Climate1_Weather Exercise

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Portland Community College *

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421

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Geography

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Dec 6, 2023

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docx

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4

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UNST 125D – Winter 2020 Name: Climate Inquiry I: Climate v Weather Fill out this Word document, save with your name and “Climate-1” and turn in via d2L. Background Weather is defined as the condition of the atmosphere at a specific time, often for a specific place. As such, it is directly observable making it much less abstract than climate. In observing weather, we break it down into elements that are discrete characteristics which vary over time and that are readily measurable (e.g. temperature or cloud cover). Climate on the other hand, is a statistical portrait of the weather for specific places, regions, or even the entire planet. To determine climate for a single location we use years or, more typically, decades of data to calculate averages and extremes of each of the weather parameters measured at that station. To determine the climate of a region, we calculate these same statistics using data from several of the stations in the region. To calculate these statistics for the entire planet we use data from as many stations around the planet as is possible, as well as data from satellites and other mobile data collection platforms. A weather report is like snapshot. By comparison, a climate record is a broad, long view of the data. The climate record enables us to see trends over the “noise” of the short-term fluctuations of our local, daily weather. In this activity, you will be looking at information derived from a single ground-based station, an NWS facility at the international airport in Portland Oregon. Activity A - Reading data from a single weather station To begin this activity, access the page: https://www.wrh.noaa.gov/mesowest/getobext.php?wfo=pqr&sid=KPDX&num=72&raw=0 Once you have it you should see something like figure 1. This page includes station metadata (e.g. latitude, longitude, and elevation), graphs showing temperature, dew point, humidity, wind speed and gust, and 1-hour precipitation over a three-day period, and the raw data used to construct these graphs. The latter table also includes wind direction, visibility, air pressure, and cloud types. Figure 1. Main weather data page for the Portland International Airport. Questions to investigate with these data (Answer in spaces provided. Turn in Word doc through d2L): 1) What's the latitude/ longitude and elevation of the station? Elevation = 20 ft Latitude = 45.59578 Longitude = -122.60917 2) What kinds of data are being shown for this station? (List) Look up any you aren’t familiar with. Dew point, Station Pressure, altimeter setting, wind Chill 3) Look at the graph of temperature, dew point, and relative humidity. What seems to be the relationship between air temperature and relative humidity? Explain. Relative Humidity is increasing while Temperature is decreasing because in the graph it shows both temperature and relative humidity having the same starting place but changes drastically in 2 days after which results in showing Humidity’s line to increase while temperature line to decrease. This exercise modified from Granshaw, F., 2019. Climate Toolkit: A Resource Manual for Science and Action , PSU.
Activity B - Calculating “climate statistics” for a single station Many of the data pages on the NWS site include a mixture of observational weather data (what is at the moment), forecast (what's likely), and climate data (what's normal and what's extreme). The page you are looking at right now is unique in that it's only observational data. What we will do with it now is to calculate some “short-range climate” statistics. (Note: Climate statistics are usually calculated on the basis of what’s normal for at least 30-year periods.) Questions to investigate with these data: 1) What is the highest and lowest temperature for all three days? January 13 Highest: 39 degrees Lowest: 40 degrees January 12 Highest: 42 degrees Lowest: 39 degrees January 11 Highest: 46 degrees Lowest: 42 degrees 2) What is the temperature range for all three days? January 13 th : 39 – 40 January 12 th : 39-42 January 11 th : 42-46 3) Now, copy the data table, starting with the header rows and grabbing all the hourly data for the last three days. Open Microsoft Excel and paste the data into a blank worksheet. (If data doesn’t paste cleanly, try Paste Special Text). Either insert a new line just below header or go to blank row below the data and use “=average(cell range)” to calculate the average values of temperature for all of the hourly temperature data. Then simply copy that formula over into adjacent columns to calculate the average values for all the other parameters. What is the average temperature for all the data? 43.27F 4) Find or insert a blank column. Write an equation that takes the difference in temperature between the temperature value in first row of data and your newly calculated average (i.e., subtract average from the reported value), adding $ before the row number of the average value only to lock that row in. Now copy the equation down to the bottom of the column, so each value represents the difference between that hour’s temperature and the average temperature. The difference between the daily averages and the longer period average is referred to as a temperature anomaly . This exercise modified from Granshaw, F., 2019. Climate Toolkit: A Resource Manual for Science and Action , PSU.
Now construct a line graph of all these temperature anomaly values, with date & hour values from 1 st column as x-axis and temperature anomalies (in deg. F) on y-axis. Click on y-axis and change the value for “Horizontal axis crosses” to whatever the lowest (negative) value is in order to get the x-axis on bottom of graph. Click on x-axis and set Major unit to 0.5 or 1.0 and play with Category and Type settings under “Number” tab to get a clean-looking x-axis. Add suitable labels to both x- and y-axis. Copy and paste the resulting graph into third page of this Word document. The resulting graph should have approximately the same shape as the precipitation graph at the top of the NOAA page, but with hourly temperature anomaly values instead of measured temperatures. ( Bonus: Set up a column to convert degrees Fahrenheit to Celsius (=(deg F – 32) x 5/9) & graph temp. anomaly in deg C instead!) How might this type of graph be useful? it shows the average rate of change for the Temperature Celsius over the span of time. The data can be utilize to tell when certain times in the graph when it’s cooler. Paste your graph here: This exercise modified from Granshaw, F., 2019. Climate Toolkit: A Resource Manual for Science and Action , PSU.
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This exercise modified from Granshaw, F., 2019. Climate Toolkit: A Resource Manual for Science and Action , PSU.