Case Study: A Climatology Example

Case Study...

I recommend that you use the example climatology below as a reference guide. It serves as a template that you can follow when you collect climatological information for forecast cities this semester. The most important parts of the example are the annotations (gray text), which provide additional explanations and descriptions about how each piece of data was collected (much of which came from the Local Climatological Data Document (LCD) for Los Angeles International Airport (KLAX), a KLAX wind rose, and the NWS Climate Page for Los Angeles).

Climatology of Los Angeles International Airport (KLAX)

Time Period

This climatology focuses on February 9-12 and 16-19 (These were the forecast dates for Los Angeles in the WxChallenge forecasting competition in 2021. General time periods are listed on the WxChallenge schedule page.)

Topography and Geography

Local Time Zone (Conversion from UTC): Pacific Standard Time (UTC-8) (note that if daylight saving time was in effect, the conversion would be to Pacific Daylight Time (UTC-7)).

Station Elevation: 97 feet (Taken from page 2 of the LCD for KLAX (see the annotation for station elevation in the LCD).

Station Location: KLAX is located about 10 miles southwest of downtown Los Angeles, just inland from the Pacific Coast, as shown on this Google map. (Make sure to describe the location of the station with respect to the center of the nearest major city, which can help to assess urban heat island effects).

Important Topographical Features: The Pacific Ocean is located just west and about 15 miles south of KLAX. The southern California coastal ranges line the inland side of the coastal plain that surrounds the airport. About 12 miles south of the airport, the Palos Verdes Hills rise to about 1,500 feet, while the Hollywood and Santa Monica Hills rise to near 2,000 feet about 12 miles north of the station. Other various hills rise to several hundred feet between the airport and downtown LA. A bit farther away, elevations rise in excess of 3,000 feet about 40 miles to the east-southeast and up to 6000-7000 feet + in Angeles National Forest, about 20 miles north and northeast. These high elevations extend off to the northwest, as well. (Generally, bodies of water and regional elevation differences are the important features to note. Make sure to include some reference distances from the station for significant bodies of water, mountain ranges, and even hills (along with representative elevations).

Winds

Wind Rose: The February wind rose is below. (Note that the color-coded key to quantify wind speeds at the bottom of the wind rose can be a bit difficult to read, so here is a zoomed-in version. Winds on these particular wind roses are expressed in meters per second.)

Frequency (percentage) of the single most common wind direction: Winds from the west-southwest occur roughly 16.5 percent of the time during February (estimate based on the length of the longest spoke).

Directions that are most and least common: Clearly, winds from the WSW, W, and E are the most common (largest spokes). Winds from the SSW, S, SSE, and WNW-NNW are the least common (smallest spokes).

Direction(s) most likely to produce the fastest winds: W, WSW (largest area of green (and some cyan) shading).

Direction(s) least likely to produce the fastest winds: NE, ENE, SSW. Winds from these directions almost never blow faster than 16.5 knots (smallest blue shading along the spokes and minimal, if any, green areas).

Impacts of wind direction on local weather: Onshore wind directions (large westerly components or southerly components) will bring moist, moderating flow, that can lead to fog or low cloud development. Considerable downsloping occurs on winds from the northwest all the way around clockwise to the southeast, as air flows down surrounding hills and mountains into the LA basin. Such flow would have a warming, drying contribution. The most substantial downsloping comes on northeast and east-northeast flow. (Make sure to note the implications of the wind interacting with the topographic features you mentioned earlier in the climatology. Impacts on precipitation and temperature caused by wind flow interacting with bodies of water or elevation changes are particularly important.)

Maximum observed two-minute wind speed for the month (or months): 38 mph = 33 knots (converted from mph to knots from the "Normals, Means, and Extremes" table on page 3 of the LCD—see the annotation for monthly maximum wind speed). (Make sure that you don't use the table containing data only from the most recent year).

Temperatures

All data taken from the NWS local climate page for Los Angeles by clicking on "NOWData." I chose data for "Los Angeles Intl, CA" so that I could get data specifically for KLAX (I did not choose the "LA Airport Area" or "LA Downtown Area" options since those are the ThreadEx datasets that may incorporate multiple sites). For normals, I selected "Daily / Monthly Normals" and "Daily" for data type, then retrieved the relevant values from the tables (not the graph). For records, I used "Calendar Day Summaries" and selected my variables accordingly. For daily records, make sure you're using the entire period of record at the station (you may need to enter "por" into the year range if it's not there by default).

Each WxChallenge city consists of two forecasting weeks (4 days each) and forecasts are submitted Monday - Thursday, so the days we're actually forecasting for are Tuesday - Friday. In this section, you want to list the data for each day we're forecasting for (Tuesday - Friday each week). This climatology was prepared in February, 2021 and our forecast days were February 9-12 and 16-19, so the data below come from those dates.

Precipitation

All data taken from the NWS local climate page for Los Angeles. Data can be retrieved by similar methods used to retrieve daily temperature data.

With a complete climatology at your disposal, you should keep these statistics in the back of your mind (refer to your climatology as often as needed) as you make your daily forecasts. In addition to helping you get to "know" your forecast location and common weather influences, this data will help you see when weather patterns depart dramatically from "normal" and near the extremes. During these times, you'll want to employ some strategies that we'll learn about later in the course.