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What is forecasting? This might seem like a silly question, but it's an important one! Of course, to forecast means to make a prediction about the future state of something. So, in weather forecasting, we're trying to predict the future state of the atmosphere (duh!).

But, what exactly are we trying to forecast? That question could have a variety of answers. It depends on the atmospheric variable(s) that you are trying to forecast and what exactly you want to know about those variable(s). Indeed, before making any weather forecast, we have to start with a few important questions:

1. What do I want to know (what variables am I forecasting)?
2. What time period am I interested in?
3. What geographic region am I interested in?

Answering these questions helps you to define the objective(s) of your forecast. These questions can have many answers, and thus, many different types of weather forecasts exist. But, before we address these questions, we have to cover a basic distinction that you're already aware of -- the difference between deterministic and probabilistic forecasts.

• A deterministic forecast is one in which forecasters provide only a single solution. For example, "tonight's low will be 31 degrees Fahrenheit," or "0.46 inches of rain will fall tomorrow."
• A probabilistic forecast is one in which forecasters convey uncertainties by expressing forecasts as probabilities of various outcomes. For example, "the probability that tonight's low will be below 32 degrees Fahrenheit is 40 percent," or "the probability of receiving at least 0.25 inches of rain tomorrow is 60 percent."

I'm sure you have encountered both classifications of forecasts before. The daily high and low temperature forecasts produced by the National Weather Service, most private forecasting companies, and your local TV weathercaster are usually deterministic. Probabilistic forecasts, on the other hand, are more commonly produced for precipitation (for example, "tomorrow's chance of rain is 80 percent"), but the Storm Prediction Center also uses a probabilistic approach for their convective outlooks (see the tornado, damaging wind, and hail forecast graphics in this example from May 20, 2019).

In many cases, probabilistic forecasting is a more realistic approach because it allows forecasters to acknowledge and express the fact that the forecast contains uncertainty (and just about all weather forecasts do contain some degree of uncertainty). But, despite their shortcomings, deterministic formats still rule the day in the form of the five, seven, or even 10-day "tombstone forecasts" that you see on TV (see the example on the right) or on many weather apps.

Some private forecasting firms produce similar deterministic forecasts out a few months into the future, which is a completely ridiculous format for that length of time. Such deterministic forecasts imply that the forecaster has the exact same confidence in the first day of the forecast period as he or she does in the 50th day, for example (which isn't true), and the accuracy of the exact forecast values that far into the future is laughable. If you're interested in reading more about the differences between deterministic and probabilistic forecasts, check out the links in the Explore Further section below.

Forecasting Issues of Space and Time

Now that we've discussed probabilistic and deterministic forecasts, we can deal with those key questions that I mentioned at the top of the page. Think about it: The format of your forecast and the variables that you forecast will differ based on what you want to know (as well as when and where). If you're forecasting for a sailing race between Chicago, Illinois and Mackinac Island, Michigan, you need to predict weather conditions (especially wind and threats of thunderstorms) along your race path for more than a day. On the other hand, if you just want to know whether the afternoon will be hot enough to take the kids to the pool, you're interested in the afternoon weather conditions (particularly temperature, sky conditions, wind, and chance of precipitation) at a single specific location for a time period of a few hours.

To account for the variety of variables, geographic areas, and time periods that forecasters may be interested in, forecasts take on a variety of formats. The most common are point forecasts (or zone forecasts) and areal forecasts.

• Point forecasts, as you might have guessed, are forecasts valid at a given point in space (or a very small area), such as your backyard, neighborhood, or zip code. When you look at daily forecasts (which often include high and low temperature, probability of precipitation, amount of precipitation, and wind speed) from the National Weather Service or most private forecasting companies, you're looking at point forecasts.

  Another twist on a point forecast is a "zone forecast."  The forecast format is similar to that of a point forecast, but instead of the forecast being valid for a single point (or very small area), it's valid for a "zone"--an area with similar topographical and climatological characteristics (usually the size of a county or part of a county). In the modern era of automation in forecasting, point forecasts are the norm because computers are able to interpolate gridded computer model output to many specific points. Thus, zone forecasts are being utilized less and less.

• Areal forecasts are typically issued for individual forecast parameters over a much larger area (portions of states, or entire regions of a country, for example). Because of the size of the forecast area, it's common to only view one forecast variable at a time (an areal forecast for precipitation, for example).

You're already quite familiar with areal forecasts, too, I'm sure. The example below is a Day 1 quantitative precipitation forecast (QPF) map from the Weather Prediction Center.

During the 24-hour period covered by this forecast, a large area of the central and eastern U.S. was predicted to receive at least 0.01 inches of rain (light green shading). Successive colors (darker greens, blues, purples, and reds) indicate areas of heavier rain, with a few areas predicted to receive at least 3.00 inches of rain during the 24 hour period (associated in part with Hurricane Zeta). Although areal QPF maps are quite common, other regularly published areal forecasts include snowfall accumulation maps, as well as daily Convective Outlooks from the Storm Prediction Center (SPC).

Of course, in addition to the point or area that we're forecasting for, we also need to define what time span we're interested in. We can forecast for the very near future (perhaps a few hours), or produce short (1-2 days), medium (3-7 days), or long-range forecasts (you may find varying definitions of these forecast ranges, but these will give you the basic idea). Obviously, as we move from the short range, to the medium range, to the long range, the types of forecasts that we can reasonably make change, because the uncertainties become greater. For example, check out the format of the medium and long range temperature and precipitation forecasts produced by the Climate Prediction Center. They merely identify areas (probabilistically) where temperatures and precipitation will either be above, below, or near long-term averages for the given time period.

So far, I've just scratched the surface in describing various types of forecasts because I want you to realize that forecasting can be a very broad field! Different forecasting scenarios will have different objectives, so answering the three questions near the top of the page is important any time you begin to make a forecast. Check out the WxChallenge Application section below to better understand our forecasting focus in this course.

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You've probably heard the old adage, "weather forecasters are the only people who can be wrong most of the time and still get paid." Hilarious, right? Most folks just don't realize the complex science that is involved in making good forecasts. Throwing darts won't cut it! I don't think most people even realize weather forecasters have methods of verifying and assessing the accuracy of their forecasts (or even if the methods exist, they assume that nobody cares enough to use them). Well, methods for verifying and assessing the accuracy of forecasts do exist--many types, in fact. Here, we're just going to cover a few of the most commonly used ones. If you're a bit "math averse," don't worry too much. We'll limit the discussion to simple arithmetic and other basics.

Mean Absolute Error

Perhaps the simplest way to assess the accuracy of a deterministic point forecast is to calculate the absolute error. The absolute error is merely the absolute value of the difference between the observed and the forecast value (absolute value just means that we're ignoring the sign of the difference). So, if you forecast a high temperature of 65 degrees Fahrenheit, and the observed high temperature ends up being 68 degrees Fahrenheit, then the absolute error would be |65ºF - 68ºF| = |-3ºF| = 3ºF. Or, if you forecast 0.82 inches of rain to fall in a 24-hour period, and 0.50 inches actually falls, the absolute error would be |0.82 inches - 0.50 inches| = 0.32 inches. Simple enough, right? Then, if you take the absolute errors over a number of forecast periods and average them, you get mean absolute error (which you may see abbreviated as MAE). The mean absolute error is useful for telling us the average difference between our forecasts and the observed values that occur. As you probably realize, more accurate forecasts yield smaller absolute errors (and smaller mean absolute errors over the long haul).

You might be asking, "why do we take the absolute value of our forecast errors to eliminate the sign?" If we didn't, then we would get misleading results when we calculate the average of our errors. Take the example from above: You forecast a high temperature of 65 degrees Fahrenheit, and the observed high temperature is 68 degrees Fahrenheit. The error would be 65ºF - 68ºF = -3ºF. The next day, you forecast a high of 65 degrees Fahrenheit again, but the observed high is 62 degrees Fahrenheit. Your forecast error would be 65ºF - 62ºF = +3ºF. If we averaged your two errors we would get an average error of zero degrees Fahrenheit over the two day period. That's not an accurate reflection of your error because the positive and negative errors canceled each other out. But, your mean absolute error over the two-day period would be 3 degrees Fahrenheit, which is more telling. Calculating mean error (including the signs) over time doesn't tell us much about the size of the difference between forecasts and observations, but it can tell us about whether or not forecasts exhibit any bias (as in, whether forecasts tend to be lower or higher than the observed values over time).

Check your understanding of absolute error and mean absolute error with the practice questions below:

Brier Scores

How would we assess the accuracy of a probabilistic forecast? Mean absolute error doesn't really help us. So, I introduce to you the Brier Score, a common way of assessing the accuracy of probabilistic forecasts. Calculating the Brier Score (BS) for a single forecast event is pretty simple, using this formula:

BS = (p - o)²

In this formula, p is the forecast probability of an event occurring, and o is the occurrence of the event ("0" if the event does not occur, or "1" if the event does occur). The Brier Score for a single forecast event, then, is just the difference between the forecast probability and "0" or "1" depending on whether the event occurs. Let's think about a simple example. Say you forecast a 70 percent chance of measurable rain at your hometown on a given day, and indeed, measurable rain ends up falling. The Brier Score calculation would be:

BS = (0.7 - 1)² = (-0.3)² = 0.09

So, your Brier Score for the forecast would be 0.09, which is pretty good! Now, if you had made the same forecast (a 70 percent chance of measurable rain), and no rain fell at all, then your Brier Score would be:

BS = (0.7 - 0)² = (0.7)² = 0.49

This time, your forecast wasn't as good because you predicted a 70 percent probability of something that ended up not happening. From these two examples, it's clear that lower Brier Scores are more desirable. A perfect Brier Score for a single event would be "0" (from a forecast of 0 percent or 100 percent that ends up being correct), while the worst possible Brier Score for a single event would be "1" (from a forecast of 0 percent or 100 percent that ended up being incorrect...a complete bust). If we want to track Brier Scores for a particular forecast variable (say, precipitation) over a long period of time, we can merely average them. Or, if we wanted to combine Brier Scores for multiple forecast events on a given day (say, the probability of measurable rain, the probability of the high temperature exceeding 80 degrees Fahrenheit, and the probability of a thunderstorm occurring), we can add up the individual Brier Score for each event to compute a Total Brier Score.

Check your understanding of Brier Scores (and their interpretation) with the practice questions below:

Threat Scores

Mean absolute error and Brier Scores help us assess the accuracy of deterministic and probabilistic forecasts at a single point, but what about areal forecasts? That's where threat scores come into play. Forecasters at the Weather Prediction Center (WPC), for example, use threat scores to assess their areal forecasts for heavy precipitation. For a given event, forecasters compute threat scores by comparing the area where they predicted heavy precipitation with the area that ultimately received heavy rain or heavy snow.

You can think of a threat score as the ratio of the area where the forecast was accurate to the area where the forecast didn't verify correctly. In the figure below, the Forecast area (F) is the region for which WPC forecasted heavy precipitation (shaded in red). The observed area (OB) indicates the region where heavy precipitation fell and is shaded in green. The hatched area, C, represents the region where the forecast for heavy precipitation was correct. By definition, the threat score (T) is calculated using the equation: T = C / (F + OB - C)

Unlike with Brier Scores, higher threat scores indicate better forecasts. A threat score for a perfect forecast is "1", while a completely busted forecast gets a big fat "0" (you can read more about computing threat scores, if you're interested).

The figure below shows the trend in annual threat scores based on predictions for one inch of rain (or a liquid-equivalent of one inch) during each year from 1960 through 2020. To put this graph in proper context, I point out that WPC bases all QPF verifications on the 12Z-to-12Z period. As for the graph itself, please note that Day 1 represents the forecast period from 12 to 36 hours (WPC calculates forecast hours based on the 00Z model runs). Day 2 forecasts cover 36 to 60 hours, while Day 3 spans from 60 to 84 hours.

These threat scores indicate that WPC typically attains a threat score right around 0.28 for Day-2 forecasts of one inch of rain (or liquid equivalent). Such a score translates to WPC getting only about 45 percent of the predicted area of one inch of rain or liquid equivalent correct 36 to 60 hours in advance. The Day-3 threat scores are a bit lower yet. Keep in mind that the forecasters at WPC are very good!

The take-home message here is that threat scores in the field are improving (note the increasing trends on the graph), but our ability to accurately forecast regions of heavy rain or snow 48-hours or more from an event is not very good. Kind of paints extended forecasts for blizzards and daily deterministic point forecasts weeks into the future in a sobering light, doesn't it?

Before we move on, check out how what you learned in this section applies to forecasts in the WxChallenge competition in the WxChallenge Application section below.

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How do good forecasters go about making a forecast? What general approach do they use? As we've already discussed, anybody can interpret model guidance with a little bit of training, but the goal here is to put you on the path to becoming a good weather forecaster (a rare breed). To get us started in discussing the forecasting philosophy advocated in this course, I'm going to use an analogy that involves baseball (although softball would work, too). So, if you're not into baseball, forgive me, but I think the analogy is still effective at getting the point across even for those who aren't into baseball or softball.

When it comes to forecasting, you can take one of two approaches, which I associate with a batter's approach in baseball (or softball). I'll call one approach the "Dave Kingman approach" and the other the "Tony Gwynn approach." The first paragraphs of their Wikipedia biographies highlight their contrasts in hitting style. To boil down the differences, here are a few key stats from their careers:

• Dave Kingman (played from 1971-1986): 16 seasons, 442 home runs, 1,816 strike outs
• Tony Gwynn (played from 1982-2001): 20 seasons, 135 home runs, 434 strike outs

What should you note from those stats? Well, for starters, both players had fairly long, but different careers. Dave Kingman hit a lot of home runs, but he also struck out a lot. In fact, when he retired, he had the fourth most strike outs in Major League Baseball history. On the other hand, Tony Gwynn didn't hit many home runs, but he rarely struck out. Tony Gwynn was the ideal "singles hitter," he had a very high batting average (top-20 all-time, in fact), so he was very effective at getting hits and getting on base (not making outs).

When it comes to forecasting, the "Tony Gwynn approach" is more successful over the long haul. When forecasting, I try to hit a lot of "singles," and I'm very careful about taking large risks to avoid strike outs (major forecast busts). When you take the "Dave Kingman approach" and always try to swing for the fences to hit home runs in weather forecasting, you might hit the occasional home run (make risky forecast that turns out great), but more often you're going to strike out (like Dave Kingman) and end up with a bad forecast.

The name of the game in forecasting is to minimize your error as often as possible, which is akin to consistently hitting "singles" in baseball. Such an approach will allow you to minimize or eliminate huge forecast busts. Now, you might be thinking, "but I'd rather hit home runs -- home runs are better!" I assure you, however, that even great weather forecasters have a hard time hitting home runs consistently. Also, keep this in mind: Tony Gwynn, the ultimate singles-hitter, was inducted into the baseball hall of fame on the first ballot. Dave Kingman, the monster home-run hitter, hardly got hall-of-fame consideration, despite the fact that when he retired, 400 home runs was almost a guarantee for hall-of-fame induction.

What does applying this philosophy to a weather forecast look like? Allow me to show an example: METEO 410 students had to grapple with a rather challenging forecast for Atlantic City, New Jersey a number of years ago, when a powerful low-pressure system was slated to move up the Atlantic Coast and dump heavy rain on Atlantic City. In fact, one model predicted nearly three and a half inches for the day! You can see the basic set-up in the GFS four-panel forecast prog below.

But, the daily record for precipitation at Atlantic City was only 1.14 inches, meaning that many models were forecasting double or even triple the daily-record rainfall. That was a red flag that predicting such huge precipitation amounts could be risky. In the final analysis, the models were too high (surprise, surprise) and students that swung for the fences along with the models had huge forecast errors (they struck out big time). Atlantic City did set a daily rainfall record, but only 1.24 inches actually fell.

How could a forecaster have gone beyond mere "model reading" and come up with a prudent forecast? The key is to assess the overall weather pattern to look for clues that could make or break a forecast. For example, you may recall from previous studies that forecasters look for strong low-level jet streams at 850 mb to import deep moisture in synoptic-scale heavy rain events. A skilled forecaster in this case may have noticed that the strongest portion of the low-level jet stream was likely to be offshore, as suggested by this GFS forecast from the day before the event (note the 850-mb prog, bottom middle), which meant that the strongest deep moisture convergence might be east of Atlantic City. With only a peripheral encounter with the best moisture convergence in the cards, doubling or tripling the daily rainfall record was pretty unlikely, and a forecaster could have gone with a more conservative forecast. This case is a microcosm of the notion that swinging for the fences and/or forecasting exactly what the models predict will happen (or what you want to happen) in an extreme situation is a risky approach that often doesn't work well.

The attributes of a good forecaster ...

The Atlantic City case is pretty scary, isn't it? Models (and many forecasters) predicted double or triple the amount of rain that actually fell. The models can be your worst enemy if you don't use them in an insightful (and cautious) way. Of course, insight comes with experience, and to gain worthwhile forecasting experience, you need to develop specific attributes. What attributes to good forecasters possess? The list below shouldn't surprise you after all of your time in the certificate program. To become a good forecaster, you must:

1. Possess a working knowledge of the behavior of the atmosphere.
2. Possess a working knowledge of forecasting principles and techniques.
3. Gain enough experience to know which principles to apply to any given situation.
4. Develop the ability to interpret statistical and numerical model guidance.
5. Acquire knowledge of how a general forecast must be modified to account for local effects at a specific site.

Hopefully, all the certificate courses have given you a solid foundation for attribute #1. Don't get too comfortable, though. Number 1 is a "work in progress," even for experienced forecasters! Weather forecasting is all about life-long learning.

This course provides a solid foundation for you to learn and apply a working knowledge of forecasting principles and techniques (#2), and the process of making forecasts in this course will be critical to building your body of forecasting experience (#3). This, too, will be a "work in progress." The lessons in this course will also enhance your ability to interpret statistical and numerical model guidance (#4). Finally, #5 is crucial: Know your local climatology! We'll talk more about the types of climatological information that good forecasters need to know later in the lesson.

Good forecasters also develop a forecasting routine that allows them to apply the knowledge and skills described above. How exactly do good forecasters go about making a forecast? Ultimately, different forecasters will approach the forecasting process a bit differently based on personal preferences for data types and their forecasting objective in a particular situation. Still, for making a short-range forecast, good forecasters tend to follow the same basic recipe, which I've outlined below for you:

You will learn that statistical guidance like the National Blend of Models (NBM) and some ensemble mean forecasts tend to be good over the long haul. But, they may or may not be good for tomorrow's specific forecast. Of course, you should always consider these tools, but don't automatically accept them or reject them out of hand. Indeed, you must have a clear "big picture" in mind (be one with the atmosphere) before you make any decisions about tomorrow's forecast. In other words, you must put model guidance into a proper meteorological context. Developing this kind of an approach takes practice, of course, and you'll get plenty of it this semester.

While sorting out the details of the forecast, you have to keep your wits about you. The amount of data can be overwhelming, and sometimes there's little consensus among the models. Especially in cases with great model uncertainty, I think it's wise to establish an internal dialogue with yourself. For example, if the range in ensemble precipitation forecasts for tomorrow is from 0.10 inches to 0.75 inches, ask yourself questions like "Is it more likely that something closer to 0.10 inches or 0.75 inches will fall tomorrow? Why?" Then back up your answers to your internal questions with sound meteorological reasoning (perhaps based on various lifting mechanisms available and the overall moisture characteristics of the air mass, for this example). Even though we'll focus on deterministic forecasts this semester, it's still important to think probabilistically. This will help you hit more "singles" and avoid swinging for the fences, trying to hit a home run instead (but often striking out with a big forecast bust).

I hope you noticed in the forecasting process outlined above that good forecasters start with observations and the atmospheric "big picture." On that note, we're going to look at a case study to give you an example of how forecasters can use the big picture to diagnose the expected weather in a particular region. Keep reading!

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Since good forecasters start their forecasting process with observations, we need to look more closely at how weather observations are taken--especially observations of temperature, wind speed and precipitation. While measuring these weather variables might seem straightforward, as you're about to see, measurements aren't taken in the same way at all stations. Furthermore, for decades, weather observations were taken solely by humans, but the number of completely automated observations taken with no human intervention continues to increase. Even today, however, students at Penn State still take daily observations for the campus weather station at University Park. Check out the observation card (below) from January 25, 1985. For the record, the icon under the heading of general observations is the universal symbol for blowing snow.

Why do I show this antique observation card? I want you to note the variety of different observations it includes. It's not just high and low temperature, wind speed, and total precipitation! Even something as seemingly simple as "temperature" has observations for maximum, minimum, and set. What's the "set" temperature?  Let's find out, and explore some other nuances of measuring temperature.

The Highs and Lows of Temperature

Students at Penn State aren't alone in taking these "old fashioned" observations. Many cooperative observers and some smaller airports rely on the same type of liquid-in-glass max / min thermometers that have been used for decades (see image on the right). While modern digital and electronic equipment has largely replaced less-sophisticated tools and methods, these "low-tech" thermometers are critical for recording daily high and low temperatures at locations where observations are not electronically monitored throughout the day.

In locations where observations aren't constantly monitored electronically, maximum and minimum thermometers (yes, there are two!) are typically housed in Cotton Region Shelters, or "CRS." Cotton Region Shelters are typically painted white to reflect sunlight because too much absorption of direct sunlight could dramatically raise the temperature of the thermometer above the true air temperature. CRS are also mounted about five feet above the surface, away from the drastic temperature changes that occur very near the ground. Their louvers allow ventilation so that the air in contact with the thermometer has essentially the same temperature as the outside air (check out the interior of the CRS).

But, the two thermometers in the CRS are not ordinary thermometers. The most important part of the maximum thermometer is the constriction in the bore. Think of the constriction as a trap door. As the air temperature increases, the mercury expands and forges through the constriction. But, as the air temperature eventually starts to decrease, the mercury remains trapped beyond the constriction so that the weather observer can determine the high temperature later on. Like a clinical thermometer used to measure a patient's temperature, the observer resets the maximum thermometer to the current temperature (called the "set temperature") by shaking or spinning (centrifuging) the mercury back through the constriction.

The minimum thermometer typically contains alcohol (which has a lower freezing point than mercury). Within the bore of this thermometer, there is a barbell-shaped marker called an index. The barbell on the right of the index registers the minimum temperature. How? As air temperature decreases, the alcohol contracts, and when its retreating meniscus encounters the right side of the barbell, it drags the index along with it. When temperatures increase, however, the expanding alcohol draws the meniscus away from the index and alcohol flows up the bore around the index (the index doesn't move; it stays in a position that corresponds to the minimum temperature). The weather observer can simply read-off the 24-hour minimum temperature (the right barbell) at the observation time and reset the minimum thermometer to the current temperature by tilting the thermometer downward.

Instead of using max / min thermometers inside a CRS, increasing numbers of cooperative observers are using the Maximum Minimum Temperature System, or MMTS. Essentially, an MMTS is an electrical thermometer quartered in what I can only describe as a beehive (a circular, louvered shelter). More specifically, the MMTS is a special type of sensor called a thermistor which electronically measures temperature based on the electrical resistivity of a circuit. The MMTS transmits the information from the temperature sensor to a digital display, which can be easily read by the observer.

Meanwhile, at most primary airports, the temperature sensor is an integral component of an Automated Surface Observing System (ASOS). Smaller airports may instead have an Automated Weather Observing System (AWOS), which is basically a less sophisticated version of an ASOS. ASOS and AWOS sites commonly measure temperature with a type of thermistor called a platinum-wire resistance sensor (PRT). A hygrothermometer houses the PRT sensor and also a chilled mirror that measures dew-point temperature.

Getting a Handle on the Wind

Of course, ASOS stations measure more than temperature. The ASOS sensor that measures wind speed is an ultrasonic anemometer, which is mounted atop a tower that is typically 10 meters high (some are a bit lower, depending on local restrictions). How can ultrasonic sound waves measure the wind? Well, the transmission of sound waves through the air varies with wind speed. If the wind speed is blowing in the same direction as the sound waves are traveling, the sound waves travel faster (or slower, if the wind is blowing in the opposite direction). With three equally spaced ultrasonic transducers (a transducer is a device that can convert energy from one form to another), the sensor measures how long it takes for ultrasonic waves to travel between transducers. A total of six measurements are taken (one in each direction along each path between transducers) and from those measurements, a computer calculates wind speed and direction.

The two common wind speeds that get reported are sustained wind speeds and gusts. What does "sustained" really mean? At ASOS stations, It's a two-minute average speed, but the two-minute averages are computed from three-second averages of one-second wind speeds, so measuring a sustained wind speed is not straightforward. The smoothing of data through averaging provides a fairly reliable measurement, though. Gusts, on the other hand, are quick "bursts" of faster winds, technically defined as three-second averages that are faster than those immediately before or after.

The ASOS standards for wind measurement, however, are not universal. Reported sustained winds in tropical cyclones over the ocean, for example, are often measured or estimated one-minute averages. But, some agencies, like the Japan Meteorological Agency, use 10-minute averages as a standard for sustained wind speeds in tropical cyclones. In addition to using different averaging periods than ASOS sites, other observing networks don't always take observations at a standard height of around 10 meters. Wind observations from home weather stations, for example, are often measured much lower to the ground and therefore suffer from increased friction and various sheltering issues because of surrounding trees and buildings inherent in many neighborhood settings. The height of a wind observation really matters (in terms of frictional effects and surrounding obstructions), so beware of observation height when interpreting or comparing wind observations. Wind observations that aren't taken near a standard height of 10 meters can be notably different!

Reining in Precipitation

Several types of rain gauges are commonly used to measure precipitation. At the Penn State Weather Station, we use a standard rain gauge to measure precipitation (see image below). For starters, the circle of triangular metal slats helps to buffer the gauge from the wind, lessening its effect on the amount of precipitation falling into the gauge (allowing precipitation, particularly snow, to fall more vertically into the gauge). The outer shell of a standard rain gauge is a metallic cylinder eight inches in diameter. Inside the outer shell, there is a clear, plastic inner cylinder just over 2.5 inches in diameter into which rain funnels. Given that the inner cylinder's cross-sectional area is one-tenth of the outer cylinder's area, rain funneling into the inner cylinder will rise to a height ten times the actual rainfall - horizontal "squeezing" yields vertical "stretching." Thus, a meager 0.01 inches of rain balloons to a depth of 0.10 inches on a measuring stick, making the otherwise unwieldy task of obtaining 0.01" accuracy much easier.

When the forecast calls for snow, weather observers remove the inner cylinder and the snow accumulates in the hollow outer cylinder. They then determine the liquid equivalent by taking the gauge indoors, melting the snow, and then pouring the liquid into the small beaker to accurately measure its depth. Observers similarly obtain the liquid equivalent of unmelted hail and ice pellets (sleet).

The weighing gauge is another instrument that measures precipitation. Rain collected by the gauge funnels into a hole, below which is a catch bucket. As water accumulates in the bucket, its weight gets converted to a liquid-equivalent depth. Routinely, a mechanically driven pen scrolls across a chart attached to a rotating drum and traces out a record of this depth as a function of time (the drum rotates once every 24 hours). Yet another instrument for measuring rainfall is the tipping-bucket rain gauge. For this gauge, collected rainwater gets funneled into a two-compartment bucket; 0.01 inches of rain will fill one compartment and overbalance the bucket so that it tips, emptying the water into a reservoir and moving the second compartment into place beneath the funnel (see a close-up view of the top of a tipping-bucket rain gauge). As the bucket tips, it actuates an electric circuit that records rainfall.

Observing stations that report hourly precipitation are likely equipped with a Fischer Porter automatic rain gauge The gauge is approximately five feet tall and has a diameter of two feet. Much like a weighing gauge, precipitation collecting in a catch bucket presses down on a scale. Every 15 minutes, holes are punched on a "ticker tape" (as a function of the weight of the bucket). In this way, the punched tape keeps a running tally of precipitation since the catch bucket was last emptied. Check out the "guts" of a Fisher Porter and a close-up of the punched ticker tape.

Naturally, different instruments and standards for taking weather observations can lead to some issues with consistency and quality. If you're interested in reading about a few of these issues, along with a more technical description of ASOS instruments, check out the Explore Further section below. In the meantime, now that we've examined the ways that temperature, precipitation, and wind are measured, we need to familiarize ourselves with the published format of all of these observations. Put on your decoder rings: It's time to learn about decoding METAR observations!

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Throughout the certificate program, you've surely come across METAR observations at least a few times. But, you never actually had to know how to decode them. You might be wondering, "Why do we need to know how to decode METARS? We can get already decoded versions lots of places online!" That's certainly true, but by looking at raw METARS, you can glean more information than from the standard decoded versions (many decoded versions don't include the complete observation). Plus, if you become well-versed decoding raw METARS, you'll find that you can actually gather information from the coded versions much more quickly since the data is presented in a denser format. So, by knowing how to decode raw METARS, you can more easily and quickly see how your forecasts are doing, and gather data as you prepare your forecast for the next day!

Note that this page is quite long, but I don't intend for you to read every single part of it in detail. Focus on the sample METAR observations below and the parts emphasized in the Prioritize statement (they're in red as you go down the page). Also, focus on the Key Skill and Quiz Yourself sections, because they'll outline important things you'll need to know and give you some practice. Consider the rest of the page as a reference guide for times when you need to look up the meaning of a specific code or remark.

Let's get right to it. Here are a couple of METAR observations from Concord, New Hampshire on May 13, 2006.

• METAR KCON 131151Z AUTO 09009KT 1 3/4SM +RA BR OVC010 09/07 A3005 RMK AO2 CIG 007V013 SLP177 P0015 60056 70066 T00890072 10094 20089 53018
• METAR KCON 131751Z AUTO 00000KT 2SM +RA BR OVC013 08/06 A3012 RMK AO2 SLP202 P0017 60115 T00780061 10089 20072 53004

What do all of these letters and numbers mean? Let's break it down and decode the following METAR:

METAR KCON 131151Z AUTO 09009KT 1 3/4SM +RA BR OVC010 09/07 A3005 RMK AO2 CIG 007V013
SLP177 O0015 60056 7066 T00890072 10094 20089 53018

• METAR - METAR is a French acronym that, loosely translated, means "routine aviation weather observation." But sometimes you'll see "SPECI", which translates to a special (unscheduled) report.
• KCON is the four-character ICAO (International Civil Aviation Organization) identifier for Concord, New Hampshire. You can use the station list at the National center for Atmospheric Research to help you decipher any identifier.
• 131151Z is the observation date and time. The observation was taken on the 13th (May, 2006) at 1151Z (the month and the year are not part of the report, so it's important to know whether you're looking at a current or past observation).
• AUTO indicates a fully automated report with no human intervention. If an observer takes or augments observations, this tag does not appear. Sometimes you might see COR, which indicates a corrected observation.

• 09009KT indicates wind direction and speed. Here, the wind blew from 90 degrees (an easterly wind) at 9 knots. What happens if winds are gusty? Let's look at a METAR from Mount Washington in New Hampshire (see photograph below) at the same time as the first METAR from Concord:

  KMWN 131147Z 13043G58KT 1/16SM FZRA PL FZFG VV001 M01/M01 RMK PLB40 VRY LGT GICG 60074 70148 931000 10017 21013

  Winds were blowing from 130 degrees sustained at 43 knots and gusting to 58 knots. That's just a ho-hum "breeze" compared to the world-record setting 231 miles an hour clocked at the summit on April 12, 1934! Yes, Mount Washington is a windy place indeed.

  

  Mount Washington, New Hampshire, in December, 2005. If you look closely, you can see the Mount Washington Observatory.

  Credit: Mount Washington Observatory

  In stark contrast to windy Mount Washington, a METAR entry of 00000KT represents a calm wind. When the wind is light (a speed of six knots or less) and it varies in direction with time, the data encoded on a METAR might look like VRB004KT (variable direction blowing at four knots). If the wind speed is greater than six knots and the wind direction varies, the data encoded on a METAR might look like "32014KT 290V350". Translation: the wind direction was 320 degrees and the wind speed was 14 knots, but the direction varied from 290 to 350 degrees. Such a varying wind direction might occur in the immediate wake of a cold front. Variable wind directions are always encoded in the clockwise direction (just for the record).

• 1 3/4SM translates to a horizontal visibility of one and three-fourths statute miles. Visibilities below one fourth of a mile appear as M1/4SM in METARS from automated stations.
• +RA BR is the present weather in this case. "+RA" represents heavy rain, while "BR" is the METAR code for mist. You should become familiar with the other codes for precipitation and restrictions to visibility.

On with the rest of our METAR translation:

• OVC010 represents the current sky condition, which, at this time, was overcast at 1000 feet (the three-digit code corresponds to the ceiling (or cloud base) in hundreds of feet). In general, please note that METARS can list data about more than one layer of clouds. Moreover, when the sky is obscured, METARS should include the vertical visibility in hundreds of feet. For example, VV004 corresponds to an obscured sky with a vertical visibility of 400 feet.
• A3005 is the altimeter setting - in this case, 30.05 inches of mercury.
• 09/07 represent the temperature and the dew point reported to the nearest degree Celsius (more precise data sometimes appear near the end of METARS - more in a bit). In this observation, the temperature was 9 degrees Celsius and the dew point was 7 degrees Celsius. A letter "M" appearing before either number indicates a value below 0 degrees Celsius.
• RMK stands for "Remarks." There are a multitude of possible remarks, but I'll only go over the ones in this particular METAR. In this case, A02 indicates that the automated station has a precipitation sensor (A01 means that the automated station does not have a precipitation sensor).
• CIG 007V013. When the ceiling (as measured by a ceilometer, which uses a laser or other light source to determine the height of a cloud base) is less than 3000 feet and variable, this group typically appears in METARS. In this case, the ceiling was variable between 700 and 1300 feet.
• SLP177 indicates the sea-level pressure in millibars, using the same convention as on a standard station model (1017.7 mb, in this case).
• P0015 is the hourly liquid precipitation (in hundredths of an inch). In this case, 0.15 inches of rain fell in the hour ending at 12Z.
• 60056 represents the three- or six-hour total liquid precipitation (in hundredths of an inch). In this case, 0.56 inches of rain fell in the six-hour period ending at 12Z. For the record, six-hour totals appear at 00Z, 06z, 12Z and 18Z. Three-hour totals appear at 03Z, 09Z, 15Z and 21Z. 60000 translates to a trace of liquid precipitation during the three- or six-hour period.
• 70066 indicates the total 24-hour liquid precipitation ending at 12Z (in hundredths of an inch). In this case, 0.66 inches fell at Concord from 12Z on May 12 to 12Z on May 13.
• T00890072, the T-group, indicates the hourly temperature and dew point to the nearest tenth of a degree Celsius. The "0" after the "T" indicates that the temperature is greater than or equal to 0 degrees Celsius (a "1" will follow the "T" when the temperature is lower than 0 degrees Celsius). After the three digits for temperature, a "0" indicates that the dew point is greater than or equal to 0 degrees Celsius (a "1" indicates that the dew point is less than 0 degrees Celsius). In this case, the 12Z temperature at Concord was 8.9 degrees Celsius (48 degrees Fahrenheit) and the dew point was 7.2 degrees Celsius (45 degrees Fahrenheit).
• 10094, the 1-group, indicates the highest temperature, in tenths of a degree Celsius, during the previous six-hour period. If the digit following the "1" is a "0", then the temperature is higher than 0 degrees Celsius (a "1" following the first "1" indicates that the temperature is less than 0 degrees Celsius). So the highest temperature at Concord between 06Z and 12Z on May 13, 2006, was 9.4 degrees Celsius (49 degrees Fahrenheit). Note that the "1" group is only reported at 00Z, 06Z, 12Z and 18Z.
• 20089, the 2-group, indicates the lowest temperature during the previous six-hour period. If the digit following the "2" is a "0", then the temperature is higher than 0 degrees Celsius (a "1" following the "2" indicates that the temperature is less than 0 degrees Celsius). So the lowest temperature at Concord between 06Z and 12Z on May 13, 2006, was 8.9 degrees Celsius (48 degrees Fahrenheit). Like the "1" group, the "2" group is only reported at 00Z, 06Z, 12Z and 18Z.
• 53018 indicates the pressure tendency (the "5 group"). The digit following the "5", which can vary from 0 to 8, describes the behavior of the pressure over the past three hours (for guidance, consult the table below). The last three digits represent the amount of pressure change in tenths of a millibar. Thus, the pressure at Concord increased 1.8 mb in the three-hour period ending at 12Z on May 13, 2006.

I realize that translating one METAR hardly qualifies as an entire lesson, but at least you now know the general guidelines and where to find information in case you run across a METAR that gives you pause. I encourage you to expand your aptitude for decoding METARS - they hold a lot of information! If you want to completely master METAR code, you can study Chapter 12 of the Federal Meteorological Handbook No. 1, which is basically the METAR "bible" (section 12.7 in particular covers the wide variety of "remarks" that can appear). If you have aspirations for becoming an official weather observer, you may be interested in checking out the entire handbook (it's very long, but there is a table of contents to help you find specific topics).

What are the key take-away points from this section? The Key Skill box below highlights some key data groups with which you must become proficient in this course, and the Quiz Yourself section will give you a chance to get a little practice, too. Once you get the hang of it and memorize the general format and some common codes, it's a snap!

All of these observations we've been talking about, over long periods of time, constitute a station's climatology, and good forecasters investigate the climatology of the location they're forecasting for. We'll take a look at common climate data and what components are needed for a good understanding of a city's climatology next.

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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?

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.

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.

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.

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.

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).

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.

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The temperature and precipitation statistics that we covered in the last section are a big piece of a particular location's climatological puzzle, but they don't paint the whole picture. A location's wind climatology also provides important clues about typical / atypical weather, and perhaps more importantly, when coupled with knowledge of the surrounding terrain like bodies of water and elevation changes, can help forecasters understand local influences that winds can have on other weather variables (like temperatures and precipitation).

First off, however, it's important to gauge what constitutes "windy" conditions at your forecast location. Perhaps you've never dug into wind data before, but your experience has probably told you that some locations are typically windier than others. Unfortunately, climatological wind data can be somewhat hard to come by because of a relative lack of properly sited observation stations (many privately owned and home weather observing equipment is not set up to measure wind speed at a standard height of 10 meters). Furthermore, publicly available wind data from ASOS sites is generally limited to hourly observations and some summary statistics for stations at larger airports (anything more typically requires some data mining).

Still, we can get a sense for what constitutes a windy day at select forecast locations by viewing summary statistics compiled by the National Centers for Environmental Information. For larger airports in the U.S., they produce "Local Climatological Data" documents, which contain a wealth of statistics, including mean and record wind speeds (sustained and gusts) for each month. For example, compare the historical wind data at Cold Bay, Alaska and Seattle, Washington. Cold Bay is clearly a windier place, with monthly mean wind speeds that are roughly twice those in Seattle. Furthermore, the monthly record sustained wind speeds in Cold Bay are significantly higher, as are the record gusts (multiple months have had gusts of over 80 miles per hour in Cold Bay). The data for monthly record sustained wind speeds and gusts can be particularly helpful for gauging what constitutes unusually fast winds at a particular location during various times of the year.

A very common visualization for studying a location's wind climatology is a wind rose, which is a plot that displays historical wind direction and speed information. You encountered wind roses during your previous studies, and if you need a refresher on how to interpret them, I recommend this quick discussion on the basics (keep in mind, however, that display conventions can vary from website to website). Ultimately, wind roses are a great way to quickly assess from which directions winds most and least commonly blow, and assess which directions are more (or less) prone to particularly speedy winds.

Recurrent patterns in winds may become apparent at a particular location because of regional terrain (winds being channeled through mountains or valleys, for example), persistent mesoscale circulations (like sea breezes or mountain-valley circulations), or persistent storm tracks, and these patterns may change throughout the year. For example, check out the wind roses for Miami, Florida (above) from January and July.

Note that the Miami wind roses for January and July look very different. The most frequent wind direction (marked by the longest spoke) is from the north-northwest in January (blowing from that direction nearly 14 percent of the time). Winds from the east and southeast are also fairly common in January, but not quite as common as north-northwest winds. In July, however, east-southeast winds are the most common (occurring nearly 19 percent of the time), and winds from the north-northwest are much less frequent. It's not even close! In fact, if we sum the percentages of winds from the east, east-southeast, and southeast (the three longest spokes), winds from those directions occur nearly 50 percent of the time combined!

Any ideas why winds from those three directions are so common in July? A big part of it is the regular occurrence of sea breezes as the Florida peninsula warms up during the day and a mesoscale sea-breeze circulation develops, resulting in onshore flow at Miami. During the summer, this onshore flow ushers in slightly cooler, moist air into south Florida. The reliable sea breeze is a big reason why temperatures have only hit 100 degrees in Miami once since records began in 1895 (per the ThreadEx data). Daily record highs in Miami during the summer are typically in the middle and upper 90s, which is comparable to or even lower than many locations that are much farther north in the U.S. So, wind direction, and the trajectory of the air as it approaches your forecast location are important considerations!

Given the forecasting impacts that air trajectories can have, getting the lay of the land around your forecast site and the surrounding region is critical. Large bodies of water and elevation changes are particularly influential. To get a feel for the lay of the land around your forecast location, it's always a good idea to study a detailed topographic map of the local area, in order to judge the local elevation changes and distance from your forecast site. I'll use Los Angeles International Airport (KLAX) as an example. As you can see from the Google map below, the terrain around Los Angeles is complicated! Many terrain features influence the weather at KLAX!

For starters, the Pacific Ocean is just west of the airport, and the coastline wraps around such that the ocean also lies about 15 miles to the south. Therefore, any time low-level winds blow onshore (having a large westerly or southerly component), they'll bring a substantial maritime influence, which during certain times of year can lead to fog or low cloud development. But, given the substantially higher elevations that are located around the Los Angeles Basin (KLAX sits at an elevation of 97 feet), considerable downsloping occurs when low-level winds blow from the northwest all the way around to the southeast.

Such downsloping brings a warming and drying influence (from compressional warming), with the greatest downsloping occurring on northeast and east-northeast flow since that's where the highest elevations are locally. Indeed, within 30 to 40 miles northeast of KLAX, multiple elevations in excess of 7,000 feet can be found! That's a lot of downsloping! Even downsloping from smaller ranges of hills and mountains (like the Hollywood Hills about 12 miles north of the site, or the Palos Verdes Hills about 12 miles south) is considerable. If air parcels descended the Palos Verdes Hills dry adiabatically (warming at a rate of 5.5 degrees Fahrenheit per 1,000 feet), that's worth almost 8 degrees Fahrenheit of compressional warming between the hills and the airport (assuming a starting elevation of 1,500 feet)! So, it pays to study the terrain surrounding your forecast location even for fairly subtle elevation changes. It's worth considering the impacts for even elevation changes of a few hundred feet or more within several miles.

The farther you go from your forecast site, it takes a more significant topographic feature (body of water or elevation change) to make a significant contribution to a location's climatology. In general, it's wise to assess the terrain within several hundred miles for major bodies of water or elevation changes. And, sometimes, the elevation changes don't take the form of well-defined, obvious hills or mountains. To get an idea of how a forecaster might assess the larger-scale terrain around a forecast location, check out the short video below (4:16), which contains a few examples.

From the video, take note that quantitatively estimating elevation changes and the distance of a particular feature from your forecast location is important, as it gives context for how significant a particular feature may be. Performing these types of analyses also helps forecasters understand the influences that various wind directions may have on the weather at their forecast location in terms upsloping (which brings cooling and potential cloud / precipitation development) or downsloping (warming and drying), or a potential maritime influence.

Ultimately, each location can have its own unique topographic influences that are an integral part of its weather and climate, and it's up to the forecaster to analyze the location's surroundings and determine the relevant features and impacts. I hope the sampling of examples gives you an idea of the types of features and influences that forecasters need to look for, and with your fundamental knowledge from your previous studies, you should be able to perform basic analyses like these on your own. If you want a little practice in making these kinds of assessments for yourself, check out the Quiz Yourself section below for a few more examples. In the next section, I'll show you a Case Study detailing how a forecaster might compile relevant climatology information for a particular city.