From M.I.T. and the “Total outgoing radiation is actually one of the biggest uncertainties in climate change” department:
Batches of shoebox-sized satellites could improve estimates of Earth’s reflected energy
The researchers, led by Sreeja Nag, a former graduate student in MIT’s Department of Aeronautics and Astronautics (AeroAstro), simulated the performance of a single large, orbiting satellite with nine sensors, compared with a cluster of three to eight small, single-sensor satellites flying together around the Earth. In particular, the team looked at how each satellite formation measures albedo, or the amount of light reflected from the Earth — an indication of how much heat the planet reflects.
The team found that clusters of four or more small satellites were able to look at a single location on Earth from multiple angles, and measure that location’s total reflectance with an error that is half that of single satellites in operation today. Nag says such a correction in estimation error could significantly improve scientists’ climate projections.
“Total outgoing radiation is actually one of the biggest uncertainties in climate change, because it is a complex function of where on Earth you are, what season it is, what time of day it is, and it’s very difficult to ascertain how much heat leaves the Earth,” Nag says. “If we can estimate the reflectance of different surface types, globally, frequently, and more accurately, which a cluster of satellites would let you do, then at least you’ve solved one part of the climate puzzle.”
Nag, who is now a research engineer at the Bay Area Environmental Research Institute, NASA Ames Research Center, and NASA Goddard Space Flight Center, has co-authored the paper with Oli de Weck, an AeroAstro professor at MIT; Charles Gatebe of NASA Goddard Space Flight Center; and David Miller, NASA Chief Technologist and the Jerome C. Hunsaker Professor in AeroAstro.
A 3-D view
Nag says that to accurately estimate the reflectance of any ground spot on Earth requires measurements taken of that spot from multiple angles at the same time.
“The Earth does not reflect equally in all directions,” Nag says. “If you don’t get these multiple angles, you might under- or overestimate how much it’s reflecting, if you have to extrapolate from just one direction.”
Today, satellites that measure the Earth’s albedo typically do so with multiple cameras, arranged on a single satellite. For example, NASA’s Multi-angle Imaging SpectroRadiometer (MISR) instrument on the Terra satellite houses nine cameras that take images of the Earth from a fan-like arrangement of angles. Nag says the drawback of this design is that the cameras have a limited view, as they are not designed to change angles and can only observe within a single plane.
Instead, the team proposes a cluster of small satellites that travel around the Earth in a loose formation, close enough to each other to be able to image the same spot on the ground from their various vantage points. Each satellite can move within the formation, taking pictures of the same spot at the same time from different angles.
“Over time, the cluster would cover the whole Earth, and you’d have a multiangular, 3-D view of the entire planet from space, which has not been done before with multiple satellites,” Nag says. “Moreover, we can use multiple clusters for more frequent coverage of the Earth.”
Estimating error
Nag and her colleagues simulated formations of three to eight small, orbiting satellites, and developed an algorithm to direct each satellite to point to the same ground spot simultaneously, regardless of its position in space. They programmed each formation to measure a theoretical quantity known as bidirectional reflectance distribution function, or BRDF, that is used to calculate albedo and total outgoing radiation, based on the angles at which measurements are taken and the angle of the sun’s incoming rays.
For each formation, Nag calculated the satellites’ error in measuring BRDF and compared these errors with those of the MISR instrument on the Terra satellite. She validated all errors against data from the NASA Goddard’s Cloud Absorption Radiometer, an airborne instrument that obtains tens of thousands of angular measurements of a ground spot. She found that every formation with seven or more single-sensor satellites performed better than the nine-sensor monolith satellite, with lower estimation errors. The best three-satellite clusters generated half the error of MISR’s estimates of albedo. The accuracy of overall estimates improved with the number of satellites in the cluster.
“We found that even if you can’t maintain your satellites perfectly, the worst-case error is less than what the single satellite is able to do,” Nag says. “For the best-case scenario, if you are more than halving the error that you currently get, you’re halving the amount of error you would get in reflected heat leaving the Earth. That’s really important for climate change.”
“This work is significant not only for demonstrating the capability for instantaneous multiangular BRDF measurements from space for different land surface types and biomes, but also for establishing a strong methodological bridge between the systems engineering of future small satellite clusters and high fidelity Earth science simulations,” de Weck says. “Our team fully expects that a demonstration mission of this type could be flown within the next decade.”
While multisatellite formation flights have been deemed expensive endeavors, Nag says this assumption mostly pertains to satellites that need to maintain very strict formations, with centimeter-level accuracy — a precision that requires expensive control systems. The satellites she proposes would not have to keep to any single formation as long as they all point to the same location.
There’s another big advantage to monitoring the Earth with small satellites: less risk.
“You can launch three of these guys and start operating, and then put three more up in space later — your performance would improve with more satellites,” Nag says. “If you lose one or two satellites, you don’t lose the whole measurement system — you have graceful degradation. If you lose the monolith, you lose everything.”
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Trust us, we’re “Scientists”. Nothing can go wrong, (click) can go wrong (click) can go wrong………
I find science amazing…
When you can’t measure something accurately….but can accurately know the range of error
What price accuracy?
I could envision swarms of pico satellites, about 1 cc, which are configured with solar arrays and a small nano battery as well as a solar sail and nano actuators. They would communicate sensor data to a local mother and on down the line to the home office.Several million with various sensors dispersed in various orbits. Also throw in a self destruct atomizer if they need to be removed for transit…
Did I miss anything?
The article is completely spot on. I’m totally baffled at “why didn’t anyone think of it before”.
The earth reflectance is indeed irrelevant if it’s measured directly below the satellite. The only decent approximation would be to measure the point that reflects the sunlight on the surface of earth, but the reflectance function is outgoing through the whole hemisphere from any point of earth’s surface. About halving the error, well, it depends where the satellites are.
How much will this thing cost? And is it worth that price? Perhaps the money could be spent better on Earth or perhaps a permanent Moon base. As others have said, if they don’t like the data, then it will be ignored anyway.
Cube sats are cheap. Many hobbists have launched them. hmm I think over 130 hobbists got rides, many failed, but they got a ride.
Hey, at least they are talking about actually measuring something instead of just modeling it. This is a step in the right direction! And since they would be in low altitude orbits, they would not be up there a long time. Probably just a few years before they reentered. So they don’t have to be designed to last a long time, are small, so they are (relatively) cheap to build. Launch costs are lower because of the light weight and low altitude orbit. And with players like SpaceX pushing launch costs down, this may actually be a good idea.
all measurement includes modelling.
I’m a nobody who had read about these matters for about a decade, and there is this idea that has at times rattles around in my ignorant skull . .
Water molecules at the edge of space, being “aligned” into sheet-like thin layers, sometimes, by magnetic fields, which creates a slight “sheen”, sometimes, deflecting some light in some places . .
So, please abuse me of my affliction if it’s silly, experts ; )
So what? Why do we need to know something to that accuracy when it is not something in our control. We should be content with decent readings and let nature do its thing. It is hubris to think that we have control of the climate. What egotists we have in our power positions.
of course it is under our control.
For example. emitting c02 causes the planet to green as skeptics have argued.
Greening the planet changes albedo.
“The world’s clouds are in different places than they were 30 years ago” !!
https://www.washingtonpost.com/news/energy-environment/wp/2016/07/11/the-worlds-clouds-are-in-different-places-than-they-were-30-years-ago/?postshare=7531468269939618&tid=ss_tw
ROTFLMAO !
sattelite swarms – enhanced air pollution ->
https://www.google.at/search?ie=UTF-8&client=ms-android-samsung&source=android-browser&q=space+pollution&gfe_rd=cr&ei=ldeFV6LYB8PR8geHyJTwDQ
“You can launch three of these guys and start operating, and then put three more up in space later — your performance would improve with more satellites,” Nag says. “If you lose one or two satellites, you don’t lose the whole measurement system — you have graceful degradation.”
Oh well.
Lots of negativity here on this idea…but, in general, I’m supportive of efforts to improve empiricism in climate science. We all (mostly) agree that there’s too much computer modeling being done in lieu of actual observations, and too many untested assumptions being used without question. Plans and ideas to reduce the number of untested assumptions and gather more observations / data should be encouraged.
rip
of course there is negativity. the reason is simple. nobody here is actually interested in improved observation.
Mr. Mosher.
This is why you fail as a commenter here in my viewpoint. You just made a broad assumption based on observations of a few regulars, without knowing what the people who don’t comment think. So much your your application of the scientific method.
Mosher,
You said, “nobody here is actually interested in improved observation.”
It is strange that you would make such a statement in reply to Rip when he said, “I’m supportive of efforts to improve empiricism in climate science.” Did you not read his comment before replying?
I believe from what I read that there are others who similarly would like to see improved observations. However, I won’t attempt to speak for them.
Your statement about “nobody” is categorically false. I’m interested in improved observation because I think it is woefully inadequate. One of the problems is that many involved in the field are not the classic disinterested observer. Instead, they have already decided what the problem is and focus on finding confirmatory’ evidence.’
Before this, I think sensor at L2, looking at the ‘dark side’ of the earth, would get us a lot closer approximation.
No amount of accuracy or precision determines cause and effect. The only thing that these satellites can measure is an effect of a proposed plausible set of drivers. one of which serves as the null hypothesis. It must also be the case that both the null and proposed driving mechanism must be plausible and undergirded with sufficient research to make them plausible. Natural intrinsic oscillation has not been subjected to enough research to rule it in or rule it out. Therefore any extrinsic to natural variation “added” driver must be held suspect. Which is why I am skeptical of both solar and anthropogenic greenhouse gas drivers.
unicorns have not been ruled out.
My null is simple. Invisible unicorns cause the warming. falsify that null.
I wonder if the hight of the radiation is taken into account. The radiation from a hight of 3km have 1 thousends more surface and will then radiate a bit more energy than the ground.
It is small amounts, but the TOA difference is in the same order.
A friend of mine here on the Front Range , Jack Rudd , was one of the implementers of the space debris database in APL tracking every object over about 10cm . Had less than 10k items some time ago .
While the equation for the equilibrium temperature of a ball with an arbitrary absorption=emission spectrum , I’d be inclined to call it the Kirchhoff spectrum except several others deserve equal recognition , or even a spherical map of spectra , ie : color , heated by an arbitrary power spectrum is quite simple , measuring the
aespectrum clearly is not . This is something I would really like to see some details on how its done from some of the people spending their lives doing it .The specs of the Multi-angle Imaging SpectroRadiometer (MISR) , http://www-misr.jpl.nasa.gov/Mission/misrInstrument/ , mentioned are a quite informative overview . I find it interesting that the “Temperature of main structure: +5 degrees C” is very close to our calculated gray body orbital temperature .
BTW : my recent Hangout with the Silicon Valley Forth Interest Group demonstrating how more usable by ordinary humans 4th.CoSy is getting , and the invitation to this year’s MidSummer Party are linked at the top of http://CoSy.com .
Once again, to the studious reader of technical papers, regarding “Climate Science” … the required accuracy of measurements is grossly insufficient to support the conclusion that the Earth is retaining heat due to Mann-kind’s CO2 emissions. Trenberth and pals said the Earth was retaining about 0.9 Watts per square meter. Hansen, at first, said 0.85 Watts/m^2, and later, 0.58W/m^2. Stephens 2012 said 0.6W … Allan 2014 said 0.34 W/m^2 and 0.62 W for another time period. Apparently, the “consensus” determination is that the measure of “Global Warming” is bigger than half-a-watt, but less than a whole watt. Per meter squared, of course … or ¾W/m^2
Here, Nag et al. 2016 proposes an array of cube-sats, as opposed to the traditional method of one satellite, to measure the Earth’s albedo. Mostly, this allows for a broader capture area, since the reflectance of the Earth has serious angular differences that get missed by the traditional single-satellite sensor. Kimes and Sellers 1985 points out that most natural surfaces are not isotropic, and errors in the angular distribution of albedo can reach as high as 45%. Strugnell & Lucht 2001 quantify the absolute errors in albedo retrieval in the visible light spectrum, are approximately ±1.5% and absolute errors, in the NIR, and total shortwave, are ±3%.
A 3% error in albedo translates to about 4W/m^2; a 1.5% error is about 2W/m^2, according to Nag 2016. Compare that to the ¾W/m^2 magnitude of “Global Warming”.
”An albedo error of 0.001 [0.1%] translates to 1.36 W/m^2 in Earth’s outgoing radiation error.”
An albedo error of 0.00055 (0.055%) translates to 0.748W/m^2, or ¾W/m^2 according to Nag 2016
Just one parameter – the Earth’s albedo – as a “budget item” in the Earth’s heat balance – has intrinsic errors in the measurement, that dwarf the size of the supposed “budget surplus”. Each and every other parameter in this “budget” has similar errors.
Quotes from some references:
Nag 2016: ”Three satellites, in some specific formations, show average albedo errors of less than 2% with respect to airborne, ground data; and seven satellites in any slotted formation outperform the monolithic error of 3.6%. In fact, the maximum possible albedo error, purely based on angular sampling, of 12% for monoliths, is outperformed by a five-satellite formation in any slotted arrangement and an eight satellite formation can bring that error down four fold to 3%.”
Nag, Sreeja, et al. 2016 “Effect of satellite formations and imaging modes on global albedo estimation.” Acts Astronautica
http://www.sciencedirect.com/science/article/pii/S0094576516303149
Strugnell & Lucht 2001: ”Early investigators proposed various methods to estimate albedo over large areas from aerial photography, airborne scanners, and satellite system [see Starks et al. (1991) and references therein] but with the exception of Kriebel (1979) these methods assumed a Lambertian terrestrial surface, a necessity due to the nadir-viewing sensors used by the investigators. This assumption is still made where albedo measurements are required, at such a fine scale, that only nadir data are available, for example, when investigating the urban heat island effect (Soler and Ruiz 1994). Most natural surfaces, however, are anisotropic diffusers of incident radiation and the Lambertian assumption can result in errors of up to 45% in the calculation of albedo (Kimes and Sellers 1985). In order to accurately retrieve albedo from remotely sensed measurements, the directional nature of the reflected radiation needs to be taken into account.”
”…estimated [bidirectional reflectance distribution function] is integrated to produce the albedo.”
”…if we are able to accurately quantify the surface BRDF, [the bidirectional reflectance distribution function,] we can calculate albedo at any solar zenith angle, and under any skylight conditions. This … allows us to predict surface albedo under cloud cover, which is not currently possible using albedo measurements from remote sensing … BRDF is not directly measurable, …”
”Expected relative retrieval accuracies for albedo are between 1.9% and 11.4% (ignoring errors in atmospheric corrections) depending on scene cloudiness, and whether one, or both instruments are used (Lucht 1998).”
”… a land cover class such as, boreal needleleaf forest, will have a [ bidirectional reflectance distribution function] that varies from one location to another … the assumption of similarity made for members of a [ bidirectional reflectance distribution function] family may no longer be fulfilled as the underlying radiation scattering mechanisms change.”
”…the use of the multiplicative factor dramatically improves the albedo retrieval, compared to using the archetypal BRDF for all pixels. For the needleleaf tree landcover type we used a PARABOLA Jack Pine BRDF as the archetype and PARABOLA Black Spruce as the test BRF dataset. The differences in albedos of the two datasets were 17.295% in the red and 19.714% in the near-infrared (NIR). By using the a factor to constrain the archetypal BRDF to the observed test BRFs we reduced this error to 12.534% in the red and 0.357% in the NIR. For dense grass we used a PARABOLA prairie BRDF (Deering and Leone 1990) as the archetype and a PARABOLA mixed grass test dataset. Errors were reduced from 49.431% to 12.020% in the red and from 61.077% to 0.546% in the NIR. The two datasets have similarly shaped BRDFs in the NIR that accounts for the small error. In the visible, however, the two BRDFs appear quite different, which accounts for the larger error. It should be noted that as albedos are usually small in the visible, even quite large relative errors result in only small changes in the surface albedo and hence the radiation budget.”
”To investigate the robustness of the algorithm, we took the archetypal BRDFs in Table 2, and varied the three weights parameterizing the BRDF by up to ±10%, mimicking the variation we might expect in a given BRDF class. We then compared the white-sky albedo of the new BRDFs with those of the archetypal BRDFs. The errors vary according to BRDF class, with the largest error seen in the visible band of the urban BRDF (class 25). When all three weights were reduced by 10% in this class, the archetypal albedo overestimated the real albedo by 30.4%; however, the constrained inversion albedo only overestimate the real albedo by 8.4%. The class 25 archetype is based on poorly sampled AVHRR data (the only urban AVHRR dataset available at the time of writing). A new urban BRDF dataset from Meister et al. (Meister et al. 1999) will be used in future work. The mean error across all classes using the archetypal albedo, to estimate real albedos, is 6.61% in the visible and 6.44% in the NIR. Using the constrained inversion errors are reduced to 0.53% in the visible and 0.48% in the NIR.”
”The errors in the visible albedo varied from 15% to 45% with a mean of 25%. To put this into perspective, a typical visible albedo of 0.05 would have an error of ±0.0125. Mean errors in the NIR and total shortwave were much lower, 11% and 10%, respectively.”
”These are, again, relative errors, and we emphasize that, as visible albedos are generally low (≪0.1), absolute errors, in the visible, are of the order of ±0.015 [1.5%]. Typical absolute errors, in the NIR, and total shortwave, are ±0.03 [3%].”
A 3% error in albedo translates to about 4W/m^2; a 1.5% error is about 2W/m^2, according to Nag 2016
Strugnell, Nicholas & Wolfgang Lucht 2001. “An algorithm to infer continental-scale albedo from AVHRR data, land cover class, and field observations of typical BRDFs.” Journal of Climate
http://journals.ametsoc.org/doi/full/10.1175/1520-0442%282001%29014%3C1360%3AAATICS%3E2.0.CO%3B2