Download - Prism Climate Group Oregon State University

Prism Climate GroupOregon State University

Christopher DalyDirector

Based on presentation developed Dr. Daly“Geospatial Climatology” as an emerging discipline

PRISM Overview 5-8-08

Leveraging Information Content of High-Quality Climatologies to Create New Maps with Fewer Data and Less Effort

Climatology knowledge used to convert a DEM into a PRISM predictor grid tomore accurately represent climate variables using weather station data.

Products

• Monthly and Annual (yearly and averages)– Precipitation

–Maximum Temperature

–Minimum Temperature

– Dewpoint Temperature

–% Annual Precipitation (by month)

• 2.5 arcmin (4 km) raster

• United States by state.

Basic Process• Y = a + b X , where X is elevation • Moving Window Regression• Local Interpolation using regression

• Spatial climate knowledge-base is used to weight stations in the regression function by their physiographic similarity to the target grid cell.

• The best method may be a statistical approach that is automated, but developed, guided and evaluated with expert knowledge.

1. Elevation Influence on Climate 3D Representation

2. Weighting the Weather Stations

Knowledge-based Technology

• Improving the results by applying our knowledge on the climate process.

• Each station is assign a weight in estimating the climate variable at a grid cell location.

• Designed to minimize the effects of factors other than elevation on the regression prediction.

• Weights are based on:– Distance– Elevation – Clustering– Topographic Facet (orientation)– Coastal Proximity– Vertical Layer (inversion)– Topographic Index (cold air

pooling)– Effective Terrain Height

(orographic profile)

Weights

• Distance – inverse Euclidean distance, more weight for closer stations

– Similar to Inverse Distance Weighting Interpolation

• Elevation – more weights for stations with the same elevation.

• Cluster – will down-weight individual stations that are “clustered” together so as to not over-sample a given location

Terrain-Induced Climate Transitions (topographic facets, moisture index)

• Stations on the same side of a terrain feature as the target grid cell are weighted more highly than others.

• Orthographic effects on precipitation.


Rain Shadow: 1961-90 Mean Annual PrecipitationOregon Cascades

Portland

Eugene

Sisters

Redmond

Bend

Mt. Hood

Mt. Jefferson

Three Sisters

N

350 mm/yr

2200 mm/yr

2500 mm/yr

Dominant PRISM KBSComponents

Elevation

Terrain orientation

Terrain steepness

Moisture Regime


1961-90 Mean Annual Precipitation, Cascade Mtns, OR, USA


Olympic Peninsula, Washington, USA

FlowDirection


Topographic Facets

= 4 km

= 60 km


Oregon Annual Precipitation

Full Model3452 mm

3442 mm

4042 mm

Max ~ 7900 mm

Max ~ 6800 mm

Mean Annual Precipitation, 1961-90


Facet Weighting Disabled

Max ~ 4800 mm

3452 mm

3442 mm

4042 mm


The 7900-mm precipitation maximum has “collapsed” under the weight of the more numerous and nearby dry-side stations


Oregon Annual Precipitation

Elevation = 0

Max ~ 3300 mm

3452 mm

3442 mm

4042 mm


Vertical extrapolation above the highest stations is “turned off”, leaving us with a map that is similar to that produced by an inverse-distance weighting interpolation algorithm

Coastal Effect

• Coastal Cooling – a band near the coast.• Coastal proximity is estimated with the PRISM

coastal influence trajectory model, which performs a cost-benefit path analysis to find the optimum path marine air might take, given prevailing winds and terrain.

• Penalties are assessed for moving uphill, and for the length of the path, requiring the optimal path to be a compromise between the shortest path, and path of least terrain resistance.


Coastal Effects: 1971-00 July Maximum Temperature

Central California Coast – 1 km

Monterey

San Francisco

San Jose

Santa Cruz

Hollister

Salinas

Stockton

Sacramento

Pacifi

c Oce

an

Fremont

N

PreferredTrajectories

DominantPRISM KBS Components

Elevation

Coastal Proximity

Inversion Layer

34°

20° 27°

Oakland

Two-Layer Atmosphere and Topographic Index

• Temperature Inversions are common in mountains especially during the winter

• Temperatures in the boundary layer are partly or totally decoupled from the free atmosphere.

• Based on an a priori estimation of the inversion top, PRISM divides the atmosphere into two layers, and performs the elevation regressions on each layer separately, allowing for a certain amount of crosstalk between layers near the inversion top.

• This allows temperature profiles with sharp changes in slope due to atmospheric layering to be simulated.


TMAX-Elevation Plot for January

TMIN-Elevation Plot for January

1971-2000 January Temperature, HJ Andrews Forest, Oregon, USA

Layer 1 Layer 2

Layer 1 Layer 2


United States Potential Winter Inversion


Western US Topographic Index

Another factor that influence’s a site’s temperature regime is its susceptibility to cold air pooling.

A useful way to assess this is to determine a site’s vertical position relative to local topographic features, such as valley bottom, mid slope, or ridge top.

A “topographic index” grid was created, which describes the height of a pixel relative to the surrounding terrain height.

PRISM uses this information to further weight stations during temperature interpolation.


Central Colorado Terrain and Topographic Index

Terrain Topographic Index

Gunnison Gunnison


January Minimum

Temperature Central

Colorado

Gunnison

Gunnison

Valley BottomElev = 2316 mBelow InversionLapse = 5.3°C/kmT = -16.2°C


January Minimum

Temperature Central

Colorado

Gunnison

Mid-SlopeElev = 2921 mAbove InversionLapse = 6.9°C/kmT = -12.7°C


January Minimum

Temperature Central

Colorado

Gunnison

Ridge TopElev = 3779 mAbove InversionLapse = 6.0°C/kmT = -17.9°C


Inversions – 1971-00 January Minimum TemperatureCentral Colorado

DominantPRISM KBS Components

Elevation

Topographic Index

Inversion LayerGunnison

Lake City

Crested ButteTaylor Park Res.

-18°C-13°

-18°

N

Orographic Effectiveness of Terrain (Profile)

• 3D vs 2D interpolation – does the terrain have an impact on precipitation.

Comments

• Based on my Arizona experience.

• Provides good representation for temperature.

• Provides good representation for precipitation where frontal events (warm or cold) are the dominate precipitation type. Good in winter in AZ.

• Provides poorer spatial representation of a single year when convective events dominate (i.e. monsoon), although long-term averages are OK.

Download - Prism Climate Group Oregon State University

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