Pedagogy
To students:
Academia can be an odd place to navigate, especially as a first-generation or minority-identifying student/ scholar. There are a lot of "unwritten rules" for how to behave within the academy, how to communicate, and how to do your work.
As a first-generation scholar myself, I am still learning the systems too, and I urge all students and prospective graduate researchers to remember the following: there are no stupid questions.
A secret students should know? No one has all of the answers. The professors/ researchers you idolize or are afraid of? They are still learning too, they have just gotten very good at asking questions (and finding the answers, which takes years of practice).
A thirst to never stop learning and to continuously improve goes much farther than "book smarts", and drive (sometimes with a dash of stubbornness) will get you through the times that you wonder if finishing is worth it. Along the way though, don't struggle alone. Find your people. Whatever stage you are at in your career, find people that support you in the direction of your dreams.
People believing in me as a scrappy, first-gen student who didn't know if I was smart or not (and was working 30+ hours a week at a restaurant to pay for college) are the reason I am in this position today. To give back some of what they gave to me is a gift.
Teaching
Research without education and outreach accomplishes very little. The terms “climate change” and “global warming”, along with the increasing severity of extreme weather events, has moved climate science to the forefront of public discourse. I believe that education is powerful; it builds confidence and creates advocates for our planet.
Environmental sciences are powerful tools in situational awareness: the subject matter is always around us. Regardless of the course level, my first teaching objective is to have students situate themselves within the discipline. I do this through mixed methods, such as including relevant news articles involving climate and the environment, photos and videos of areas undergoing environmental change, and by incorporating project work that engages students in research.
Throughout my teaching, I keep in mind the following: How can I empower students to confidently discuss, and understand, such pertinent subject matters? How, through my teaching, can I do the most good, both for the students, but also for the planet?
Courses taught at OSU:
- Seminar in Climate Science (GEOG 5930):
- - Graduate-level course in climate science for students in environmental science fields interested in incorporating climatic methods in their research. Begins with the physical foundations of climate science before diving into seminal research works, climate data types, research methods, and study design. Topics such as surface-atmosphere feedbacks, teleconnections, climate modelling, and ocean-atmosphere relationships are discussed at length.
- Meteorology (GEOG 3033):
- - Introduces weather processes, including how energy, moisture, and variation in land cover surfaces influence weather phenomena across space. The course begins with an introduction to the atmosphere and how energy exchanges and physics dictate atmospheric motion before diving into atmospheric processes and specific weather phenomena, including winds, hurricanes, tornadoes, cyclones, etc., and how all are linked to the atmosphere’s state of stability. Meteorological hazards and extreme events are also discussed.
- Climatology (GEOG 3023):
- - Provides an overview of the components of the Earth’s climate system, including the atmosphere, oceans, land and sea ice, and the biosphere. We explore how the climate system works, how it affects us, how we affect it, and how it has changed and is changing. Climate, and its influences, are complicated. This course will build a foundation in atmospheric science before diving into real world examples, complex interactions with the atmosphere and other Earth “Spheres”, and relevant research on climate issues.
- Climate Change and Humanity (GEOG/GEOL 1022):
- - Introduces Earth and environmental sciences, with an emphasis on the atmosphere and how it impacts life on Earth as well as how living things have changed it. Outlines the basics of climate change and what makes it complicated, how climate interacts with all components of the Earth-system, and where changes are most magnified. Future climate impacts and scenarios are also discussed.
Research Resources
Below you will find a collection of resources that have provided useful over the years, including places to find data, some online handbooks for learning R, and a vague outline of how I break a new/ large project into chunks to make it less overwhelming. I'm happy to update this page - let me know if there is something great you think I should be here!
Finding Data
There are many climate data sources, all in various formats and resolutions, and they are spread out all over the internet. This page could never contain them all, but should be a good resource to start your data digging process. I have predominantly organized things by the format most of the data from the source is available in (which determines what skills needed to work with it). In addition to those below, NCEI and Data.gov are huge collections of data that you can search by key words and file types. Some important concepts and specific data products are also described by the NCAR Climate Data Guide.
No spreadsheets or coding required:
- Climate Toolbox
- - A (huge) collection of web tools for visualizing past and projected climate and hydrology of the contiguous United States. There are so many things available here... If you go to "tools" and "variable lookup" you will be given a list of options for your variable of interest and what tools it is included in. Most of the graphics the tools make are pretty decent, and each tool has a mini tutorial included to make sure you know how to use it. Most of them also give you the option to download the data as well if you want to create your own graphics/ conduct your own analysis on it. You can also just go straight to "Data download" if you want to take the data elsewhere.
- Climate Engine
- - Free to use, needs a google account, works best with google chrome. Another tool that you can use to make graphics from a variety of datasets using many different variables/ covering various spatial and temporal resolutions. Like above, you can create and download graphics from specific points or polygons (aka, states/ regions). You can access both the images and a csv of the data after creation (if you want to take the data elsewhere). This one requires some pre-understanding of the variables; not much context is given on what each is/ how they are derived.
- Daily Composites
- - Plot daily composites (averages) of the mean or anomalies (mean - total mean) of variables from the NCEP/NCAR Reanalysis and other datasets. Long term means (climatologies) are based on 1991-2020. Data is available from Jan 1948 to previous current day for most variables.
- Monthly Composites
- - Plot seasonal composites (averages) of the mean or anomalies (mean - total mean) of variables from the NCEP reanalysis and other datasets. NCEP data is available from Jan 1948 to Mar 2024 . Other datasets have different time ranges. Note the climatology used for the anomaly and long term mean plots is now 1991-2020 to match the new climate normal time period.
- Historical Doppler Radar Maps
- - Create maps of various Doppler products (as well as some satellite imagery) for specific times in the past (you set the date/ hours/ map location).
- Billion Dollar Weather Events
- - Create maps and histograms/ calculate trends in costly weather events (nationwide or statewide). Graphics produced can be downloaded directly, OR you can download the data used to make the graphic and use another software to make your own.).
- NOAA Climate at a Glance
- - The tool provides near real-time analysis of monthly and annual temperatures (globally, nationally, statewide, regions, and counties) and is intended for the study of climate variability and change. You can create maps, look at time series, create Haywood plots, look at rankings, etc. All data to make graphics can ALSO be downloaded for analysis in other environments.
- Climate.gov
- - This resource is both educational/ informative (it has decent descriptions for some of the climate tools presented) but it also has maps and paths to various climate data available (things like teleconnection and drought indices, sea ice change, sea level rise, among many others).
- Climate Charts
- - You can choose a state (or climate division) and create monthly precipitation and temperature (compared to the 30-year mean) plots as well as climate trends charts of temperature and precipitation.
- FEMA Hazards Index
- - Create maps (or download data) of risky, vulnerability, expected losses, and community resilience to various weather hazards across the US. The maps look nice, but the data used to make them can also be downloaded for use in a variety of formats.
Spreadsheets required:
- Mesonets
- - "Mesoscale Networks" are weather station observation networks that are maintained by various states/ organizations. These are often going to be the highest resolution temporal coverage (at a point scale) data that you will be able to get for a state, although often data collection only began somewhere between the 1990s- early 2000s. From the national mesonet program website, you can navigate to "partners" to find state mesonets. Not every state has a high-res mesonet (they require substantial funding and upkeep) and not every mesonet collects the same exact data. To access the data, you will have to navigate to the state mesonet website you are interested in, and then either download the data straight from the website if it is allowed (by navigating through various tabs and links...) or you may have to put in a data request to the folks who maintain and QA/QC the data.
- Daily Global Historical Climatology Network, v3
- - Daily climate observations from approximately 30 different data sources (downloads are in csv format). Contains station-based measurements from well over 90,000 land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth.
- Hourly Global Historical Climatology Network, v1
- - Contains approximately 110 separate data sources and will be updated daily using the United States Air Force and NOAA Surface Weather Observations data streams (downloads in psv format). Has many different weather variables included and will eventually replace the ISD (below).
- Integrated Surface Dataset
- - Worldwide surface weather observations (many variables, downloads in csv format) from over 35,000 stations. Hourly, synoptic (3-hourly), and daily weather observations are stored. For some stations, data may go as far back as 1901, though most data show a substantial increase in volume in the 1940s and again in the early 1970s.
- Northern Hemisphere Teleconnections Dataset
- - Indices for download of the major teleconnection patterns associated with Northern Hemisphere weather behaviors. Each link to a pattern also has descriptions of the pattern's behavior/ graphics of the index over time, as well as downloads of the raw values in text file formats).
- National Weather Service
- - Weather data organized by local climate offices. You can select the office nearest your study area and browse the "NowData" variables available. They provide a table that can easily be copied into a spreadsheet for analysis.
- Gridded Weather Type Classification, v2
- - The gridded weather typing classification (GWTC) system is a geographically and seasonally relative classification of multivariate surface weather conditions (weather types) for North America. Using six near-surface weather variables (temperature, dew point, sea-level pressure, cloudiness, wind speed, and wind direction) from the North American Regional Reanalysis (NARR) the GWTC classifies every day since 1979 into one of 11 different weather types at over 9000 locations. Data is downloaded in csv format.
- Spatial Synoptic Classification, v3
- - The SSC is based solely on surface based observations at individual stations. Four-times daily observations of temperature, dew point, wind, pressure, and cloud cover are incorporated into the model. Based on these variables, relative to the climatological norm, each day is given a weather type classification. Within the SSC scheme, weather-type characteristics change from station to station and day to day. Data is available for download as text files (by station).
Coding/ GIS required:
- NCEP-NCAR Reanalysis
- - The NCEP/NCAR Reanalysis 1 project is using a state-of-the-art analysis/forecast system to perform data assimilation using past data from 1948 to the present. Temporal coverage is 4-times daily, daily, and monthly values for 1948/01/01 to 2024/04/11 on a 2.5 degree x 2.5 degree global grids (144x73) from 0.0E to 357.5E, 90.0N to 90.0S. Reanalysis II is similar, but with different temporal coverage.
- WorldClim
- - WorldClim is a database of high spatial resolution global weather and climate data. These data can be used for mapping and spatial modeling, and both historical and future conditions are included. Future conditions are downscaled CMIP6 model output. Monthly values of minimum temperature, maximum temperature, and precipitation were processed for 23 global climate models (GCMs), and for four SSPs: 126, 245, 370 and 585. They also have some bioclimatic variables calculated. Files come in TIFF format. The monthly values were averages over 20 year periods (2021-2040, 241-2060, 2061-2080, 2081-2100).
- PRISM Climate Data
- - Contains climate observations from a wide range of monitoring networks, applies sophisticated quality control measures, and develops spatial climate datasets to reveal short- and long-term climate patterns. The resulting datasets incorporate a variety of modeling techniques and are available at multiple spatial/temporal resolutions, covering the period from 1895 to the present. Gridded products come in a. BIL raster format. This package in R was made for working with PRISM data.
- LOCA Downscaled CMIP6
- - Downscaled CMIP6 projections (at 6km for most of North America) which includes many different models/ ensemble members and multiple SSP scenarios. Files are in netCDF format.
- Standardized Drought Indices
- - GeoTiff files of multiple drought indices across many different drought lengths and from many different sources. More metadata on each source is also available at the link.
- Drought Monitor GIS Data
- - Drought monitor data back to 2000 and the associated shapefiles needed to conduct GIS analysis.
Coding in R
Many climate data products need something beyond spreadsheets to work with them. This often means either learning to code (R or Python, typically) or using ArcGIS. Personally, once I became comfortable coding, I never used ArcGIS anymore. Thankfully, it is easier now to learn to code than ever… there are numerous help guides online, tons of help forums, and AI can even help troubleshoot simple code (don’t always trust it, though…). Below is a basic “starter kit” for R, my preferred programming language.
- R and R Studio
- - The first step in working with R. This is a free and easy download. Studio is the "Environment" that you interact with R in (basically, where you will do your writing, running, and troubleshooting, etc.). There are other IDEs out there, but Studio is pretty good and very intuitive to learn and get used to.
- Environmental Computing
- - An online textbook utilizing R for environmental data analysis. The book starts very basic/ beginner friendly and uses examples that are relevant/ could transfer to atmospheric sciences.
- GIS and Spatial Analysis
- - Explains/ teaches how many different GIS tasks can be done in R through a series of walkthroughs and example code.
Starting a Research Project
Integrating ideas, thinking across disciplines, and condensing many seemingly separate studies to have some sort of holistic understanding of a field/ gaps in the literature is not easy. You must learn how to be systematically and strategically curious and to make connections across fields that there is a good chance you are very new to. Then, once you have a good idea… it turns out someone has done it already. Time to come up with something new!
Sounds easy, right?? Maybe not.. and it shouldn’t be! But like most things, breaking the process down into steps can make it much better.
- Some steps I think are important:
- 1. Conduct a literature search on the general topic that interests you (use key words that cover the topic, the geography, the field, etc., and try many different combinations of AND/EITHER/OR while adding/ removing different phrases if in Scopus/ Web of Science)
- 2. Read, read, read... but also make sure you are keeping track of what you have read (annotating/ making notes/ etc.) Also read stuff that is more adjacent/ less specialized (I call this the funnel down approach, start "big picture" and get more specific as you go, eventually honing in on the niche of interest)
- 3. Develop an annotated bibliography of the literature - this should be detailed and organized by theme
- 4. THINK and ANALYZE - what knowledge gaps exist within this body of work? What hypothesis could aid in filling that gap (aka, what could YOU do?)
- 5. Write an essay to justify (justify being a KEY word - cite ample literature here that makes what you are proposing make sense) the hypothesis you propose to test (this becomes an excellent Introduction to a proposal!)
- 6. Write a lengthy description of what type of data you will collect (and WHY), including from where (metadata - strengths/ weaknesses), how much (temporal period/ spatial coverage, etc.), and how you will analyze it with an emphasis on WHY the methods were chosen (this will be a great draft of your Data and Methods section of your proposal/ eventual manuscript)
- 7. Get the data (or create it!) and do any necessary pre-processing (be sure to document everything you do in detail for reproducability)
- 8. Analyze data like you said you would; if you change any plans, be sure to document them and why
- 9. Digest the results of analysis (do they make sense??) and put in context of previous literature (would this make sense to others/ is it consistent with what you have read?)
- 10. Assuming everything seems good, check to make sure no more work on this topic has been published since you started (keep your literature base up to date)
- 11. Prepare a manuscript draft - clean it up so it is easy to read, but accept that it may still be ugly at this point
- 12. Send manuscript to at least one person you trust to give the first round of constructive feedback (advisor, other professors, colleagues, peers, etc.)
- 13. Revise, send it to other trusted individuals
- 14. Revise, revise, revise (never put "_final" at the end of a doc... you are lying)
- 15. Submit, eventually.
- Creating new knowledge is a never ending cycle. Be prepared to have your views challenged and scrutinized - this is normal. Keep it up, and with time, the whole process will become second-nature.
