the dispersion models, there is generally little/no cost to obtain and process the data. There are a few items to keep in mind when considering MM5 data as a potential source of meteorological data for dispersion modeling. The data sets can be significantly larger (typically on the order of several megabytes) than traditional meteorological data files; thus, increased computing resources may be needed to run, store, and manage MM5 data. One must also be aware of the increased preparation/processing time and CALMET execution time that may be required. Although MM5 has been used in regulatory applications for the past few years, many regulatory agencies have not yet worked with MM5 data, so they would not likely have the computational resources to properly review a CALPUFF analysis that uses MM5 data. It is also important to note that, although a great deal of interpolation is eliminated when using MM5 in conjunction with CALMET, some data interpolation may still be required if the underlying forecast model used to initialize the MM5 data set does not generate hourly output. Although dispersion modeling with MM5 is relatively new, U.S. EPA has documented some basic guidance pertaining to meteorological model analysis data use in regulatory applications. These include (but are not limited to):

• Only forecast model initial analyses (i.e., 0-hour data) should be used to derive meteorological data sets for modeling, not forecast predictions.5

• It is not recommended that individual meteorological model data points be

represented as “observations” or “pseudo-stations” in CALMET without also including traditional observations. However, U.S. EPA will allow certain CALMET adjustments to be made (i.e., use small radius of influence values to adjust diagnostic winds in Step 2 to preserve prognostic winds) as a measure of compensation. 5

• Modelers utilizing meteorological model analysis data (such as MM5) may be

allowed to conduct their regulatory air quality studies with less than the “traditional” five years of meteorological data that is normally required when modeling solely with NWS data.

RUC2 BENEFITS As with MM5 data sets that are initialized using output data from other forecast models, RUC2 (either alone or in combination with MM5) offers a number of the same benefits over traditional meteorological data. Since RUC2 is specifically designed to provide accurate, short-term weather forecasts, RUC2 has the added advantage of more frequent (hourly) output than most forecast models, which may offer output on only a 3- or 6-hour basis. Thus, the RUC2 hourly representations of atmospheric conditions further reduce the amount of interpolation required by CALMET to derive the hourly meteorological fields for CALPUFF. RUC2 can also provide higher horizontal data resolution over most MM5 applications since its meteorological grid can be derived with spacing as small as 20 km. Another consideration is the number of actual meteorological observations that are assimilated into RUC2. RUC2 includes many observations that may not be incorporated into other forecast models, including (but not limited to):

• NEXRAD-measured Winds • Wind profiles from VHF Wind Profilers • Satellite Data • Temperature and wind soundings from commercial Aircraft • Observations from buoys and ships

This feature allows RUC2 to provide higher-quality estimates of certain meteorological parameters than other models. For example, NWS surface winds are measured by anemometers that have a starting threshold (generally between 0.5 and 1.5 meters per second) below which the observations are subject to inaccuracies.5 Ironically, these light or calm wind conditions often result in worst-case pollutant impacts due to the lack of atmospheric dispersion. Since NEXRAD Doppler radar is a remote-sensing platform (i.e., it does not involve physical instrumentation), NEXRAD wind observations are typically more accurate at lower speeds. RUC2 is also able to better characterize upper air conditions since its underlying observations (e.g., 6 or 10-minute NEXRAD-measured winds) are much more frequent than the twice-daily NWS soundings used in other models. As a result, transient boundary-layer phenomena such as nocturnal low-level jets are more likely to be incorporated into RUC2 data, and subsequently CALMET, during processing. These boundary-layer phenomena can be important in atmospheric dispersion. These additional benefits of RUC2 over MM5 data sets that are initialized using other forecast model output allow CALMET to further reduce the amount of interpolation required when deriving meteorological fields, even further minimizing errors in the calculations.6 RUC2 IMPLEMENTATION To summarize, the main operational advantage of using RUC2 in dispersion modeling is that it incorporates more “actual” observations than any other meteorological data source.

RUC2 data sets are particularly well suited to simulate rapid changes in atmospheric motion over short time scales due to its design as a short-term forecast model. Although it appears that RUC2 is poised to become the future standard in meteorology for air pollution modeling, it has some limitations. Due to the extremely large amount of data that are assimilated into RUC2 output, the computing resources required to run, store, and manage these data sets are currently cost-prohibitive for most users in the air quality community. At the time of this writing, only one vendor (Software Solutions and Environmental Services Company [SSESCO] of St. Paul, MN) currently offers CALMET-ready RUC2 at a premium price. Unlike most forecast models, RUC2 is presently a limited area model (i.e., its domain does not cover the entire planet); therefore, data may not be available for certain geographical areas. It is also important to note that RUC2 was designed to forecast synoptic (as opposed to mesoscale) weather conditions; therefore, its boundary layer characterization may not be as accurate as MM5 in some cases. See the bulleted list in the “MM5 Implementation” section of this paper for a summary of basic U.S. EPA guidance when utilizing RUC2. Since it is a meteorological model analysis data set, this same collection of guidelines applies to RUC2. CONCLUSIONS The recent updates to the federal guideline for air quality modeling (40 CFR Part 51, Appendix W) specifically included a number of references regarding the use of prognostic meteorological data for long-range transport and complex wind situations.1 In addition, the revised guideline emphasizes that the ability of a meteorological data set to accurately construct boundary layer profiles/three-dimensional fields and minimize reducible uncertainty in modeled results are key criteria when selecting meteorology for dispersion modeling. U.S. EPA indicates a clear preference in the revised guideline towards the use of mesoscale meteorological data for regulatory dispersion modeling by stipulating that a shorter time period can be modeled (minimum of three years, not necessarily consecutive) versus traditional meteorological data (five consecutive years) to derive accurate results.1 It is clear that meteorological model analysis data sets such as MM5 and RUC2 represent the best available means to satisfy these criteria in a regulatory modeling setting. As these data sets evolve and become more widespread, it is expected that official, detailed guidance will be developed by the air quality community to ensure that meteorological model analysis data sets are properly used by CALPUFF modelers. ACKNOWLEDGMENTS The author would like to thank a number of Trinity Consultants peer reviewers (Bruce Turner, Dr. Erwin Prater, Ryan Gesser, and Tony Schroeder) who reviewed of the draft manuscript. The author would also like to acknowledge the efforts of the Lignite Vision 21 Program, Barr Engineering Company, Russell Lee, and Gale Biggs and Associates in providing the basis for this study and helping to develop much of the information presented within.

REFERENCES 1. Code of Federal Regulations, Title 40, Part 51, Appendix W (Guideline on Air Quality

Models), July 1, 2003. 2. MM5 Community Model Homepage (www.mmm.ucar.edu/mm5/overview.html). 3. SSESCO Weather Data Archives, RUC2 Overview

(www.ssesco.com/weatherdata.html). 4. Memorandum to Jeff Burgess (Lignite Vision 21 Program) from Eric Edwalds (Barr

Engineering Company), Russell Lee, Gale Biggs (Gale Biggs and Associates), and Jeff DeToro (Trinity Consultants), MM5 Meteorological Data for CALPUFF Modeling, March 25, 2003.

5. Report to the North Dakota Department of Health (NDDH) from ENSR International,

Response to NDDH Comments on ENSR’s Initial CALPUFF Submittal, March 2003. 6. Memorandum to Jeff Burgess (Lignite Vision 21 Program) from Eric Edwalds (Barr

Engineering Company) and Jeff DeToro (Trinity Consultants), RUC2 Data Application for CALPUFF Modeling, May 22, 2003.