prism climate group oregon state university
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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. - PowerPoint PPT PresentationTRANSCRIPT
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.
PRISM Overview 5-8-08
PRISM Overview 5-8-08
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
PRISM Overview 5-8-08
PRISM Overview 5-8-08
1961-90 Mean Annual Precipitation, Cascade Mtns, OR, USA
PRISM Overview 5-8-08
1961-90 Mean Annual Precipitation, Cascade Mtns, OR, USA
PRISM Overview 5-8-08
Olympic Peninsula, Washington, USA
FlowDirection
PRISM Overview 5-8-08
Topographic Facets
= 4 km
= 60 km
PRISM Overview 5-8-08
Oregon Annual Precipitation
Full Model3452 mm
3442 mm
4042 mm
Max ~ 7900 mm
Max ~ 6800 mm
Mean Annual Precipitation, 1961-90
PRISM Overview 5-8-08
Facet Weighting Disabled
Max ~ 4800 mm
3452 mm
3442 mm
4042 mm
Mean Annual Precipitation, 1961-90
The 7900-mm precipitation maximum has “collapsed” under the weight of the more numerous and nearby dry-side stations
PRISM Overview 5-8-08
Oregon Annual Precipitation
Elevation = 0
Max ~ 3300 mm
3452 mm
3442 mm
4042 mm
Mean Annual Precipitation, 1961-90
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 5-8-08
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.
PRISM Overview 5-8-08
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
PRISM Overview 5-8-08
United States Potential Winter Inversion
PRISM Overview 5-8-08
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 5-8-08
Central Colorado Terrain and Topographic Index
Terrain Topographic Index
Gunnison Gunnison
PRISM Overview 5-8-08
January Minimum
Temperature Central
Colorado
Gunnison
Gunnison
Valley BottomElev = 2316 mBelow InversionLapse = 5.3°C/kmT = -16.2°C
PRISM Overview 5-8-08
January Minimum
Temperature Central
Colorado
Gunnison
Mid-SlopeElev = 2921 mAbove InversionLapse = 6.9°C/kmT = -12.7°C
PRISM Overview 5-8-08
January Minimum
Temperature Central
Colorado
Gunnison
Ridge TopElev = 3779 mAbove InversionLapse = 6.0°C/kmT = -17.9°C
PRISM Overview 5-8-08
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.