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UID:20251201T035506EST-0775PNDV8E@132.216.98.100
DTSTAMP:20251201T085506Z
DESCRIPTION:Please join us as we welcome Dr. Paul Roebber\, a Distinguished
  Professor\, College of Letters and Science in the Department of Mathemati
 cal Sciences at the University of Wisconsin-Milwaukee\, for a seminar titl
 ed 'The Application of Data Analytics to Atmospheric Science'. Coffee will
  be served.\nThe Application of Data Analytics to Atmospheric Science\nEve
 n my 87-year old mother has heard of “Big Data” and “Data Analytics.” But 
 stripping away the marketing noise\, what is it\, really\, and why should 
 physical scientists such as us care about it? The ability to leverage data
  to improve understanding has always been important\, but is becoming incr
 easingly so as data becomes more readily available and the need increases 
 to extract some measure of value from its rising volume. Data analytics pr
 ovides the methodology. The requirements for a practitioner in this field 
 are application-oriented math and statistics knowledge allied with substan
 tive domain expertise. Since the software tools needed to perform the nece
 ssary analyses are not mature\, and often must be custom-designed\, progra
 mming skills are also important. \n\nMultiple linear regression (MLR) has 
 seen wide use in economics and affiliated fields\, as it is a useful techn
 ique for assessing the relationships between variables and thereby develop
 ing understanding from data. MLR represents an early\, simple application 
 of data analytics to weather prediction in the form of Model Output Statis
 tics (MOS)\, which seeks to map numerical weather prediction model output 
 to observations. More sophisticated techniques\, like artificial neural ne
 tworks (ANN)\, including its extension to Deep Learning\, or various machi
 ne-learning approaches such as Evolutionary Programming\, are now gaining 
 currency in many fields\, and have excellent potential for use in atmosphe
 ric sciences. \n\nA straightforward example of an atmospheric science ques
 tion that can be answered with data analytics is “Can we forecast daily pe
 ak electricity load given available atmospheric inputs?”  Rather than buil
 d a comprehensive\, numerical model that encompasses both the meteorology 
 and the built-environment energy usage that results\, using data analytics
 \, we would start by collecting relevant data and building a data model us
 ing MLR or and ANN. Given the curse of dimensionality\, which requires an 
 exponential increase in the length of time-series data as the number of va
 riables considered increases\, we would need to know something about energ
 y usage to guide our choice of data to collect. The built data model would
  confirm that the most predictive variable by far is temperature\, and in 
 the warm season\, apparent temperature (the combination of temperature and
  humidity)\, but that other information such as time-of-day\, wind speed a
 nd direction\, cloud cover\, and snow on the ground are also relevant in s
 ome situations\, and likewise\, that changing energy usage patterns over t
 ime need to be accounted for in the analysis. \n\nA question of interest t
 o a fan of American football might be “What is the contribution of penalty
  calls to NFL home field advantage?” Rather than simply argue about it ove
 r a beer\, data analytics can provide an answer. One would collect play-by
 -play data (available online) to build a model of the contribution of fact
 ors like position on the field\, time remaining in the game\, down-and-dis
 tance\, score\, and so on to estimate for any situation the win probabilit
 y. Using that model\, we would find that the answer to our original questi
 on is approximately 18%. Data analytics methods are similar in each exampl
 e\, but the specifics in each are guided by an understanding of the domain
  under study.\n\nIn this seminar\, I will provide specific examples in the
  meteorological domain using MLR\, multiple logistic regression\, ANN\, an
 d Evolutionary Programs. I will present some future directions I am develo
 ping\, including Deep Learning applications\, which are highly suited to t
 he ubiquitous pattern recognition problems of weather prediction and are l
 ikely to gain increasing importance in meteorology. \n\n
DTSTART:20161027T193000Z
DTEND:20161027T203000Z
LOCATION:Room 934\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue 
 Sherbrooke Ouest
SUMMARY:Seminar: Dr. Paul Roebber
URL:/meteo/channels/event/seminar-dr-paul-roebber-2625
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