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