Minimum temperature mapping in complex terrain for fruit frost warning Jin I. Yun Kyung Hee University Suwon, Korea

Spatial Interpolation = Objective Analysis Optimum Interpolation

Model for T min (~1km) T i : observed temperature at station 'i', (synoptic) d i : distance from the site to station 'i', z : elevation of the site z i : 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:

January Tmin of South Korea (30 year normal, 1km resolution) - Elevation correction - UHI correction

Tmin on January 6, 2003

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

T min model at landscape scale E1 : correction for thermal belt effect E2 : correction for cold air accumulation effect

Study Area

Determining Inversion Cap Height and Strength for Quantifying Thermal Belt Effect

Correction for Thermal Belt Effect

Flow of Cold Air

Flow Direction

Flow Accumulation 8 4

Searching for Relationship between T min 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 log 10 of Flow Accumulation Potential Temperature Estimation Error, C

Potential Error from Cold Air Accumulation R and R max : daily temperature range FA 5 : 5 cell average flow accumulation

T min model at landscape scale

Validation at Hydrologic Units (watershed)

Application : Fruit Frost Warning 1.Flowering date estimation by a phenology model which requires daily T min and T max (T max estimated by BioSIM of Canadian Forest Service) since last fall 2. Site-specific T min for tomorrow morning based on official T min forecasts at nearby synoptic stations

Flowering Date of Pear

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