What can we learn from Hurricane Sandy?

Nearly a week after Hurricane Sandy struck the Mid-Atlantic coast of the United States, the affected region is still reeling from the shock. This really was an unprecedented storm in the truest sense of that word.

Among the amazing aspects of the event was the extraordinarily accurate and early forecasting of the storm. Numerical forecast models were latching on to the correct scenario, including the unusual and rapid leftward turn off the Mid-Atlantic coast, as early as five to seven days before the event (depending on the particular model in question).

This kind of forecast accuracy was a pipe dream, even for run-of-the-mill storms, just 20 years ago. We are all living through a quiet revolution in weather forecasting and high-profile events such as Sandy make it very clear. The advance warning offered by these accurate medium-range forecasts undoubtedly saved dozens, perhaps hundreds of lives, and literally billions of dollars in damage in New York City alone.

From what we know in its immediate aftermath, the storm itself was the result of an unusually strong interaction between a late-season tropical cyclone (the original Hurricane Sandy) and a mid-level extratropical disturbance that grew in intensity as it approached the coast. Such interaction is a common feature of the transition seasons — fall or spring.

There are reasons to believe that in a warming climate the frequency of these kinds of interactions may increase potentially leading to an increase in strong examples, like Sandy, of this kind of severe weather.

Category: Tropical
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Are wind turbines detected by weather radars?

While a single wind turbine is unlikely to confuse a radar return signal, a wind farm, particularly one 20 square miles or larger, will pose a problem. For example, the radar returns from the weather radar in Sullivan continually measures what looks to be a rain cloud to the north. This signal is always there and is the location of a wind farm.

A radar consists of a transmitter and a receiver. The transmitter emits pulses of radiowaves outward in a circular pattern. Precipitation scatters these radiowaves, sending some energy back to the transmitting point where it is detected by the radar’s receiver as a radar return signal. The wind turbine towers are made of metal and have a strong radar return signal and will appear as a fixed object in a radar image. Many of the rotating blades are made of materials transparent to radar, but they often have metal lightning ground wires running through each blade — increasing the radar signature. The various speeds of the different turbines can modulate the radar return, so the radar image will continually change.

There are ways to get around to this problem, ranging in expense. Perhaps the simplest is to know that the farm is there and thus recognize that when it exists in an isolated manner that it is not raining.

Wind farms can also show up on surveillance radar used for aviation airspace control, which perhaps poses a more serious problem. As the number of wind farms continues to increase, their impact on radar is important to understand.

Category: Meteorology
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How were recent heavy rains so well-predicted so many days in advance?

Though we have been relatively dry for much of the autumn, on the weekend of Oct. 13-14 we received a soaking rain of 0.86 inches on Saturday followed by 1.74 inches on Sunday. Two aspects of this heavy rain event are noteworthy to us. First, though Madison averages an inch of rain in a single day about six times each year, the 1.74 inches that fell on Sunday was the most in a single calendar day in Madison since 3.61 inches of rain fell on July 22, 2010. That long stretch includes two full summers (2011 and 2012) in which we never received such a rain.

Second, the rainy weekend was clearly in the forecast for almost seven days in advance. In other words, at the end of the prior weekend, it was clear that next weekend was going to be a washout.

That forecasting success is a particular example of a revolution that has quietly occurred in the science of numerical weather prediction (forecasting with the aid of high-speed computers) over the last 30 years. It used to be that the one- to two-day forecast was the apex of forecasting skill but the combination of more powerful computers and continued research into the underlying science of weather systems has led to our present ability to forecast the weather sometimes several days in advance.

It is exciting to imagine where the state of the science may be in another 30 years.

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What is an air mass?

An air mass is a large body of air whose properties of temperature and humidity are similar in any horizontal direction. Air masses can cover hundreds of thousands of square miles. Air masses are formed when air stagnates for long periods of time over a uniform surface. The characteristic temperature and moisture of air masses are determined by the surface over which they form. An air mass acquires these attributes through heat and moisture exchanges with the surface.

Air masses are classified according to the temperature and moisture characteristics where they develop. Cold air masses originate in polar regions and are therefore called polar air masses. Warm air masses usually form in tropical or subtropical regions and are called tropical air masses. Moist air masses form over oceans and are referred to as maritime air masses. Dry air masses that form over land surfaces are called continental air masses.

In a typical year, weather changes resulting from the movement of air masses kill more people in the United States than all other weather phenomena combined. For instance, people from warmer winter climates (like Dallas, Texas) might be at greater risk from a cold air outbreak than people living in colder climates (like Madison). Conversely, Madisonians may be at greater risk to heat waves than residents of Dallas.

Socioeconomic status, cultural backgrounds and the size of the elderly population can also put a city’s residents at greater or lesser risk. For example, households with heaters in the north and air conditioners in the south may reflect socioeconomic status, whereas cultural backgrounds may influence the amount of time people spend outdoors.

Category: Meteorology
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What are cooling degree days?

A degree day is a measure of heating or cooling. A degree day is defined as a departure of the mean daily temperature from a given standard: one degree day for each degree of departure above (or below) the base temperature during one day. The degree day is related to the outside temperature and is not related to time.

A cooling degree day is every degree that the mean temperature is above 65 degrees during a day. So, if the high temperature for the day is 95, and the minimum is 51, the average temperature for the day is 73. That would be 8 cooling degree days (73-65).

The amount of energy required to maintain a building’s temperature in the summer is proportional to the accumulated cooling degree days. Cooling degree days, or CDD for short, are used to estimate fuel consumption needs.

The typical accumulated CDD for Madison is 664. The recent hot summer has, so far, registered 1065 CDDs — well above normal.

Of course, cooling a building depends on total energy gains and losses of the building and not just the mean temperature outside. Other factors, such as cloudiness, insulation and the number and efficiency of electrical appliances running will influence energy needs.

We also compute growing degree days, GDD, which is used by farmers to predict plant growth such as when a flower will bloom or when a crop should reach maturity. During winter, we keep track of heating degree days, or HHD. Each degree that the mean temperature is below 65 degrees is one heating degree day.

The Wisconsin State Climate office publishes this data at go.madison.com/madisonclimate.

Category: Climate
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