Improving the Usage of the Korea Meteorological Administration's Digital Forecasts in Agriculture: V. Field Validation of the Sky-condition based Lapse Rate Estimation Scheme

Abstract

The aim of this study was to confirm the improvement of efficiency for temperature estimation at 0600 and 1500 LST by using a simple method for estimating temperature lapse rate modulated by the amount of clouds in comparison with the case adopting the existing single temperature lapse rate (-6.5°C/km or -9°C/km). A catchment of the 'Hadong Watermark2,' which includes Hadong, Gurye, and Gwangyang was selected as the area for evaluating the practicality of the temperature lapse rate estimation method. The weather data of 0600 and 1500 LST at 12 weather observation sites within the catchment were collected during the entire year of 2015. Also, the 'sky condition' of digital forecast products of KMA in 2015 (5 × 5km lattice resolution) were overlapped with the catchment of the 'Hadong Watermark2,' to calculate the spatial average value within the catchment, which were used to simulate the 0600 and 1500 LST temperature lapse rate of the catchment. The estimation errors of the temperatures at 0600 LST were ME -0.39°C and RMSE 1.45°C in 2015, when applying the existing temperature lapse rate. Using the estimated temperature lapse rate, they were improved to ME -0.19°C and RMSE 1.32°C. At 1500 LST, the effect of the improvements found from the comparison between the existing temperature lapse rate and the estimated temperature lapse rate were minute, because the estimated lapse rate of clear days is not very different from the existing lapse rate. However, the estimation errors of the temperatures at 1500 LST during cloudy days were improved from ME -0.69°C, RMSE 1.54°C to ME -0.51°C, RMSE 1.19°C.

Farmstead-Specific Weather Risk Prediction Technique Based on High-Resolution Weather Grid Distribution

“…In the case of temperature, there is an RMSE of ±1 • C, and the growth stage shows an RMSE of ±2 days based on the blooming period of the peach. In addition, in the case of forecast data, errors may occur in the process of detailing them because errors are implied by themselves [4,22,34,35,38,39].…”

“…For temperature, the first step in reflecting the effects of terrain onto the background distribution map is to correct for elevation deviation. This corrects the temperature using a temperature value at an observation point the temperature lapse rate at which the temperature changes with the changing altitude [34]. Moreover, the maximum temperature can be corrected by reflecting the increase in temperature through solar radiation reflecting the terrain effect [35,36].…”