The PRISM Approach to Mapping Climate in Complex Regions Christopher Daly Director Spatial Climate Analysis Service Oregon State University Corvallis,

The PRISM Approach to The PRISM Approach to Mapping Climate in Mapping Climate in

Complex RegionsComplex Regions

Christopher DalyDirector

Spatial Climate Analysis ServiceOregon State University

Corvallis, Oregon

Spatial Climate Analysis Service Spatial Climate Analysis Service MissionMission

• Service• Provide innovative, state-of-the science spatial

climate products and services to clients worldwide

• Research• Maintain scientific research and development

programs that provide the basis for products and services

• Education• Advance “geospatial climatology” as an

emerging discipline

SCAS and PRISM are UniqueSCAS and PRISM are Unique

• SCAS is the only center in the world dedicated solely to the mapping of climate

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

• PRISM climate maps are the “gold standard” by which others are evaluated

• SCAS has become a leader in climate mapping products and technology worldwide

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 KBS

- Generates gridded estimates of climatic parameters

- Moving-window regression of climate vs. elevation for each grid cell- Uses nearby station observations

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

PRISM

Parameter-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

PRISM Moving-Window Regression Function

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

Weighted linearregression

Governing EquationGoverning Equation

Moving-window regression of climate vs elevation

y = 1x + 0 Y = predicted climate elementx = DEM elevation at the target cell0 = y-intercept

1 = slope

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

Station WeightingStation WeightingCombined weight of a station is:

W = f {Wd, Wz, Wc, Wf, Wp, Wl, Wt, We}

- 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, 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

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°


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



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

Upcoming ProductsUpcoming Products

- 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

- Targeted climatologies for NWS River Forecast Centers (NWS Western Region)

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

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

Documents

The PRISM Approach to Mapping Climate in Complex Regions Christopher Daly Director Spatial Climate Analysis Service Oregon State University Corvallis,