The Importance of Fundamentals and Climatology

This is a sample lesson page from the Certificate of Achievement in Weather Forecasting offered by the Penn State Department of Meteorology. Any questions about this program can be directed to: Steve Seman


When you've completed this page, you should be able to identify a station's period of record, discern between a city's ThreadEx data and climatology data from an individual station, differentiate between daily raw averages and normals, and retrieve and interpret daily normals and records for temperature and precipitation.


You can gain all the sophisticated knowledge of advanced forecasting techniques in the world, but if you fail at the fundamentals, you're not going to be a very good forecaster over the long haul. That's why the fundamentals in this lesson, while perhaps seeming very basic, are critically important to quality forecasting.

We've focused on observations so far because adding ground truth to your forecasting procedure through observations is vital, especially over short time periods (say, 12 hours or less). Current observations can still offer clues even for longer-range forecasts, but as we go out in time, specific current observations may be less helpful. Meanwhile, the importance of various types of numerical weather prediction (NWP) models and statistically post-processed forecasting products grows. Forecasters, however, must still view the guidance with well-grounded conceptual models of the atmosphere in mind. We'll be looking more at NWP and statistically post-processed forecasting products in upcoming lessons, but before we move on, there's one more critical "fundamental" we must cover--climatology.

Studying climatological statistics and local and regional topography around a given location can provide important insights about the historical bounds of weather variables and how the weather "behaves" in various weather patterns. This background knowledge gives forecasters essential context for what's typical and atypical at their forecast location. In order to assess a location's climatology, let's look at some important variables and issues that forecasters must be aware of.

Know Your Location and Period of Record!

This might seem obvious, but forecasters need to know the specific location where their forecasts will be verified and how long weather observations have been taken there (the "period of record") in order to understand a station's climatology. Imagine you're given the task of forecasting for Pittsburgh, Pennsylvania. If you're verifying your forecast at a single point (as is the case for many forecasters), your first question should be, "Where in Pittsburgh?" Are you forecasting for a location in downtown Pittsburgh? In a suburb? At the airport?

Map showing the location of KPIT compared to the old observation site in Pittsburgh
Pittsburgh International Airport (the current location of the official weather observations for Pittsburgh) is about 10 miles west and 200 feet higher than the old observation site downtown.
Credit: Applied Climate Information System

If you're forecasting for the downtown Pittsburgh area (where the city's official observations were taken from 1875 until 1952), its location is about 200 feet lower in elevation and about 10 miles east of Pittsburgh International Airport--KPIT (where the city's official observations have been taken since 1952). Recalling from previous studies that higher elevation locations tend to be a bit cooler than their lower elevation counterparts (all else being equal), it stands to reason that the specific climatology of the two locations might differ, at least slightly.

Since the location of official observations has changed over time in many large cities, scientists at NOAA have created what's called "ThreadEx" data, which is a "threaded" historical record that stitches together observations from multiple historical observation sites into one "continuous" thread. For cities where a ThreadEx data set exists, it's considered the city's official climate record. For Pittsburgh, the period of record goes back to 1875, even though the period of record from KPIT at the airport doesn't go back nearly that far.

ThreadEx data is a great starting point to understand a city's climatology, but using it by itself can be problematic. Are the observations taken at the old observation site in downtown Pittsburgh (200 feet lower in elevation and more in the midst of the urban heat island) representative of conditions at the airport? Not really. For example, if we examine the history of the hottest days on record in Pittsburgh (highs of 100 degrees Fahrenheit or higher), it's easy to see that most of them occurred prior to 1952, when the official observation site switched to KPIT. In other words, historically, 100-degree days are more common downtown than they are at KPIT. In fact, two dates exist when the downtown observation site reached 100 degrees Fahrenheit and weather records were also being taken at KPIT. The highs at KPIT on those days were 99 degrees and 96 degrees, providing further evidence that 100 degrees is a harder threshold to reach at the higher elevation of the airport.

Forecast Tip

If you want to really dig into a city's climatology, just using the official climate record (the ThreadEx data) may not be sufficient. If climatological data exists for the exact location where your forecasts are being verified, the data for that site may be more useful, even if its period of record is shorter (as long as it's not too short). More information for retrieving a city's climate data is coming later in this section.

What Exactly is "Normal" Anyway?

When studying a location's climatology, a common place to start is with "normal" temperatures and precipitation. But, what does "normal" really mean? In your previous studies, you learned that normal temperatures are essentially 30-year averages, which get updated once every decade. That's basically true, but there's a little more to it than that. As it turns out, raw averages over a 30-year period can be a bit noisy, where one or two very warm / cold / wet days in the sample can skew the average for that date a bit. So, to eliminate the noise and paint a clearer picture of day-to-day and seasonal trends, the raw averages undergo some statistical smoothing to create the official climate normals for a given city. To see the difference between raw averages and official 1991-2020 normal high temperatures for State College, Pennsylvania, check out the graph below.

Graph of 1991-2020 raw averages and official normal high temperatures at State College, PA.
Raw 30-year averages (blue line) can be "noisy" from day to day, so official climate normals (orange line) are statistically smoothed to create a clearer picture of day-to-day and seasonal variability.
Credit: Steve Seman (Data from ACIS)

The blue line on the graph, which represents the raw average of daily high temperatures from 1991 to 2020 shows a number of "bumps" that are just noise and have no meteorological significance (a few are highlighted on this annotated version of the graph). So, to avoid being misled by the noise in day-to-day raw averages, forecasters typically prefer using the official (smoothed) normals (orange line). With precipitation, the "noise problem" with raw averages gets even worse (check out the corresponding graph for daily raw average and official normal precipitation at State College). The abrupt spikes and lulls that occur on a day-to-day basis are noise, so official (smoothed) normals are calculated to better identify seasonal trends. Note that there is a slight uptick in daily normal precipitation in the warm season (daily normals are generally greater than 0.10 inches), but it's hard to tell from the raw averages.

Forecast Tip

Official climate normals (which are commonly communicated to the public) are typically more useful for understanding average day-to-day weather behavior at a particular location than 30-year raw averages. Official normals are statistically smoothed values and typically get reported as whole numbers for temperature and as inches of liquid (to the nearest hundredth) for precipitation, which is consistent with measurement practices in both cases in the U.S.

What Records are Important?

While normals tell forecasters the (approximate) 30-year average conditions at a location, it's also helpful to know the extreme (record) values of temperature and precipitation. You may be most accustomed to "record high" and "record low" temperatures since they're what most commonly get reported, but they don't tell the entire story of temperature extremes.

For example, if forecasters wanted to know what constituted an extremely hot day at a particular location during a particular time of year, they would look up the record maximum temperature (or, "record high") for the date (and perhaps some surrounding days, too). But, the record maximum temperature only tells us the historical upper bound for maximum temperatures on a given date. The historical lower bound for maximum temperatures is important, too, because it lets forecasters know what constitutes an extremely cool (or cold) "daytime" in most cases. Knowing the record maximum temperature and recorded lowest maximum temperature for any given date tells the forecaster what the historical range of maximum temperatures is for the date.

Graph of 1991-2020 raw averages and official normal high temperatures at State College, PA.
Daily record maximum temperatures (red dots) and record lowest maximum temperatures (blue dots) tell forecasters what the historical range of maximum temperatures is for any given date.
Credit: Steve Seman (Data from ACIS)

Knowing the range of historical maximum temperatures at a given time of year can be of great use to forecasters. For example, the graph above shows record daily maximum (red dots) and daily record lowest maximum temperatures (blue dots) at State College, Pennsylvania for each day during the year. Very quickly, we can see that an extremely hot day during the hottest time of year (mid-July) in State College would have high temperatures in the upper 90s to low 100s. On the flip side, a very cool mid-July day would have high temperatures in the 60s! In mid-winter, the warmest days in January historically have had high temperatures in the 60s mainly, while the coldest days have had high temperatures in the single digits, or (in a few cases) even below zero.

The same idea applies to daily minimum temperatures. The daily record minimum (or "record low") tells forecasters the lower bound for minimum temperature. But, to get a complete picture of daily minimum temperatures, we need the upper bound, too -- the record highest minimum temperature, which tells us what constitutes an extremely mild or warm night (in most cases) at a given time of year. Precipitation, on the other hand, is a bit of a different story. Yes, daily record maximum precipitation values tell us the greatest amount of liquid precipitation that has fallen on any particular day, but the daily record minimum precipitation value would presumably be zero everywhere on Earth (it's pretty safe to assume that it's been totally dry at least once on every calendar day, everywhere).

Key Data Resource

Where can you find daily temperature and precipitation normals and records for a particular city? On the NWS Climate Page, click on the NWS forecast office on the map that includes your desired location, and then select NOWData to access the data you need. On the list of NOWData stations, note that "[CityName] Area" represents the city's ThreadEx data. If you want data from a specific site (such as a major airport), you'll need to find the specific location on the list. Select, "Daily / Monthly Normals" for normal daily high and low temperatures and precipitation and the official normals will be displayed in several tables beneath a graph. For record temperatures and precipitation, select "Calendar Day Summaries" and the appropriate variables (for example, the variable "Min Temp" and summary type "Daily Maximum" will give you the record highest minimum temperatures for each day).

The bottom line: For maximum temperatures, the relevant extremes that mark the historical upper and lower bounds are record maximum temperatures and record lowest maximum temperatures. For minimum temperatures, the relevant extremes are record minimum temperatures and record highest minimum temperatures.

In the next section, we'll focus on how forecasters can analyze local terrain and historical wind observations to further assess a location's climatology. But, before you move on, check out the Quiz Yourself tool, so that you can test your ability to retrieve basic climatological statistics for a given city. The Explore Further section has some additional links that may be of interest to you, as well.

Quiz Yourself...

Imagine that you are going to be forecasting for Pittsburgh, Pennsylvania, with forecasts verified at Pittsburgh International Airport (KPIT). To retrieve some basic climatological stats, use the NWS Climate Page, along with the guidance above, to answer the questions in the quiz tool below. Can you answer all five questions correctly? If not, make sure that you're searching for the correct variable and using the correct data set.

Explore Further...

If you want to dig a bit deeper into a particular location's temperature and precipitation climatology, you may be interested in the following links:

  • Threaded Station Extremes: The home of ThreadEx data. You can investigate the official station history for many large U.S. cities., as well as gather a limited set of daily records from each available city's ThreadEx data set.
  • xmACIS: This site contains a complete set of temperature and precipitation records for thousands of U.S. stations. The data is from the Applied Climate Information System (ACIS), which is the same database that runs the NWS Climate Page (linked above), but this site has a larger set of reports and graphs that can be generated (and therefore, is a bit less user friendly). Various interfaces into the ACIS database exist online in addition to xmACIS and the NWS Climate Page (SC-ACIS is another option with similar functionality), and if you're want to dissect a location's climatology, these sites have tons of data to explore.