Prism Climate Group Oregon State University Christopher Daly Director Based on presentation developed Dr. Daly “Geospatial Climatology” as an emerging discipline

PRISM Overview 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 to more 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.

PRISM Overview

Rain Shadow: Mean Annual Precipitation Oregon Cascades Portland Eugene Sisters Redmond Bend Mt. Hood Mt. Jefferson Three Sisters N 350 mm/yr 2200 mm/yr 2500 mm/yr Dominant PRISM KBS Components Elevation Terrain orientation Terrain steepness Moisture Regime

PRISM Overview

Mean Annual Precipitation, Cascade Mtns, OR, USA

PRISM Overview Mean Annual Precipitation, Cascade Mtns, OR, USA

PRISM Overview Olympic Peninsula, Washington, USA Flow Direction

PRISM Overview Topographic Facets  = 4 km  = 60 km

PRISM Overview Oregon Annual Precipitation Full Model 3452 mm 3442 mm 4042 mm Max ~ 7900 mm Max ~ 6800 mm Mean Annual Precipitation,

PRISM Overview Facet Weighting Disabled Max ~ 4800 mm 3452 mm 3442 mm 4042 mm Mean Annual Precipitation, The 7900-mm precipitation maximum has “collapsed” under the weight of the more numerous and nearby dry-side stations

PRISM Overview Oregon Annual Precipitation Elevation = 0 Max ~ 3300 mm 3452 mm 3442 mm 4042 mm Mean Annual Precipitation, 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.

PRISM Overview Coastal Effects: July Maximum Temperature Central California Coast – 1 km Monterey San Francisco San Jose Santa Cruz Hollister Salinas Stockton Sacramento Pacific Ocean Fremont N Preferred Trajectories Dominant PRISM 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.

PRISM Overview TMAX-Elevation Plot for January TMIN-Elevation Plot for January January Temperature, HJ Andrews Forest, Oregon, USA Layer 1 Layer 2

PRISM Overview United S tates Potential Winter Inversion

PRISM Overview 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.

PRISM Overview Central Colorado Terrain and Topographic Index Terrain Topographic Index Gunnison

PRISM Overview January Minimum Temperature Central Colorado Gunnison Valley Bottom Elev = 2316 m Below Inversion Lapse = 5.3°C/km T = -16.2°C

PRISM Overview January Minimum Temperature Central Colorado Gunnison Mid-Slope Elev = 2921 m Above Inversion Lapse = 6.9°C/km T = -12.7°C

PRISM Overview January Minimum Temperature Central Colorado Gunnison Ridge Top Elev = 3779 m Above Inversion Lapse = 6.0°C/km T = -17.9°C

PRISM Overview Inversions – January Minimum Temperature Central Colorado Dominant PRISM KBS Components Elevation Topographic Index Inversion Layer Gunnison Lake City Crested Butte Taylor 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.