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METR 150: Computers in Meteorology (San Jose State University)
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Plotting with GRADS Practice
Above are some practice plots using the GRADS program in Linux. Aside from a few touching ups they were pretty easy to produce, and I will most likely use the program again in the future.
Midterm Project Part 1: Temperature and Winds for Two California Stations
The first part of the midterm assignment was to collect data from two California cities and plot their monthly diurnal temperature and wind. This was done for Half Moon Bay and Cordoza, where the average daily values for all 12 months were taken to get temperature and wind plots. For temperature, Half Moon Bay is the warmest in September while Cordoza temperatures peak earlier in the summer (July and August). However, the lowest temepratures occur in January for both cities.
There is a greater peak in warm temperatures mid-day for Half-Moon Bay than for Cordoza, and this could be due to mixing of the air from higher wind speeds in the wind plots. They also represent a similar trend of peaking much more for Half Moon Bay. However, winds are abnormally high for Cordoza in April, and, upon further review, this turns out to be instrumentation errors.
While the last two digits on the x-axis accurately represent the time throughout the day, the other numbers ended up labeled as 04-10 and should be neglected since all months were used, not just April.
This part of the assignment shows the monthly averaged temperature and winds. I decided to plot them together to show the relation of them together. As mentioned before, wind mixes the air which generally warms it up, so it would make sense for the temperature to rise with higher wind speeds. This is shown really well with Cordoza, where the temperature drops almost exactly when wind speed does. Half Moon Bay doesn't have as prominent of a pattern, especially in the summer. Otherwise, the winds do increase and decrease with temperature in the winter months. The error bars, or standard deviations, are also shown as the vertical lines in the figures.
Temperatures and winds for the cities were plotted again, but as diurnal yearly values. Here, the temperatures have the expected rise during the summer months, and drop during the winter, and the winds do the same. Both plots are extremely similar when taking dirunal yearly averages.
Midterm Project Part 2: Skew-T and Globally Averaged Temperatures by Season
Above is a vertical profile, or Skew-T, using data from a new location: Punta Caucedo, which is located in the tropics. We were only required to get the data for January and July, along with the averaged monthly winds going up in the atmosphere. Temperatures for January and July are almost an exact match, which is feasible in a tropical climate. The dew points differ considerably, with higher dew points in the summer because of increased moisture. There are a couple of things that require correction in this plot, I simply have been unable to access the file to do so. But when I do, I need to switch the labels for temperature and pressure on the axises, and well as attempt to plot the dry abiabats again, which would not originally appear. (It might seem weird to include an errorous plot on my website, but I think it's a good opportunity for myself and other students to see what to avoid during analysis).
For the last part of the midterm, we were required to plot NCEP data, which shows the global temperatures of the requested time period. We were all assigned different decades to analyze, and I got 1980-1989, where I looked at the seasonal temperature change. January was the coldest month while July was the hottest, as expected, but if I can access these files again, it would be good to change the color scheme to be the same for all 4 plots, so it will be easier to see the differences between the seasons. I mainly made them these colors because they were associated with that season.
Final Project: Running WRF
For the final, we were required to pick a case to use for running WRF, or the Weather Research Forecasting model. I used precipitation for the intense period of Hurricane Harvey as it made landfall on the Texas Coast. The winds acquire a definite counter-clockwise rotation going forward in time, and the precipitation settles in while Harvey creeps up on August 26th. In the future, I want to plot other important variables that most likely influenced the storm. This certainly was new, and a challenge at points, but I learned many interesting coding techniques to carry with me through to graduate school.
