Tag Archives: Radar

Raindrops Keep Falling on my Radar - Part 1

What's the most complicated way to say it's raining? Well, if you know me, you know it will involve electronics, sensors, and signal processing! This post was originally going to compare the fall velocity for rain, sleet, and snow. Unfortunately, I haven't been lucky enough to be home to run my radar when it was snowing. It will happen this winter, but we'll start looking at some data now. Want to review radar before we get started? We have already talked about looking at the doppler signature of cars and got a tour of a mobile weather radar.

Back in October we had a couple of squall lines come through. On the 3rd, there was a significant event with two lines of storms. I had just been experimenting with measuring rainfall velocity with the modified X-band radar, so I decided to try another experiment. I put the radar unit in a trashcan and covered it with plastic bags. Then I sat it outside on our balcony and recorded for about 2.5 hours.

Testing the radar setup before the rain with some passing cars as targets.

Testing the radar setup before the rain with some passing cars as targets.

There is a radar in there! My make-shift rain proof radome. The only problem was a slight heat buildup after several hours of continuous operation.

There is a radar in there! My make-shift rain proof radome. The only problem was a slight heat buildup after several hours of continuous operation.

Not only do we get the doppler shift (i.e. velocity of the raindrops), but we get the reflected power. I'm not going to worry about calibrating this, but we can confidently say that the more (or larger) raindrops that are in the field of view of the radar, the more power will be reflected back.

First, let's look at a screenshot of the local weather service radar. You can see my location (blue cross) right in front of the second line of showers. At this point we had already experienced one period of heavy rain and were about to experience another that would gradually taper off into a very light shower. This was one of the nicer systems that came through our area this fall.

A capture of our local weather radar, my location is the blue cross directly ahead of the storm.

A capture of our local weather radar, my location is the blue cross directly ahead of the storm.

Now if we look at the returned power to the radar over time, we can extract some information. First off, I grouped the data into 30-second bins, so we calculate the average returned power twice per minute. Because of some 32-bit funny business in the computations, I just took the absolute value of the signal from the radar mixer, binned it, and averaged.

Reflected power received by the radar over time. The vertical red line is the time that the radar screen shot above was taken.

Reflected power received by the radar over time. The vertical red line is the time that the radar screen shot above was taken. We can see the arrival and tapering off of the storms.

From this chart we can clearly see the two lines of storms that came over my location. We also see lots of little variations in the reflected power. To me the rain-rate seemed pretty constant. My best guess is that we are looking at skewing of the data due to wind. This could be solved with a different type of radar, which I do plan to build, but that doesn't help this situation.

Let's look at what inspired this in the first place, the rainfall velocity. From a chart of terminal velocities, we can see that we expect to get drops falling between 4.03-7.57 m/s for moderate rain and 4.64-8.83 m/s for heavy rain. Taking a 5 minute chunk of data starting at 60 minutes into the data (during high reflectivity on the chart above), we can compute the doppler frequency content of the signal. Doing so results in the plot below, with the velocity ranges above shaded.


Doppler frequency content of 5-minutes of data starting at 60 minutes into recording. The blue box shows doppler frequencies corresponding to moderate rain, and the red box corresponding to heavy rain.

Based on what I see above, I'd say that we fall right in line with the 0.25"-1" rain/hour data bracket! There is also the broad peak down at just under 100 Hz. This is pretty slow (about 1 m/s). What could it be? I'm not positive, but my best guess is rain splattering and rebounding off the top of my flat radar cap. I'm open to other suggestions though. Maybe part of this could be rain falling of the eve of the building in the edge of the radar view? The intensity seems rather high though. (It was also suggested that this could be a filter or instrument response artifact. Sounds like a clear air calibration may help.)

So, what's next? We'll take some clear-air calibration data and the use data from a Penn State weather station to see what the rain rate actually was and what the winds were doing. Maybe we can get a rain-rate calibration for this radar from our data. See you then!

Thank you to Chuck Ammon for discussions on these data!


Doppler On Wheels - A Tour of a Mobile Radar


Recently, Penn State was lucky enough to have the "Doppler on Wheels" or DOW visit for two weeks through an NSF education grant! The truck, owned and operated by the Center for Severe Weather Research, is probably familiar to you if you have watched any of the storm chasing television shows or are interested in severe storms.  Dr. Yvette Richardson and Dr. Matt Kumjian were the faculty hosts and incorporated the radar into classes they are teaching.

I've always believed in getting students involved with data collection.  If students collect the data, they are attached to it and begin to see the entire scientific process.  Data doesn't just appear, real data is collected, often with complex instruments, and processed to remove various problems, corrections, etc.  It's not everyday that students get to collect data with a state-of-the-art radar though!

For this entry we're going to try a video format again.  Everyone seemed to like the last video entry (Are Rocks like Springs?).  Keep the feedback coming! It was a bit windy, but I've done what I can with the audio processing.  A big thanks to everyone who let me talk with them!  As always, keep updated on what's happening by following me on twitter (@geo_leeman).  This week I'll be off to New York to hear Edward Tufte talk about data visualization, so expect updates about that!

Doppler Radar at Home: Experiments with a CW Radar Part 1

When you hear "radar", you probably think of weather radar and a policeman writing a ticket.  In reality there are many kinds of radar used for everything from detecting when to open automatic doors at shops to imaging cracks in concrete foundations.  I've always found radar and radar data fascinating.  Some time back I saw Dr. Gregory Charvat modify an old police radar on YouTube and look at the resulting signal.  I happened to see that model of radar (a 1970's Kustom Electronics) go by on EBay and managed to buy it.  I'm going to present several experiments with the radar over a few posts.  If you want to learn more about radar and the different types of radar I highly recommend Dr. Charvat's book Small and Short-Range Radar Systems.  I haven't bought a personal copy yet, but did manage to read a few chapters of a borrowed copy.

The doppler radar I purchased.  I'm not using the head unit.

The doppler radar I purchased. I'm not using the head unit.

The radar I have outputs the doppler shift of a signal that is transmitted, reflected, and received.  Doppler is familiar to all of us as we hear the tone of a train horn or ambulance change as it rushes past us.  Since there is relative motion of the transmitter (horn) and receiver (your ears), there is a shift in received frequency.  Let's say that the source emits sound at a constant number of cycles per second (frequency).  Now let's suppose that the distance between you and the source begins to close quickly as you move towards each other.  The apparent frequency will go up because the source is closer to you each emitted cycle and you are closer to the source!

The doppler effect of a moving source.  Image: Wikipedia

The doppler effect of a moving source. Image: Wikipedia

This particular radar transmits a signal at a frequency of 10.25 GHz.  This outgoing signal is continually transmitted and reflected/scattered off of objects in the environment.  If the object isn't moving, the signal returns to the radar at 10.25 GHz.  If the object is moving, the signal experiences a doppler shift and the returned frequency is higher or lower than 10.25 GHz (depending on the direction of travel).  This particular radar can be easily hacked and we can record the doppler frequency out of a device called a mixer.  The way this unit is designed, we can't tell if the frequency went up or down, just how much it changed.  This means we don't know if the targets (cars) are coming or going, just how fast they are traveling.  Maybe in a future set of posts, we'll build a more complex radar system such as the MIT Cantenna Radar.  Be sure to comment if that's something you are interested in.

Since we'll be measuring speeds that are "slow" compared to the speed of light, we can ignore relativistic effects and calculate the speed of the object knowing the frequency change from the mixer, and the frequency of the radar.

Simplified doppler velocity.

I took the radar out to the street and recorded several minutes of traffic going by, including city busses.  Making a plot of the data with time increasing as you travel left to right and doppler frequency (speed) increasing bottom to top, we get what's known as a spectrogram.  Color represents the intensity of the signal at a given frequency at a certain point in time.

Speeds of several cars on my street.  1000 Hz is about 33 mph and 500 Hz is about 16 mph.

Speeds of several cars on my street. 1000 Hz is about 33 mph and 500 Hz is about 16 mph.

The red lines are strong reflectors (the cars).  Most of the vehicles slow down and turn on a side street in front of the radar.  About 30 seconds in there are three vehicles, two slow down and turn, the third again accelerates on past.  Next I'll be lining up a video of these cars passing the radar with the data and you'll be able to hear the doppler signal.  To do that I'm learning how to use a video processing package (OpenCV) with Python.

In the next few installments, we'll look at videos synced with these data, radar signatures of people running, how radar works when used from a moving car, and any other good targets that you suggest!

Teaching Field Camp Week 2 - Ground Penetrating Radar

Week 2 of camp for the geophysics students was at the new University of Oklahoma Bartell Field Camp. Students were split into three groups and each group rotated through three main geophysical methods: gravity, magnetics, and ground penetrating radar (GPR).  I was responsible for the GPR all week, but we'll briefly discuss everything they did and some problems we had along the way.

Monday the students went on an intro field trip to learn about the geology of the area.  First students walked up the road to 'high camp' noting the sediment basement contact (and what we interpret as a large fault breccia) on the way.  When into the granitic basement there are many mafic dikes, some locations even have dikes crosscut by later intrusions.  The students this year really seemed well prepared to tie the geology into their reports and were very careful in noting/interpreting features.  Next we drove to Tunnel Drive, a short hike that exposes lots of basement deformation and some classic fault examples.  There were also a couple fun stops like Skyline Drive where dinosaur footprints have been preserved as trace fossils.  In the picture below we are looking up at the bottom on an impression likely left by a foot of an ankylosaurus.

The next three days the groups rotated through the geophysical methods.  In this post I'm only going to discuss the GPR collection and data.  The gravity data is currently being processed (so expect a post about it early next week) and the magnetics are posing problems.  Our main magnetometer has an internal problem that prevents us from downloading the data collected.  It is being sent back to the factory and the students will collect new data with an older system next week.  There is also a special magnetic surprise I found in an outcrop that I want to discuss in a more detailed post.

Ground penetrating radar is a technique we haven't really used much recently at OU, but I'm hoping to make a come back with it! The system needed lots of tweaking, adjusting parameters, and fiddling with; after that it obtained some really interesting data.  A ground penetrating radar sends a signal into the rock, which is reflected from various objects/interfaces, so data is interpreted similar to seismic data (only at a different time scale).  Seismic waves travel through rock at around 2200km/second while radar waves are much faster at about 0.1m/nanosecond.  GPR is used extensively in archeology to look for near surface targets and to find bodies during criminal investigations (we have in fact used this system over a mass grave before in Norman... but that's another post all together).

The first day we had students experiment with different parameters over a known target (a metal culvert under a road).  While the target isn't necessarily geologic, we know what it is, where it is, and how big it is.  Using this we setup an ideal parameter set to then examine more interesting geologic features.  Other groups during the week also targeted the culvert for practice, then picked more interesting areas to examine.

First I had to patch together some codes to convert the GPR data from the proprietary DT1 format to a more standard SEGY format.  We then worked up some seismic unix command flows to process that data.  The images shown are not migrated and could benefit from migration, spiking deconvolution, etc.

The first image shown is on the high camp road.  There was a culvert near the surface, but below that are other diffractions from some interesting geological structures.  I'll currently not say any more so students can think about what these are.  The second image shows why this tool could be so valuable.  There was little geology at the surface, but according to the data there is a dipping reflector just under our feet.  What could it be? Maybe with a few more trips up there I'll be able to find it in outcrop somewhere.  There were also some diffractions deeper in this image.  While I do have lots of comments about the GPR parameters, setup, etc I don't think it's so important to discuss.  My goal is to show that there is so much beauty in the complexity of what happened here.  The basement rock is very very old (without an extensive literature search we'll say pre-cambrian, which is ~540 Million years ago).

Friday we took the students on another field trip.  Early in the morning I had to take our other TA, Cullen Hogan, to the airport.  He is leaving us for an internship and will be greatly missed in the last week of the course.  After returning from the Colorado Springs airport the students piled in to drive to one of my favorite views in southern Colorado, Spiral Drive in Salida.  On the way to Salida from Cañon City small sedimentary 'hanging basins' can be found in the mountain sides as we drive through a thrust zone between sediment and basement.  Salida lies in the San Lúis Valley, part of the slow Rio Grande Rift.  The view is always amazing and some complex geology is observed on the way.  Below is a panorama overlooking the collegate peaks I took at this location last year (there wasn't as much snow this time).

Wisconsin Meteor - A Great Time to Play with Radar Data

As I'm sure you've heard by now last week, what is believed to be a meteor, passed into our atmosphere and exploded over Wisconsin.  The light was seen as far away at St.Louis, MO and was captured on a camera at the University of Wisconsin-Madison.  The video frames have been played on most news networks and are available everywhere online.
As you can imagine the 911 call center (actually 911 call centers over 6 states) was/were flooded with reports of the light, the sonic boom, and other observations.  The NWS also noticed a new trail appear on the radar.  I downloaded the level 2 data and plotted it up.  First we'll look at the reflection.

You can see the trail in the SW corner of Iowa county.  (KDVN radar)  Next is just a blow up of this image.  The meteor path was from west to east.  According to NASA scientists the meteorite was likely not from the current Gamma Virginids meteor shower, but a rock from the asteroid belt.
Next it would be interesting to look at this trail in 3D.  Using level II radar data this is possible.  The next images show this from several different angles.   The directions are labeled so it's easy to get bearings on which way you're looking.  If you notice the trail is sloping down slightly towards the SE.

The average hight for the event was right around 24,000-25,000 ft.  Looking at the plot you can see how small the plot was and how large the strongest reflector in the center is! A few back of the envelope calculations can be done using basic trig to determine some interesting things.  You can try this yourself.  I've posted a link to the radar data at the bottom and a link to a website where you can download a 21 day trail of GR2Analyst.  Just open the data and start slicing it!

Finally a sample of the meteorite has been recovered at is being examined currently.  There should be many other samples in the area also and hunters are already out looking for them.  With all this in mind you should remember it's not that uncommon for meteorites to enter the atmosphere.  Washing machine size chunks of rock are not abnormal and they burn up in the atmosphere.  If any material makes it to the ground it's probably never seen.  (Since most of the Earth is covered in water most probably hit there.)
Below: Scientists prepare a sample of the meteorite for a test

Gibson Ridge Software
Data for This Radar Scan