Stunning new first images available from NASA-JAXA Global Rain and Snowfall Satellite

Remote sensing of weather just got a lot more detailed and interesting

NASA and the Japan Aerospace Exploration Agency (JAXA) have released the first images captured by their newest Earth-observing satellite, the Global Precipitation Measurement (GPM) Core Observatory, which launched into space Feb. 27.

The images show precipitation falling inside a March 10 cyclone over the northwest Pacific Ocean, approximately 1,000 miles east of Japan. The data were collected by the GPM Core Observatory’s two instruments: JAXA’s Dual-frequency Precipitation Radar (DPR), which imaged a three-dimensional cross-section of the storm; and, NASA’s GPM Microwave Imager (GMI), which observed precipitation across a broad swath.

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“It was really exciting to see this high-quality GPM data for the first time,” said GPM project scientist Gail Skofronick-Jackson at NASA’s Goddard Spaceflight Center in Greenbelt, Md. “I knew we had entered a new era in measuring precipitation from space. We now can measure global precipitation of all types, from light drizzle to heavy downpours to falling snow.”

The satellite’s capabilities are apparent in the first images of the cyclone. Cyclones such as the one imaged — an extra-tropical cyclone — occur when masses of warm air collide with masses of cold air north or south of the tropics. These storm systems can produce rain, snow, ice, high winds, and other severe weather. In these first images, the warm front ahead of the cyclone shows a broad area of precipitation — in this case, rain — with a narrower band of precipitation associated with the cold front trailing to the southwest. Snow is seen falling in the northern reaches of the storm.

The GMI instrument has 13 channels that measure natural energy radiated by Earth’s surface and also by precipitation itself. Liquid raindrops and ice particles affect the microwave energy differently, so each channel is sensitive to a different precipitation type. With the addition of four new channels, the GPM Core Observatory is the first spacecraft designed to detect light rain and snowfall from space.

In addition to seeing all types of rain, GMI’s technological advancements allow the instrument to identify rain structures as small as about 3 to 9 miles (5 to 15 kilometers) across. This higher resolution is a significant improvement over the capability of an earlier instrument flown on the Tropical Rainfall Measurement Mission in 1997.

“You can clearly see them in the GMI data because the resolution is that much better,” said Skofronick-Jackson.

The DPR instrument adds another dimension to the observations that puts the data into high relief. The radar sends signals that bounce off the raindrops and snowflakes to reveal the 3D structure of the entire storm. Like GMI, its two frequencies are sensitive to different rain and snow particle sizes. One frequency senses heavy and moderate rain. A new, second radar frequency is sensitive to lighter rainfall and snowfall.

“Both return independent measurements of the size of raindrops or snowflakes and how they are distributed within the weather system,” said DPR scientist Bob Meneghini at Goddard. “DPR allows scientists to see at what height different types of rain and snow or a mixture occur — details that show what is happening inside sometimes complicated storm systems.”

The DPR data, combined with data from GMI, also contribute to more accurate rain estimates. Scientists use the data from both instruments to calculate the rain rate, which is how much rain or snow falls to Earth. Rain rate is one of the Core Observatory’s essential measurements for understanding where water is on Earth and where it’s going.

“All this new information comes together to help us better understand how fresh water moves through Earth’s system and contributes to things like floods and droughts,” said Skofronick-Jackson.

3-d models of clouds, with cutaway showing towers of color - precipitation rates
3D view inside an extra-tropical cyclone observed off the coast of Japan, March 10, 2014, by GPM’s Dual-frequency Precipitation Radar. The vertical cross-section approx. 4.4 mi (7 km) high show rain rates: red areas indicate heavy rainfall while yellow and blue indicate less intense rainfall.Image Credit: JAXA/NASA

Earth Right Now: Your planet is changing. We're on it.
Five new NASA Earth science missions are launching in 2014 to expand our understanding of Earth’s changing climate and environment.
satellite flys over earth, recording a swath of colorized data over a cyclone
An extra-tropical cyclone seen off the coast of Japan, March 10, 2014, by the GPM Microwave Imager. The colors show the rain rate: red areas indicate heavy rainfall, while yellow and blue indicate less intense rainfall. The upper left blue areas indicate falling snow.
A satellite swath over a Pacific storm transforms gray clouds into colors
On March 10 the Core Observatory passed over an extra-tropical cyclone about 1,055 miles (1,700 km) east of Japan’s Honshu Island. Formed when a cold air mass wrapped around a warm air mass near Okinawa on March 8, it moved NE drawing cold air over Japan before weakening over the North Pacific.
Image Credit: NASA/JAXA
drawn storm clouds over a color-swatch style display of GPM data
The GMI instrument has 13 channels, each sensitive to different types of precipitation. Channels for heavy rain, mixed rain and snow, and snowfall are displayed of the extra-tropical cyclone observed March 10, off the coast of Japan. Multiple channels capture the full range of precipitation.
Image Credit: NASA/JAXA
The Dual-frequency Precipitation Radar observes rainfall and snowfall that occurs within clouds in three dimensions, across the surface of Earth and upward into the atmosphere. An extra-tropical cyclone was observed over the northwest Pacific Ocean off the coast of Japan on March 10, 2014.
Image Credit: JAXA/NASA

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Every weather freak’s dream.
Does it come with an Android App? 🙂


Wow. That is amazing. My first question-will they be able to fudge the data it collects?


and of course that cyclone was directly caused by global warming/climate change/climate disruption.

Paul Westhaver

Question for the tech wonks here:
I see a 3D plot about 6 km deep.
Is it a fact that material nearest the satellite detector in high altitude does not obscure or distort the information for clouds beneath? Does moisture in the upper atmosphere impact the ability to resolve data in the lower atmosphere?


Why, really why?
Just more dissecting of the earths atmosphere, eventually leading to what will be the controlling of the earths atmosphere


Cool now the alarmists can do denial in H.D.


looks very exciting but I think I would have been more interested if they had sent up a replacement for The Glory satellite that blew up, that was a replacement for the other one that blew up.
The Glory satellite was a planned NASA satellite mission that would have collected data on the chemical, micro-physical and optical properties—and the spatial and temporal distributions—of sulfate and other aerosols, and would have collected solar irradiance data for the long-term climate record. The science focus areas served by Glory included: atmospheric composition; carbon cycle, ecosystems, and biogeochemistry; climate variability and change; and water and energy cycles.


Paul Westhaver says:
March 26, 2014 at 9:43 am
Question for the tech wonks here:
I see a 3D plot about 6 km deep.
Is it a fact that material nearest the satellite detector in high altitude does not obscure or distort the information for clouds beneath? Does moisture in the upper atmosphere impact the ability to resolve data in the lower atmosphere?
RADAR, as in all other light, has limited ability to pass through water. So, radar’s ability to “see” vertically through or horizontally through heavy precipitation is limited by the density of the precipitation.

michael hart

GMI’s technological advancements allow the instrument to identify rain structures as small as about 3 to 9 miles (5 to 15 kilometers) across.

Interesting. What is the smallest modeled rain structure that can be produced by coupled GCMs? Considerably larger, I would guess.


…also remember one can stand under a thunder storm in the Mohave Desert and not get wet. Looking vertically from above would show a high column of water, but it never reaches the ground.


Oh Goodie!
Now the Earth’s precipitation will be base line by the Global Precipitation Measurement (GPM) Core Observatory starting in March 2014. Any deviation, however small, will promptly be blamed on Catastrophic Anthropogenic Global Warming.

this of course people will say is weather and not climate but no doubt will be used to imply accuracy in climate assertion. How can people with 3d be wrong on causes of climate?

Mike Bromley the Kurd

Yep, pretty neat in itself. Once the spin is applied, it’ll become a never-ending drizzle.


Interesting. What is the smallest modeled rain structure that can be produced by coupled GCMs? Considerably larger, I would guess.
No guess. GCMs typically use a cell size of 3×3 degrees or 5×5 degrees in a spherical polar “rectilinear” grid. At the equator, the circumference is ballpark 25,000 miles, divide by 360 degrees or roughly 70 miles per degree. So equatorial cells are typically between 200 and 350 miles square. The cells get thinner as one proceeds towards the poles, so that 45 degrees north latitude they are roughly 0.7 that (that’s how big they are where I live, for example) and of course AT the poles you could step from one cell to another (one of many reasons spherical polar grids suck).
One of many, many complaints concerning GCMs is that they are incapable of resolving individual thunderstorms, which are almost invariably far, far smaller than the grid size. They aren’t terribly good at resolving weather FRONTS. You’ll note that the grid size is a poor match for covering tropical storms — an entire tropical storm (hurricane, that is) might be covered with four to nine cells. They have to replace all of the actual dynamics of a thunderstorm heat transport too small to be resolved at the grid size with the approximate transport for some sort of “average” number of thunderstorms in the cell applied to the entire cell.
Thunderstorms are in many locations the outgrowth of the daily building of convective instability in a moist atmosphere — warming damp ground creates an updraft that lofts warm wet air until it cools enough to form clouds, which then spread out and shade the ground underneath and eventually creates a strong local low pressure center with air inflowing over the moisture and creating a stable cycle of inflow, uplift, precipitating with the release of latent heat (perpetuating the uplift), and downdraft of the eventually cooler drier air elsewhere.
as always has a lovely discussion of the cycle. Interesting fact — thunderstorms have an average size of 15 miles, so you could pack 5 of them into a one degree equatorial grid. They have a lifetime on the order of 30 minutes which is the time of one (or sometimes two or three) timesteps in a typical GCM. GCMs are literally blind to thunderstorms — they are an order of magnitude too small to be seen, and last for the briefest flicker of time that a GCM can resolve at all.
This is a shame, as thunderstorms are responsible for a significant vertical transport of heat (in the super-efficient form of latent heat) straight through most of the greenhouse layer, adding a double-whammy of strong evaporative cooling and the direct, significant modulation of incoming insolation as thunderstorm clouds create a high albedo layer, high in the troposphere or even into the stratosphere, where it reflects a significant fraction of broadband incoming insolation before it even has a chance to pass through the troposphere at all. And while individual thunderstorms are perhaps an invisible flicker in a GCM cell, in aggregate thunderstorms are far from “negligible” or easily average into cell dynamics as they are collectively significant features/causes of the evolution of fronts, diurnal heating and cooling cycles, and much more.
A second problem with GCMs is that we really have no idea how to initialize them. Basically, even with the coarse gridding we have, we have at best an incredibly sparse set of measurements in actual cells (bearing in mind that each lateral cell, large as it is, is split into numerous atmospheric layers at say 1 km intervals, and may or may not include one or more ocean layers). We have a laughably small set of actual measurements to initialize almost all of the grid (while having far too many measurements, far too corrupted with UHI, in the highly populated areas that make up most of the 3D solution grid).
In this sense this new instrument is a godsend. There are already papers being published suggesting that GCMs underestimate vertical heat transport and the effective cooling efficiency of global thunderstorms by a large factor (greater than order unity). This instrument may well give us detailed, ongoing, truly global data on the distribution and dynamics of deep ocean thunderstorms all over the world include places that we are currently nearly blind to except for the very indirect, low resolution inferences one can make from IR spectra in orbit (mostly reflecting cloud tops without necessarily resolving what is going on underneath). In time, it could give us ways of accurately initializing one aspect of a multilayered global tessellation and lead us to a much better representation of cloud physics in GCMs. This in turn might make them work much better, in particular might all by itself be enough to show that total water vapor feedback is neutral to negative (as has been postulated many times) rather than positive, with the strongest negative feedback coming in the high-humidity tropics where insolation is the strongest.
Combined with matching (same cell, same time) LTT measurements and surface temperature (sea or land) measurements, we might actually be able to build a working functional measurement based picture of how surface temperature, water vapor/precipitation, and lower troposphere temperature all combine in a locally consistent process of heat absorption and transport at a granularity small enough to resolve many if not most thunderstorms. Then all we have to do is scrap spherical polar tessellation in favor of icosahedral tessellation at that resolution, rewrite the intercell physics routines to work with an icosahedral tessellation, initialize on the basis of a fine-grained simulation that matches actual stations where possible and uses the actual measured statistical moments to form a statistical interpolation where not, and wait for there to be enough computer power to integrate the (say) 10 km tessellation in 1 minute timesteps out to a century or so, a few thousand times to get a decent picture of the statistical ensemble of final states, given reasonable perturbations of the initial conditions and various (ideally measured) assumptions about the statistical distribution of volcanic events, ENSO events, and so on, presuming that the model cannot accurately come up with ENSO, the NAO, the PDO, etc on its own. In a good world, it would do these on its own and get them and the entire thermohaline and hadley cell circulation “right” for decade-plus timescales, but I suspect we won’t get there in the next thirty to fifty years if ever.
If ever is a real possibility, with the Navier-Stokes equation. It’s that damn butterfly. If we could just smash it flat so it would stop shifting the global climate by whole degrees centigrade 100 years into the future things would be ever so much simpler.



Henry Galt.

Robert. Vis-a-vis your “… AT the poles you could step from one cell to another”.
You would enjoy this, I’m certain:
Both protagonists known to yours truly. The search observed over decades and much learned during.
For everyone:
no, not that one 😉
“”His measurements produced a result of 110.46 km for one degree of latitude, which gives a corresponding terrestrial radius of 6328.9 km. The polar radius has now been measured at just over 6357 km. This was an error only 0.44% less than the modern value.””
The degree of latitude he measured, at the degree of longitude he measured it, was by chance, the prime one for determining that measure – being half way from the equator to a pole but not>/em> at the 45 degree position.


You would enjoy this, I’m certain:

Ah, but the basis for the English foot is the humble barleycorn:
I quote:
An Anglo-Saxon unit of length was the barleycorn. After 1066, 1 inch was equal to 3 barleycorn, which continued to be its legal definition for several centuries, with the barleycorn being the base unit.[10] One of the earliest such definitions is that of 1324, where the legal definition of the inch was set out in a statute of Edward II of England, defining it as “three grains of barley, dry and round, placed end to end, lengthwise”.
An English foot, my friend, is 36 barleycorns, dry and round, placed end to end, lengthwise, no more and no less.
Time to go home and hoist a glass of the fruit of the barley to King William.
So any discovery of numerological coincidence with earlier or later systems of measure are just that — evolutionary coincidence.

Henry Galt.

Neolithic metrology pre-dates that by some margin 😉
As soon as ‘an inch’ became monetized it was in someone’s interest to define it at a mundane/useful level.
Prior (roughly) to Compositio it was ‘only’ of architectural significance. Until the Romans started paying by the mile marched.
I will raise your toast in footwear.
“”This law, attributed to either Henry III or his successor Edward I, instituted a new foot that was exactly 10/11 the length of the old foot, with corresponding reductions in the size of the yard, ell, inch, and barleycorn. Miles, furlongs and rods, however, remained the same. The furlong remained an eighth of a mile, but changed from 600 old feet to 660 new feet. The rod remained the same length, but changed from 15 old feet to 161⁄2 new feet.””
Royal edict. That’ll do it all right. “We” have reduced the size of a barleycorn (and a yard, ell, and inch to boot).
A moveable feast. Until global comparisons were performed without (nationalistic) confirmation bias.
As Nosh says “You can’t give it away”.

John Coleman



Paul Westhaver says:
March 26, 2014 at 9:43 am
Question for the tech wonks here:
Research Inverse Convolutions.

george e. smith

“””””…..rgbatduke says:
March 26, 2014 at 11:12 am
Interesting. What is the smallest modeled rain structure that can be produced by coupled GCMs? Considerably larger, I would guess.
No guess. GCMs typically use a cell size of 3×3 degrees or 5×5 degrees in a spherical polar “rectilinear” grid. At the equator, the circumference is ballpark 25,000 miles, divide by 360 degrees or roughly 70 miles per degree. ….”””””
Well at one time, a nautical mile was one minute of arc , equatorially, which made the circumference, 21,600 nautical miles. Nowadays, a nautical mile is 1852 meters.


If you really dig down, you can’t sell anything you don’t own.
That is why there are surveyors, and courts to try to keep down the gunfire.
Alleys in Chicago are (generally) 16 feet, unless otherwise noted.
So you measure the entire block, less the 16 foot alley, and the remainders are split up by the number of lots (proportionally).
The old surveying marks were hacked into the sidewalks, or alley concrete, that have been replaced.
Long story short….
Don’t try to survey in the urban areas.
Unless you are willing to use occupation as evidence.