Doppler capabilities added much later allowed for the detection of tornadoes and downbursts, as scientists could now "see" the wind via the movement of energy returns back to the radar and infer motion from these returns. If you have ever stood near a railroad track as a train was approaching then passing by, or listened as an emergency vehicle with siren blaring passed by, you have heard the change in pitch as the sound comes towards you then departs away from you. This is the Doppler effect. In it's most basic form, the radar sends out a pulse of energy which strikes an object and returns back to the radar dish. The time it takes to leave and return is used to compute the distance of the object (like a raindrop) from the radar. The change in the frequency of that emitted pulse tells whether the object it struck is moving towards or away from the radar, thus the computer in the radar infers motion.
2. The radar looks within the cloud while the gauge is at the ground. The difference in height between where the radar is looking and the gauge can be thousands of feet. A lot can happen to those drops as they fall out of the cloud: some might evaporate and others might be blown far downwind from where the radar is looking.
3. The radar beam might be blocked by hills, trees or building and might not be able to see the cloud producing the rain.
4. The radar volume at all ranges is very much larger than the sample volume of a rain gauge on the ground. This large volume may have a variety of reflectors - i.e. snow, hail, raindrops and cloud drops, all of which contribute to the reflectivity but which have very different water contents.
Radar also showed its utility in estimating precipitation. The traditional way of turning radar measurements into rainfall data is to relate the power returned from the cloud to the radar (meteorologists refer to this as the "radar reflectivity") to some estimate of rain intensity measured on the ground. Once this so called Z-R relationship is determined, the radar data can be converted directly to rainfall over the entire coverage region of the radar. Usually, the rainfall estimates in the Z-R relationship come from rain gauge networks.
The great benefit of using radar is that it can estimate rainfall over a huge area, including most places where there are no rain gauges. However, it turns out that using radar to estimate rainfall is much more difficult than one might think for a number of reasons:
1. The radar reflectivity and rainfall relationship is not unique, it changes constantly within a storm, among different types of storms, and from place to place.
The great benefit of using radar is that it can estimate rainfall over a huge area, including most places where there are no rain gauges. However, it turns out that using radar to estimate rainfall is much more difficult than one might think for a number of reasons:
1. The radar reflectivity and rainfall relationship is not unique, it changes constantly within a storm, among different types of storms, and from place to place.
2. The radar looks within the cloud while the gauge is at the ground. The difference in height between where the radar is looking and the gauge can be thousands of feet. A lot can happen to those drops as they fall out of the cloud: some might evaporate and others might be blown far downwind from where the radar is looking.
3. The radar beam might be blocked by hills, trees or building and might not be able to see the cloud producing the rain.
4. The radar volume at all ranges is very much larger than the sample volume of a rain gauge on the ground. This large volume may have a variety of reflectors - i.e. snow, hail, raindrops and cloud drops, all of which contribute to the reflectivity but which have very different water contents.
Content for this post was adapted from a similar post on the CoCoRaHS blog.
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