Peering at Precipitation
Peering at PrecipitationPrioritize...
Upon completing this section, you should be able to interpret 85-91-GHz imagery and 36-37-GHz imagery, as well as discuss their primary uses and how these types of images are derived. Furthermore, you should be able to discuss the primary uses of the precipitation radar and microwave imager instruments aboard the GPM satellite. Finally, you should be able to discern whether a particular product discussed on the page comes from an active or passive remote sensor.
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Our studies of remote sensing from satellites so far have mostly focused on techniques and products that are based on conventional satellite imagery. Even multi-spectral images are merely created from various wavelengths used to create visible and infrared images. Now, however, we're going to transition into some more sophisticated applications of remote sensing from satellites. In this section, I'm going to focus on satellite-based detection of precipitation structures and rates. Satellites play a crucial role in this area because tropical cyclones spend so much time outside of the range of land-based radar networks. First, we'll investigate imagery created from satellite detection of microwave radiation between 85 GHz and 91 GHz.
85-91-GHz Imagery
One of the characteristics that you've learned about a tropical cyclone's eye is that it is generally rain free, but it is not often completely cloud free. Either some low clouds exist in the eye and/or high clouds obscure the presence of the eye altogether on conventional satellite imagery. For example, check out the enhanced infrared satellite image at 2250Z on July 2, 2026 (below), which shows Typhoon Bavi. At the time, Bavi was in the midst of rapid intensification and had maximum sustained wind speeds of 105 miles per hour (a Category 2 storm on the Saffir-Simpson Wind Scale). Given that data, you might suspect that Bavi would display a well-defined eye on conventional satellite imagery. But, alas, high clouds obscured Bavi's eye, and it would be tough to get a fix on the storm's center under these circumstances.
Even though enhanced IR imagery didn't provide a good look at Bavi's core structure, thanks to passive microwave imagery, forecasters could still see that Bavi had an eye. How? Microwave imagers aboard polar-orbiting satellites detect emitted microwave radiation at frequencies from 85-91 GHz, and at the wavelengths used to create these images, we can "see" right through high-altitude cirrus clouds into the eye. Therefore, detecting the high-level structure of the core of tropical cyclones is a primary use of 85-91-GHz imagery. The image below happened to utilize 89-GHz microwave radiation and is superimposed on conventional infrared imagery in the areas outside the viewing swath of the polar-orbiting satellite (the pass didn't capture the entire storm). The 89-GHz image, which was from less than 30 minutes after the enhanced infrared image shown above, clearly revealed that Bavi did have a well-defined eye.
So, how should we interpret this image? Why is Bavi's eye evident on this image, but not the enhanced infrared image? After all, both images are plotting the same variable, called "brightness temperature," which is the temperature of a hypothetical object that absorbs all radiation that strikes it (brightness temperature is also sometimes referred to as "equivalent blackbody temperature"). But, because the two images are utilizing different wavelengths (frequencies) of radiation, they're showing us different things. The 89-GHz image doesn't really show us high, cold cloud tops like conventional infrared imagery does. Focusing on the spiraling pattern of low brightness temperatures around Bavi's center (red, green, and yellow), it stands to reason that not much 89-GHz radiation was reaching the satellite at this time. The short video below will help you understand why.
85-91-GHz Imagery (2:51)
Transcript: 85-91-GHz Imagery (2:51)
To understand the utility of 85-91 GHz imagery, we need to study the sources and behavior of microwave radiation at these frequencies. So, we’ll start at the bottom and work our way up through a thunderstorm. For starters, the surface of the ocean is a source of 85-91 GHz radiation. However, this radiation upwelling from the ocean gets strongly attenuated – that is, absorbed and scattered -- by cloud droplets and raindrops at below the melting layer within a tall thunderstorm, like those within the eye wall or a spiral band of a hurricane. However, these raindrops and cloud droplets also emit some radiation at these frequencies upward. This upwelling radiation from the top of the rain layer is primarily what reaches the satellite, but not before it gets scattered and absorbed above the freezing level by precipitation-sized ice particles like hail and graupel. Graupel pellets are softer ice pellets that form when supercooled water droplets freeze onto a snow crystal. Higher up in the storm, tiny ice crystals in cirrus clouds are virtually transparent to 85-91-GHz radiation. It gets transmitted right through the tiny ice crystals, which is why we can’t see cirrus clouds on 85-91 GHz images. But, in a tall thunderstorm where there’s lots of attenuation from the large ice particles, there’s not much of a signal left to reach the satellite. The weak signal that reaches the satellite correlates to very low brightness temperatures.
So, let’s apply this to an actual 85-91 GHz image. This one happened to utilize 89 GHz radiation. In this case, the polar-orbiting satellite wasn’t able to scan the entire storm, but it did capture the core of the storm. The yellows and reds surrounding the center of the storm represent brightness temperatures less than 228 Kelvin, and in some areas even less than 200 Kelvin – that’s less than -73 degrees Celsius! The brightness temperatures are so low because these are areas of deep convection in the eyewall – the upwelling 89-GHz radiation from beneath the melting level was largely attenuated by the large precipitation-sized ice particles high in the deep convective clouds. Not much radiation ultimately reached the satellite in those areas, yielding low brightness temperatures. Contrast that with the brightness temperatures in the eye, which were much higher – up near 280 Kelvin. The brightness temperatures in the eye were higher because the path of the 89-GHz radiation was much less impeded on its way to the satellite, allowing more radiation to reach the satellite. Upwelling radiation from the ocean or from the tops of low clouds had a pretty clear shot to the satellite from there, since there were no deep convective clouds with large ice particles in them over the eye, so there was minimal attenuation at higher altitudes, and 89 GHz radiation gets transmitted right through cirrus clouds, rendering them basically invisible.
The bottom line from the video is that when we see very low brightness temperatures on 85-91-GHz imagery, we're really seeing the signature of deep convection (characterized by the areas where emissions of 85-91-GHz radiation have been weakened the most by large ice particles like hail and graupel high up in convective clouds). For practical purposes, this trait of 85-91-GHz imagery:
- allows forecasters to see the eye of a hurricane that's shrouded by high clouds
- allows forecasters to assess the structure of hurricanes over remote seas by revealing the patterns of deep, moist convection in the storm's eye wall and outer rain bands
For the record, a few "twists" on 85-91-GHz images actually exist. Scientists have made some tweaks to the basic product in order to make it more useful. If you're interested in reading about these "twists," check out the upcoming Explore Further page.
One of the major limitations of 85-91-GHz imagery is that the satellites housing the sensors that create the imagery do not provide constant, universal coverage (as hinted at by the fact that the 89-GHz image above only includes a partial scan of Bavi). In fact, one of the several satellites equipped with a microwave sensor passes over a tropical cyclone, on average, every 2-3 hours (though time lags can be shorter or longer). So, there can be long gaps between data for any tropical cyclone. Researchers at the University of Wisconsin devised a creative technique, called MIMIC (Morphed Integrated Microwave Imagery at CIMSS), to fill in the time gaps with morphed 85-91-GHz images, and it can be very helpful for assessing changes to the structure of a tropical cyclone's core structure (and thus, its intensity). For example, check out this MIMIC loop of Hurricane Helene (opens in a new window) as it made landfall in Florida in September, 2024. The loop really shows the breakdown of Helene's eye wall (the partial ring of yellows and oranges) after landfall. If you really enjoy following tropical cyclones in real-time, I highly recommend keeping an eye on the recent MIMIC loops posted on the CIMSS site (opens in a new window).
36-37-GHz Imagery
While 85-91-GHz imagery is useful for identifying areas of deep convection within a tropical cyclone, it's not particularly useful at looking at the low-altitude structure of a storm because of the impacts that the large ice particles above the freezing level have on upwelling 85-91-GHz radiation. To get a better view of the low-level structure of a tropical cyclone, forecasters turn to imagery based on 36-37-GHz radiation, which works much like 85-91-GHz imagery, with one key difference. The 36-37-GHz radiation that upwells from the top of the "rain layer" is not scattered and absorbed by large ice particles or tiny ice crystals above the freezing level (here's a visual schematic outlining the process (opens in a new window)).
As a result, brightness temperatures are higher because the passive microwave sensor aboard the satellite detects a relatively large portion of the upwelling 36-37-GHz radiation from its source -- raindrops below the freezing level. And, because the majority of the radiation from lower altitudes reaches the satellite, 36-37-GHz imagery gives forecasters a better sense of the overall low-level structure of tropical cyclones. For example, we can see the low-level structure of Bavi from this corresponding 37-GHz image (opens in a new window). You might have noticed that the signature of Bavi's eye was smaller on the 37-GHz image than it was on the 89-GHz image shown earlier. The ability of 36-37-GHz imagery to detect low-level structure also makes it a better choice than 85-91-GHz imagery for pinpointing a tropical cyclone's center. For a more in-depth explanation of this advantage of 36-37-GHz imagery, check out upcoming Explore Further page.
Before moving on, however, I want to point out that forecasters can use 36-37-GHz imagery in tandem with 85-91-GHz imagery to assess the vertical structure of tropical cyclones. Since 36-37-GHz imagery gives a better look at the low-level structure, and 85-91-GHz imagery gives a better look at the high-level structure, forecasters can compare the locations of the low-altitude center and high-altitude center to see if the center of the storm tilts with increasing height. If the center notably tilts with height, that's often a sign that the storm isn't healthy and may be hindered by strong vertical wind shear.
Quantitative Precipitation Estimates
While 85-91-GHz and 36-37-GHz imagery do a good job of showing us the overall precipitation structure of a tropical cyclone (by highlighting deep convection and the details of the low-level rain layer, respectively), they don't quantitatively indicate rainfall rates or totals. Remote sensing from satellites can help with that, too, as suggested by the rainfall estimates (below) from Hurricane Harvey as it approached and made landfall in Texas in late August, 2017.
How can microwave sensors be used to attain quantitative precipitation rates and totals? The key lies in a multispectral approach. Imagery generated using a single frequency between 85-91-GHz or 36-37-GHz can't display precipitation rates, but imagery developed from multiple frequencies can. The data in the image come from NASA's Integrated Multi-Satellite Retrievals for GPM (IMERG) product. The "GPM" in the name stands for the Global Precipitation Measurement mission, which consists of several satellites with microwave sensors. The workhorse for providing quantitative precipitation data, however, is the core GPM satellite itself, which provides nearly global coverage (though not simultaneously since it's a polar-orbiting satellite) and houses two key instruments--the GPM Microwave Imager (GMI) and a Dual-frequency Precipitation Radar (DPR). If you're interested in learning more details about these key instruments aboard the GPM satellite, feel free to read more (DPR overview (opens in a new window); GMI overview (opens in a new window)), but I'll summarize the main points.
GMI is a passive microwave sensor, carefully measuring weak microwave energy naturally emitted by the Earth and the atmosphere and using it to infer rainfall rates. What makes the GMI different from 85-91-GHz imagery and 36-37-GHz imagery (which do not quantitatively estimate precipitation) is its use of multiple frequencies. Meanwhile, DPR is an active microwave sensor, which transmits pulses of microwave radiation and waits for return signals, much like a ground-based radar. DPR also uses multiple frequencies (within the "Ka-Band" at 35.5 GHz and the "Ku-Band" at 13.6 GHz), and is capable of depicting the surface rain rate as well as the three-dimensional precipitation structure of storms. DPR also provides higher-resolution data than GMI; however, the tradeoff is that its scanning swaths are narrower, so DPR is more likely to only partially scan any individual storm on a single pass, as illustrated by the schematic below.
So, GMI offers coarser information about surface precipitation over a larger area, while DPR offers more detailed, three-dimensional information over a smaller area. DPR is also more sensitive to areas of light rain and snow, and its sensitivity allows it to help calibrate the passive microwave data that go into IMERG precipitation analyses (which include passive microwave data and even rain gauge data, if available). Ultimately, the passive microwave imagery that go into IMERG precipitation estimates provide perhaps the best look at where it's precipitating (and how hard) across much of the globe where ground-based radar imagery is lacking. The 7-day loop of IMERG estimates below gives a spectacular example. Can you pick out the signatures of precipitation from mid-latitude cyclones tracking across the Southern Hemisphere and the typhoon approaching China?
Global 7-Day Precipitation Rate (0:13)
Transcript: Global 7-Day Precipitation Rate (0:13)
Video has no audio. It shows a loop of IMERG 7-day precipitation rate data across most of the globe. Some key features are mid-latitude cyclones swirling from west-to-east through the southern hemisphere and a typhoon approaching China in the western Pacific. All are marked by areas of locally high precipitation rates, in yellows, oranges, and reds.
Now that you're familiar with satellite-based qualitative and quantitative looks at precipitation within tropical cyclones, I recommend checking your knowledge in the Quiz Yourself section below. You also might be wondering where you can access all of this data. For more on data resources and some of the products available, check out the Explore Further page that follows. Otherwise, we'll stick with the theme of remote sensing using microwaves and explore microwave sounders (a sounder provides a vertical profile of a meteorological variable) used in tropical forecasting.
Read on.
Quiz Yourself...
Check your basic knowledge of the microwave remote sensors and products covered in this section: