Western- State Air Quality Modeling Study (WSAQS) Weather Research Forecast 2014 Meteorological Model Application/Evaluation

Transcription

1 Western- State Air Quality Modeling Study (WSAQS) Weather Research Forecast 2014 Meteorological Model Application/Evaluation Prepared for: Tom Moore Western Regional Air Partnership c/o CIRA, Colorado State University 1375 Campus Delivery Fort Collins, CO Prepared by: Jared Bowden, Kevin Talgo, Zac Adelman 100 Europa Drive, Suite 490 Chapel Hill, NC January 1, 2016

2 CONTENTS EXECUTIVE SUMMARY... ES INTRODUCTION Meteorological Modeling WSAQS WRF Meteorological Modeling METHODOLOGY WRF Model Inputs Evaluation Approach WRF SURFACE MODEL PERFORMANCE EVALUATION RESULTS Seasonal Quantitative Model Evaluation Results Monthly Quantitative Model Performance Additional Results TABLES Table 2-1. Physics options used in the WSAQS WRF Version simulation Table 2-2. Vertical layer definition for WRF simulations Table 3-1. Meteorological model performance benchmarks for simple and complex conditions Table 3-2. Winter season 2- m temperature (T2), mixing ratio (Q2), wind speed (WS) and direction (WD) average bias and MAE over the 4- km domain, Colorado, Wyoming, and Utah Table 3-3. Spring season 2- m temperature (T2), mixing ratio (Q2), wind speed (WS) and direction (WD) average bias and MAE over the 4- km domain, Colorado, Wyoming, and Utah Table 3-4. Summer season 2- m temperature (T2), mixing ratio (Q2), wind speed (WS) and direction (WD) average bias and MAE over the 4- km domain, Colorado, Wyoming, and Utah Table 3-5. Fall season 2- m temperature (T2), mixing ratio (Q2), wind speed (WS) and direction (WD) average bias and MAE over the 4- km domain, Colorado, Wyoming, and Utah Figure AMET WRF 4- km June 2014 spatial surface plot of daily mean temperature bias FIGURES Figure 2-1. Figure km CONUS, 12- km WESTUS (d02), and 4- km WAQS (d03) WRF modeling domains Locations of MADIS surface meteorological modeling sites with the WRF 4- km WAQS modeling domain.... Error! Bookmark not defined. i

3 Figure 3-1. Figure 3-2. Figure 3-3. Figure 3-4. Figure 3-5. Figure 3-6. Figure 3-7. Figure 3-8. Figure 3-9. Winter season 4- km WSD diurnal average 2m temperature/specific humidity and 10m wind speed/direction time series of standard deviation, MAE, and bias Spring season 4- km WSD diurnal average 2m temperature/specific humidity and 10m wind speed/direction time series of standard deviation, MAE, and bias Summer season 4- km WSD diurnal average 2m temperature/specific humidity and 10m wind speed/direction time series of standard deviation, MAE, and bias Fall season 4- km WSD diurnal average 2m temperature/specific humidity and 10m wind speed/direction time series of standard deviation, MAE, and bias Soccer plot of monthly temperature error and bias (K) for 4- km WSD over Colorado Soccer plot of monthly temperature error and bias (K) for 4- km WSD over Utah Soccer plot of monthly temperature error and bias (K) for 4- km WSD over Wyoming Soccer plot of monthly mixing ratio error and bias (g/kg) for 4- km WSD over Colorado Soccer plot of monthly mixing ratio error and bias (g/kg) for 4- km WSD over Utah Figure Soccer plot of monthly mixing ratio error and bias (g/kg) for 4- km WSD over Wyoming Figure Soccer plot of monthly wind speed error and bias (m/s) for 4- km WSD over Colorado Figure Soccer plot of monthly wind speed error and bias (m/s) for 4- km WSD over Utah Figure Soccer plot of monthly wind speed error and bias (m/s) for 4- km WSD over Wyoming Figure Soccer plot of monthly wind direction error and bias for 4- km WSD over Colorado Figure Soccer plot of monthly direction error and bias for 4- km WSD over Utah Figure Soccer plot of monthly wind direction error and bias for 4- km WSD over Wyoming Figure AMET WRF 4- km annual summary plot of temperature for all sites in the 4- km WSD ii

4 EXECUTIVE SUMMARY The Western- State Air Quality Study (WAQS) is intended to assess the environmental impacts of emissions sources in the region, in particular those related to oil and gas development and production. The WAQS is developing regional photochemical grid model (PGM) modeling platforms to assess the contributions of emissions to ozone, fine particulate matter (PM 2.5 ), visibility, sulfur and nitrogen deposition, and other potential air quality and air quality related values (AQRVs). Meteorological data are key inputs for PGM modeling. These data include wind speed and direction, temperature, water vapor concentrations (mixing ratio), sunlight intensity, clouds, precipitation, and vertical mixing. The meteorological inputs for PGM models are generated using prognostic meteorological models that solve the fundamental equations of the atmosphere. The WAQS used the Weather Research Forecast (WRF Version 3.6.1) Advanced Research WRF (WRF- ARW, or WRF in this Executive Summary) for the 2014 calendar year with a 36 km continental U.S. (CONUS), 12 km western U.S. (WESTUS), and 4 km western- state domain (WSD) covering the states of Colorado, Utah, New Mexico, Wyoming, and neighboring areas. The WAQS 2014 WRF 36/12/4- km simulation was run in 5- day segments with a half- day initialization per segment. The annual WRF simulation used 16 processors per run segment and each segment took on average two wall clock days to complete. The 2014 annual WRF simulations were evaluated for surface wind, temperature, and mixing ratio observations. The 2014 WRF model performance for surface winds, temperature, and mixing ratios was evaluated across the states of Colorado, Utah, and Wyoming as well as across the 4- km WSD and 12- km WESTUS domains. The WRF model performance statistics were compared against meteorological model performance benchmarks. These benchmarks were developed by analyzing the meteorological model performance of approximately 30 good performance prognostic model simulations conducted to support air quality modeling. Most of these simulations were for urban ozone modeling of stagnation episodes in fairly flat locations. More recently, new sets of meteorological model performance benchmarks have been developed for more complex conditions (e.g., complex terrain and complex meteorological phenomena) that were also used to help assess WRF model performance. Quantitative evaluation of surface winds, temperature, and water mixing ratio show that WRF represented these fields in the 4- km WSD with acceptable model performance. The monthly bias and error statistics rarely exceed the model performance benchmarks when the fields are evaluated on a domain- wide and state- by- state basis. The evaluation reveals a number of minor biases in the WRF simulation that are worth noting. WRF consistently underestimates wind speed by approximately 0.5 m/s throughout the entire year across much of the modeling domain. Mixing ratio is generally over- predicted during the cooler months, peaking during the daytime hours. Conversely, mixing ratio is under- predicted across much of the domain during the warmer months. We also find that for temperature, there is a tendency for a warm bias during the overnight hours into the early morning and the temperature bias becomes smaller and sometimes negative during the daylight hours. ES- 1

5 1.0 INTRODUCTION The University of North Carolina (UNC) at Chapel Hill is participating in the Western Air Quality Modeling Study (WAQS). The WAQS includes cooperators from U.S. Environmental Protection Agency (EPA), U.S. Forest Service (USFS), Bureau of Land Management (BLM), National Park Service (NPS), Fish and Wildlife Service (FWS), Region 8 of the U.S. Environmental Protection Agency (EPA) and the state air quality management agencies of Colorado, New Mexico, Utah, and Wyoming. The WAQS is intended to facilitate air resource analyses for federal and state agencies in the states of Colorado, New Mexico, Utah, and Wyoming. 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 on assessing the air quality impacts of emissions within the western- 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 developing a meteorological input data for future 2014 PGM modeling. The meteorological model evaluation is described in this document. 1.1 Meteorological Modeling Over the past decade, emergent requirements for numerical simulations of urban and regional scale air quality have led to intensified efforts to construct high- resolution emissions, meteorological, and air quality datasets. The concomitant increase in computational throughput of low- cost modern scientific workstations has ushered in a new era of regional air quality modeling. It is now possible, for example, to exercise sophisticated mesoscale prognostic meteorological models and Eulerian and Lagrangian photochemical/aerosol models for multi- seasonal periods over near- continental scale domains in a matter of weeks with the application tailored to a specific air quality modeling project. We used the Weather Research and Forecasting (WRF) Advanced Research dynamic core (WRF- ARW, henceforth simply called WRF) model for this meteorological modeling study. 1,2 WRF is a next- generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. 3,4,5 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 1 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 2 model.org/index.php 3 Skamarock, W. C Evaluating Mesoscale NWP Models Using Kinetic Energy Spectra. Mon. Wea. Rev., Volume 132, pp December. ( 4 Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang and J. G. Powers A Description of the Advanced Research WRF Version 2. National Center for Atmospheric Research (NCAR), Boulder, CO. June. ( 5 Skamarock, W. C Positive- Definite and Monotonic Limiters for Unrestricted- Time- Step Transport Schemes. Mon. Wea. Rev., Volume 134, pp June. ( 1

6 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. This report describes an application and performance evaluation of WRF for an atmospheric simulation for calendar year 2014 over a modeling domain that covers the continental United States at 36 km grid spacing (CONUS), the Western and Midwestern United States at 12 km grid spacing (WESTUS) and the western- state domain at 4 km resolution (WSD). 1.2 WSAQS WRF Meteorological Modeling Prior meteorological modeling tests were performed for an annual 2011 WRF configuration, which included numerous first- order WRF meteorological modeling simulations for the 36/12/4- km domains used here in the 2014 WRF simulation. An optimal physics configuration from the 2011 WRF simulation tests was chosen based on model performance, see Table 2-1 in Chapter 2. The WRF 2011 analysis revealed issues with model performance for winter ozone within the WSD. 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 meteorological model evaluation identified that WRF did not reproduce the low wind speeds, temperature inversions, and dry conditions associated with elevated winter ozone events. Additional work was tasked to improve the representation of meteorological processes important in the buildup of wintertime ozone precursors. The work on improving winter season meteorology modeling in the WSD considered lessons learned from prior WRF 2011 meteorological modeling, prior studies, and input from other groups working on similar issues, including the U.S. EPA, the University of Utah (UUtah), the Utah Department of Environmental Quality Division of Air Quality (UTDAQ), and the Wyoming Department of Environmental Quality (WDEQ) 6. In the WAQS WRF winter modeling study, one task was to improve the snow depth initial conditions. The study found improvements in near surface fields within the WSD when using input from the Snow Data Assimilation System (SNODAS). This WRF- SNODAS configuration was also found to improve the representation of cold air pools when comparing with field campaign data. Below we describe in detail the WRF configuration for the 2014 annual simulation, which uses optimal input data including SNODAS, and optimal physics options from prior testing. We then present the results using this configuration for 2014 WRF simulation. 6 Bowden, J., Talgo, K., Adelman, Z., 2015: Western Air Quality Modeling Study WRF Meteorological Model Winter Modeling Application/Evaluation, UNC- IE, Chapel Hill, NC, August 2015, available online at: 15.pdf 2

7 2.0 METHODOLOGY The WAQS 2014 WRF modeling used the WRF model to simulated 2014 meteorology using our assessment of the best physics options for the region. We then evaluated the WRF results (e.g., wind speeds, wind directions, and temperatures) with available, standard meteorological observations. 2.1 WRF Model Inputs A brief summary of the WRF input data preparation procedure used for this annual modeling exercise is provided below. Model Selection: The publicly available version of WRF (version 3.6.1) was used in the modeling study. The WPS 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 2-1. 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 2-2. 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 WSD was based on the 30 sec. (~900 m) data. Vegetation Type and Land Use Inputs: Vegetation type and land use information was developed using the most recently released WRF databases provided with the WRF distribution. 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 WSD. 3

8 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 WSD. 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. 7 SNOW Cover and Depth Inputs: The snow cover and depth are initialized with SNODAS data 8. 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 5x10-4 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 (MADIS 9 ) observation archive. The MADIS archive includes the National Climatic Data Center (NCDC 10 ) observations and the National Data Buoy Center (NDBC) Coastal- Marine Automated Network (C- MAN 11 ) 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 this application are presented in Table 2-1. The best- performing physics options configurations are the same as those used in the 2011 WRF configuration. 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 was included in each 5- day block before the data were used in the subsequent evaluation. The model was run at the 36-7 Real- time, global, sea surface temperature (RTG- SST) analysis. 8 Snow Data Assimilation System. 9 Meteorological Assimilation Data Ingest System National Climatic Data Center National Data Buoy Center. 4

9 km, 12- km and 4- km grid resolution from December 16, 2013 through January 1, 2015 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). 2.2 Evaluation Approach The model evaluation approach was based on quantitative analysis. The quantitative analysis was divided into monthly summaries of 2- m temperature, 2- m mixing ratio, and 10- m wind speed using the boreal seasons to help generalize the model bias and error relative to a set of standard model performance benchmarks. The evaluation focused on the 4- km WSD in the states of Colorado, Wyoming, and Utah and was supplemented with select diurnal and 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 WSD are shown in Figure 2-2. In general, the evaluation was developed to summarize the meteorological model performance to inform downstream PGM activities of the model errors. 5

10 Table 2-1. Physics options used in the WSAQS WRF Version simulation. WRF Treatment Option Selected Notes Microphysics Thompson A scheme with ice, snow, and graupel processes suitable for high- resolution simulations. Longwave Radiation RRTMG Rapid Radiative Transfer Model (RRTM) for GCMs includes random cloud overlap and improved efficiency over RRTM. Shortwave Radiation RRTMG Same as above, but for shortwave radiation. Land Surface Model (LSM) NOAH Two- layer scheme with vegetation and sub- grid tiling. Planetary Boundary Layer (PBL) scheme Cumulus parameterization Analysis nudging Observation Nudging Initialization Dataset YSU Kain- Fritsch in the 36- km and 12- km domains. None in the 4- km 3SD. 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 WSD 12 km North American Model (NAM) 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. Temperature and moisture nudged above PBL only moisture observation nudging produces excessive rainfall 6

11 Table 2-2. Vertical layer definition for WRF simulations. WRF Meteorological Model WRF Layer Sigma Pressure (mb) Height (m) Thickness (m)

12 Figure km CONUS, 12- km WESTUS (d02), and 4- km WAQS (d03) WRF modeling domains. 8

13 Figure 2-2. Locations of MADIS surface meteorological modeling sites with the WRF 4- km WAQS modeling domain 9

14 3.0 WRF SURFACE MODEL PERFORMANCE EVALUATION RESULTS 3.1 Seasonal 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 12. AMET calculates statistical performance metrics for bias, error and correlation for surface winds, temperature and mixing ratio and can produce 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. An electronic docket is available through the Intermountain West Data Warehouse (IWDW) that includes additional plots for evaluating the 2014 WRF model performance (see the Model Performance Evaluation Plots Tool at: nfo). Overall comparisons are offered in this report to judge the model efficacy, but this review does not necessarily cover all potential user needs and applications. To evaluate the performance of the 2014 WRF simulation for the U.S., we used a number of performance benchmarks for comparison. 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. 13 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 and 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 conditions 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. 14 Kemball- Cook et al. also proposed model Emery, C., E. Tai, and G. Yarwood, 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. nceevaluation.pdf 14 McNally, D. E., km MM5 Performance Goals. Presentation to the Ad- hoc Meteorology Group. 25- June. 10

15 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. 15 Based on these reviews, we have adopted simple and complex model performance benchmarks for surface temperature, mixing ratio (humidity), wind speed, and wind direction bias and error as shown in Table 3-1. The equations for bias and error are given below. 1 N Bias = ( ) N i= 1 P i O i Error = 1 N N i= 1 P i O i Table 3-1. Meteorological model performance benchmarks for simple and complex conditions. Parameter Simple Complex Temperature Bias ±0.5 K ±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 Winter 2014 Winter season (December, January, and February) bias and error statistics for the entire 4- km WAQS modeling domain and the states of Colorado, Wyoming, and Utah are presented in Table 3-2. The WRF wintertime performance for temperature, specific humidity, wind speed and direction over the 4- km WSD is within both the simple and complex benchmark conditions. Being within both the simple and complex terrain benchmarks is indicative of acceptable model performance. There is a slight cool bias ( K) in the winter months when all stations in the 4- km WSD are aggregated, which is well within the temperature bias benchmark for both simple ( ±0.5 K) and complex ( ±1.0 K) conditions. There is also a near surface wet bias ( g/kg) within the WSD, but is within the simple ( ±0.5 g/kg) and complex ( ±1.0 g/kg) condition performance benchmarks. A negative wind speed bias ( m/s) is also shown, but is within 15 Kemball- Cook, S., Y. Jia, C. Emery, and R. Morris, 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. 11

16 the simple ( ±0.5 m/s) and complex ( ±1.0 m/s) condition performance benchmarks. The wind direction biases are inconsistent between the three states. The winter season average diurnal time series bias and error across all stations in the 4- km WSD is shown in Figure 3-1. Winter season 4- km WSD diurnal average 2m temperature/specific humidity and 10m wind speed/direction time series of standard deviation, MAE, and bias. For temperature, the late night and morning hours, roughly from UTC, experience a domain- wide warm bias. There is a marked cool bias in the evening hours, especially immediately following sunset. This diurnal pattern of temperature bias suggests that WRF may have some difficulty modeling temperature inversions over complex terrain. The inversion could be either too shallow and/or mixing out too quickly in the model, causing temperatures to be too warm at night. Similarly, there is a diurnal pattern in the specific humidity with smaller bias (close to 0 g/kg) during the overnight hours compared to the daytime (approximately 0.3 g/kg). Overall, WRF is generally too moist throughout the entire day for the WSD. The winter season wind speed bias indicates winds are roughly 0.5 m/s too slow in WRF when averaged across all stations in the 4- km WSD. The wind direction biases are small when averaged across all stations in the 4- km WSD. Table 3-2. Winter season 2- m temperature (T2), mixing ratio (Q2), wind speed (WS) and direction (WD) average bias and MAE over the 4- km domain, Colorado, Wyoming, and Utah. Bias / MAE (K) T2 (K) Q2 (g/kg) WS (m/s) WD (deg.) 4-km Domain -0.2 / / / / 29.3 CO -0.2 / / / / 33.7 WY -0.4 / / / / 29.3 UT -0.1 / / / / 38.1 ble 1: 2-m temperature (T2), mixing ratio (Q2), wind speed and direct 12

17 Figure 3-1. Winter season 4- km WSD diurnal average 2m temperature/specific humidity and 10m wind speed/direction time series of standard deviation, MAE, and bias. 13

18 3.1.2 Spring 2014 Spring season (March, April, May) bias and error statistics for the entire 4- km WSD and the states of Colorado, Wyoming, and Utah are presented in Table 3-3. WRF has an overall slight cool bias (approximately 0.2 K) across the domain. Temperature under- prediction is greatest in Colorado and Wyoming, but the simple and complex bias and error benchmarks are not exceeded. The specific humidity bias varies across the region with a wet bias in the springtime for Wyoming/Colorado and a dry bias in Utah. However, the humidity bias is small (±0.1 g/kg) for each state and falls within both simple and complex benchmarks. Similarly, we find a negative wind speed bias for Wyoming and Colorado ( m/s) compared to Utah, which has an average bias near zero. Overall, these performance metrics indicate that during the spring season the model errors are more sensitive to the local circulation features. Figure 3-2 shows the diurnal time series of average spring season bias and error across all stations in the 4- km WSD. The temperature bias shows that temperatures are under- predicted during the nighttime hours, but then shift quickly to a warm bias immediately after sunrise (approximately UTC). This performance trend contributes to our hypothesis that WRF is mixing the nighttime temperature inversion too quickly. By mixing out the inversion, WRF is producing warmer than observed temperatures immediately following sunrise when the PBL starts the mixing process. During the overnight hours the specific humidity bias becomes slightly negative, but then also quickly becomes positive immediately after sunrise, lending further evidence that the model is misrepresenting the diurnal dynamics within the PBL. As for wind speed, we find a negative wind speed bias that seems to improve during the daytime (near zero during parts of the day) relative to the overnight hours. The wind direction bias does not have a large change (< 10 degrees) throughout the day. Table 3-3. Spring season 2- m temperature (T2), mixing ratio (Q2), wind speed (WS) and direction (WD) average bias and MAE over the 4- km domain, Colorado, Wyoming, and Utah. Bias / MAE (K) T2 (K) Q2 (g/kg) WS (m/s) WD (deg.) 4-km Domain -0.2 / / / / 30.2 CO -0.3 / / / / 35.0 WY -0.3 / / / / 30.5 UT -0.1 / / / /

19 Figure 3-2. Spring season 4- km WSD diurnal average 2m temperature/specific humidity and 10m wind speed/direction time series of standard deviation, MAE, and bias. 15

20 3.1.3 Summer 2014 Table 3-4 shows that there is a warm bias (0.1 K) during each of the three summer months (June, July, August) averaged across all observation sites in the 4- km WSD. While the warm bias is largest over Wyoming, the bias and error statistics are within both the simple and complex performance benchmarks. The specific humidity bias is negative throughout the domain with a consistent magnitude around 0.5 g/kg. Both the wind speed error and bias are within the simple and complex performance benchmarks. The wind speed is underestimated on average by 0.4 m/s for the 4- km WSD, but again the bias and error statistics for wind speed are within the benchmark thresholds on average. The wind direction bias is around 1 m/s on average for the domain with an error of 36 degrees. Figure 3-4 shows the diurnal time series of average summer season bias and error across all stations in the 4- km WSD. The temperature bias shows that temperatures are over- predicted during the late nighttime hours through the early morning ( ), but then shift to a near zero bias during the afternoon and evening. The warm nighttime bias is mainly responsible for the slight warm bias in the daily average. The specific humidity exhibits a dry bias throughout the day. The wind speed bias is underestimated throughout the days and exhibits similar performance as the winter and spring seasons. The wind direction bias is again small throughout the day and much smaller than the benchmark performance thresholds. Table 3-4. Summer season 2- m temperature (T2), mixing ratio (Q2), wind speed (WS) and direction (WD) average bias and MAE over the 4- km domain, Colorado, Wyoming, and Utah. Bias / MAE (K) T2 (K) Q2 (g/kg) WS (m/s) WD (deg.) 4-km Domain 0.1 / / / / 36.4 CO 0.1 / / / / 40.0 WY 0.3 / / / / 37.0 UT 0.1 / / / / 41.6 ble 1: 2-m temperature (T2), mixing ratio (Q2), wind speed and direction a 16

21 Figure 3-3. Summer season 4- km WSD diurnal average 2m temperature/specific humidity and 10m wind speed/direction time series of standard deviation, MAE, and bias. 17

22 3.1.4 Fall 2014 The fall season (September, October, November) WRF performance shown in Table 3-5 features a domain- average 0.2 K warm bias. Despite the domain average warm bias, the largest temperature bias and error occur at observation sites in Utah and are biased low. The cool bias at the Utah sites contrasts to positive biases at sites in Colorado and Wyoming. Hence, domain wide temperature statistics of bias have a tendency cancel each other out and the average statistics are less representative of the local conditions and biases. The domain and state average temperature bias and error statistics are within the simple and complex performance benchmarks. The specific humidity bias is consistently negative throughout the domain with an average of 0.1 g/kg, and is again largest averaged across sites in Utah. The wind speed is underestimated throughout the WSD and in combination with results for other seasons illustrates that low wind speeds is a consistent problem for the entire WRF annual simulation. The wind direction error is also largest for Utah, indicating that warm/cold air advection may be responsible for the larger temperature and specific humidity bias at the Utah observation sites. Overall, the fall season bias and error statistics for all WRF variables are within both the simple and complex performance benchmarks. Figure 3-4 shows the diurnal time series of average summer season bias and error across all stations in the 4- km WSD. The temperature bias shows over- predictions through most of the day, with the exception of the afternoon. Specific humidity exhibits a dry bias for most of the day. The wind speeds are negative throughout the day as seen for all seasons. The wind direction bias is again small throughout the day and never exceeds 10 degrees on average for the WSD. Table 3-5. Fall season 2- m temperature (T2), mixing ratio (Q2), wind speed (WS) and direction (WD) average bias and MAE over the 4- km domain, Colorado, Wyoming, and Utah. Bias / MAE (K) T2 (K) Q2 (g/kg) WS (m/s) WD (deg.) 4-km Domain 0.2 / / / / 29.4 CO 0.3 / / / / 33.2 WY 0.1 / / / / 30.0 UT -0.6 / / / /

23 Figure 3-4. Fall season 4- km WSD diurnal average 2m temperature/specific humidity and 10m wind speed/direction time series of standard deviation, MAE, and bias. 19

24 3.2 Monthly Quantitative Model Performance The monthly model performance of the 2014 WRF 4- km simulation for surface meteorological variables within the states of Colorado, Utah and Wyoming is 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 center box of the plot, they achieve the benchmark (score a goal) Monthly Temperature Performance Figure 3-5 is the WRF 4- km WSD average monthly temperature Soccer plot for Colorado. All months meet the complex temperature performance benchmark. The simple terrain temperature benchmark is achieved for all months other than February, which is too cool. The plot illustrates the seasonal bias in temperatures, with late summer and early fall having a warm bias and late winter early spring a cool bias. Figure 3-5. Soccer plot of monthly temperature error and bias (K) for 4- km WSD over Colorado. 20

25 Figure 3-6 is the WRF 4- km WSD average monthly temperature Soccer plot for Utah. There is more spread in the temperature bias in Utah compared to Colorado. WRF achieves the simple temperature benchmark in Utah for 7 of 12 months. Again, there is a seasonal bias with late summer and early fall/winter having a warm bias and late winter/early spring a cool bias. The months that fall outside of the simple temperature benchmark include: January, February, August, October, and November. All of the months fall within the complex benchmark for temperature. State UT WRF_2014_BASE_04k Temperature Temperature Error (K) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Temperature Bias (K) Figure 3-6. Soccer plot of monthly temperature error and bias (K) for 4- km WSD over Utah. 21

26 Figure 3-7 is the WRF 4- km WSD average monthly temperature Soccer plot for Wyoming. Only January and February fall outside the simple terrain performance benchmark for bias. Most of the months have a slight warm bias, especially during the summer. The temperature error during the summer months is also smaller. The late winter and early spring have a cool bias and larger errors compared to the summer season. This trend suggests that performance problems at individual sites persists in the winter season and may be associated with cold air pool meteorology. All months fall within the complex benchmarks. Figure 3-7. Soccer plot of monthly temperature error and bias (K) for 4- km WSD over Wyoming. 22

27 3.2.2 Monthly Mixing Ratio Performance Figure 3-8 is the WRF 4- km WSD average monthly water mixing ratio Soccer plot for Colorado. WRF achieves the simple terrain performance benchmark for mixing ratios averaged across the Colorado sites for 8 of the 12 months; the summer months fall outside of the simple benchmark. As reported in the seasonal performance analysis, WRF is too try in the summer months at sites in Colorado. The bias reverses sign during the winter months (too wet), but the errors are typically smaller. It is important to note that as the moisture capacity of air is reduced at lower temperatures, we anticipate the mixing ratio errors to be smaller during the cool months. Figure 3-8. Soccer plot of monthly mixing ratio error and bias (g/kg) for 4- km WSD over Colorado. 23

28 Figure 3-9 is the WRF 4- km WSD average monthly water mixing ratio Soccer plot for Utah. The monthly mixing ratio performance in Utah achieves the simple terrain performance benchmark for 8 of the 12 months. As in Colorado, the months with largest error are the warm months: July, August, and September. The bias during these months is negative and August falls outside of the complex benchmark. January and February are the only months with positive mixing ratio biases. Figure 3-9. Soccer plot of monthly mixing ratio error and bias (g/kg) for 4- km WSD over Utah. 24

29 Figure 3-10 is the WRF 4- km WSD average monthly water mixing ratio Soccer plot for Wyoming. The Wyoming monthly mixing ratio performance is similar to Colorado and Utah with a slight wet bias in the cooler months and a dry bias in the warmer months. The simple mixing ratio bias benchmark is achieved for 10 of the 12 months. The complex terrain benchmark for error is achieved for all months. Only July and August fail to achieve the simple terrain benchmark. Figure Soccer plot of monthly mixing ratio error and bias (g/kg) for 4- km WSD over Wyoming. 25

30 3.2.3 Monthly Wind Speed Performance Figure 3-11 is the WRF 4- km WSD average monthly wind speed Soccer plot for Colorado. Monthly wind speed model performance in Colorado is characterized by winds that are too slow with monthly bias falling between the simple (- 0.5 m/s) and complex (- 1.0 m/s) benchmarks for almost all months. Monthly wind speed error is clustered around the simple benchmark (2.0 m/s). All months achieve the complex terrain wind speed performance benchmark. Figure Soccer plot of monthly wind speed error and bias (m/s) for 4- km WSD over Colorado. 26

31 Figure 3-12 is the WRF 4- km WSD average monthly wind speed Soccer plot for Utah. Monthly wind speed performance in Utah is fairly good with near zero bias. WRF meets the simple terrain wind speed benchmark for all months, with biases ranging from approximately 0.0 to m/s and error ranges from approximately 1.5 to 2.0 m/s. Figure Soccer plot of monthly wind speed error and bias (m/s) for 4- km WSD over Utah. 27

32 Figure 3-13 is the WRF 4- km WSD average monthly wind speed Soccer plot for Wyoming. Similar to Colorado, the WRF monthly wind speed bias shows that the winds are too slow, with biases generally falling between to m/s and errors clustered around the 2.0 m/s simple terrain benchmark. The largest errors occur during in January and February. The mid- summer to early winter months all achieve the simple terrain performance benchmark for wind speeds. Figure Soccer plot of monthly wind speed error and bias (m/s) for 4- km WSD over Wyoming. 28

33 3.2.4 Monthly Wind Direction Performance Figure 3-14 is the WRF 4- km WSD average monthly wind direction Soccer plot for Colorado. The WRF monthly averaged wind direction in Colorado achieves the complex terrain performance error benchmark ( ±10 degrees) for all months. The wind direction error is larger than the simple terrain metric of 40 degrees. The wind direction error is generally between degrees averaged across all Colorado sites. Figure Soccer plot of monthly wind direction error and bias for 4- km WSD over Colorado. 29

34 Figure 3-15 is the WRF 4- km WSD average monthly wind direction Soccer plot for Utah. While the WRF wind direction performance in Utah has high error ( degrees) and a mostly negative bias, the model achieves the complex terrain benchmark in all months. January and February have the largest average error and bias, respectively. Figure Soccer plot of monthly direction error and bias for 4- km WSD over Utah. 30

35 Figure 3-16 is the WRF 4- km WSD average monthly wind direction Soccer plot for Wyoming. Similar to Utah, the WRF monthly wind direction performance in Wyoming mostly has a negative bias. The cooler months have negative biases, beginning in September and running through March. While the errors are largest in the summer months, these months exhibit low biases. The complex terrain wind direction bias and error performance benchmarks are for all months. The simple terrain performance benchmark for bias is achieved for the warm months. Figure Soccer plot of monthly wind direction error and bias for 4- km WSD over Wyoming. 31

36 3.3 Additional Results An electronic docket is included with this report that contains additional plots illustrating the 2014 WRF 4- km model performance. These plots are accessible through Intermountain West Data Warehouse (IWDW) Model Performance Evaluation Plots Tool (select 2014 WRF à AMET à WRF_2014_BASE_04k): nfo Quantitative performance plots for the WSAQS 2014 WRF modeling on the 4- km modeling domains are included in the docket. The four types of plots available in this electronic docket include soccer, spatial surface, summary, and time series plots. Examples of the soccer and time series plots are included in this report. An example summary plot is shown in Figure 3-17 for WRF 4- km WSD temperature annual performance. The summary plot contains an extensive list of monthly model performance metrics and displays for all sites across the 4- km WSD. Performance metrics include number of data points used, correlation coefficient, standard deviation, and three types of bias and error. Graphical displays include a scatter plot, a comparison of the bias and mean absolute error (MAE) as a function of the observed quantity, and box plots of the distribution of the observed and predicted meteorological variables. Figure 3-18 is an example spatial surface plot. This plots show the June 2014 daily mean temperature bias for all observations sites in the 4- km WSD. The plot illustrates that on average, WRF is generally too cool across the entire domain during June. 32

37 Figure AMET WRF 4- km annual summary plot of temperature for all sites in the 4- km WSD. 33

38 Figure AMET WRF 4- km June 2014 spatial surface plot of daily mean temperature bias. 34