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Prism Climate Group Oregon State University

Prism Climate Group Oregon State University. Christopher Daly Director Based on presentation developed Dr. Daly “Geospatial Climatology” as an emerging discipline. Leveraging Information Content of High-Quality Climatologies to Create New Maps with Fewer Data and Less Effort.

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Prism Climate Group Oregon State University

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

  2. 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.

  3. 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.

  4. 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.

  5. 1. Elevation Influence on Climate 3D Representation

  6. 2. Weighting the Weather Stations Knowledge-based Technology 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) • 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.

  7. 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

  8. 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.

  9. Rain Shadow: 1961-90 Mean Annual Precipitation Oregon Cascades Portland Mt. Hood Eugene Dominant PRISM KBS Components Elevation Terrain orientation Terrain steepness Moisture Regime Mt. Jefferson 2500 mm/yr 2200 mm/yr Sisters Three Sisters 350 mm/yr Redmond N Bend

  10. 1961-90 Mean Annual Precipitation, Cascade Mtns, OR, USA

  11. 1961-90 Mean Annual Precipitation, Cascade Mtns, OR, USA

  12. Olympic Peninsula, Washington, USA Flow Direction

  13. Topographic Facets  = 4 km  = 60 km

  14. Mean Annual Precipitation, 1961-90 Oregon Annual Precipitation Max ~ 7900 mm Full Model 3452 mm 3442 mm 4042 mm Max ~ 6800 mm

  15. Mean Annual Precipitation, 1961-90 Max ~ 4800 mm 3452 mm 3442 mm 4042 mm Facet Weighting Disabled The 7900-mm precipitation maximum has “collapsed” under the weight of the more numerous and nearby dry-side stations

  16. Mean Annual Precipitation, 1961-90 Oregon Annual Precipitation Max ~ 3300 mm 3452 mm 3442 mm 4042 mm Elevation = 0 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

  17. 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.

  18. Coastal Effects: 1971-00 July Maximum Temperature Central California Coast – 1 km Sacramento Stockton Dominant PRISM KBS Components Elevation Coastal Proximity Inversion Layer 34° SanFrancisco Oakland Fremont SanJose Preferred Trajectories Santa Cruz 27° 20° Pacific Ocean Hollister Monterey Salinas N

  19. 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.

  20. 1971-2000 January Temperature, HJ Andrews Forest, Oregon, USA TMAX-Elevation Plot for January Layer 1 Layer 2 TMIN-Elevation Plot for January Layer 1 Layer 2

  21. United States Potential Winter Inversion

  22. 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.

  23. Central Colorado Terrain and Topographic Index Gunnison Gunnison Terrain Topographic Index

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

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

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

  27. Inversions – 1971-00 January Minimum Temperature Central Colorado N Dominant PRISM KBS Components Elevation Topographic Index Inversion Layer Taylor Park Res. Crested Butte -18° Gunnison -13° -18°C Lake City

  28. Orographic Effectiveness of Terrain (Profile) • 3D vs 2D interpolation – does the terrain have an impact on precipitation.

  29. 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.

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