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Principal aspects taken into account in PRISM model:

Principal aspects taken into account in PRISM model: Relationship between precipitation and elevation: Precipitation increases with elevation, with a maximum in mountain crests Relationship between precipitation and elevation can be described by a linear function

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Principal aspects taken into account in PRISM model:

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  1. Principal aspects taken into account in PRISM model: • Relationship between precipitation and elevation: • Precipitation increases with elevation, with a maximum in mountain crests • Relationship between precipitation and elevation can be described by a linear function • 2. Spatial scale of orographic precipitation (orographic elevation) • Mismatch in scale when using actual elevation of stations • “Orographic” elevation estimation in order to avoid this mismatch • The orographic scale depends on the scale of the prevailing storm type • 5 min-DEM appears to approximate the scale of orographic effects explained by available data • 3. Spatial patterns of orographic precipitation (facets) • PRISM divides the mountainous areas into “facets “ • Each “facet” is a contiguous area of constant slope orientation

  2. PRISM components: • FACET • PRISM • GRADIENT FACET: • A contiguous area with a constant slope orientation • FACET assigns a slope orientation to each DEM cell by estimating elevation gradients W to E and S to N. column i, row j o o o (i-1,j+1) (i,j+1) (i+1,j+1) W-E gradient: elev(i-1, j) – elev(i+1, j) o * o (i-1,j) (i,j) (i+1,j) S-N gradient: elev(i, j-1) – elev(i, j+1) o o o (i-1,j-1) (i,j-1) (i+1,j-1)

  3. FACET: • The slope is flat if: WE and SN gradient <= MINSTP • If slope is NOT flat: cell is categorized facing: N, S, E, or W • To define a facet, important: number of data points (stations) • In many cases, number of points is very small a 5-point filter applied • (then, the spatial extent of a facet can be broadened): • Unfiltered DEM, 8 filtering passes, 16, 24, 33 and 40 passes • Assimilations: • At least 2 grids needed to define a facet (if there is a single, is assimilated into surrounding facets) • For narrow facets at the crest (net slope flat): precipitation at crest is more similar to that of windward side than leeward (W and S)

  4. PRISM: • Estimation of “orographic” elevations for each precipitation • station: • a. Locating the DEM grid cell center nearest to station • b. Find 4 surrounding grid cells • c. Calculate the weightd mean elevation of the 5 DEM cells (each • cell weighted inversely by its distance from the station) o o o o o o * o o o

  5. 2. Station retrieval: • Stations <= MAXRAD (maximum radius from DEM grid cell) • If station does not fall on cell’s facet: omitted • If station have same but a separate facet: ommited • If number of stations < MINSTA (minimum number), then use • filtered FACET grid until MINSTA is reached • If NO station is available even with filtering, the ONE station • nearest to DEM grid cell is selected (regardless of facet) • 3. Precipitation-DEM elevation regression functions • Monthly and annual linear functions are generated using the station • data retrieved. • Condition: MIN and MAX slope of the calculated regression lines • ( B1MIN, B1MAX)

  6. If slope falls outside the bounds: model tries to find an outlier • station. • If outlier station is found: is deleted • If it is not found: the slope is set to a prescribed value (DB1) • If only one station is in the regression dataset: DB1 is used. • Regression slopes are not expressed in units. They are expressed as • a proportion of average precipitation • 4. Precipitation estimation: • Monthly or annual precipitation is estimated at each grid cell: • Pij = b0ij + b1ijEij

  7. 5. Vertical extrapolation adjustment for coarse-grid simulations • Smoothed DEM reduce the height of narrow or isolated mountains • Algorithm to adjust precipitation estimates for locally high • elevations: • Change the slope of the function to a prescribed value B1EX. The • new function begins on the old regression line at the elevation of • the highest station • 6. Estimation of prediction intervals • Degree of uncertainty, 95% prediction intervals GRADIENT: • Check slopes of regressions to avoid discontinuities in predictions • between adjacent facets

  8. PRISM APPLICATIONS (1994) • Willamette River Basin (Oregon) • Most precipitation produced by cyclonic storms (fall, winter, spring) • Dataset: annual precipitation 1982-88 at 52 stations (15 from SNOTEL and 37 from NWS) • SNOTEL: Soil conservation Service, USDA • 5-min DEM (NGDC)

  9. 2. Northern Oregon • Different orographic regimes and extreme spatial gradients in precipitation • Dataset: 181 stations (more than 20 years of record) of monthly and annual precipitation, 130 from MWS and 51 from SNOTEL

  10. 3. Western US • Dataset: annual precipitation from 3514 stations (3091 from NWS and 423 from SNOTEL)

  11. Limitations • The NWS and SNOTEL data have not been subjected to rigorous checks for quality or accuracy • The use of a coarse-grid DEM oversmoothes the elevations of sharply regions • However and “optimal” DEM resolution presents a problem of scale matching (different orographic scales and limitation of available data) • Assumption that maximum precipitation occurs at the crest of the mountains

  12. CHANGES IN PAPER 2002 • A KNOWLEDGE-BASED SYSTEM: • Relationship precipitation-elevation • Topographic facets (can be defined at different scales) • Coastal effects (proximity to ocean) • Two-layer atmosphere: if moist boundary layer is shallow relative to terrain height, then the max precip is mid-slope) • Orographic effectiveness of terrain

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