From Scripps Institute New Airborne GPS Technology for Weather Conditions Takes Flight
First-time demonstration led by Scripps Institution of Oceanography captures key meteorological data from aircraft, along with the potential to improve hurricane forecasting
A new GPS system aboard airplanes tracks key atmospheric conditions.
GPS technology has broadly advanced science and society’s ability to pinpoint precise information, from driving directions to tracking ground motions during earthquakes. A new technique led by a researcher at Scripps Institution of Oceanography at UC San Diego stands to improve weather models and hurricane forecasting by detecting precise conditions in the atmosphere through a new GPS system aboard airplanes.
The first demonstration of the technique, detailed in the journal Geophysical Research Letters (GRL), is pushing the project’s leaders toward a goal of broadly implementing the technology in the near future on commercial aircraft.
Current measurement systems that use GPS satellite signals as a source to probe the atmosphere rely on GPS receivers that are fixed to ground and can’t measure over the ocean, or they rely on GPS receivers that are also on satellites that are expensive to launch and only occasionally measure in regions near storms. The new system, led by Scripps Institution of Oceanography geophysicist Jennifer Haase and her colleagues, captures detailed meteorological readings at different elevations at targeted areas of interest, such as over the Atlantic Ocean in regions where hurricanes might develop.
“This field campaign demonstrated the potential for creating an entirely new operational atmospheric observing system for precise moisture profiling from commercial aircraft,” said Haase, an associate researcher with the Cecil H. and Ida M. Green Institute of Physics and Planetary Physics (IGPP) at Scripps. “Having dense, detailed information about the vertical moisture distribution close to the storms is an important advancement, so if you put this information into a weather model it will actually have an impact and improve the forecast.”
“These are exciting results, especially given the complications involved in working from an airplane,” says Eric DeWeaver, program director in the National Science Foundation’s (NSF) Division of Atmospheric and Geospace Sciences, which funded the research. “Satellite-based measurements are now regularly used for weather forecasting and have a big impact, but airplanes can go beyond satellites in making observations that are targeted right where you want them.”
The GRL paper details a 2010 flight campaign aboard NSF aircraft and subsequent data analysis that demonstrated for the first time that atmospheric information could be captured by an airborne GPS device. The instrumentation, which the scientists labeled “GISMOS” (GNSS [Global Navigation Satellite System] Instrument System for Multistatic and Occultation Sensing), increased the number of atmospheric profiles for studying the evolution of tropical storms by more than 50 percent.
“We’re looking at how moisture evolves so when we see tropical waves moving across the Atlantic, we can learn more about which one is going to turn into a hurricane,” said Haase. “So being able to look at what happens in these events at the early stages will give us a lot longer lead time for hurricane warnings.”
“This is another case where the effective use of GPS has the potential to improve the forecast and therefore save lives,” said Richard Anthes, president emeritus of the University Corporation for Atmospheric Research, which currently runs the satellite based GPS measurements system called COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate).
While the current GISMOS design occupies a refrigerator’s worth of space, Haase and her colleagues are working to miniaturize the technology to shoe box size. From there, the system can more feasibly fit onto commercial aircraft, with hundreds of daily flights and a potential flood of new atmospheric data to greatly improve hurricane forecasting and weather models.
The technology also could improve interpretation of long-term climate models by advancing scientists’ understanding of factors such as the moisture conditions that are favorable for hurricane development.
Paytsar Muradyan, who recently received a Ph.D. from Purdue University in atmospheric sciences, started working with Haase in 2007 as a graduate student during the formative stages of GISMOS’s design and development. She eventually flew with the group in the 2010 campaign and took away a wealth of experience from the demands of the project.
“It was a lot of responsibility but certainly rewarding to work with a group of world-known scientists in an interdisciplinary project,” said Muradyan.
In addition to Haase and Muradyan, coauthors of the project include students Brian Murphy and Kuo-Nung Wang, and Professor James Garrison of Purdue University; F. Felipe Nievinski of Universidade Estadual Paulista, Presidente Prudente (Brazil); and Professor Kristine Larson of the University of Colorado, Boulder.
Funding for the project was provided by NSF, NASA, the Ross Fellowship, the Schlumberger Faculty for the Future Fellowship, the Capes/Fulbright Graduate Student Fellowship, and a NASA Earth System Science Research Fellowship.
will be issues with mtx crews and calibrations.
conceptually probably not a bad idea though.
My only concern is that trans-oceanic tracks are typically moved away from big weather events if at all possible, so that you may not get the information that is of most interest when you could really use it. Airliners are not hurricane hunters; they are hurricane avoiders; the same goes for avoiding storms in the ITCZ when flying N-S or S-N.
To further elaborate, moving the tracks to avoid weather could mean you end up with favorable selection of samples, because, if at all possible, the aircraft tracks will be located in fair winds over following seas, instead of flying into the teeth of a storm. Thus over time, all the samples in a given are will be more likely than not to be representative only of good weather in the area, but not provide the overall picture, including stormy weather.
Why do gremlins come to mind? ;-(
Ya think?
A system regularly transmitting GPS locations would also provide yet another way of tracking of Malaysian Airlines planes. It would be particularly useful if mounted in a place where the pilots can’t turn it off
The article is here:
http://onlinelibrary.wiley.com/doi/10.1002/2013GL058681/full
It discusses how doppler readings are used to extract the refractive index of the air along the ray path. This is the same technique that has been used to measure the atmospheres of other planets as spacecraft fly behind them, just applied at Earth, and with a dozen satellites overhead. a measurement is obtained each time a sat rises and sets (1 to 2 per hour), getting a humidity measurement along each line of sight.
Ric – Akamai is a site which maximizes a response speed on the Net by caching the original web page in a server near your location. .gov sites are free to use akamai services.
The paper seems to indicate that GPS signals themselves are used to measure meteorological data. Not impossible in itself; let’s say the GPS device gets signal from 7 satellites in different directions. The problem is that each signal is distorted by – let’s say we are interested in water vapor – an overall water vapor along this signal’s path. To deduce a vertical distribution of water vapor from these 7 signals seems next to impossible to me. It would be nice to see error margins.
Ya think?
Well, OK, maybe that was a bit over the top, but they should be aware of all of those things:
http://en.wikipedia.org/wiki/Geodesic_grid
Note scaling in figures — note also that this tiling has been in use since at least 1968 for geophysical problems. Yet it is my belief that basically all of the GCMs use lat-long spherical polar tiling with completely asymmetric tile shapes and areas. I quote from the article (referring to the universally used lat-long grid):
“However, such a pattern does not conform to many of the main criteria for a statistically valid discrete global grid,[2] primarily that the cells’ area and shape are not generally similar; this is especially noticeable as the cells are developed towards the poles.”
As you say, “Ya think?”
Then there is Kriging:
http://en.wikipedia.org/wiki/Kriging
Note well two things from this article. First of all, note the enormous uncertainty of the interpolation in one dimension with a moderately sparse grid. This uncertainty strictly depends on assumptions made about the underlying process being fit — again I quote: “Under suitable assumptions on the priors, kriging gives the best linear unbiased prediction of the intermediate values.” That’s sort of like saying “if the process being interpolated is nonlinear, or if the prior assumptions one makes are unsuitable, kriging will not give you the best unbiased prediction of the intermediate values”. Second, note from the figure itself note that the specific place where the interpolation is quite different from other e.g. spline interpolations is in interpolated segments where an extremum (probably) occurs. Here’s where those prior assumptions come into play — cubic splines are well-known to be very good for certain classes of problem, and still other interpolating polynomials can be good for still other classes of problem.
It seems reasonably likely that kriging underestimates extrema, possibly systematically, and it is virtually certain that it leads to very large errors across the interpolated regions. One is then left with nothing but a “fond hope” that those errors are symmetric and on average cancel, as opposed to systematic so that they don’t.
Finally, there is a well-known connection between interpolating functions and quadrature (numerical integration):
http://en.wikipedia.org/wiki/Numerical_integration#Quadrature_rules_based_on_interpolating_functions
The idea is that one numerically interpolates a set of data points with some smooth function (e.g. piecewise polynomial) according to some criterion, and then integrate the interpolating function one constructs to estimate the integral over some sampled domain. Note well that error estimation is serious business and is based on all sorts of assumptions that any numerical/computational scientist knows can easily be defeated by nothing more than bad luck for any given non-adaptive grid. This is why using adaptive methods are absolutely key.
Note also the comments in the article about multidimensional integrals and the curse of dimensionality. Note the solutions — Monte Carlo (which I mention above) or some very sophisticated methods one can implement for sparse, non-uniform grids on hypersurfaces or in hypervolumes that may or may not be flat. Again, errors are enormously difficult to control for sparse, non-uniform, non-random grids on curved surfaces, and most oversimplified forms of linear interpolation quadrature are going to really suck.
Since estimating global surface temperature is precisely taking an irregular, sparse, non-uniform sampling of points locally averaged in 5×5 degree lat-long surface cells, kriging to interpolate, integrating the interpolation over the area, and then dividing out the total “spherical” area, all of these remarks are entirely apropos, and IMO are serious criticisms of the GCMs right out of the box(es).
I could, of course, be wrong — maybe there are GCMs built on rescalable icosahedral grids — but none of the ones I’ve looked at so far were, and a lot of them (if not all of them) seem to share “DNA” at this level — use common lat-long input data, use a similar gridding, etc.
rgb
Oops, didn’t close the blockquote properly. The equation shows that the refractive index is mostly sensitive to pressure over temperature, less than 10% as much to humidity over temp, and also to humidity over temp^2. I’m not sure how they back out “e” from the “n” measurement with P & T unknown.
spdrdr says:March 18, 2014 at 8:56 pm “How much would it cost … to install a proper GPS locator for all commercial aircraft, which could NOT be disabled by a person or persons unknown? ”
Apparently not much considering that trucking companies and DPW’s already install them in semi’s and snow plows. (However… I believe those rely on ground cell service to rely the GPS info to the home office so airplanes would have to use something like Airfone over the USA unless we start allowing cell phones from airplanes.)
But your point brings up an interesting dovetail to GPS weather reporting from airplanes in that it could easily be incorporated into long-haul semi tractor-trailer trucks right now. There are a lot more trucks, distributed over a lot more area than there are airliners resulting in finer resolution of barometric pressure and temperature, (well.. temperature on asphalt but consistently so for 99%).
Plus, with so many trucks going in and out of cities every day, imagine how it could refine the issue of UHI magnitude?
Doug Jones gave the URL for the article. If you read that, or if you glance at the illustration at the top of this post, you will see that the GPS satellites used by this method are the ones that are extremely low to the horizon. These satellites are not very useful in navigation because the path of the signals passes through so much of the atmosphere, causing timing errors. An airliner flying at altitude, above the weather, has plenty of GPS satellites with clean signals to establish a position. The border-line GPS satellites near the horizon can then be analyzed and their errors turned into information about the atmosphere along that signal path which grazes the earth, far from the actual position of the plane. Think of it as a kind of over the horizon “radar”.
great idea. SWA is doing soundings out of Love Field here in Dallas now for the local NWS office.