Minimum temperature mapping Minimum temperature mapping in complex terrain for fruit frost in complex terrain for fruit frost

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Jin I. YunKyung Hee University

Suwon, Korea

Spatial Interpolation = Objective Analysis Optimum Interpolation

Model for Tmin (~1km)

Ti : observed temperature at station 'i', (synoptic)di : distance from the site to station 'i', z : elevation of the site zi : elevation of the station 'i'

Γ : temperature change per unit change in the elevation  ε : interpolation error

Inverse Distance Weighting (IDW)

Elevation Correction (lapse rate)

Urban Heat Island Correction

Choi et al.(2003) J. Appl. Meteorol. 42:1711-1719

January Tmin of South Korea (30 year normal, 1km resolution)

- Elevation correction

- UHI correction

Tmin on January 6, 2003

1 km

1 km

Scope

• Description of a spatial interpolation scheme incorporating local topography

• Applying this scheme to production of gridded minimum temperature data

• Combining this scheme with phenology model for fruit frost warning in spring

Tmin at Synop Station

Z0

Dry Adiabatic Line

Z2

Z1

Thermal Belt Effect

Cold Air Effect

Tmin model at landscape scale

E1 : correction for thermal belt effectE2 : correction for cold air accumulation effect

Study Area

Determining Inversion Cap Height and Strength for Quantifying Thermal Belt Effect

0

100

200

300

400

500

600

6 8 10 12 14

TEMPERATURE,degC

ALTI

TUD

E, m

200

300

400

800

Correction for Thermal Belt Effect

• Flow of Cold Air

• Flow Direction

• Flow Accumulation

84

Searching for Relationship between Tmin and Topographic Cold Air Accumulation Potential

Elevation Contour (vector)

Digital Elevation Model : DEM (raster)

Potential Flow Accumulation

Regression Analysis

1. Produce temperature map by applying the conventional model to DEM of the study area

2. Extract the estimated – measured temperature deviation at 8 HOBO sites

3. Extract the flow accumulation at 8 HOBO sites (zonal averages of cell radius from 1 to 10)

4. Regress the temperature estimation error to Log 10 of zonal averages of flow accumulation

Best Fit Equation

Temperature estimation error at observation site is linearly related to log10 of Flow Accumulation Potential

y = 0.9879x - 0.3432

R2 = 0.7808

0 1 2 3 4

LOG(FA)

3

2

1

0

4

5

Temperature Estimation Error, C

Potential Error from Cold Air Accumulation

R and Rmax : daily temperature range

FA5 : 5 cell average flow accumulation

Tmin model at landscape scale

Validation at Hydrologic Units (watershed)

-15

-10

-5

0

5

10

-15 -10 -5 0 5 10

Observed Tmin, C

Est

imat

ed T

min

, C

OLD

NEW

Winter 04/05, Baikgu Vineyards

RMSE=2.4

RMSE=1.5

Application : Fruit Frost Warning

1. Flowering date estimation by a phenology model which requires daily Tmin and Tmax (Tmax estimated by BioSIM of Canadian Forest Service) since last fall

2. Site-specific Tmin for tomorrow morning based on official Tmin forecasts at nearby synoptic stations

FloweringDate ofPear

As of

21 May

Conclusion• Potential effects of cold air accumulation and

inversion profile on minimum temperature were added to the large area estimation model

• This new interpolation scheme was successful in estimating minimum temperature mapping at landscape scale

• Combination with a phenology model showed a strong feasibility for development of a site-specific frost warning system