Climate observing network design
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Transcript of Climate observing network design
Climate Observing Network Design
Phil Mote, Karin Bumbaco, Guillaume Mauger, &Greg Hakim
University of Washington
Phil Mote, Karin Bumbaco, Guillaume Mauger, & Greg Hakim Climate Observing Network Design
Observing Network Design
Many networks grow “organically” (e.g. ASOS)Others are designed before implementation (e.g. CRN)
Objective network design
Given a performance measure & constraints, find sites.New sites are conditional on previous.Can design networks with a suite of metrics.Optimal if linear & Gaussian.
Phil Mote, Karin Bumbaco, Guillaume Mauger, & Greg Hakim Climate Observing Network Design
Climate Reference Network Performance
CRN explained precipitation varianceMM5 4 km monthly precipitation.Regress precip onto 11 CRN stations.Map: percentage variance explained by the regression.
Phil Mote, Karin Bumbaco, Guillaume Mauger, & Greg Hakim Climate Observing Network Design
Results for Monthly Precipitation
Metric: area averaged precipitation
Five stations explain 95% of variance.Stations are located in the mountains.Distribution is not intuitive.
Phil Mote, Karin Bumbaco, Guillaume Mauger, & Greg Hakim Climate Observing Network Design
Summary
Climate Network Design
Objectively site observationsMaximize available resources (or reduce costs)Can rapidly evaluate metricsCan incorporate other constraints
Federal landCement trucks
Phil Mote, Karin Bumbaco, Guillaume Mauger, & Greg Hakim Climate Observing Network Design
Theory
Metric = J, with sample vector J; state = x with sample X
σ2 =1
N − 1δJδJT . (1)
Leading-order Taylor approximation:
δJ =
[∂J∂x
]T
δX, (2)
δσ2i =
[∂J∂x
]T
(Bi−1 − Bi)
[∂J∂x
](3)
Kalman filter:Bi−1 − Bi = KHB (4)
Phil Mote, Karin Bumbaco, Guillaume Mauger, & Greg Hakim Climate Observing Network Design