Western Air Quality Modeling Study (WAQS) Weather Research

Western Air Quality Modeling Study (WAQS) Weather Research Forecast (WRF) Meteorological Model Winter Modeling Application/Evaluation Prepared by: Jared Bowden, Kevin Talgo, Zac Adelman UNC Institute for the Environment 100 Europa Dr., Suite 490 Chapel Hill, NC 27517 [email protected] August 19, 2015 CONTENTS 1 EXECUTIVE SUMMARY ...................................................................................................... ES-‐1 2 Introduction ........................................................................................................................ 2-‐1 3 4 5 6 7 2.1 2.2 Meteorological Modeling ......................................................................................................... 2-‐1 WSAQS Winter WRF Modeling ................................................................................................. 2-‐2 WRF Input and Physics Sensitivity ....................................................................................... 3-‐1 3.1 3.2 3.3 3.4 3.5 WRF Model Inputs for WRF-‐BASE Configuration ..................................................................... 3-‐1 WRF-‐SNODAS Configuration .................................................................................................... 3-‐3 WRF-‐SNODAS-‐NO_OBSNUDGE Configuration ......................................................................... 3-‐3 WRF-‐PX Configuration .............................................................................................................. 3-‐4 Evaluation Approach ................................................................................................................ 3-‐4 Quantitative Evaluation Using Surface Meteorological Observations ................................ 4-‐1 4.1 4.2 4.3 Monthly Quantitative Model Evaluation Results ..................................................................... 4-‐1 Diurnal Quantitative Model Evaluation ................................................................................. 4-‐11 Additional Results .................................................................................................................. 4-‐13 Cold Air Pool Meteorological Process Evaluation Using Observations .............................. 5-‐1 5.1 5.2 Persistent Cold Air Pool (PCAPS) Field Campaign Comparison ................................................ 5-‐1 Upper Green River Winter Ozone Study (UGWOS) Comparison ............................................. 5-‐6 Sensitivity Comparison ........................................................................................................ 6-‐1 6.1 6.2 WRF-‐SNODAS Comparison to WRF-‐BASE ................................................................................. 6-‐1 WRF-‐PX Comparison to WRF-‐BASE .......................................................................................... 6-‐5 Summary and Recommendations ....................................................................................... 7-‐1 TABLES Table 3-‐1. Model configuration used in the WRF-‐BASE simulation of winter seasons 2010-‐2011 and 2012-‐2013. .................................................................................................................... 3-‐5 Table 3-‐2. WRF Wintertime Sensitivity Experiments. ................................................................. 3-‐6 Table 3-‐3. Vertical layer definition for WRF simulations. ............................................................ 3-‐7 Table 3-‐4. Stations sites used in the for the WRF evaluation and sensitivity comparisons. ....... 3-‐8 Table 3-‐5. Dates of events evaluated for the different field campaigns. .................................... 3-‐9 Table 4-‐1. Meteorological model performance benchmarks for simple and complex conditions. ............................................................................................................................ 4-‐2 August 19, 2015 i FIGURES Figure 2-‐1. Conceptual model of meteorological processes that impact cold air pool formation and demise. .......................................................................................................................... 2-‐3 Figure 3-‐1. 36-‐km CONUS, 12-‐km WESTUS (d02), and 4-‐km 3SAQS (d03) WRF modeling domains. ............................................................................................................................. 3-‐9 Figure 3-‐2. Snow height (m) initial conditions on Feb. 8, 2011 from NAM (top) and SNODAS (bottom) ............................................................................................................................. 3-‐10 Figure 3-‐3. Locations of MADIS surface meteorological modeling sites with the WRF 4-‐km 3SAQS modeling domain. ................................................................................................... 3-‐11 Figure 4-‐1. Winter 2010-‐2011 soccer plot of monthly temperature error and bias (K) over CO, UT, and WY for WRF-‐BASE, WRF-‐SNODAS and WRF-‐PX configurations. .............................. 4-‐4 Figure 4-‐2. Winter 2012-‐2013 soccer plot of monthly mixing ratio error and bias (g/kg) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS and WRF-‐PX configurations. ....................... 4-‐5 Figure 4-‐3. Winter 2012-‐2013 soccer plot of monthly wind speed error and bias (g/kg) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS and WRF-‐PX configurations. ....................... 4-‐6 Figure 4-‐4. Winter 2010-‐2011 soccer plot of temperature error and bias (K) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS. ................................................................................. 4-‐8 Figure 4-‐5. Winter 2010-‐2011 soccer plot of mixing ratio error and bias (g/kg) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS. ................................................................................. 4-‐9 Figure 4-‐6. Winter 2010-‐2011 soccer plot of wind speed error and bias (K) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS. ...................................................................................... 4-‐10 Figure 4-‐7. Diurnal average temperature time series of standard deviation (red), mean absolute error (blue), and bias (green) for January 2013 over all stations in the 4-‐km domain. ...... 4-‐12 Figure 4-‐8. January and February 2011 and 2013 time series of temperature (K) for WRF-‐BASE, WRF-‐SNODAS, and observations over Utah. Highlighted sections for 2011 are known ozone exceedance events. ............................................................................................................ 4-‐13 Figure 4-‐9. January and February 2013 time series of temperature (K) for WRF-‐SNODAS, WRF-‐
PX, and observations over Utah. ........................................................................................ 4-‐13 Figure 5-‐1. Time-‐height plot of potential temperature and wind speed for January 1-‐10, 2011. The height levels (m) are provided on the y-‐axis for PCAPS. The corresponding sigma levels are plotted on the y-‐axis for the matching height levels from the WRF simulations. .......... 5-‐3 Figure 5-‐2. Initial snow depth from NAM used in the WRF-‐BASE configuration and SNODAS used in the WRF-‐SNODAS and WRF-‐PX configurations. ................................................................ 5-‐4 Figure 5-‐3. Time-‐height plot of potential temperature and wind speed for December 1-‐7, 2010. The height levels (m) are provided on the y-‐axis for PCAPS. The corresponding sigma levels are plotted on the y-‐axis for the matching height levels from the WRF simulations. .......... 5-‐6 Figure 5-‐4. Comparison between observations of WRF-‐BASE (red), WRF-‐SNODAS (green), WRF-‐
SNODAS-‐No_OBSNUDGE (blue) for snow depth (m), 2-‐m temperature (°C), wind speed (m/s), wind direction (°), relative humidity (%), and incoming shortwave (W/m2) radiation over Juel Springs, WY from March 1-‐4, 2011. ...................................................................... 5-‐8 Figure 5-‐5. Comparison between observations of WRF-‐BASE (red), WRF-‐SNODAS (green), WRF-‐
SNODAS-‐No_OBSNUDGE (blue) for snow depth (m), 2-‐m temperature (°C), wind speed August 19, 2015 ii (m/s), wind direction (°), relative humidity (%), and incoming shortwave (W/m2) radiation over Boulder, WY from February 13-‐16, 2011. .................................................................... 5-‐9 Figure 6-‐1. Time series of snow depth from March 1-‐5, 2011 for WRF-‐SNODAS (black) and WRF-‐
BASE (green). Two locations shown are Pinedale, WY and Roosevelt, UT. .......................... 6-‐3 Figure 6-‐2. Time-‐height difference plot (WRF_SNODAS-‐WRF_BASE) of potential temperature and wind speed. Two locations shown are Pinedale, WY and Roosevelt, UT as in Figure 6-‐1.
.............................................................................................................................................. 6-‐3 Figure 6-‐3. Difference plots (WRF_SNODAS –WRF_BASE) of snow depth, 2-‐m temperature, and 10-‐m wind speed on March 1, 2011 at 1200 UTC. ............................................................... 6-‐4 Figure 6-‐4. Time-‐height plot of potential temperature (top) and wind speed (bottom) for WRF-‐
SNODAS and the difference (WRF-‐SNODAS minus WRF-‐BASE) from March 1-‐5, 2011 for Altamont, UT. ....................................................................................................................... 6-‐4 Figure 6-‐5. WRF PBL height for WRF-‐SNODAS (black) and WRF-‐BASE (green) from March 1-‐5, 2011 for Altamont, UT. ......................................................................................................... 6-‐5 Figure 6-‐6. WRF PBL height (m) for WRF-‐PX (black) and WRF-‐BASE (green) from March 1-‐5, 2013 for two sites in UT. ............................................................................................................... 6-‐6 Figure 6-‐7. WRF PBL height (m) for WRF-‐PX (black) and WRF-‐BASE (green) from January 21-‐27, 2013 for two sites in WY. ...................................................................................................... 6-‐6 August 19, 2015 iii 1
EXECUTIVE SUMMARY The University of North Carolina (UNC) at Chapel Hill investigated improving the meteorological modeling for wintertime ozone formation near oil and gas development areas in the Rocky Mountain region. The pilot (October 2012 through September 2014) Three State Air Quality Study (3SAQS) performed meteorological modeling for the year 2011 using the Weather Research and Forecasting (WRF) model. The meteorological simulations used a 36 km continental U.S. (CONUS), 12 km western U.S. (WESTUS), and 4 km three-‐state domain (3SD) covering the states of Colorado, Wyoming, and Utah and neighboring areas. The 2011 meteorological fields were subsequently used in photochemical grid model (PGM) application and model performance evaluation (MPE). The MPE for 2011 identified areas of poor model performance within the three-‐state study region.1,2 Of particular concern was the wintertime model performance in two locations in the intermountain west. High winter ozone events have occurred in the Jonah-‐Pinedale Anticline Development (JPAD) area in the Upper Green River area of southwest Wyoming and the Uintah Basin, Utah, both sites for rural oil and gas development. The 3SAQS identified that WRF did not reproduce the low wind speeds, temperature inversions, and dry conditions associated with elevated winter ozone events. This work is to improve the representation of meteorological processes important in the buildup of wintertime ozone precursors. WRF configuration improvements will be implemented in an improved 2011 air quality modeling platform in the winter months, and in a preliminary 2014 modeling platform. Different groups have documented WRF configurations for simulating specific winter ozone episodes,3 but there has not been an effort to develop and test a wintertime WRF configuration that yields acceptable performance across all Western states. Here we take lessons learned from the 3SAQS meteorological modeling, prior studies, and input from other groups working on similar issues including the EPA, the University of Utah (UUtah), the Utah Department of Environmental Quality Division of Air Quality (UTDAQ), Wyoming Department of Environmental Quality (WDEQ), to improve the meteorological model performance during the winter months. The different WRF meteorological model configurations tested are 1) quantified for near surface observations as in the prior 2011 WRF MPE;4 2) compared with field campaign data for meteorological process evaluation; and 3) compared against each other to test the sensitivity of the WRF configuration. 1
UNC and ENVIRON. (2014a, August). Three-‐State Air Quality Study (3SAQS): weather research and forecasting model – year 2011 Modeling Application/Evaluation. Chapel Hill, NC: University of North Carolina–Chapel Hill and ENVIRON International Corporation. Retrieved from http://vibe.cira.colostate.edu/wiki/Attachments/Modeling/3SAQS_2011_WRF_MPE_v8_draft_Aug04_2014.pdf. 2
UNC and ENVIRON. (2014b, November). Three-‐State Air Quality Study (3SAQS): CAMx photochemical grid model draft model performance evaluation – simulation year 2011. Chapel Hill, NC: University of North Carolina–Chapel Hill and ENVIRON International Corporation. Retrieved from http://views.cira.colostate.edu/tsdw/Demo/Documents.aspx. 3
Ahmadov, R., McKeen, S., Trainer, M., Banta, R., Brewer, A., Brown, S., . . . Zamora, R. (2014). Understanding high wintertime ozone pollution events in an oil and natural gas producing region of the western US. Atmospheric Chemistry Physics, 14, 20295-‐20343. 4
UNC and ENVIRON, 2014a,b August 19, 2015 ES-‐1 Of particular concern are cold air pool meteorology processes, which are important contributors to poor air quality in the oil and gas development regions.5 One input considered to be an important source of error in the pilot study is snow cover. A misrepresentation in the snow cover or amount will change the albedo and near-‐surface actinic flux, thus impacting the photolysis rates and ozone concentrations.6 Misrepresenting the snow cover can also impact the mixing height in the oil and natural gas basins, such as changing the diurnal upslope/downslope flows from the surrounding mountains. In this study, we change snow cover and snow depth initial conditions from the North American Model (NAM) used in the 3SAQS WRF pilot study to that available from the Snow Data Assimilation System.7 We tested the WRF-‐
SNODAS configuration for two winter seasons, December 2010 – March 2011 and December 2012 – March 2013. The WRF-‐SNODAS configuration was run in 5-‐day segments as in the prior 3SAQS with a half-‐day spinup period, enabling each run segment to be updated frequently back to the SNODAS values. Our results indicate that the WRF-‐SNODAS configuration improves the wintertime meteorology for reasons listed below. •
WRF-‐SNODAS configuration statistics of near surface temperatures improve within the 3SD when compared with MADIS sites. In particular, warm bias at night improves in the WRF-‐SNODAS configuration for sites within Utah. The WRF-‐SNODAS temperature improvements are relative to the snow cover and height and can change from year to year. We recommend modeling multiple years because the snow cover exhibits large interannual variability and impacts the local meteorology bias and error and PGM results. Other near surface variables, such as mixing ratio and wind speed/direction, show smaller improvements at the MADIS sites, but some of the largest differences for these meteorological fields extend above the near surface. •
WRF-‐SNODAS configuration improves the representation of cold air pool meteorological processes when comparing with the Persistent Cold Air Pool Study (PCAPS) field campaign data. For the Uintah Basin and the Upper Green River Winter Ozone Studies, most observations are near surface or limited to areas below the inversion and therefore limit the ability to do process evaluation of the boundary layer structure and depth. There is inconclusive evidence of model improvements relative to observations in the Upper Green River Study, likely a result of the deeper snowpack in the Upper Green River Basin limiting the feedback between the land surface and the atmosphere. •
WRF-‐SNODAS configuration significantly alters the local circulation important for transport and mixing of pollutants. Differences in the vertical structure of temperature and wind speed are very sensitive to the location and amount of snow not only in the immediate vicinity of the station of interest, but also the surrounding area. The downslope flow transports colder air from higher 5
Neeman, E.M., Crosman, E.T., Horel, J.D., & Avey, L. (2014). Simulations of a cold-‐air pool associated with elevated wintertime ozone in the Uintah Basin, Utah. Atmospheric Chemistry and Physics, 15, 15953-‐16000. doi: 10.5194/acpd-‐14-‐
15953-‐2014. 6
Schnell, R.C. Oltmans, S.J., Neely, R.R., Endres, M.S., Molenar, J.V., & White, A.B. (2009). Rapid photochemical production of ozone at high concentrations in a rural site during winter, Nature Geoscience, 2, 120-‐122. doi: 10.1038/NGEO415. 7
Barrett, A. (2003). National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS) Products at NSIDC. NSIDC Special Report 11. Boulder, CO: NSIDC. August 19, 2015 ES-‐2 elevations down into the valley resulting in a significantly colder basin. Changes in the diurnal flow may help to mix pollutants from various sources in the basin and boost ozone formation. •
Turning off observational nudging did not consistently improve near surface temperature when used in combination with the WRF-‐SNODAS configuration. The use of observational nudging in combination with SNODAS needs further investigation. An additional test was performed for both seasons to determine if the 3SAQS configuration can be improved upon when applying SNODAS. Discussions with UUtah and UTDAQ provided insight into a potential problem with the timing and frequency of reinitializing in WRF. In particular, reinitializing the model in the middle of a cold air pool event may create problems when trying to model these events. This is a difficult operational problem given the timing of these events and that they are likely occuring at different times throughout the domain. Reinitializing during a cold pool event is a problem that may be avoided by carefully constructing the reinitialization time for known events or by using the Pleim-‐Xiu (PX) land surface model (LSM). The PX LSM directly assimilates SNODAS data, thus avoiding reinitialization, and is a well-‐known option in WRF to drive retrospective meteorological simulations for regulatory air quality applications.8 Additionally, the WRF-‐PX configuration includes additional configuration changes thought to be important including the land-‐use land-‐cover data and the planetary boundary layer scheme. •
Experiments using the WRF-‐PX configuration in the Uintah Basin consistently demonstrated problems with the diurnal temperature range. WRF-‐PX was warmer than observations during the day in the Uintah Basin generating more mixing and higher PBL heights. The WRF-‐PX simulation is also continuous and takes longer to run and was unstable for the 2011 simulation. We do not currently recommend using WRF-‐PX because of these issues which need further investigation. Overall, the WRF-‐SNODAS configuration is equal to or better than the WRF 2011 application used in the 3SAQS.
8
Gilliam. R.C., & Pleim, J.E. (2010). Performance assessment of new land surface boundary layer physics in the WRF-‐ARW. Journal of Applied Meteorology and Climatology, 49, 760-‐774. doi: http://dx.doi.org/10.1175/2009JAMC2126.1. August 19, 2015 ES-‐3 2
Introduction The University of North Carolina (UNC) at Chapel Hill is working to improve the meteorological modeling important for wintertime ozone formation currently available from the Three State Air Quality Modeling Study (3SAQS, conducted October 2012 through September 2014). The 3SAQS includes cooperators from U.S. Environmental Protection Agency (EPA) Region 8, U.S. Forest Service (USFS), Bureau of Land Management (BLM), National Park Service (NPS), Fish and Wildlife Service (FWS), and the state air quality management agencies of Colorado, Utah, and Wyoming. The meteorological modeling in this extended study, now called Western Air Quality Study (WAQS), is intended to facilitate air resource analyses for federal and state agencies in the states of Wyoming, Colorado, and Utah. Funded by the EPA, BLM, and USFS and with in-‐kind support from the NPS and Colorado, Utah, and Wyoming state air agencies, the main focus of the study is to assess the air quality impacts of emissions within the three-‐
state region with particular emphasis on sources related to oil and gas development and production. To quantify the impacts of proposed oil and gas development projects on current and future air quality, including ozone visibility levels in the National Parks and Wilderness Areas, we are working to improve the regional air quality modeling platform by improving the meteorological inputs for the photochemical grid models (PGMs). The meteorological inputs are an important component of the PGM modeling platform, and their development and evaluation for wintertime ozone are described in this document. 2.1
Meteorological Modeling We used the Weather Research and Forecasting (WRF) Advanced Research dynamic core (WRF-‐ARW, henceforth simply called WRF) model for this meteorological modeling study.9,10 WRF is a next-‐
generation mesoscale prognostic meteorological model designed to serve both operational forecasting and atmospheric research needs.11,12,13. WRF is routinely used for urban-‐ and regional-‐scale photochemical, fine particulate and regional haze regulatory modeling studies. Developed jointly by the National Center for Atmospheric Research and the National Centers for Environmental Prediction, WRF is maintained and supported as a community model by researchers and practitioners around the globe. WRF contains separate modules to compute different physical processes such as surface energy budgets and soil interactions, turbulence, cloud microphysics, and atmospheric radiation. Within WRF, the user has many options for selecting the different schemes for each type of physical process. There is a WRF Preprocessing System (WPS) that generates the initial and boundary conditions used by WRF, based on topographic datasets, land use information, and larger-‐scale atmospheric and oceanic models. 9
Skamarock, W.C., J.B. Klemp, J. Dudhia, D.O. Gill, M. Barker, M.G. Duda, X.-‐Y. Huang, W. Wang, and J.G. Powers, 2008: A description of the Advanced Research WRF version 3. NCAR Technical Note NCAR/TN475+STR 10
http://www.wrf-‐model.org/index.php 11
Skamarock, W. C. 2004. Evaluating Mesoscale NWP Models Using Kinetic Energy Spectra. Mon. Wea. Rev., Volume 132, pp. 3019-‐3032. December. (http://www.mmm.ucar.edu/individual/skamarock/spectra_mwr_2004.pdf) 12
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang and J. G. Powers. 2005. A Description of the Advanced Research WRF Version 2. National Center for Atmospheric Research (NCAR), Boulder, CO. June. (http://www.mmm.ucar.edu/wrf/users/docs/arw_v2.pdf) 13
Skamarock, W. C. 2006. Positive-‐Definite and Monotonic Limiters for Unrestricted-‐Time-‐Step Transport Schemes. Mon. Wea. Rev., Volume 134, pp. 2241-‐2242. June. (http://www.mmm.ucar.edu/individual/skamarock/advect3d_mwr.pdf) August 19, 2015 2-‐1 2.2
WSAQS Winter WRF Modeling The 3SAQS discovered deficiencies in modeling the wintertime ozone formation that may be a result of problems modeling complex meteorological processes within the oil and natural gas basins in the Rocky Mountain region.14 Of particular concern are cold air pool meteorological processes, see Figure 2-‐1. These meteorological processes and interactions are complicated and can have significant impact on the PGM. These meteorological processes include: •
synoptic scale processes, such as the preconditioning of cold air near the surface, warm air advection aloft, and large-‐scale atmospheric subsidence. •
mesoscale processes, such as the spillover of warm air into the valley creating dynamic instability and thermally driven upslope and downslope flows; and •
surface and boundary layer processes, such as the amount of snow cover and/or cloud cover, temperature inversions, boundary layer heights, wind speed and direction, and net solar radiation. Several different WRF configurations, described in Section 2, were conducted for the 2010-‐2011 and 2012-‐2013 winter seasons. We selected March 2011 to study the meteorological processes leading to high surface winter ozone formation. High observed ozone concentrations and periodic cold air pools occurred during March in some of the oil and natural gas basins in Colorado, Utah, and Wyoming.15 The modeling domains are the same as in the 3SAQS and cover the continental United States at 36-‐km grid spacing (CONUS), the Western and Midwestern United States at 12-‐km grid spacing (WESTUS) and the three-‐state domain 4-‐km resolution (3SD). This document provides recommended changes in the model configuration for wintertime ozone to optimize the WRF meteorological model performance for simulating wintertime ozone. The recommended configuration is generalized based on statistical performance for near surface fields as well as cold air pool meteorological processes throughout the 3SD. The WRF configuration recommendations will be implemented into future meteorological modeling applications available in the Western Air Quality Data Warehouse. 14
UNC and ENVIRON. (2014a, August). Three-‐State Air Quality Study (3SAQS): weather research and forecasting model – year 2011 Modeling Application/Evaluation. Chapel Hill, NC: University of North Carolina–Chapel Hill and ENVIRON International Corporation. Retrieved from http://vibe.cira.colostate.edu/wiki/Attachments/Modeling/3SAQS_2011_WRF_MPE_v8_draft_Aug04_2014.pdf. 15
Ibid. August 19, 2015 2-‐2 Figure 2-‐1. Conceptual model of meteorological processes that impact cold air pool formation and demise. August 19, 2015 2-‐3 3
WRF Input and Physics Sensitivity The WRF configuration used in this study leverages the WRF configuration from the previous 3SAQS study. For instance, the 3SAQS production WRF options are used in this study as the “WRF-‐BASE” configuration. The WRF-‐BASE configuration was selected after evaluation of standard near surface meteorological variables (e.g., wind speeds, wind directions, temperatures, and precipitation). Please refer to UNC and ENVIRON (2014a) for prior comparisons. The WRF model physics options for the WRF-‐
BASE configuration are included in Table 3-‐1. Below we discuss the WRF model inputs into the WRF-‐
BASE configuration and the additional WRF input and physics sensitivity simulations in Table 3-‐2. 3.1
WRF Model Inputs for WRF-‐BASE Configuration A brief summary of the WRF input data preparation procedure and physics options used for the WRF-‐
BASE winter modeling exercise is provided below. Model Selection: The publicly available version of WRF (version 3.6.1) was used in the modeling study. The WRF preprocessor programs including GEOGRID, UNGRIB, and METGRID were used to develop model inputs. Horizontal Domain Definition: The WRF 36/12/4-‐km domains are defined with at least a 5-‐grid cell buffer in all directions from the CAMx/CMAQ air quality modeling domains to minimize any potential numeric noise along WRF domain boundaries which affect the air quality model meteorological inputs. Such numeric noise can occur near the boundaries of the WRF domain solution as the boundary conditions come into balance with the WRF numerical algorithms. The WRF horizontal domains are presented in Figure 3-‐1. The grid projection was Lambert Conformal with a pole of projection of 40 degrees North, -‐97 degrees East and standard parallels of 33 and 45 degrees. Vertical Domain Definition: The WRF modeling was based on 37 vertical layers with a surface layer approximately 12 meters deep. The vertical domain is presented in both sigma and approximate height coordinates in Table 3-‐3. Topographic Inputs: Topographic information for the WRF was developed using the standard WRF terrain databases. The 36-‐km CONUS domain was based on the 10 min. (18 km) global data. The 12-‐km WESTUS domain was based on the 2 min. (~4 km) data. The 4-‐km 3SD was based on the 30 sec. (~900 m) data. Vegetation Type and Land Use Inputs: USGS 24-‐category vegetation type and land use information available from the standard WRF distribution was used. The standard WRF surface characteristics corresponding to each land use category were employed. Atmospheric Data Inputs: The first guess fields were taken from the 12-‐km (Grid #218) North American Model (NAM) archives available from the National Climatic Data Center (NCDC) National Operational Model Archive and Distribution System (NOMADS) server. Time Integration: Third-‐order Runge-‐Kutta integration was used with a fixed time step of 90 seconds for the 36-‐km CONUS domain, 30 seconds for 12-‐km WESTUS domain, and 10 seconds for the 4-‐km 3SD. August 19, 2015 3-‐1 Diffusion Options: Horizontal Smagorinsky first-‐order closure with sixth-‐order numerical diffusion and suppressed up-‐gradient diffusion was used. Lateral Boundary Conditions: Lateral boundary conditions were specified from the initialization dataset on the 36-‐km CONUS domain with continuous updates nested from the 36-‐km domain to the 12-‐km WESTUS domain and continuous updates nested from the 12-‐km domain to the 4-‐km 3SD. Top and Bottom Boundary Conditions: The top boundary condition was selected as an implicit Rayleigh dampening for the vertical velocity. Consistent with the model application for non-‐idealized cases, the bottom boundary condition was selected as physical, not free-‐slip. Water Temperature Inputs: The water temperature data were taken from the NCEP RTG global one-‐
twelfth degree analysis.16 SNOW Cover and Depth Inputs: The snow cover and depth are initialized from the NAM archives that also provide the atmospheric input. FDDA Data Assimilation: The WRF model was run with a combination of analysis and observation nudging (i.e., Four Dimensional Data assimilation [FDDA]). Analysis nudging was used on the 36-‐km and 12-‐km domain. For winds and temperature, analysis nudging coefficients of 5.0x10-‐4 and 3.0x10-‐4 were used on the 36-‐km and 12-‐km domains, respectively. For mixing ratio, an analysis nudging coefficient of 1.0x10-‐5 was used for both the 36-‐km and 12-‐km domains. Both surface and aloft nudging were used, though nudging for temperature and mixing ratio were not performed in the lower atmosphere (i.e., within the boundary layer). Observation nudging for winds and temperature was performed on the 4-‐km grid domain using the Meteorological Assimilation Data Ingest System (MADIS17) observation archive. The MADIS archive includes the National Climatic Data Center (NCDC18) observations and the National Data Buoy Center (NDBC) Coastal-‐Marine Automated Network (C-‐MAN19) stations. The observational nudging coefficients for winds and temperatures were 1.2x10-‐3 and 6.0x10-‐4, respectively and the radius of influence was set to 60-‐km. Physics Options: The physics options chosen for the BASE configuration are presented in Table 3-‐1. Application Methodology: The WRF model was executed in 5-‐day blocks initialized at 12Z every 5 days with a 90-‐second integration time step. Model results were output every 60 minutes and output files were split at 24-‐hour intervals. Twelve hours of spin-‐up were included in each 5-‐day block before the data were used in the subsequent evaluation. The model was run at the 36-‐km, 12-‐km and 4-‐km grid resolution from December 16, 2010 through January 1, 2012 using two-‐way grid nesting with no feedback (i.e., the meteorological conditions are allowed to propagate from the coarser grid to the finer grid but not vice versa). 16
Real-‐time, global, sea surface temperature (RTG-‐SST) analysis. http://polar.ncep.noaa.gov/sst/oper/Welcome.html. Meteorological Assimilation Data Ingest System. http://madis.noaa.gov/. 18
National Climatic Data Center. http://lwf.ncdc.noaa.gov/oa/ncdc.html. 19
National Data Buoy Center. http://www.ndbc.noaa.gov/cman.php. 17
August 19, 2015 3-‐2 3.2
WRF-‐SNODAS Configuration This configuration is selected based on knowledge that the NAM snow cover is unable to resolve the steep horizontal gradients in observed snow fields. These gradients impact local meteorology and are key for simulating ozone formation within the Western U.S. oil and gas basins. Figure 3-‐2 illustrates the large differences in the representation of snow cover between the NAM and SNODAS WRF-‐initialization datasets. Below is a list of the changes in the WRF-‐SNODAS simulations. All other model options are not modified from the WRF-‐BASE configuration. SNOW Cover and Depth Inputs: The snow cover and depth are initialized from the SNODAS20 archives and replaces the NAM snow cover used in the WRF-‐BASE configuration. SNODAS integrates a physically based, spatially distributed energy and mass balance model with observed snow data from satellite and airborne platforms and ground stations. SNODAS output has high spatial resolution (~1 km) and temporal resolution (1 hour) over the United States. Our implementation of SNODAS in WRF includes adjustments to the snow albedo that are based on land use type. For snow, the albedo was adjusted for each land use cover type. This adjustment is important because the surface albedo can depend on vegetation type/coverage. For example, conifer forests have a lower snow albedo because they consist of dark trees covering the high albedo snowpack. Grasslands have a high snow albedo because they could be completely covered by the snowpack. This level of detail can have important implications for the surface energy budget and dependent issues. A special data processor was developed to format the SNODAS data for input to the WRF-‐REAL program. WRF was reinitialized every 5.5 days, which included reinitializing back to SNODAS. All other model input and options are the same as the WRF-‐BASE configuration. 3.3 WRF-‐SNODAS-‐NO_OBSNUDGE Configuration There was concern that nudging may impact the cold air pool meteorology.21 Tran et al. (2015) performed a sensitivity analysis where simulated (NAM) and observational (MADIS) nudging were turned off to assess the impact on cold air pool development and subsequent ozone formation in the Uintah Basin during the winter of 2011. They concluded that the application of analysis and observational nudging degraded WRF-‐CMAQ performance in simulating cold air pools and ozone in the Uintah Basin during that period. Their findings suggest that the NAM analysis nudging produced a planetary boundary layer (PBL) that was too shallow and overly-‐mixed. The simulation which is not representative of the local conditions aloft in the Uintah Basin, but rather RAOB sites quite a distance away. Nudging may have a more positive impact in other areas where the NAM analysis is more representative of the local conditions (e.g. Salt Lake Basin). We also modified our data assimilation strategy as outlined below to test the impact on the model results. FDDA Data Assimilation: The WRF model was run with a combination of analysis and observation nudging (i.e., Four Dimensional Data assimilation [FDDA]). Analysis nudging was used on the 36-‐km and 12-‐km domain. For winds and temperature, analysis nudging coefficients of 5.0x10-‐4 and 3.0x10-‐4 were 20
Snow Data Assimilation System. http://nsidc.org/data Tran, T., H. Tran, and E. Crosman, 2015: FDDA nudging impacts on WRF-‐CMAQ performance in simulating winter O3 formation in Uintah Basin. 2015 Western Air Quality Modeling Workshop, 13-‐15 May 2015, Boulder, CO. August 19, 2015 3-‐3 21
used on the 36-‐km and 12-‐km domains, respectively. For mixing ratio, an analysis nudging coefficient of 1.0x10-‐5 was used for both the 36-‐km and 12-‐km domains. Both surface and aloft nudging were used, though nudging for temperature and mixing ratio were not performed in the lower atmosphere (i.e., within the boundary layer). Observation nudging for winds and temperature was turned off in the 4-‐km grid domain. 3.4
WRF-‐PX Configuration The WRF-‐PX (Pleim-‐Xiu) configuration applies to many US EPA model developments to improve retrospective meteorological simulations for air quality applications. Below lists the changes from the WRF-‐BASE configuration used in the WRF-‐PX simulations. Vegetation Type and Land Use Inputs: National Land Cover Database 2006 (NLCD22 2006) 40-‐category vegetation type and land use information available from the standard WRF distribution was used. The standard WRF surface characteristics corresponding to each land use category were employed with the exception of snow cover. For snow, the albedo was adjusted for each land use cover type, similar to the WRF-‐SNODAS configuration. SNOW Cover and Depth Inputs: The snow cover and depth are initialized from the SNODAS archives and replaces the NAM snow cover used in the WRF-‐BASE configuration. Physics Options: The land surface model used is the Pleim-‐Xiu (PX) land surface model and is a well-‐
known option to drive retrospective meteorological simulations for air quality applications.23 The planetary boundary layer scheme was also changed to the Asymmetric Convective Model (ACM version, 2) for consistency with the PX land surface model and ongoing efforts within the US EPA.24 Application Methodology: This simulation does not reinitialize every 5.5 days and instead used a continuous simulation initialized on November 20. The November 20 initialization date provides a spin-‐
up period for the soil model. The continuous simulation approach was used for consistency with the most recent methodology within the US EPA and does not reinitialize back to SNODAS. Instead PX LSM directly assimilates the SNODAS dataset because simulation does not reinitialize back to SNODAS. The PX LSM directly assimilates the SNODAS dataset because it does not include a sub-‐snow model. 3.5
Evaluation Approach This report includes standard meteorological performance evaluation, such as error and bias, and a process-‐oriented evaluation of known meteorological processes during cold air pools and important for wintertime ozone formation and demise. The quantitative analysis was divided into monthly comparisons of 2-‐m temperature, 2-‐m mixing ratio, and 10-‐m wind speed to help generalize the model bias and error relative to a set of standard model performance benchmarks. The evaluation focused on the 4-‐km 3SD in the states of Colorado, Wyoming, 22
National Land Cover Database 2006. http://www.mrlc.gov/nlcd2006.php Gilliam. R.C., & Pleim, J.E. (2010). Performance assessment of new land surface boundary layer physics in the WRF-‐ARW. Journal of Applied Meteorology and Climatology, 49, 760-‐774. doi: http://dx.doi.org/10.1175/2009JAMC2126.1. 24
Ibid. August 19, 2015 3-‐4 23
and Utah and was supplemented with select diurnal time series analyses. The observed database for winds, temperature, and water mixing ratio used in this analysis was the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) Meteorological Assimilation Data Ingest System (MADIS). The locations of the MADIS monitoring sites within the 4-‐km 3SD are shown in Figure 3-‐3. We also used a process-‐oriented evaluation to discuss modeling meteorological processes for select events that correspond to high levels of ozone within the oil and natural gas basins, shown in Table 3-‐4. These events leverage both knowledge of known air quality exceedances and field campaign data. The events were evaluated at site-‐specific locations in the Salt Lake, Uintah, and Upper Green River Basins. Table 3-‐5 is a list of locations analyzed for the events selected. Table 3-‐1. Model configuration used in the WRF-‐BASE simulation of winter seasons 2010-‐2011 and 2012-‐2013. WRF Treatment Microphysics Option Selected Thompson Longwave Radiation RRTMG Shortwave Radiation RRTMG Land Surface Model (LSM) NOAH Planetary Boundary Layer (PBL) scheme YSU Cumulus parameterization Kain-‐Fritsch in the 36-‐km and 12-‐km domains. None in the 4-‐km 3SD. Analysis nudging Nudging applied to winds, temperature and moisture in the 36-‐
km and 12-‐km domains Nudging applied to surface wind and temperature only in the 4-‐km 3SD Temperature and moisture nudged above PBL only 12 km North American Model (NAM) Observation Nudging Initialization Dataset August 19, 2015 3-‐5 Notes A scheme with ice, snow, and graupel processes suitable for high-‐resolution simulations. Rapid Radiative Transfer Model (RRTM) for GCMs includes random cloud overlap and improved efficiency over RRTM. Same as above, but for shortwave radiation. Two-‐layer scheme with vegetation and sub-‐
grid tiling. Yonsie University (Republic of Korea) Asymmetric Convective Model with non-‐local upward mixing and local downward mixing. 4 km can explicitly simulate cumulus convection so parameterization not needed. Moisture observation nudging produces excessive rainfall Table 3-‐2. WRF Wintertime Sensitivity Experiments. Winter Season Dec. 2010 – Mar. 2011 Noah LSM with 25
USGS LULC data and SNODAS initialization; albedo is adjusted Dec. 2012 – Mar. 2013 Exp. 2. WRF-‐
SNODAS-‐
NO_OBSNUDGE Observation nudging of surface winds and temperature are turned off NA 25
Exp 1.WRF-‐
SNODAS USGS Land Cover Database. http://landcover.usgs.gov/ National Land Cover Database 2006. http://www.mrlc.gov/nlcd2006.php August 19, 2015 3-‐6 26
Exp 3. WRF-‐PX PX LSM & ACM2 PBL with NLCD 26
2006 LULC data and assimilated SNODAS Table 3-‐3. Vertical layer definition for WRF simulations. WRF Meteorological Model WRF Layer 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Sigma 0.0000 0.0270 0.0600 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 0.5500 0.6000 0.6400 0.6800 0.7200 0.7600 0.8000 0.8400 0.8700 0.8900 0.9100 0.9300 0.9400 0.9500 0.9600 0.9700 0.9800 0.9850 0.9880 0.9910 0.9930 0.9950 0.9970 0.9985 1.0000 Pressure (mb) 50.00 75.65 107.00 145.00 192.50 240.00 287.50 335.00 382.50 430.00 477.50 525.00 572.50 620.00 658.00 696.00 734.00 772.00 810.00 848.00 876.50 895.50 914.50 933.50 943.00 952.50 962.00 971.50 981.00 985.75 988.60 991.45 993.35 995.25 997.15 998.58 1000 August 19, 2015 3-‐7 Height (m) 19260 17205 15355 13630 11930 10541 9360 8328 7408 6576 5816 5115 4463 3854 3393 2954 2533 2130 1742 1369 1098 921 747 577 492 409 326 243 162 121 97 72 56 40 24 12 0 Thickness (m) 2055 1850 1725 1701 1389 1181 1032 920 832 760 701 652 609 461 440 421 403 388 373 271 177 174 171 84 84 83 82 82 41 24 24 16 16 16 12 12 Site Table 3-‐4. Stations sites used in the for the WRF evaluation and sensitivity comparisons. Latitude Longitude Site Latitude Longitude Salt Lake Valley 40.6006 -‐111.9252 Pinedale 42.86 -‐109.87 Duchesne 40.1615 -‐110.4011 South Pass 42.52 -‐108.72 Altamont 40.3603 -‐110.2858 Thunder Basin 44.66 -‐105.29 Lapoint 40.404 -‐109.8157 Wamsutter 41.67 -‐108.02 Cedarview 40.383524 -‐110.072563 Wyoming Range 42.98 -‐110.35 Jensen 40.3671 -‐109.3522 Tall Tower 42.42 -‐109.56 9 Mile Canyon 39.7919 -‐110.2035 Mobile Trailer 42.68 -‐109.8 Pariette Draw 40.03460278 -‐109.8300556 Big Piney 42.48 -‐110.09 Rabbit Mtn 39.8687 -‐109.0973 Pavillion 43.25 -‐108.57 DinosaurNM 40.4371 -‐109.3047 Centenial 41.37 -‐106.24 RedWash 40.1972 -‐109.3525 Pinedale 42.92 -‐109.79 Vernal 40.443273 -‐109.560983 Yellowstone 44.55 -‐110.4 Ouray 40.054768 -‐109.688001 HONO (Boulder 2) 42.71 -‐109.75 Roosevelt 40.30073056 -‐109.9784172 Boulder 42.71 -‐109.75 Fruitland 40.2087 -‐110.8403 Campbell County 44.14 -‐105.53 Horsepool 40.1437 -‐109.4672 Cheyenne 41.18 -‐104.77 Rangely 40.0869 -‐108.7616 Cloud Peak 44.33 -‐106.95 Sodar 42.71 -‐109.75 Daniel South 42.79 -‐110.05 Tethered balloon 42.68 -‐109.8 Juel Springs 42.37 -‐109.56 Muphy Ridge 41.37 -‐111.04 Moxa 41.75 -‐109.78 Pinedale 42.86 -‐109.87 Southpass 42.52 -‐108.72 August 19, 2015 3-‐8 Table 3-‐5. Dates of events evaluated for the different field campaigns. Field Campaign Dates PCAPS PCAPS PCAPS, UGRWOS, UBWOS UGRWOS, UBWOS UGRWOS, UBWOS UGRWOS, UBWOS UGRWOS, UBWOS UGRWOS, UBWOS December 1 – 6, 2010 January 1-‐10, 2011 January 26-‐31, 2011 February 11-‐16, 2011 March 1-‐5, 2011 January 21-‐27, 2013 February 1-‐7, 2013 March 1-‐5, 2013 Figure 3-‐1. 36-‐km CONUS, 12-‐km WESTUS (d02), and 4-‐km 3SAQS (d03) WRF modeling domains. August 19, 2015 3-‐9 Figure 3-‐2. Snow height (m) initial conditions on Feb. 8, 2011 from NAM (top) and SNODAS (bottom) August 19, 2015 3-‐10 Figure 3-‐3. Locations of MADIS surface meteorological modeling sites with the WRF 4-‐km 3SAQS modeling domain. August 19, 2015 3-‐11 4
4.1
Quantitative Evaluation Using Surface Meteorological Observations Monthly Quantitative Model Evaluation Results Statistical model evaluation results are presented in this section. The quantitative model performance evaluation of WRF using surface meteorological measurements was performed using the publicly available AMET evaluation tool. AMET calculates statistical performance metrics for bias, error and correlation for surface winds, temperature, and mixing ratio and can produce a time series of predicted and observed meteorological variables and performance statistics. This evaluation only summarizes the meteorological model performance using bias and error model performance statistics metrics with select plots to enhance potential users’ understanding of model performance. However, we provide an online source so data users can independently judge the adequacy of the model simulation. Overall comparisons are offered herein to judge the model efficacy, but this review does not necessarily cover all potential user needs and applications. To evaluate the WRF performance for the 2010-‐2011 and 2012-‐2013 winter seasons, a number of performance benchmarks for comparison were used. Emery and co-‐workers derived and proposed a set of daily performance “benchmarks” for typical meteorological model performance for good performing models in air quality model applications.27 These performance benchmarks were based upon the evaluation of about 30 MM5 and RAMS meteorological simulations of limited duration (multi-‐day episodes) in support of air quality modeling study applications performed over several years. These were primary ozone model applications for cities in the eastern and Midwestern U.S. and Texas, the majority of which were simple (flat) terrain with simple (stationary high pressure causing stagnation) meteorological conditions. More recently, these benchmarks have been used in annual meteorological modeling studies that include areas with complex terrain and more complicated meteorological conditions; therefore, they must be applied as guidelines and not bright-‐line numbers. The purpose of these benchmarks is to put the results of a meteorological model application into proper context with other models and meteorological datasets. Recognizing that these simple condition benchmarks may not be appropriate for more complex conditions, McNally analyzed multiple annual runs that included complex terrain conditions and suggested an alternative set of benchmarks for temperature, namely a guideline of within ±1.0 K for bias and 3.0 K for error.28 Kemball-‐Cook et al. also proposed model performance benchmarks for complex conditions as part of the Western Regional Air Partnership (WRAP) meteorological modeling, which includes the challenging meteorological conditions in the Rocky Mountain Region and Alaska.29 Based on these reviews, we have adopted “simple” and “complex” model performance benchmarks for surface 27 Emery, C., E. Tai, and G. Yarwood, 2001. “Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Ozone Episodes.” Prepared for the Texas Natural Resource Conservation Commission, prepared by ENVIRON International Corporation, Novato, CA. 31-‐August. http://www.tceq.texas.gov/assets/public/implementation/air/am/contracts/reports/mm/EnhancedMetModelingAndPerformanceEvaluati
on.pdf 28
McNally, D. E., 2009. “12km MM5 Performance Goals.” Presentation to the Ad-‐hoc Meteorology Group. 25-‐June. http://www.epa.gov/scram001/adhoc/mcnally2009.pdf 29 Kemball-‐Cook, S., Y. Jia, C. Emery, and R. Morris, 2005. Alaska MM5 Modeling for the 2002 Annual Period to Support Visibility Modeling. Prepared for the Western Regional Air Partnership, by ENVIRON International Corp., Novato, CA. http://pah.cert.ucr.edu/aqm/308/docs/alaska/Alaska_MM5_DraftReport_Sept05.pdf August 19, 2015 4-‐1 temperature, mixing ratio (humidity), wind speed, and wind direction bias and error as shown in Table 4-‐1. The equations for bias and error are given below. Bias = N
1
N
∑ (P − O )
1
N
∑ P −O
Error =
i
i
i =1
N
i
i
i =1
Table 4-‐1. Meteorological model performance benchmarks for simple and complex conditions. Parameter Temperature Bias Simple ≤ ±0.5 K Complex ≤ ±1.0 K Temperature Error ≤ 2.0 K ≤ 3.0 K Humidity Bias ≤ ±0.5 g/kg ≤ ±1.0 g/kg Humidity Error ≤ 1.0 g/kg ≤ 2.0 g/kg Wind Speed Bias ≤ ±0.5 m/s ≤ ±1.0 m/s Wind Speed RMSE ≤ 2.0 m/s ≤ 3.0 m/s Wind Direction Bias ≤ ±5 degrees ≤ ±10 degrees Wind Direction Error ≤ 40 degrees ≤ 80 degrees 4.1.1 Winter 2012-‐2013 The monthly model performance of the winter 2012-‐2013 WRF 4-‐km simulation for surface meteorological variables within the states of Colorado, Utah and Wyoming are analyzed using soccer plots. Soccer plots display monthly bias on the x-‐axis and error on the y-‐axis along with the simple and complex benchmarks. When the monthly symbols fall within the box, they achieve the benchmark (score a goal). Figure 4-‐1 displays the WRF 4-‐km monthly temperature soccer plots for the states of Colorado, Utah, and Wyoming for the winter of 2012-‐2013. In Colorado, all months achieve the complex temperature benchmark. A notable improvement for the WRF-‐SNODAS simulation is the January temperature error with all winter months falling under the 2 degree K error threshold. On the other hand, WRF-‐PX error increases for December and January in comparison to the WRF-‐BASE simulation. In Utah, January consistently has the largest error across all simulations with WRF-‐PX having the largest error. WRF-‐
SNODAS is colder than the WRF-‐BASE simulation in Utah while WRF-‐PX is warmer. For Wyoming, the WRF-‐SNODAS improves model error and bias relative to the WRF-‐BASE simulation. WRF-‐PX again has the largest error, especially the month of January. Figure 4-‐2 displays the WRF 4-‐km monthly mixing ratio soccer plots for the states of Colorado, Utah, and Wyoming for the winter of 2012-‐2013. The mixing ratio is within the complex terrain benchmark for all states and simulations and there are very small differences between the error and bias. In general, the mixing ratio is positively biased with an error of around 0.5 g/kg. August 19, 2015 4-‐2 Figure 4-‐3 displays the WRF 4-‐km monthly wind speed soccer plots for the states of Colorado, Utah, and Wyoming for the winter of 2012-‐2013. For all states and simulations the wind speed is within the complex terrain benchmark. For Colorado and Wyoming, the wind speed bias is negative. Utah is the only state where the wind speed bias becomes positive for WRF-‐BASE and WRF-‐SNODAS. Both of these simulations use the same physics options. The WRF-‐PX simulation wind speed bias remains negative for Utah. WRF-‐PX also has the largest error exceeding 2 m/s for all winter months over Wyoming. August 19, 2015 4-‐3 Figure 4-‐1. Winter 2010-‐2011 soccer plot of monthly temperature error and bias (K) over CO, UT, and WY for WRF-‐BASE, WRF-‐SNODAS and WRF-‐PX configurations. August 19, 2015 4-‐4 Figure 4-‐2. Winter 2012-‐2013 soccer plot of monthly mixing ratio error and bias (g/kg) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS and WRF-‐PX configurations. August 19, 2015 4-‐5 Figure 4-‐3. Winter 2012-‐2013 soccer plot of monthly wind speed error and bias (g/kg) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS and WRF-‐PX configurations. August 19, 2015 4-‐6 4.1.2 Winter 2010-‐2011 We also created soccer plots for the 2010-‐2011 winter season. The only exception is the WRF-‐PX simulation. The WRF-‐PX simulation for 2010-‐2011 crashed multiple times and we were not able to complete this simulation for the winter season. We were able to simulate most of the PCAPS observation period with the WRF-‐PX simulation (until late January) and will be discussed later. In the plots below, we have excluded WRF-‐PX for comparison for the quantitative monthly evaluation. For the WRF-‐BASE and WRF-‐SNODAS evaluation we refer back to Figures 4-‐1 to 4-‐3 to understand how the error/bias changes as a result of the interannual variability. This is an important component of this study because the recommended configuration will be run for 2014. Figure 4-‐4 displays the WRF 4-‐km monthly temperature soccer plots for the states of Colorado, Utah, and Wyoming for winter of 2010-‐2011. For Colorado, the WRF-‐BASE and WRF-‐SNODAS are both within the complex terrain benchmarks. The temperature errors are larger in 2010-‐2011 compared to 2012-‐
2013 for both simulations, but all months exceed 1.5 K regardless of the year. The largest notable difference between years is the bias, particularly in December. For Utah, the WRF-‐BASE simulation bias exceeds the benchmark thresholds for two months, December 2010 and January 2011. The WRF-‐
SNODAS simulation improves the error and bias for these months and brings the January error within the complex benchmark threshold. Additionally, January 2013 was also significantly improved using the WRF-‐SNODAS configuration. This illustrates that SNODAS is improving the meteorology, especially for Utah. As for Wyoming, the interannual variability of the bias and error can be very large. For instance, the WRF-‐BASE configuration temperature error is approximately 1 K larger for December 2010 compared to December 2012. The improvements over Wyoming using SNODAS are also inconsistent over Wyoming between the years. The 2012-‐2013 temperature error and bias is improved for all months using SNODAS, but the December 2010 error and bias increases in WRF-‐SNODAS. Overall, it is important to note that near surface temperature bias and error is sensitive to the year choice because the amount of snow varies between years. Metrics such as bias and error are helpful to help quantify WRF-‐SNODAS improvements but process evaluation is also needed. A process evaluation is included in the following sections. Figure 4-‐5 displays the WRF 4-‐km monthly mixing ratio soccer plots for the states of Colorado, Utah, and Wyoming for winter of 2010-‐2011. Unlike temperature, there is little sensitivity on the mixing ratio statistics between the two winters. The mixing ratio is positively biased for all months and states for both winters, but all months fall within the complex terrain benchmark. The most notable difference is the spread in the bias is reduced in the WRF-‐SNODAS simulations. Figure 4-‐6 displays the WRF 4-‐km monthly wind speed soccer plots for the states of Colorado, Utah, and Wyoming for winter of 2010-‐2011. For Colorado and Wyoming, the wind speed bias is consistently negative for all months. As for Utah, the wind speed is positively biased. Both of these findings are consistent across winters. Additionally, comparing WRF-‐BASE to WRF-‐SNODAS for each state shows little difference. The largest difference is with respect to the interannual variability and the benchmark thresholds. Almost all months for the WRF-‐BASE and WRF-‐SNODAS simulation fall within the complex threshold during 2012-‐2013, but this is not true for the 2010-‐2011 winter. For instance, all months for Wyoming fall outside the simple benchmark for 2010-‐2011 and within the simple benchmark for 2012-‐
2013. August 19, 2015 4-‐7 Figure 4-‐4. Winter 2010-‐2011 soccer plot of temperature error and bias (K) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS. August 19, 2015 4-‐8 Figure 4-‐5. Winter 2010-‐2011 soccer plot of mixing ratio error and bias (g/kg) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS. August 19, 2015 4-‐9 Figure 4-‐6. Winter 2010-‐2011 soccer plot of wind speed error and bias (K) over CO, UT, and WY for the WRF-‐BASE, WRF-‐SNODAS. August 19, 2015 4-‐10 4.2
Diurnal Quantitative Model Evaluation 4.2.1 Monthly Diurnal Temperature Performance The diurnal time series of temperature bias and error across all stations in the 4-‐km domain is shown in Figure 4-‐7. The late night and morning hours, roughly from 0600-‐1500 UTC, experience a large domain-‐
wide warm bias in the WRF-‐BASE and WRF-‐SNODAS simulations. The bias and error improvements are smaller during the afternoon hours. On average, WRF-‐SNODAS improves the bias and error throughout the day, but a persistent warm bias at night is not corrected by changing the snow cover alone. The diurnal temperature statistics indicates some significant problems modeling cold air pool processes despite improving the initialization of snow cover and depth. We discuss cold air pool processes in more detail when comparing with field campaign data, such as PCAPS. Figure 4-‐7 includes the WRF-‐PX diurnal statistics. Compared to the WRF-‐BASE and WRF-‐SNODAS simulations, the WRF-‐PX simulation has a very different diurnal statistics profile. The bias is smaller during the late night and morning hours and largest during the afternoon. The error is more persistent, above 3 K, throughout the day. In general, WRF-‐PX has a larger diurnal temperature range than WRF-‐
BASE and WRF-‐SNODAS. The diurnal statistics indicate that WRF-‐PX also has problems simulating cold air pool meteorological processes. From the soccer plots, Utah consistently performed the worst among the three states for temperature. Figure 4-‐8 is a plot of the hourly temperature over Utah for January and February. The highlighted portions for 2011 are known ozone exceedance events. During these events there are large temperature errors, especially at night. The warm bias at night for multiple days indicates a problem with modeling persistent cold air pools. Note that WRF-‐SNODAS simulation on average is colder at night, but the near surface temperature errors remain large, 3-‐5 K warmer than observed for these events. The WRF-‐PX simulation, Figure 4-‐9, is also showing similar issues at night. WRF-‐PX is also persistently warmer during the day creating the large diurnal temperature range. We discuss the near surface temperature errors and other cold air pool processes in more detail when comparing with field campaign data in the following sections. August 19, 2015 4-‐11 Figure 4-‐7. Diurnal average temperature time series of standard deviation (red), mean absolute error (blue), and bias (green) for January 2013 over all stations in the 4-‐km domain. August 19, 2015 4-‐12 Figure 4-‐8. January and February 2011 and 2013 time series of temperature (K) for WRF-‐BASE, WRF-‐
SNODAS, and observations over Utah. Highlighted sections for 2011 are known ozone exceedance events. Figure 4-‐9. January and February 2013 time series of temperature (K) for WRF-‐SNODAS, WRF-‐PX, and observations over Utah. 4.3
Additional Results Additional qualitative performance plots for the WRF winter modeling for 2010-‐2011 and 2012-‐2013 are hosted on the Intermountain West Data Warehouse. August 19, 2015 4-‐13 5
Cold Air Pool Meteorological Process Evaluation Using Observations There are several field campaigns with available data for comparison. We find the PCAPS study provides the best meteorological data for evaluating the cold air pool structure. For Uinta Basin and the Upper Green River, most of the observations are near surface or limited to areas below the temperature inversion and therefore limit the ability to do process evaluation of the boundary layer structure and depth. All of our analysis focused on the PCAPS and the Upper Green River observations because of time constraints. 5.1
Persistent Cold Air Pool (PCAPS) Field Campaign Comparison One of the main benefits of the PCAPS data is the availability of time-‐height data. The data is a combination of data collected at two Integrated Sounding System (ISS) sites, a radar wind profiler, a radio acoustic sounding system, near surface observations, and radiosonde data from the Salt Lake International Airport. A more detailed description on the generation of the PCAPS time-‐height data can be found in Lareau and Horel (2014). Figure 5-‐1 is a comparison of the PCAPS time-‐height data from January 1-‐10, 2011 to the WRF-‐
BASE, WRF-‐SNODAS, and WRF-‐PX simulations. The plot includes potential temperature and wind speed. The WRF comparisons are centered at the core of the Salt Lake Valley (closest grid point to the ISS-‐N location in Figure 1 of Lareau and Horel (2014).30 The observed potential temperature from PCAPS illustrates the cold air pool near surface temperatures being strongest (coldest) from January 1-‐2 that weakens (becomes warmer) on January 3-‐4. The cold air pool strengthens the next two days and is scoured out by January 9th in association with stronger winds aloft. There is also a diurnal cycle also in the wind speed, which is weakest at night and strongest during the day. WRF-‐BASE simulates the cold air pool, shown in Figure 5-‐1, but the intensity of the cold air pool is weaker and erodes too quickly compared to observations. In particular, the temperatures aloft (60 meters and above) in WRF-‐BASE are much warmer than observed. Additionally, the wind speeds aloft, especially at the initial time of the event, are much stronger than observed. These strong winds can mix down the warm air aloft towards the surface, weakening the cold air pool. Both WRF-‐SNODAS and WRF-‐PX have similar issues. This is despite snow cover being very different between NAM and SNODAS, see Figure 5-‐2. In particular, the snow cover is more extensive in the mountains surrounding Salt Lake when initializing WRF using SNODAS instead of NAM. The results show that the near surface boundary forcing (such as implementing SNODAS) is only one component towards improving the ability to simulate cold air pool meteorological processes. We note that this problem is not specific to one episode. Figure 5-‐3 shows the same set images but for a different event, December 1-‐7, 2010. Note that the temperatures and winds aloft are both warmer and stronger than observed. We hypothesize the NAM biases are limiting the ability to simulate the cold air pool processes. Our simulations nudge to the NAM atmospheric fields including winds and temperature above the boundary 30
Lareau, N. and J. Horel (2015). Turbulent Erosion of Persistent Cold Air Pools: Numerical Simulations. Journal of Atmospheric Science, 72,1409-‐1427. doi:http://dx.doi.org/10.1175/JAS-‐D-‐14-‐0173.1. August 19, 2015 5-‐1 layer. It is possible that biases in the NAM limit the ability to simulate cold air pool processes, especially if applying analysis nudging. The upper levels in the NAM analyses may be constructed from an upper-‐air sounding site that is not representative of the area that is being nudged. We recommend that future studies investigate analysis nudging and the use of NAM for simulating cold air meteorology. August 19, 2015 5-‐2 Figure 5-‐1. Time-‐height plot of potential temperature and wind speed for January 1-‐10, 2011. The height levels (m) are provided on the y-‐axis for PCAPS. The corresponding sigma levels are plotted on the y-‐axis for the matching height levels from the WRF simulations. August 19, 2015 5-‐3 Figure 5-‐2. Initial snow depth from NAM used in the WRF-‐BASE configuration and SNODAS used in the WRF-‐SNODAS and WRF-‐PX configurations. August 19, 2015 5-‐4 August 19, 2015 5-‐5 Figure 5-‐3. Time-‐height plot of potential temperature and wind speed for December 1-‐7, 2010. The height levels (m) are provided on the y-‐axis for PCAPS. The corresponding sigma levels are plotted on the y-‐axis for the matching height levels from the WRF simulations. 5.2
Upper Green River Winter Ozone Study (UGWOS) Comparison Observations including near surface temperature, relative humidity, wind speed and direction, PBL height, incoming radiation, and upper air observations were investigated for comparison with the WRF output. Unfortunately, the majority of the data besides the near surface fields were intermittent and did not extend above the height of the temperature inversions. This limits the ability to compare the cold air pool structure, unlike the PCAPS field campaign data. Below we compare available near surface fields for known periods with high ozone during the 2010-‐2011 winter. Figure 5-‐4 is a plot of the near surface meteorology compared to observations for Juel Springs, WY from March 1-‐4, 2011. The amount of snow in the WRF-‐BASE simulation (using NAM) of almost two feet is twice that seen in WRF-‐SNODAS. However, the differences in the amount of snow (NAM vs. SNODAS) does not equate to large temperature differences. For instance, in both simulations we find large errors in nighttime temperatures on March 1st. The large nighttime temperature errors correspond to large errors in the wind direction. The observed wind direction at night is predominately from the southwest. The model wind directions are from the northeast. It is likely cold air advection that is driving the cold bias at night. The cold August 19, 2015 5-‐6 model temperatures also influence the relative humidity, which typically goes to near 100% most nights. This is consistent with the prior statistics that the near surface specific humidity is positively biased. Similarly, we find similar temperature errors for both WRF-‐BASE and WRF-‐
SNODAS for daytime temperatures on March 3rd. On March 3rd, the model shortwave radiation is larger than observed and and corresponds to higher temperatures in WRF than observed. Figure 5-‐5 is a plot of the near surface meteorology simulation, compared to observations for Boulder, WY from February 13-‐16, 2011. The snow depth in the WRF-‐BASE simulation is shallower than that of WRF-‐SNODAS by several inches. Again, the WRF-‐SNODAS simulation has similar errors on average to that of the WRF-‐BASE simulation. The simulation without observational nudging differs more when compared against WRF-‐BASE. Removing observational nudging can make notable improvements. For instance, the WRF-‐BASE and WRF-‐
SNODAS nighttime temperatures are colder than observed on February 16. Turning off observation nudging favors warmer nighttime temperatures and helps to improve the near surface temperatures on February 16. But observational nudging did not improve the temperatures for prior days. The mixed results suggest that observational nudging needs to be investigated more thoroughly. The wind speeds are much weaker and more variable in this case and it is difficult to quantify the impact of using SNODAS. The observed incoming surface radiation indicates clear sky conditions. The simulations do well at simulating the incoming solar radiation. Interestingly, we find that the relative humidity again goes to near 100% overnight. We find this to be a consistent pattern for other locations also (e.g. Pindale and Daniel South -‐not shown). The positive bias in the specific humidity, despite being within the benchmark thresholds, may hinder the development, persistence, and demise of cold air pools and needs further investigation. August 19, 2015 5-‐7 Figure 5-‐4. Comparison between observations of WRF-‐BASE (red), WRF-‐SNODAS (green), WRF-‐SNODAS-‐No_OBSNUDGE (blue) for snow depth (m), 2-‐m temperature (°C), wind speed (m/s), wind direction (°), relative humidity (%), and incoming shortwave (W/m2) radiation over Juel Springs, WY from March 1-‐4, 2011. August 19, 2015 5-‐8 Figure 5-‐5. Comparison between observations of WRF-‐BASE (red), WRF-‐SNODAS (green), WRF-‐SNODAS-‐No_OBSNUDGE (blue) for snow depth (m), 2-‐m temperature (°C), wind speed (m/s), wind direction (°), relative humidity (%), and incoming shortwave (W/m2) radiation over Boulder, WY from February 13-‐16, 2011. August 19, 2015 5-‐9 6
Sensitivity Comparison Sensitivity comparisons were made to investigate WRF-‐SNODAS and WRF-‐PX simulations to understand changes in the model configuration on the cold air pool vertical structure. The comparisons focused on locations within the Uintah and Upper Green River Basins (UGR) for ozone exceedance events, as shown in Table 3-‐4. The comparisons include snow height, temperature, wind speeds, and planetary boundary layer height. 6.1
WRF-‐SNODAS Comparison to WRF-‐BASE Figure 6-‐1 is a time series of snow depth for Pinedale, WY and Roosevelt, UT from March 1-‐5, 2011. These sites were selected because of differences in the relative amount of snow between the WRF-‐BASE and WRF-‐SNODAS simulations. For the UGR site (Pinedale), the amount of snow in the WRF-‐SNODAS is smaller than WRF-‐BASE; however, the total amount of snow is rather large for both, exceeding 1-‐1.5 feet. The Uintah site (Roosevelt) is very different. There is very little snow on the ground. WRF-‐SNODAS has some snow (several inches) compared to almost no snow in the WRF-‐BASE configuration. Figure 6-‐2 is a time-‐height plot of potential temperature and wind speed for UGR (Pinedale) and Uintah (Roosevelt) sites. The UGR site shows very small differences in the time-‐height plots for both potential temperature and wind speed. The potential temperature / wind speed differences are generally less than 1 K / 1 ms-1, respectively. The snowpack in the WRF-‐BASE and WRF-‐SNODAS simulations are large and thus any snowpack changes result in small changes in the cold air pool structure. However, when the snowpack is shallow to begin with, the atmosphere is more sensitive to differences in the snow depth. For instance, the WRF-‐SNODAS simulation for the Uintah site has more snow and results in colder temperatures extending well above the surface, altering the boundary layer structure. The changes also significantly influence the wind speeds, with colder temperatures increasing the wind speeds in the lowest model levels. In the prior section, we compared the small differences between WRF-‐BASE and WRF-‐SNODAS and the observations. Though these sensitivity results seem to contradict the prior section, this is not the case. There is great variance in the simulation results from site to site. We find that the differences in the vertical structure of temperature and wind speed are very sensitive to the location and amount of snow not only in the immediate vicinity of the station of interest, but also the surrounding area. Figure 6-‐3 helps to illustrate this point using a spatial snapshot of the snow depth fields between WRF-‐BASE and WRF-‐SNODAS. The snapshot is taken on March 1, 2011 at 1200 UTC. SNODAS has a deeper snowpack on the surrounding peaks of the Uintah and Wasatch Ranges than the WRF-‐BASE simulation. Shallow downslope drainage flow at night is stronger in areas adjacent to the mountains. The downslope flow transports colder air from higher elevations down into the valley resulting in a significantly colder basin (3-‐6K). Changes in the diurnal flow may also help to mix pollutants from various sources in the basin and boost ozone formation. Altamont, Utah is a good illustration of the changes in diurnal flow when NAM is replaced with SNODAS (Figure 6-‐4). The August 19, 2015 6-‐1 wind speed in the lower levels of the atmosphere for WRF-‐SNODAS is significantly stronger at night then WRF-‐BASE (upwards of 3-‐4 m/s) and associated with colder near surface temperatures (3-‐4 K). Figure 6-‐5 compares the PBL heights between the simulations for the same times at Altamont, UT. The PBL heights in the WRF-‐SNODAS simulation are much lower (upwards of several hundred meters) during the afternoon. Overall, using SNODAS does impact important meteorological fields in the oil and natural gas basins for air quality. Our analysis illustrates that SNODAS implementation is important for near surface temperature, wind speeds, and boundary layer height, especially in basins such as the Uintah where the local flow is driven by changes in the snowpack. August 19, 2015 6-‐2 Figure 6-‐1. Time series of snow depth from March 1-‐5, 2011 for WRF-‐SNODAS (black) and WRF-‐BASE (green). Two locations shown are Pinedale, WY and Roosevelt, UT. Figure 6-‐2. Time-‐height difference plot (WRF_SNODAS-‐WRF_BASE) of potential temperature August 19, 2015 6-‐3 and wind speed. Two locations shown are Pinedale, WY and Roosevelt, UT as in Figure 6-‐1. Figure 6-‐3. Difference plots (WRF_SNODAS –WRF_BASE) of snow depth, 2-‐m temperature, and 10-‐m wind speed on March 1, 2011 at 1200 UTC. Figure 6-‐4. Time-‐height plot of potential temperature (top) and wind speed (bottom) for WRF-‐SNODAS and the difference (WRF-‐SNODAS minus WRF-‐BASE) from March 1-‐5, 2011 for Altamont, UT. August 19, 2015 6-‐4 Figure 6-‐5. WRF PBL height for WRF-‐SNODAS (black) and WRF-‐BASE (green) from March 1-‐5, 2011 for Altamont, UT. 6.2
WRF-‐PX Comparison to WRF-‐BASE Our sensitivity analysis also compares the WRF-‐PX changes to the WRF-‐BASE. Note that these comparisons are for the 2012-‐2013 winter. The prior WRF-‐SNODAS comparison was for 2010-‐
2011 winter. Additionally, WRF-‐PX assimilates SNODAS. These comparisons focus on differences in the PBL height for sites within the Uintah and UGR Basins. Figure 6-‐6 is a comparison for two sites in the Uintah Basin, Altamont and Roosevelt. The PBL heights are higher in the WRF-‐PX simulations compared to the WRF-‐BASE. Other sites (not shown) also show WRF-‐PX having higher PBL heights. WRF-‐PX would allow for more vertical mixing than WRF-‐BASE. Using the WRF-‐PX configuration would likely decrease the ozone concentrations in the Uintah Basin. These results are consistent with the PCAPS evaluation, see Figure 4-‐4, which illustrated WRF-‐PX having a warm bias during the day and a strong diurnal cycle. The response is not consistent in the UGR Basin. Figure 6-‐7 is a plot of the PBL height for Pinedale and Juel Springs, WY. The PBL height for Pinedale is larger in the WRF-‐BASE configuration from January 25-‐27, 2013. Compare this to the PBL heights for Juel Springs. The WRF-‐BASE configuration tends to be smaller for the same days. This illustrates the complication of the model configuration choice, WRF-‐PX vs. WRF-‐BASE or WRF-‐SNODAS, when considering multiple basins because the model configuration can be sensitive to different basins. August 19, 2015 6-‐5 Figure 6-‐6. WRF PBL height (m) for WRF-‐PX (black) and WRF-‐BASE (green) from March 1-‐5, 2013 for two sites in UT. Figure 6-‐7. WRF PBL height (m) for WRF-‐PX (black) and WRF-‐BASE (green) from January 21-‐27, 2013 for two sites in WY. August 19, 2015 6-‐6 7
Summary and Recommendations This study tested the ability of different WRF configurations to simulate wintertime ozone meteorological conditions across all Western states. By applying lessons learned from prior modeling studies, these configurations were designed to improve cold air pool meteorology processes, which are important contributors to poor air quality in the oil and gas regions. All model simulations were designed to run for two winter seasons, December 2010 – March 2011 and December 2012 – March 2013. A main focus of this study was on the implementation of SNODAS in WRF and the evaluation and sensitivity of WRF-‐SNODAS compared to the WRF-‐BASE (as used in the 3SAQS study) for the Uintah and Upper Green River Basins. These simulations used a 5.5 day reinitialization, which included initializing back to SNODAS. Our comparisons with observations used traditional observations (e.g. MADIS) and field campaign data from the Persistent Cold Air Pool Study and Upper Green River Winter Ozone Study. The WRF-‐SNODAS configuration statistics of near surface temperatures improve within the 3SD when compared with MADIS sites. In particular, the warm bias at night improves in the WRF-‐SNODAS configuration for sites within Utah. The WRF-‐SNODAS temperature improvements are relative to the snow cover and height and can change from year to year. Other near surface variables, such as mixing ratio and wind speed/direction, show smaller improvements at the MADIS sites, but some of the largest differences for these meteorological fields extend above the near surface. WRF-‐SNODAS configuration improves the representation of cold air pool meteorological processes compared with the Persistent Cold Air Pool Study field campaign data. There is inconclusive evidence of model improvements relative to observations in the Upper Green River Study and likely a result of the deeper snowpack in the Upper Green River Basin limiting the feedback between the land surface and the atmosphere. The sensitivity comparisons revealed that WRF-‐SNODAS significantly alters the local circulation important for transport and mixing of pollutants. Differences in the vertical structure of temperature and wind speed are very sensitive to the location and amount of snow not only in the immediate vicinity of the station of interest, but also the surrounding area. The downslope flow transports colder air from higher elevations down into the valley resulting in a significantly colder basin. Changes in the diurnal flow may help to mix pollutants from various sources in the basin and boost ozone formation. Overall, the WRF-‐SNODAS configuration is equal to or better than the WRF 2011 application used in the 3SAQS and we recommend it for future production runs. We also performed two additional simulations that integrate SNODAS. One experiment turned off the observational nudging applied in the 4-‐km domain. Our results were inconclusive on the use of observational nudging because near surface temperature performance varied significantly between locations. The use of observational nudging in combination with SNODAS needs further investigation. The second simulation included many changes, including the PBL scheme, land surface model, and changes to the reinitialization procedure. This simulation, WRF-‐PX, used the ACM2 PBL scheme, PX land surface model, and a continuous integration. The WRF-‐PX configuration in the Uintah Basin consistently demonstrated problems with the diurnal August 19, 2015 7-‐1 temperature range. WRF-‐PX was warmer than observations during the day in the Uintah Basin, generating more mixing and higher PBL heights. The WRF-‐PX simulation takes longer to run because it is continuous and was unstable for the 2011 simulation. We do not currently recommend using WRF-‐PX because these issues require further investigation. Overall, we find that substituting SNODAS for NAM snow fields is important and leads to incremental improvements in cold air pool meteorological modeling. The biggest improvements in performance were over the Uintah Basin where snow cover is a key factor in simulating diurnal flow patterns that influence cold air pool development and ozone formation. There are other possible improvements in cold air pool meteorology that we did not explore in this study and are likely important such as: increasing the horizontal resolution to possibly to 1-‐km or finer; use of nudging techniques including analysis and observational nudging; possibly inadequate landuse/land cover data; testing other boundary layer schemes such as the Quasi-‐
Normal Scale Elimination PBL scheme. August 19, 2015 7-‐2

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