Experimental Design for Spatial and Adaptive Observations

  • Zhan-Qian Lu
  • L. Mark Berliner
  • Chris Snyder
Part of the Lecture Notes in Statistics book series (LNS, volume 144)


Global numerical weather prediction (NWP) uses large amounts of routinely-collected observations of the atmosphere. Recently, there has been substantial interest in augmenting these routine observations with specially designed additional observations. The idea is that these new observations would be chosen adaptively in an effort to target particular spatial regions (or variables) whose observation would be especially useful in reducing errors in forecasts. These observations could be used for tracking and forecasting small-scale and shortterm phenomena such as fronts and storms. Indeed, based on current estimates of the state of the atmosphere, physical reasoning, and past experience, meteorologists would want to target particular locations whose information could help to either predict the development of a storm or predict a storm’s path or track. The enterprise is made feasible with the availability of mobile observing platforms, in particular, aircraft. This general area is known in meteorology by the phrases adaptive, targeted, or supplemental observations. A recent multinational experiment, FASTEX, was a major stimulus for a surge of research along these lines. Recent references include [Jo197], [Lor98], [Pa198], and [Bis99].


Data Assimilation Numerical Weather Prediction Numerical Weather Prediction Model Statistical Experimental Design Variational Data Assimilation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York, Inc. 2000

Authors and Affiliations

  • Zhan-Qian Lu
    • 1
  • L. Mark Berliner
    • 2
  • Chris Snyder
    • 3
  1. 1.Data Analysis Product DivisionMathsoft, IncSeattleUSA
  2. 2.Ohio State UniversityColumbusUSA
  3. 3.National Center for Atmospheric ResearchBoulderUSA

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