• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.641-645
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• 2017

This study was conducted to compare three agro-climatic indices among 22 agro-climatic zones in Northeast China area. Meteorological data produced by NASA (MERRA-2) was used to calculate growing degree days (GDD), frost free period (FFP), and growth season length (GSL) at this study sites. The three indices did not differ among 6 years (2011-2016). However, they showed statistical spatial difference among agro-climatic zones. The GDD ranged between $531.7^{\circ}C{\cdot}day$ (zone 22) and $1650.6^{\circ}C{\cdot}day$ (zone 1). The range of the FFP was from 141.5 day (zone 22) to 241.7 day (zone 1). And the GSL showed spatial distribution between 125.1 day (zone 22) and 217.9 day (zone 1).

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.108-125
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• 2015

The agro-climatic index is one of the ways to assess the climate resources of particular agricultural areas on the prospect of agricultural production; it can be a key indicator of agricultural productivity by providing the basic information required for the implementation of different and various farming techniques and practicalities to estimate the growth and yield of crops from the climate resources such as air temperature, solar radiation, and precipitation. However, the agro-climate index can always be changed since the index is not the absolute. Recently, many studies which consider uncertainty of future climate change have been actively conducted using multi-model ensemble (MME) approach by developing and improving dynamic and statistical downscaling of Global Climate Model (GCM) output. In this study, the agro-climatic index of Korean Peninsula, such as growing degree day based on $5^{\circ}C$, plant period based on $5^{\circ}C$, crop period based on $10^{\circ}C$, and frost free day were calculated for assessment of the spatio-temporal variations and uncertainties of the indices according to climate change; the downscaled historical (1976-2005) and near future (2011-2040) RCP climate sceneries of AR5 were applied to the calculation of the index. The result showed four agro-climatic indices calculated by nine individual GCMs as well as MME agreed with agro-climatic indices which were calculated by the observed data. It was confirmed that MME, as well as each individual GCM emulated well on past climate in the four major Rivers of South Korea (Han, Nakdong, Geum, and Seumjin and Yeoungsan). However, spatial downscaling still needs further improvement since the agro-climatic indices of some individual GCMs showed different variations with the observed indices at the change of spatial distribution of the four Rivers. The four agro-climatic indices of the Korean Peninsula were expected to increase in nine individual GCMs and MME in future climate scenarios. The differences and uncertainties of the agro-climatic indices have not been reduced on the unlimited coupling of multi-model ensembles. Further research is still required although the differences started to improve when combining of three or four individual GCMs in the study. The agro-climatic indices which were derived and evaluated in the study will be the baseline for the assessment of agro-climatic abnormal indices and agro-productivity indices of the next research work.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.4
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• pp.113-120
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• 2008

The increase in average air temperature over the past 100 years in northern Asia including Korea is the greatest (about ${1.5}^{\circ}C$) among the various regions of the world. Considering a further warming projected by the IPCC, fluctuations of agro-climatic indices under climate change must precede an evaluation of vulnerability. The purpose of this study is to analyze how climate changes represented by global warming have altered agro-climatic indices in Korea over various time scales. Drought index during the rice-transplanting period of 15 May to 5 June has changed toward the favorable with recently increased precipitation in the Taebaek Alpine and Semi-Alpine Zone, and Yeongnam Basin and Inland Zone. The frequency of low temperature occurrence below $13^{\circ}C$ during the rice transplanting has decreased, while climatic production index (CPI) has fallen because of the decreased sunshine hour and increased temperature during the rice ripening period. We therefore concluded that the recent change of climate conditions was against the rice productivity in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.11-22
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• 2010

According to IPCC 4th Assessment Report, concentration of carbon dioxide has been increasing by 30% since Industrial Revolution. Most of IPCC $CO_2$ emission scenarios estimate that the concentration will reach up to double of its present level within 100-year if the current tendency continues. The global warming has resulted in the agro-climate change over the Korean Peninsula as well. Accordingly, it is necessary to understand the future agro-climate induced by the increase of greenhouse gases in terms of the agro-climatic indices in the Korean peninsula. In this study, the future climate is simulated by an atmosphere/ocean/land surface/sea ice coupled general circulation climate model, Pusan National University Coupled General Circulation Model(hereafter, PNU CGCM), and by a regional weather prediction model, Weather Research and Forecasting Model(hereafter, WRF) for the purpose of a dynamical downscaling. The changes of the vegetable period and the crop growth period, defined as the total number of days of a year exceeding daily mean temperature of 5 and 10, respectively, have been analyzed. Our results estimate that the beginning date of vegetable and crop growth periods get earlier by 3.7 and 17 days, respectively, in spring under the $CO_2$-doubled climate. In most of the Korean peninsula, the predicted frost days in spring decrease by 10 days. Climatic production index (CPI), which closely represent the productivity of rice, tends to increase in the double $CO_2$ climate. Thus, it is suggested that the future $CO_2$ doubled climate might be favorable for crops due to the decrease of frost days in spring, and increased temperature and insolation during the heading date as we expect from the increased CPI.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.110-119
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• 2017

The agroclimatic indices are produced by statistical analysis based on primary climate data (e.g., temperature, precipitation, and solar irradiance) or driving agronomic models. This study was carried out to evaluate how selection of daily temperature for a climate normal (1983-2012) affected the precision of the agroclimatic indices. As a first step, averaged daily 0600 and 1500 LST temperature for a climate normal were produced by geospatial schemes based on topo-climatology ($365days{\times}1$ set, EST normal year). For comparison, 30 years daily temperature data were generated by applying the same process ($365days{\times}30sets$), and calculated mean of daily temperature (OBS normal year). The flowering date of apple 'Fuji' cultivar, the last frost date, and the risk of late frost were estimated based on EST normal year data and compared with the results from OBS normal year. The results on flowering date showed 2.9 days of error on average. The last frost date was of 11.4 days of error on average, which was relatively large. Additionally, the risk of the late frost was determined by the difference between the flowering and the last frost date. When it was determined based on the temperature of EST normal year, Akyang was classified as a risk area because the results showed that the last frost date would be the same or later than the flowering date in the 12.5% of area. However, the temperature of OBS normal year indicated that the area did not have the risk of a late frost. The results of this study implied that it would be necessary to reduce the error by replacing the EST method with the OBS method in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.1-10
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• 2010

A regional climate model (RCM) can be a powerful tool to enhance spatial resolution of climate and weather information (IPCC, 2001). In this study we conducted dynamical downscaling using Weather Research and Forecasting Model (WRF) as a RCM in order to obtain high resolution regional agroclimate indices over the Korean Peninsula. For the purpose of obtaining detailed high resolution agroclimate indices, we first reproduced regional weather for the period of March to June, 2002-2008 with dynamic downscaling method under given lateral boundary conditions from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data. Normally, numerical model results have shown biases against observational results due to the uncertainties in the modelis initial conditions, physical parameterizations and our physical understanding on nature. Hence in this study, by employing a statistical method, the systematic bias in the modelis results was estimated and corrected for better reproduction of climate on high resolution. As a result of the correction, the systematic bias of the model was properly corrected and the overall spatial patterns in the simulation were well reproduced, resulting in more fine-resolution climatic structures. Based on these results, the fine-resolution agro-climate indices were estimated and presented. Compared with the indices derived from observation, the simulated indices reproduced the major and detailed spatial distributions. Our research shows a possibility to simulate regional climate on high resolution and agro-climate indices by using a proper downscaling method with a dynamical weather forecast model and a statistical correction method to minimize the model bias.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.120-129
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• 2017

Agrometeorological advisories for farms and orchards are issued when daily weather exceeds a predefined range of the local reference climate, which is a long-term average of daily weather for the location. The reference climate at local scales is prepared by various simplification methods, resulting in uncertainty in the agrometeorological advisories. We restored daily weather data for the 1981-2010 period and analyzed the differences in prediction results of weather risk by comparing with the temporal and spatial simplified normal climate values. For this purpose, we selected the agricultural drought index (ADI) among various disaster related indices because ADI requires many kinds of weather data to calculate it. Ten rural counties within the Seomjin River Basin were selected for this study. The normal value of 'temporal simplification' was calculated by using the daily average value for 30 years (1981-2010). The normal value of 'spatial simplification' is the zonal average of the temporally simplified normal values falling within a standard watershed. For residual moisture index, temporal simplification normal values were overestimated, whereas spatial simplification normal values were underestimated in comparison with non-simplified normal values. The ADI's calculated from January to July 2017 showed a significant deviation in terms of the extent of drought depending on the normal values used. Through this study, we confirmed that the result of weather risk calculation using normal climatic values from 'simplified' methods can affect reliability of the agrometeorological advisories.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.3
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• pp.200-207
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• 2003

'||'||'||'&'||'||'||'quot;Overheating index'||'||'||'&'||'||'||'quot;, the normalized difference in incident solar energy between a target surface and a level surface, is helpful in estimating the spatial variation in daily maximum temperature at the landscape scale. It can be computed as the ratio of the 4-hour cumulative solar irradiance surplus or deficit from that over a level surface to the maximum possible deviation (15 MJ $m^{-2}$ ) during the midafternoon. Ecosystem models may, for simplicity, use an empirical proxy (exposure index) variable combining slope and aspect in place of the overheating index to account for the variation of midafternoon solar irradiance. A comparative study with real-world landscape data was carried out to evaluate the performance of exposure index in replacing the overheating index. Overheating indices for summer solstice, fall equinox and winter solstice were calculated at 573,650 grid cells constituting the land surface of Donggye-Myun, Sunchang County in Korea, based on a 10-m DEM. Exposure index was also calculated for the same area and fitted for the variation of overheating index to derive a 2$^{nd}$ -order linear regression equation. The coefficient of determination ($R^2$) was 0.50 on summer solstice, 0.56 on fall equinox, and 0.44 on winter solstice, respectively. These are much lower than the theoretically calculated $R^2$ values ranging from 0.7 in summer to 0.9 in autumn. According to our study, exposure index failed to accurately predict the cumulative solar irradiance over a complex terrain, hindering its application to daily maximum temperature estimation. We suggest direct calculation of the overheating index in preference to using the exposure index.