• Korean Journal of Agricultural and Forest Meteorology
• /
• v.2 no.4
• /
• pp.175-182
• /
• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2001.06a
• /
• pp.153-156
• /
• 2001

한 지점의 매시 기온 관측값에는 이 지점의 수평 및 수직 위치, 주변 식생, 하천이나 바다 등, 모든 기온결정인자의 영향이 녹아있다고 볼 수 있다. 만약 지표 특성이 이들 관측지점과 동질적이며, 관측점들의 표고에 의해 그 지형이 정확히 표현될 수 있는 넓은 지역이 있다면, 기존의 거리 역산가중법(Inverse Distance Weighting: IDW)에 의해 내삽되는 기온의 공간변이는 실제 기온의 공간변이와 일치할 것이다.(중략)

• Korean Journal of Remote Sensing
• /
• v.15 no.1
• /
• pp.9-20
• /
• 1999

The use of climatic information is essential in the industial society. More specialized weather servies are required to perform better industrial acivities including agriculture. Especially, crop models require daily weather data of crop growing area or cropping zones, where routine weather observations are rare. Estimates of the spatial distribution of daily climates might complement the low density of standard weather observation stations. This study was conducted to estimate the spatial distribution of daily minimum and maximum temperatures in Korean Peninsula. A topoclimatological technique was first applied to produce reasonable estimates of monthly climatic normals based on 1km $\times$ 1km grid cell over study area. Harmonic analysis method was then adopted to convert the monthly climatic normals into daily climatic normals. The daily temperatures for each grid cell were derived from a spatial interpolation procedure based on inverse-distance weighting of the observed deviation from the climatic normals at the nearest 4 standard weather stations. Data collected from more than 300 automatic weather systems were then used to validate the final estimates on several dates in 1997. Final step to confirm accuracy of the estimated temperature fields was comparing the distribution pattern with the brightness temperature fields derived from NOAA/AVHRR. Results show that differences between the estimated and the observed temperatures at 20 randomly selected automatic weather systems(AWS) range from -3.$0^{\circ}C$ to + 2.5$^{\circ}C$ in daily maximum, and from -1.8$^{\circ}C$ to + 2.2$^{\circ}C$ in daily minimum temperature. The estimation errors, RMSE, calculated from the data collected at about 300 AWS range from $1.5^{\circ}C$ to 2.5$^{\circ}C$ for daily maximum/minimum temperatures.

• Horticultural Science & Technology
• /
• v.28 no.6
• /
• pp.937-941
• /
• 2010

This study investigated the effect of global warming on full bloom date (FBD) of 'Niitaka' pear ($Pyrus$ $pyrifolia$ Nakai) tree by calculating the development stage index by hourly temperatures recorded at Pear Research Station, estimating the distribution of average FBD and the change of FBD according to temperature rising by integrating development rate at 67 locations in Korea Meteorological Administration site. Development stage index of 'Niitaka' pear tree was 0.9593 at Naju location. Differences between full bloom dates observed at Cheonan region and predictions by development stage index were 0-7 days, and matched year was 35.3%. FBDs of 67 locations were distributed from April 4 to May 28. When yearly temperature was raised 1, 2, 3, 4, and $5^{\circ}C$ at 67 locations, predicted FBD was accelerated at most of the locations. However, FBD decelerated at south coast locations from $3^{\circ}C$ rise and did not bloom at 'Gosan', 'Seogwipo', and 'Jeju' locations from $4^{\circ}C$ rise. When monthly temperature was raised 1, 3, and $5^{\circ}C$ at 67 locations, predicted FBD was the most accelerated at March temperature rise, and followed by April, February, January and December. Therefore, global warming will cause acceleration of the full bloom date at pear production areas in Korea.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.12 no.4
• /
• pp.217-224
• /
• 2010

This study was carried out to quantify potential effects of the surrounding ocean on the observed air temperature at coastal weather stations in the Korean Peninsula. Daily maximum and minimum temperature data for 2001-2009 were collected from 66 Korea Meteorological Administration (KMA) stations and the monthly averages were calculated for further analyses. Monthly data from 27 inland sites were used to generate a gridded temperature surface for the whole Peninsula based on an inverse distance weighting and the local temperature at the remaining 39 sites were estimated by recent techniques in geospatial climatology which are widely used in correction of small - scale climate controls like cold air drainage, urban heat island, topography as well as elevation. Deviations from the observed temperature were regarded as the 'apparent' sea effect and showed a quasi-logarithmic relationship with the distance of each site from the nearest coastline. Potential effects of the sea on daily temperature might exceed $6.0^{\circ}C$ cooling in summer and $6.5^{\circ}C$ warming in winter according to this relationship. We classified 25 sites within the 10 km distance from the nearest coastline into 'coastal sites' and the remaining 15 'fringe sites'. When the average deviations of the fringe sites ($0.5^{\circ}C$ for daily maximum and $1.0^{\circ}C$ for daily minimum temperature) were used as the 'noise' and subtracted from the 'apparent' sea effects of the coastal sites, maximum cooling effects of the sea were identified as $1.5^{\circ}C$ on the west coast and $3.0^{\circ}C$ on the east and the south coast in summer months. The warming effects of the sea in winter ranged from $1.0^{\circ}C$ on the west and $3.5^{\circ}C$ on the south and east coasts.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.13 no.1
• /
• pp.41-46
• /
• 2011

Reliable information on the surface solar radiation is indispensable for rebuilding food production system in the famine plagued North Korea. However, transfer of the related modeling technology of South Korea is not possible simply because raw data such as solar radiation or sunshine duration are not available. The objective of this study is restoring solar radiation data at 27 synoptic stations in North Korea by using satellite remote sensing data. We derived relationships between MODIS radiation estimates and the observed solar radiation at 18 locations in South Korea. The relationships were used to adjust the MODIS based radiation data and to restore solar radiation data at those pixels corresponding to the 27 North Korean synoptic stations. Inverse distance weighted averaging of the restored solar radiation data resulted in gridded surfaces of monthly solar radiation for 4 decadal periods (1983-1990, 1991-2000 and 2001-2010), respectively. For a direct application of these products, we produced solar irradiance estimates for each sub-grid cell with a 30 m spacing based on a sun-slope geometry. These products are expected to assist planning of the North Korean agriculture and, if combined with the already prepared South Korean data, can be used for climate change impact assessment across the whole Peninsula.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.2 no.2
• /
• pp.47-61
• /
• 2000

Agroclimatic zoning was done for paddy rice culture in North Korea based on a simulation experiment. Daily weather data for the experiment were generated by 3 steps consisting of spatial interpolation based on topoclimatological relationships, zonal summarization of grid cell values, and conversion of monthly climate data to daily weather data. Regression models for monthly climatological temperature estimation were derived from a statistical procedure using monthly averages of 51 standard weather stations in South and North Korea (1981-1994) and their spatial variables such as latitude, altitude, distance from the coast, sloping angle, and aspect-dependent field of view (openness). Selected models (0.4 to 1.6$^{\circ}C$ RMSE) were applied to the generation of monthly temperature surface over the entire North Korean territory on 1 km$\times$l km grid spacing. Monthly precipitation data were prepared by a procedure described in Yun (2000). Solar radiation data for 27 North Korean stations were reproduced by applying a relationship found in South Korea ([Solar Radiation, MJ m$^{-2}$ day$^{-1}$ ] =0.344 + 0.4756 [Extraterrestrial Solar Irradiance) + 0.0299 [Openness toward south, 0 - 255) - 1.307 [Cloud amount, 0 - 10) - 0.01 [Relative humidity, %), $r^2$=0.92, RMSE = 0.95 ). Monthly solar irradiance data of 27 points calculated from the reproduced data set were converted to 1 km$\times$1 km grid data by inverse distance weighted interpolation. The grid cell values of monthly temperature, solar radiation, and precipitation were summed up to represent corresponding county, which will serve as a land unit for the growth simulation. Finally, we randomly generated daily maximum and minimum temperature, solar irradiance and precipitation data for 30 years from the monthly climatic data for each county based on a statistical method suggested by Pickering et a1. (1994). CERES-rice, a rice growth simulation model, was tuned to accommodate agronomic characteristics of major North Korean cultivars based on observed phenological and yield data at two sites in South Korea during 1995~1998. Daily weather data were fed into the model to simulate the crop status at 183 counties in North Korea for 30 years. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to score the suitability of the county for paddy rice culture.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.2
• /
• pp.63-77
• /
• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.