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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 Presentation


  • Prism Climate GroupOregon State UniversityChristopher DalyDirector Based on presentation developed Dr. DalyGeospatial Climatology as an emerging discipline

  • PRISM Overview 5-8-08Leveraging Information Content of High-Quality Climatologies to Create New Maps with Fewer Data and Less EffortClimatology knowledge used to convert a DEM into a PRISM predictor grid tomore accurately represent climate variables using weather station data.

  • ProductsMonthly and Annual (yearly and averages)Precipitation Maximum TemperatureMinimum TemperatureDewpoint Temperature % Annual Precipitation (by month)2.5 arcmin (4 km) rasterUnited States by state.

  • Basic ProcessY = a + b X , where X is elevation Moving Window RegressionLocal Interpolation using regressionSpatial 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 StationsKnowledge-based TechnologyImproving 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:DistanceElevation ClusteringTopographic Facet (orientation)Coastal ProximityVertical Layer (inversion)Topographic Index (cold air pooling)Effective Terrain Height (orographic profile)

  • WeightsDistance inverse Euclidean distance, more weight for closer stationsSimilar 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 5-8-08Rain Shadow: 1961-90 Mean Annual PrecipitationOregon CascadesPortlandEugeneSistersRedmondBendMt. HoodMt. JeffersonThree SistersN350 mm/yr2200 mm/yr2500 mm/yrDominant PRISM KBSComponents


    Terrain orientation

    Terrain steepness

    Moisture Regime

  • PRISM Overview 5-8-08

  • PRISM Overview 5-8-081961-90 Mean Annual Precipitation, Cascade Mtns, OR, USA

  • PRISM Overview 5-8-081961-90 Mean Annual Precipitation, Cascade Mtns, OR, USA

  • PRISM Overview 5-8-08Olympic Peninsula, Washington, USAFlowDirection

  • PRISM Overview 5-8-08Topographic Facets = 4 km = 60 km

  • PRISM Overview 5-8-08Oregon Annual PrecipitationFull Model3452 mm

    3442 mm

    4042 mmMax ~ 7900 mmMax ~ 6800 mmMean Annual Precipitation, 1961-90

  • PRISM Overview 5-8-08Facet Weighting DisabledMax ~ 4800 mm3452 mm

    3442 mm

    4042 mmMean Annual Precipitation, 1961-90The 7900-mm precipitation maximum has collapsed under the weight of the more numerous and nearby dry-side stations

  • PRISM Overview 5-8-08Oregon Annual PrecipitationElevation = 0Max ~ 3300 mm3452 mm

    3442 mm

    4042 mmMean Annual Precipitation, 1961-90Vertical 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 EffectCoastal 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 5-8-08Coastal Effects: 1971-00 July Maximum TemperatureCentral California Coast 1 km MontereySan FranciscoSan JoseSanta CruzHollisterSalinasStocktonSacramentoPacific OceanFremontNPreferredTrajectories

    DominantPRISM KBS Components


    Coastal Proximity

    Inversion Layer


  • Two-Layer Atmosphere and Topographic Index Temperature Inversions are common in mountains especially during the winterTemperatures 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 5-8-08TMAX-Elevation Plot for JanuaryTMIN-Elevation Plot for January1971-2000 January Temperature, HJ Andrews Forest, Oregon, USALayer 1 Layer 2Layer 1 Layer 2

  • PRISM Overview 5-8-08United States Potential Winter Inversion

  • PRISM Overview 5-8-08Western US Topographic Index Another factor that influences a sites temperature regime is its susceptibility to cold air pooling.

    A useful way to assess this is to determine a sites 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 5-8-08Central Colorado Terrain and Topographic Index TerrainTopographic IndexGunnisonGunnison

  • PRISM Overview 5-8-08January Minimum Temperature Central Colorado GunnisonGunnisonValley BottomElev = 2316 mBelow InversionLapse = 5.3C/kmT = -16.2C

  • PRISM Overview 5-8-08January Minimum Temperature Central Colorado GunnisonMid-SlopeElev = 2921 mAbove InversionLapse = 6.9C/kmT = -12.7C

  • PRISM Overview 5-8-08January Minimum Temperature Central Colorado GunnisonRidge TopElev = 3779 mAbove InversionLapse = 6.0C/kmT = -17.9C

  • PRISM Overview 5-8-08Inversions 1971-00 January Minimum TemperatureCentral ColoradoDominantPRISM KBS Components


    Topographic Index

    Inversion Layer

    GunnisonLake CityCrested ButteTaylor Park Res.-18C-13-18N

  • Orographic Effectiveness of Terrain (Profile)

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

  • CommentsBased 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.

    This method is being used operationally at the PRISM Group to create near real time climate maps for each month, 7-14 days after the end of that month. In the above example, July 2003 (right) was one of the warmest on record in the West. Even so, the local patterns of temperature bear a striking resemblance to the long-term climatological average for July (left). This is a PRISM map of mean annual precipitation in Oregon. A well-defined rain shadow is evident in the middle of the state, produced by the blocking effects of the Cascade Mountains. Here is a 3D view of the Oregon Cascades rain shadow. Mean annual precipitation drops from 2200 mm/yr at the crest of the Cascades, to only 350 mm/yr just down the hill to the east. Lets take a closed look at how PRISM works in these areas, and visit Santiam Pass, and area with one of the steepest rain shadows in the country. Here is a graphic output of the PRISM GUI, showing mean annual precipitation vs elevation for a grid cell just west of the pass, on the windward side (white vertical line represents the Cascade crest). Here, the predicted precipitation is about 2000 per year. When we move PRISM to just east of the crest, the regression function jumps down to a new position that is much drier than the windward position, weighting stations on the east side more highly than those on the west side, despite the large number and proximity of west-slope stations. Another useful case study is the Olympic Mountains, Washington. Here, winter storms bring moisture in from the southwest, and are uplifted by the terrain, creating a precipitation maximum on the windward side and a minimum on the leeward side. The large black dots represent available station data. Note the lack of stations in the interior mountains. PRISM topographic facets are depicted at up to 6 spatial scales. On the left, facets for a DEM at 4-km effective wavelength are shown. There are many small facets that are not represented by station data. On the rights, facets at a 60-km effective wavelength are shown. At this scale, the mountain range becomes oval in shape, and has a relatively simple facet pattern. In operation, PRISM starts with the smallest wavelength facet grid, and works up to the larger wavelength grids, accumulating similarly-oriented stations along the way. Once enough have been accumulated (specified by the user), the process stops. Thus , PRISM chooses the smallest scale facet representation allowable given the data density. This is a mean annual precipitation map p


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