Statistical Analysis of Massive Data Streams Proceedings of a Workshop (2004) / Chapter Skim
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Pages 29-41
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Statistical Challenges in the Production and Analysis of Remote Sensing Earth Science Data at the Jet Propulsion Laboratory Amy Bravennan and Ralph Kahn Jet Propulsion Laboratoly California Insulate of Technology Mail Stop 169-237 4800 Oak Grove Dnve Pasadena, CA 91109-8099 Amy.Braverman~jpl.nasa.gov ASTER visible/N/R image of Washington.
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~ just want to kind of go through some observations ~ have made in my five years of various experiences working at JPL. ~ should tell you that T work in the Earth and Space Sciences Division, which is a little bit unusual.
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studying the feedbacks that are involved. The upper graphic there is kind of a little bit of an illustration of why clouds are important in this system, and it is because all the energy the Earth gets is from the Sun.
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So, MISR looks down at the Earth at nine angles and four wavelengths and has a swath width of about 300 kilometers or so. The Terra satellite is in a polar orbit.
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On the other instruments onboard are MODIS, which has a much wilier swath width and, therefore, gets a Tot more coverage. MISR, with its skinny little swath width, gets global coverage about every nine days.
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At the MISR science team meeting this week, we had the people from the data distribution center come and talk. They told us that we now have 33 terabytes -- 33 terabytes is ~ ~ percent of what we have in storage now for MISR, and that is just one instrument on one platform.
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Declare truth to be conditions producing the best match. ., The previous speaker alluded to doing retrievals and doing forward models.
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~ will just plug my own thing here and something ~ have worked with Ed Wegman a little bit about. What ~ propose to do for both MTSR and AIRS is to create a Level 3 product that puts what T call a quantized data product.
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Data milling and analysis of massive data sets: techniques that allow us to find important or unusual patterns and relationships we don't already know about. Data fusion: statistically sound techniques for combining data from different sources (different orbiting instruments, data acquired on the ground and in field experiments)
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We need to be able to combine information from ground sources with the satellite data that we get in order to validate it. So, if anybody has any good ideas about that, or would like to work on that, we would be very happy to have you help us.
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A second thing that we are heavily engaged in is collaborating with the machine learning systems people at JPL to do data mining, particularly active learning methods and tools. We got some internal money from NASA to pursue this, and we are looking forward to eventually being able to show it at the ISM interface meeting.
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In fact, they suggested us having a committee and, they don't have any money, but if we could somehow find a way to come up with some money to send young researchers and students to the AGU to show their work, that would really be great, because that is really how we are going to inject ourselves into that community. Also, ~ would like to see geoscience get a little bit higher profile at ISM.
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