### Intro to WRF

• 2. Introduction to the Weather Research & Forecast Model MSGC BOREALIS Summer 2020 https://www.needpix.com/photo/download/602154/world-globe-earth-planet-blue-earth-globe-global-ocean-sphere
• 3. What is a Numerical Weather Prediction model? 1) Identify some initial condition at some geographic point using observational data 1) Predict changes in state through time using modeling software and lots of complex mathematics https://en.wikipedia.org/wiki/Numerical_weather_prediction#/media/File:AtmosphericModelSchematic.png
• 4. What is WRF? The Weather Research & Forecast model (WRF) is: ● Freely available for community use ● Suitable for many disciplines (chemistry, hydrology, fire science, meteorology...) ● A Numerical Weather Prediction model ● Developed by NCAR, NOAA, FAA and others https://en.wikipedia.org/wiki/Weather_Research_and_Forecasting_Model#/media/File:Typhoon_Mawar_2005_computer_simulation_thumbnail.gif
• 5. Why Use WRF? ● High Resolution – temporally and spatially (horizontal and vertical planes) ● Comprehensive physics considerations ● Multi-variable output ● Speed of model runs ● Documentation – support and user community
• 7. Limited Area Model Global Model (Coarse) Regional Model (Fine) An Introduction to the WRF Modeling System. Wang, January 2020 PP Resolution - Nesting Multi-domain model (Very fine)
• 8. The Basics of Cartography in 1 2 Slides! GIS Applications, January 2019 - Kevin McManigal University of Montana PP
• 9. The Basics of Cartography in 1 2 Slides! GIS Applications, January 2019 - Kevin McManigal University of Montana PP https://pro.arcgis.com/en/pro-app/help/data/imagery/hillshade-function.htm
• 10. The Basics of Cartography in 1 2 Slides! GIS Applications, January 2019 - Kevin McManigal University of Montana PP https://pro.arcgis.com/en/pro-app/help/data/imagery/hillshade-function.htm
• 11. The Basics of Cartography in 1 2 Slides! https://pro.arcgis.com/en/pro-app/help/data/imagery/hillshade-function.htm GIS Applications, January 2019 - Kevin McManigal University of Montana PP
• 12. The Basics of Cartography in 1 2 Slides! https://pro.arcgis.com/en/pro-app/help/data/imagery/hillshade-function.htm GIS Applications, January 2019 - Kevin McManigal University of Montana PP
• 13. The Basics of Cartography in 1 2 Slides! GIS Applications, January 2019 - Kevin McManigal University of Montana PP https://pro.arcgis.com/en/pro-app/help/data/imagery/hillshade-function.htm
• 14. Back to WRF https://www.nationalgeographic.com/maps/ https://www.genuineweather.com/custom.php
• 15. WRF Preprocessing System (WPS) Meteorological Model Data Topographic Data Land Use Data Lots More.. WPS converts, or standardizes, the formatting of all the required data types so that WRF can use them WRFcoin
• 16. Static Data - Domain & Grid Size Chosen by the user https://wolfscie.wordpress.com/2017/10/05/visualizing-wrf-domain/
• 17. Static Data - Elevation, Aspect & Slope https://www.earthondrive.com/elevation-data.html From USGS
• 18. Static Data - Land Use Classification https://en.wikipedia.org/wiki/Land_use#/media/File:Europe_land_use_map.png From USGS & Satellite (MODIS)
• 19. Static Data - Vegetation Greenness Fraction https://www.pickpik.com/wood-the-leaves-twig-leaf-texture-pattern-3377 From Satellite (AVHRR & MODIS)
• 20. Static Data - Snow & Soil Albedo http://www.cocorahs-albedo.org/about/
• 21. Meteorological Data - Different Models ● Various meteorological models are available through NOAA ● A WRF model will always be “based” on observational data from the model dataset used (GFS, NAM, etc.)
• 22. Meteorological Data - Global Forecast System (GFS) ● Comprised of four different models (atmosphere, soil, ocean & sea ice) ● Global coverage with horizontal resolution of 18 miles (28 km) between grid points ● One of many model datasets compatible for use with WRF https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs
• 23. Namelist.wps ● Date ● Location ● Grid spacing and size ● Nested domain ratio & placement ● Projection
• 24. ● WRF can be run with multiple observational datasets (for example GFS & NAM) with varying priority based on location ● Horizontal grid spacing for standard elevation data in WRF is 30-arc seconds (1 km) with 3-arc seconds (90 m) in U.S. *Remember that the GFS model is 28 km! Resolution - Nesting
• 25. Resolution - Nesting ● WRF can generate simulations for nested domains and resolution can vary with domain size ● In addition to reducing computation time, nesting also reduces inaccuracies related to domain edge effects
• 26. Resolution - Nesting An Introduction to the WRF Modeling System. Wang, January 2020
• 27. Resolution - Nesting Terrain Land use An Introduction to the WRF Modeling System. Wang, January 2020
• 28. Resolution - Nesting An Introduction to the WRF Modeling System. Wang, January 2020
• 29. Resolution - Nesting An Introduction to the WRF Modeling System. Wang, January 2020
• 31. Physics - Free Atmosphere Radiation Processes and Microphysics Overview of WRF physics. Dudhia, October 2019
• 32. Physics - Surface Model Processes Overview of WRF physics. Dudhia, October 2019
• 35. Skamarock - Winter WRF Tutorial January 2020, Boulder, CO Physics - Gravity Waves
• 36. Physics - Putting It All Together Overview of WRF physics. Dudhia, October 2019
• 38. Why Use WRF? ● High Resolution – temporally and spatially (horizontal and vertical planes) ● Comprehensive physics consideration ● Multi-variable output ● Speed of model runs ● Documentation - support and user community
• 40. Multi-Variable Output - Example Temperature in Kelvin at 2 meters
• 41. Speed of Model Runs It all depends on: ○ Chosen physics options ○ Chosen domain size, resolution and nesting ○ Power of machine running WRF - We can use a supercomputer at NCAR for fast runs as a student experience through NSF funding
• 42. An Introduction to the WRF Modeling System. Wang, January 2020
• 43. Documentation - Active user community https://forum.mmm.ucar.edu/phpBB3/ https://www2.mmm.ucar.edu/wrf/users/
• 44. Eclipse & Gravity Wave Research https://www.atmos-chem-phys.net/16/5949/2016/acp-16-5949-2016.pdf https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2018MS001314
• 45. Eclipse Research - Chile 2019 Observed and modeled irradiance for Collowara, Chile from 15:00-22:30 UTC July 2, 2019. Measurements are show in black, WRF-eclipse in blue, and WRF-3.8 in red. The solid grey vertical lines represent the time of first contact (C1) and fourth contact (C4). The dashed grey vertical line represents the time of totality (T). Differences in irradiance simulated by WRF-eclipse and WRF-3.8 for Collowara, Chile from 15:00-22:30 UTC July 2, 2019. The solid grey vertical lines represent the time of first contact (C1) and fourth contact (C4). The dashed grey vertical line represents the time of totality (T).
• 46. Eclipse Research - Chile 2019 Observed and modeled temperature for Collowara, Chile from 15:00-22:30 UTC July 2, 2019. Measurements are show in black, WRF-eclipse in blue, and WRF-3.8 in red. The solid grey vertical lines represent the time of first contact (C1) and fourth contact (C4). The dashed grey vertical line represents the time of totality (T). Differences in temperature simulated by WRF-eclipse and WRF-3.8 for Collowara, Chile from 15:00-22:30 UTC July 2, 2019. The solid grey vertical lines represent the time of first contact (C1) and fourth contact (C4). The dashed grey vertical line represents the time of totality (T).
• 47. Other Applications ● Improve balloon trajectory modeling using WRF ● Collect observational data for further validation through nudging of WRF model (i.e. with radiosonde, UAS, etc.) ● Mountain wave prediction ● Other ideas? We have been running WRF for 7 years and can help students with interest in using it for a project.

### Editor's Notes

1. Let’s talk about WhaRF! I did not have a strong background in meteorology in 2018 when I started working with WRF for UM-BOREALIS. I was interesting in improving wildfire models, and knew that meteorological variables were an important part of that process. I was familiar with high level principles that govern weather like stability (really important for fire and smoke behavior), but I also knew basically nothing about computer programming. My first few weeks were a dive into the deep end of working from command line and scouring help forums for errors I was receiving in my WRF models. While this felt like a painful process at the time, just like anything else I look back now and can appreciate how much I was really learning. I definitely could not have explained what WRF actually does or described it to someone, I just knew it could create weather maps when operated properly.
2. The WRF model has been used for over thirty years in various applications of weather forecasting and research
3. The Earth is divided into grid cells, and a NWP applies “rules” based on physical equations govern the behavior and interaction of cells.
4. This image shows radar reflectivity of typhoon Mawar. It was generated using WRF with 2 mile horizontal resolution. For visualization, vortex tracking was used which is why the storm appears to stay in the center of the domain. Gravity waves can be seen in the northwest corner of the domain approximately 12 frames in. You can also visualize multiple variables at once, such as temperature, wind and topography. dBZ is decibels relative to the radar reflectivity factor (Z) of precipitation (used in radar weather forecasting)
5. Resolution when looking at an image matters a lot. In a 3d model space this is even more important- you can imagine each 3d grid cell just like a “pixel” of this image in the horizontal. But remember also that we can also look at our vertical atmospheric profile like the layers of a slice of cake, where resolution would also dictate what information was conveyed. Also, not just in knowing the fineness or coarseness of geographic space your image representing, but also the size of the file and the computational requirements to generate it. You must also recognize the limitations of varying resolutions among datasets (this image of a sheep is not time-series data. It is just one snapshot in time and our models use data of a similar static structure in conjunction with time-dependent meteorological data). Within our different groups of static data, there will likely be varying resolutions. Additionally, the pre-existing climate model used as original input has a resolution associated with it
6. A global model might be fine if you wanted to demonstrate opposite seasonality in the Earth’s hemispheres, but maybe you want to examine that same global climate data, but need a higher resolution image of just the United States, say for a balloon flight trajectory prediction. A multi-domain model can give you the ability to achieve a very high resolution for a small portion of the area of interest, while still accounting for the surrounding area at a coarser resolution. This way the model does not take weeks to run on our office computer. Can anyone think of another advantage of the multi domain model over a Limited Area model? Answer- explain edge effects
7. The fact that the earth is roundish makes mapping or modeling geographic data (which occurs on the surface of a sphere) harder to represent, or visualize, accurately with 2-d means such a map or a screen.
8. A DEM is a Digital representation of the 3d surface of a planet (earth) Explain color in maps
9. A DEM is a Digital representation of the 3d surface of a planet (earth) Using cartographic computer software, you can run some geometry and add a light source to a DEM and get..
10. A hillshade. Now this is actually recognizable as the topographic features of a landscape. But why does this image make sense to us basically immediately? Can anyone tell me the direction of the light source (where its coming from?) Cartographers have been drawing maps like this with the light source from the northwest for so long that all you have to do is flip the image upside down, and now our two y-shaped valley features become mountains. Like this ->
11. Duh-da-na! But now we’ve talked a bunch about cartography and you’re probably wondering, why do I need to know about hillshades for a weather model?
12. A topographic model not only gives us elevation data which is obviously a large driver of something like temperature, but it allows us to use slope and aspect which are important drivers of processes like evapotranspiration (-based on irradiance, which differs based on how much sunlight a given aspect receives), groundwater-surface water interaction and watershed delineation (which is based in part on slope) as well as atmospheric dynamics in mountainous terrain such as the simulation of topographic gravity waves or mountain waves
13. Okay enough cartography, back to weather modeling!
14. When we want to run the WRF model, we have to do some preprocessing of data first. Fortunately, this step is mostly automated using the WPS. WPS is a series of three executable commands which prepare and convert all the different datasets we need into an intermediary format that WRF can ingest. I think about this a bit like converting different currencies into a common system, however is examples is not highly technical as the datasets retain their individual nature, so you might just think of it as changing how it’s formatted so that WRF can actually use it.
15. “In addition to computing the latitude, longitude, and map scale factors at every grid point, geogrid will interpolate soil categories, land use category, terrain height, annual mean deep soil temperature, monthly vegetation fraction, monthly albedo, maximum snow albedo, and slope category to the model grids”. WRF USER MANUAL CHAPTER 3- WPS USGS 24 category and MODIS 20 category land datasets; USGS GTOPO30 elevation dataset; 10-min greenness fraction data based on AVHRR and 30-sec greenness fraction data based on 10 years MODIS
16. “In addition to computing the latitude, longitude, and map scale factors at every grid point, geogrid will interpolate soil categories, land use category, terrain height, annual mean deep soil temperature, monthly vegetation fraction, monthly albedo, maximum snow albedo, and slope category to the model grids”. WRF USER MANUAL CHAPTER 3- WPS USGS 24 category and MODIS 20 category land datasets; USGS GTOPO30 elevation dataset; 10-min greenness fraction data based on AVHRR and 30-sec greenness fraction data based on 10 years MODIS
17. “In addition to computing the latitude, longitude, and map scale factors at every grid point, geogrid will interpolate soil categories, land use category, terrain height, annual mean deep soil temperature, monthly vegetation fraction, monthly albedo, maximum snow albedo, and slope category to the model grids”. WRF USER MANUAL CHAPTER 3- WPS USGS 24 category and MODIS 20 category land datasets; USGS GTOPO30 elevation dataset; 10-min greenness fraction data based on AVHRR and 30-sec greenness fraction data based on 10 years MODIS
18. “In addition to computing the latitude, longitude, and map scale factors at every grid point, geogrid will interpolate soil categories, land use category, terrain height, annual mean deep soil temperature, monthly vegetation fraction, monthly albedo, maximum snow albedo, and slope category to the model grids”. WRF USER MANUAL CHAPTER 3- WPS USGS 24 category and MODIS 20 category land datasets; USGS GTOPO30 elevation dataset; 10-min greenness fraction data based on AVHRR and 30-sec greenness fraction data based on 10 years MODIS
19. THAT’S IT FOR STATIC DATA
20. WRF creates a weather model based on a pre-existing weather or climate model. Conditions from the original model are used to initialize the model as the original state
21. What are the sources of an observational dataset like this? …. Radar, Satellite imagery, radiosonde profiles, etc..
22. Wrf runs off of options chosen in namelists. There is a namelist.wps and a namelist.input. These namelists can be commented & customized What feature does the lambert conformal projection preserve? (Shape)
23. 50 % of the gtopo30 data comes from the Digital Terrain Elevation Data (DTED), a raster-based topographic data base with a horizontal grid spacing of 3-arc seconds (approximately 90 meters)
24. Here we will drive home the idea of domain nesting and resolution
25. Reminder that nests are still projected from a geoid surface
26. Increased resolution applies to all the data, not just the topography
27. Another example of what our nested model domains look like.
28. Reminder that vertical resolution is unaffected in nesting, WRF calculates the same number of layers (both soil and atmosphere) in all domains THAT’S IT FOR RESOLUTION
29. Longwave and shortwave radiation plus (This is what is changes for the eclipse model) Microphysics of resolving clouds - cloud fraction, type of cloud, timing, and inner cloud species included. Additionally consider types condensation, evaporation, deposition, sublimation, freezing, melting, cloud water, rain drops, ice crystals, snow, graupel, hail, aerosols and processes of aggregation, accretion, growth, fall-out then interaction with radiation.
30. Includes land use categories like vegetation /fuel type This is a general model which has applications for many disciplines (we can control all the physics options and make certain choices for certain runs, eclipse, fire etc).
31. Precipitation processes are modeled using appropriate physical and chemical interactions Cumulus processes are convective and could be examined to help model GWs
32. Planetary Boundary Layer – turbulent lowest level and diffusion phenomena – large eddy simulation gives localized values. WRF can model topographic GWs using certain PBL schemes (configs) and can simulate topographic gravity waves for help in isolating eclipse driven waves
33. This image shows how gravity waves can be modeled. Choice in model can affect the behavior and accuracy of these waves
34. “These equations are used to evolve the density, pressure, and potential temperature scalar fields and the air velocity (wind) vector field of the atmosphere through time. The equations used are nonlinear partial differential equations which are impossible to solve exactly through analytical methods, with the exception of a few idealized cases. Therefore, numerical methods obtain approximate solutions.”
35. We can get a lot more than the basics- Temperature, Irradiance, U, V, W, Relative Humidity. And we can plot
36. Very hardware dependent.
37. Describe steps more in depth, i.e. static data, terratin, L/U, met data (in grib and ungrib process), and what the output (NETCDF) is (file compression)
38. The WRF forum has lots of users questions and topic threads, and the WRF user website has best practices, downloads, all versions of the user manual, and more helpful information
39. Montornes et al 2016 used an eclipse version of WRF, which uses the geometric movement of celestial bodies (bessel’s method) to affect irradiance by calculating obscuration of the solar disk accordingly based on NASA’s five millennium catalog of solar eclipses. Wu et al 2018 compares wrf simulation of cyclone generated gravity waves to the parent climate model of the WRF run, WACCM, and examines “sensitivity of GW structure to choice of model horizontal resolution”
40. Both WRF models were run from 06:00 UTC July 2 - 00:00 UTC July 3 2019, allowing for 12 hours of model spin up.
41. Some possible reasons for the bias in temperature (and to a lesser degree, irradiance), could be: Observational temperature data displayed is raw, uncorrected data Steep slope of the Andes increasing edge effect-related inaccuracies Projection issues Clouds disabled in model Potentially others? Temperature is influenced by a TON of factors
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