The document discusses methods for optimally designing climate observing networks. It notes that some networks grow organically while others are designed before implementation. The authors describe using objective network design which involves finding sites that maximize a performance measure given constraints. They apply this approach to the Climate Reference Network and find that five stations explain 95% of the variance in monthly precipitation for a region, with stations located in mountains. The summary provides the key details about network design methods and findings in a concise manner.

1. 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

2. 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

3. Climate Reference Network Performance
CRN explained precipitation variance
MM5 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

4. 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

5. Summary
Climate Network Design
Objectively site observations
Maximize available resources (or reduce costs)
Can rapidly evaluate metrics
Can incorporate other constraints
Federal land
Cement trucks
Phil Mote, Karin Bumbaco, Guillaume Mauger, & Greg Hakim Climate Observing Network Design

6. Theory
Metric = J, with sample vector J; state = x with sample X
1
σ2 = δJδJT . (1)
N −1
Leading-order Taylor approximation:
T
∂J
δJ = δX, (2)
∂x
T
∂J ∂J
δσi2 = (Bi−1 − Bi ) (3)
∂x ∂x
Kalman filter:
Bi−1 − Bi = KHB (4)
Phil Mote, Karin Bumbaco, Guillaume Mauger, & Greg Hakim Climate Observing Network Design