the prism approach to mapping climate in complex regions

PRISM Overview 5-8-08PRISM Overview 5-8-08

The PRISM Approach to Mapping Climate in The PRISM Approach to Mapping Climate in Complex RegionsComplex Regions

The PRISM Approach to Mapping Climate in The PRISM Approach to Mapping Climate in Complex RegionsComplex Regions

Christopher DalyChristopher DalyDirector, PRISM GroupDirector, PRISM Group

Northwest Alliance for Computational Science and EngineeringNorthwest Alliance for Computational Science and EngineeringDepartment of GeosciencesDepartment of Geosciences

Oregon State UniversityOregon State UniversityCorvallis, Oregon, USACorvallis, Oregon, USA


PRISM Group FactsPRISM Group Facts

5-FTE applied research team at Oregon State University, 100% externally 5-FTE applied research team at Oregon State University, 100% externally fundedfunded

The PRISM Group is the only center in the world dedicated solely to the The PRISM Group is the only center in the world dedicated solely to the spatial analysis of climatespatial analysis of climate

PRISM climate mapping technology has been continuously developed, and PRISM climate mapping technology has been continuously developed, and repeatedly peer-reviewed, since 1991repeatedly peer-reviewed, since 1991

The PRISM Group is the The PRISM Group is the de factode facto climate mapping center for the US climate mapping center for the US

The PRISM Group is advancing “Geospatial Climatology” as an emerging The PRISM Group is advancing “Geospatial Climatology” as an emerging disciplinediscipline


Oregon Annual Precipitation


RationaleRationale- Observations are rarely sufficient to directly represent the

spatial patterns of climate

- Human-expert mapping methods often produce the best products, but are slow, inconsistent, and non-repeatable

- Purely statistical mapping methods are fast and repeatable, but rarely provide the best accuracy, detail, and realism

Therefore…- The best method may be a statistical approach that is

automated, but developed, guided and evaluated with expert knowledge


- Knowledge acquisition capability – Elicit expert information

- Knowledge base – Store of knowledge

- Inference Engine – Infer solutions from stored knowledge

- User interface – Interaction and explanation

- Independent verification – Knowledge refinement

Knowledge-Based System KBSKnowledge-Based System KBS


- Generates gridded estimates of climatic parametersGenerates gridded estimates of climatic parameters

- Moving-window regression of climate vs. elevation for Moving-window regression of climate vs. elevation for each grid celleach grid cell

- Uses nearby station observationsUses nearby station observations

- Spatial climate knowledge base Spatial climate knowledge base weights stationsweights stations in the in the regression function by their physiographic similarity to the regression function by their physiographic similarity to the target grid celltarget grid cell

PRISMPRISMParameter-elevation Regressions on Independent Slopes Model



Interface


PRISM

Knowledge Base

- Elevation Influence on Climate



1961-90 Mean January Precipitation, Sierra Nevada, CA, USA



1961-90 Mean August Max Temperature, Sierra Nevada, CA, USA


1963-1993 Mean November Precipitation, Puerto Rico


1963-93 Mean June Maximum Temperature, Puerto Rico


1971-90 Mean February Precipitation, European Alps



1961-90 Mean September Max Temperature, Qin Ling Mountains, China


Oregon Annual PrecipitationPRISM Moving-Window Regression Function

1961-90 Mean April Precipitation, Qin Ling Mountains, China

Weighted linearregression


Governing EquationGoverning Equation

Moving-window regression of climate vs elevationMoving-window regression of climate vs elevation

y = y = ββ11x + x + ββ00

Y = predicted climate elementY = predicted climate element

x = DEM elevation at the target cellx = DEM elevation at the target cell

ββ00 = y-intercept = y-intercept

ββ11 = slope = slope

x,y pairs - elevation and climate observations from x,y pairs - elevation and climate observations from nearby climate stations nearby climate stations


Station WeightingStation Weighting

Combined weight of a station is:Combined weight of a station is:

W = W = ff {W {Wdd W Wzz W Wcc W Wff W Wpp W Wll W Wtt W Wee}}

- Distance- Elevation - Clustering- Topographic Facet

(orientation)

- Coastal Proximity- Vertical Layer (inversion)- Topographic Index (cold air pooling)- Effective Terrain Height (orographic

profile)


- Terrain-Induced Climate Transitions (topographic facets, Terrain-Induced Climate Transitions (topographic facets, moisture index)moisture index)

PRISM

Knowledge Base



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


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


Olympic Peninsula, Washington, USA

FlowDirection


Topographic Facets

= 4 km

= 60 km



Full Model3452 mm

3442 mm

4042 mm

Max ~ 7900 mm

Max ~ 6800 mm

Mean Annual Precipitation, 1961-90


Facet Weighting Disabled

Max ~ 4800 mm

3452 mm

3442 mm

4042 mm




Elevation = 0

Max ~ 3300 mm

3452 mm

3442 mm

4042 mm




Full Model3452 mm

3442 mm

4042 mm

Max ~ 7900 mm

Max ~ 6800 mm




PRISM

Knowledge Base


- Coastal Effects


Coastal Effects: 1971-00 July Maximum TemperatureCentral California Coast – 1 km

Monterey

San Francisco

San Jose

Santa Cruz

Hollister

Salinas

Stockton

Sacramento

Pac

ific

Oce

an

Fremont

N

PreferredTrajectories

DominantPRISM KBS Components

Elevation

Coastal Proximity

Inversion Layer

34°

20° 27°

Oakland


1961-90 Mean July Maximum Temperature, Central California, USA

Coastal Proximity Weighting OFF Coastal Proximity Weighting ON



PRISM

Knowledge Base


- Coastal Effects- Two-Layer Atmosphere and Topographic Index


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


Mean Annual Precipitation, Hawaii


United States Potential Winter Inversion


Western US Topographic Index


Central Colorado Terrain and Topographic Index

Terrain Topographic Index

Gunnison Gunnison


January Minimum

Temperature Central Colorado

Gunnison

Gunnison

Valley BottomElev = 2316 mBelow InversionLapse = 5.3°C/kmT = -16.2°C


January Minimum


Gunnison

Mid-SlopeElev = 2921 mAbove InversionLapse = 6.9°C/kmT = -12.7°C


January Minimum


Gunnison

Ridge TopElev = 3779 mAbove InversionLapse = 6.0°C/kmT = -17.9°C


Inversions – 1971-00 January Minimum TemperatureCentral Colorado


Elevation

Topographic Index

Inversion LayerGunnison

Lake City

Crested ButteTaylor Park Res.

-18°C-13°

-18°

N


PRISM 1971-2000 Mean January Minimum Temperature, 800-m

“Banana Belt”

Cold air drainage

Snake Plain


Inversions – 1971-00 July Minimum TemperatureNorthwestern California

Ukiah

Cloverdale Lakeport

Willits

Cle

ar

Lak

e

Pacific Ocean

Lake Pilsbury.

N


Elevation

Inversion Layer

Topographic Index

Coastal Proximity

12°

17°

9°

16°

10°

17°


- Terrain-Induced Climate Transitions (topographic facets, moisture index)Terrain-Induced Climate Transitions (topographic facets, moisture index)

PRISM

Knowledge Base- Elevation Influence on Climate

- Coastal Effects- Two-Layer Atmosphere and Topographic Index- Orographic Effectiveness of Terrain (Profile)


United States Effective TerrainUnited States Effective TerrainUnited States Orographically Effective Terrain


- Terrain-Induced Climate Transitions (topographic facets, Terrain-Induced Climate Transitions (topographic facets, moisture moisture index)index)

PRISM

Knowledge Base- Elevation Influence on Climate

- Coastal Effects- Two-Layer Atmosphere and Topographic Index- Orographic Effectiveness of Terrain (Profile)- Persistence of climatic patterns (climatologically-aided interpolation)


Oregon Annual PrecipitationLeveraging Information Content of High-Quality Climatologies to Create New Maps with Fewer Data and Less Effort

Climatology used in place of DEM as PRISM predictor grid


PRISM Regression of “Climate vs Climate”or “Weather vs Climate”

PRISM Results

18

20

22

24

26

28

30

32

34

16.5

17.5

18.5

19.5

20.5

21.5

22.5

23.5

24.5

25.5

26.5

71-00 Mean July Maximum Temperature

Dai

ly M

axim

um T

empe

ratu

re (C

)

21D12S

21D35S

21D13S

353402

21D08S

5211C70E

324045CC

3240335C

21D14S

Regression

Stn: 21D12SDate: 2000-07-20Climate: 21.53Obs:26.0Prediction: 25.75Slope: 1.4Y-Intercept: -4.37

20 July 2000 Tmax vs 1971-2000 Mean July Tmax


Recent ProjectsRecent Projects

- Updated 1971-2000 mean monthly P, Tmax, Tmin maps for the US at 800-m resolution (USDA-NRCS, NPS, USFS)

- Spatial-Probabilistic QC system for SNOTEL observations (NRCS)

- 1971-2000 monthly precipitation climatologies for NW Oregon conditional on 700-mb flow direction (NWS Western Region)

- Extended monthly time series maps of P, Tmax, Tmin, Tdew for climate monitoring (USFS)


Future DirectionsFuture Directions- Engage in collaborative projects to develop the use of PRISM and

PRISM climatologies for downscaling numerical weather prediction models

- Continue to develop technology to move to smaller time steps and towards real time operation

- Explore using remotely-sensed data to improve PRISM accuracy in under-sampled areas (and vice-versa)

- Continue to develop PRISM’s Spatial Climate Knowledge Base

the prism approach to mapping climate in complex regions

Documents

usaprism overview

climateprism overview

chinaprism overview

expert knowledgeprism

puerto ricoprism overview

european alpsprism overview

mapping climate

prism approach