References
- Ahn, J.-B., J. Hur, and A.-Y. Lim, 2014: Estimation of fine-scale daily temperature with 30 m-resolution using PRISM. Atmosphere 24(1), 101-110. https://doi.org/10.14191/Atmos.2014.24.1.101 (in Korean with English abstract)
- Baigorria, G. A., J. W. Jones, and J. J. O'Brien, 2008: Potential predictability of crop yield using an ensemble climate forecast by a regional circulation model. Agricultural and Forest Meteorology 148(8-9), 1353-1361. https://doi.org/10.1016/j.agrformet.2008.04.002
- Ban, H.-Y., J.-B. Ahn, and B.-W. Lee, 2019: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions. Plos One 14(2), e0211874. https://doi.org/10.1371/journal.pone.0211874
- Battisti, R., P. C. Sentelhas, P. S. Parker, C. Nendel, G. M. D. S. Camara, J. R. B. Farias, and C. J. Basso, 2018: Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil. Crop and Pasture Science 69(2).
- Bohra, A., S. Basu, E. Rajagopal, G. Iyengar, M. D. Gupta, R. Ashrit, and B. Athiyaman, 2006: Heavy rainfall episode over Mumbai on 26 July 2005: Assessment of NWP guidance. Current Science, 1188-1194.
- Boote, K., J. Jones, W. Batchelor, E. Nafziger, and O. Myers, 2003: Genetic coefficients in the CROPGRO-Soybean model: Links to field performance and genomics. Agronomy Journal 95(1), 32-51.
- Brunet, D., E. R. Vrscay, and Z. Wang, 2011: On the mathematical properties of the structural similarity index. IEEE Transactions on Image Processing 21(4), 1488-1499. https://doi.org/10.1109/TIP.2011.2173206
- Cantelaube, P., and J.-M. Terres, 2005: Seasonal weather forecasts for crop yield modelling in Europe. Tellus A: Dynamic Meteorology and Oceanography 57(3), 476-487. https://doi.org/10.3402/tellusa.v57i3.14669
- Castro, J. C., F. G. Dohleman, C. J. Bernacchi, and S. P. Long, 2009: Elevated CO2 significantly delays reproductive development of soybean under Free-Air Concentration Enrichment (FACE). Journal of Experimental Botany 60(10), 2945-2951. https://doi.org/10.1093/jxb/erp170
- Chen, J., F. P. Brissette, and R. Leconte, 2011: Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. Journal of Hydrology 401(3-4), 190-202. https://doi.org/10.1016/j.jhydrol.2011.02.020
- Chipanshi, A., Y. Zhang, L. Kouadio, N. Newlands, A. Davidson, H. Hill, R. Warren, B. Qian, B. Daneshfar, F. Bedard, and G. Reichert, 2015: Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape. Agricultural and Forest Meteorology 206, 137-150. https://doi.org/10.1016/j.agrformet.2015.03.007
- Coucheney, E., H. Eckersten, H. Hoffmann, P.-E. Jansson, T. Gaiser, F. Ewert, and E. Lewan, 2018: Key functional soil types explain data aggregation effects on simulated yield, soil carbon, drainage and nitrogen leaching at a regional scale. Geoderma 318, 167-181. https://doi.org/10.1016/j.geoderma.2017.11.025
- Daly, C., 2006: Guidelines for assessing the suitability of spatial climate data sets. International Journal Climatology 26(6), 707-721. https://doi.org/10.1002/joc.1322
- Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain. Journal of Applied Meteorology and Climatology 33(2), 140-158. https://doi.org/10.1175/1520-0450(1994)033<0140:Astmfm>2.0.Co;2
- Daly, C., E. H. Helmer, and M. Quinones, 2003: Mapping the climate of Puerto Rico, Vieques and Culebra. International Journal Climatology 23(11), 1359-1381, https://doi.org/10.1002/joc.937
- Daly, C., W. P. Gibson, G. H. Taylor, G. H. Taylor, and P. Pasteris, 2002: A knowledge-based approach to the statistical mapping of climate. Climate Research 22(2), 99-113. https://doi.org/10.3354/cr022099
- Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal Climatology 28(15), 2031-2064. https://doi.org/10.1002/joc.1688
- De Wit, A. d., and C. Van Diepen, 2007: Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology 146(1-2), 38-56. https://doi.org/10.1016/j.agrformet.2007.05.004
- Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of Atmospheric Sciences 46(20), 3077-3107. https://doi.org/10.1175/1520-0469(1989)046<3077:Nsocod>2.0.Co;2
- Folberth, C., R. Skalsky, E. Moltchanova, J. Balkovic, L. B. Azevedo, M. Obersteiner, and M. van der Velde, 2016: Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nature Communications 7(1).
- Fujimori, S., T. Iizumi, T. Hasegawa, J. y. Takakura, K. Takahashi, and Y. Hijioka, 2018: Macroeconomic impacts of climate change driven by changes in crop yields. Towards Sustainable Global Food Systems 332.
- Gardner, A., I. Maclean, K. Gaston, and L. Butikofer, 2021: Forecasting future crop suitability with microclimate data. Agricultural Systems 190, 103084. https://doi.org/10.1016/j.agsy.2021.103084
- Gijsman, A. J., P. K. Thornton, and G. Hoogenboom, 2007: Using the WISE database to parameterize soil inputs for crop simulation models. Computers and Electronics in Agriculture 56(2), 85-100. https://doi.org/10.1016/j.compag.2007.01.001
- Hong, K.-O., M.-S. Suh, D.-K. Rha, D.-H. Chang, C. Kim, and M.-K. Kim, 2007: Estimation of high resolution gridded temperature using GIS and PRISM. Atmosphere 17(3), 255-268. (in Korean with English abstract)
- Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). Asia Pacific Journal of Atmospheric Sciences 42(2), 129-151.
- Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review 134(9), 2318-2341. https://doi.org/10.1175/mwr3199.1
- Hur, J., and J.-B. Ahn, 2015: Seasonal prediction of regional surface air temperature and first-flowering date over South Korea. International Journal of Climatology 35(15), 4791-4801. https://doi.org/10.1002/joc.4323
- Irmak, A., J. W. Jones, and S. S. Jagtap, 2005: EVALUATION OF THE CROPGRO-SOYBEAN MODEL FOR ASSESSING CLIMATE IMPACTS ON REGIONAL SOYBEAN YIELDS. Transactions of the ASAE 48(6), 2343-2353. https://doi.org/10.13031/2013.20073
- Jiang, Z., Z. Chen, J. Chen, J. Liu, J. Ren, Z. Li, L. Sun, and H. Li, 2014: Application of crop model data assimilation with a particle filter for estimating regional winter wheat yields. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(11), 4422-4431. https://doi.org/10.1109/JSTARS.2014.2316012
- Kaeomuangmoon, T., A. Jintrawet, C. Chotamonsak, U. Singh, C. Buddhaboon, P. Naoujanon, S. Kongton, Y. Kono, and G. Hoogenboom, 2019: Estimating seasonal fragrant rice production in Thailand using a spatial crop modelling and weather forecasting approach. The Journal of Agricultural Science 157(7-8), 566-577. https://doi.org/10.1017/S0021859619000881
- Kain, J. S., 2004: The Kain-Fritsch Convective Parameterization: An Update. Journal of Applied Meteorology 43(1), 170-181. https://doi.org/10.1175/1520-0450(2004)043<0170:Tkcpau>2.0.Co;2
- Kim, H.-J., and J.-B. Ahn, 2015: Improvement in prediction of the Arctic Oscillation with a realistic ocean initial condition in a CGCM. Journal of Climate 28(22), 8951-8967. https://doi.org/10.1175/jcli-d-14-00457.1
- KREI (Korea Rural Economic Institute), 2021: "농업전망 2021", 328pp.
- Lee, J., M.-N. Shin, B.-I. Ku, K.-B. Shim, and W.-T. Jeon, 2021: Current status and direction of weed management according to cropping systems. KOREAN JOUNAL OF CROP SCIENCE 66(4), 459-466.
- Lee, K.-D., C.-W. Park, S.-I. Na, M.-P. Jung, and J. Kim, 2017: Monitoring on crop condition using remote sensing and model. Korean Journal of Remote Sensing 33(5_2), 617-620. https://doi.org/10.7780/KJRS.2017.33.5.2.1
- Lee, S.-J., E. H. Berbery, and D. Alcaraz-Segura, 2013: Effect of implementing ecosystem functional type data in a mesoscale climate model. Advances in Atmospheric Sciences 30(5), 1373-1386. https://doi.org/10.1007/s00376-012-2143-3
- Liu, Y., K. S. Kim, R. M. Beresford, and D. H. Fleisher, 2020: A generic composite measure of similarity between geospatial variables. Ecological Informatics 60.
- Liu, Y., J. Kim, D. H. Fleisher, and K. S. Kim, 2021: Analogy-based crop yield forecasts based on temporal similarity of leaf area index. Remote Sensing 13(16), 3069. https://doi.org/10.3390/rs13163069
- Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research Atmospheres 102(D14), 16663-16682. https://doi.org/10.1029/97JD00237
- Na, S., S. Hong, Y. Kim, and K. Lee, 2014: Estimation of corn and soybean yields based on MODIS data and CASA model in Iowa and Illinois, USA. Korean Journal of Soil Science and Fertilizer 47(2), 92-99. https://doi.org/10.7745/KJSSF.2014.47.2.092
- NASS (National Agricultural Statistics Service), 2021: Quick Stats, https://quickstats.nass.usda.gov/ (Accessed on 2022 March 21)
- Ovando, G., S. Sayago, and M. Bocco, 2018: Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data. ISPRS Journal of Photogrammetry and Remote Sensing 138, 208-217. https://doi.org/10.1016/j.isprsjprs.2018.02.015
- Palmer, T. N., A. Alessandri, U. Andersen, P. Cantelaube, M. Davey, P. Delecluse, M. Deque, E. Diez, F. J. Doblas-Reyes, and H. Feddersen, 2004: Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bulletin of the American Meteorological Society 85(6), 853-872. https://doi.org/10.1175/BAMS-85-6-853
- Paulson, C. A., 1970: The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. Journal of Applied Meteorology and Climatology 9(6), 857- 861. https://doi.org/10.1175/1520-0450(1970)009<0857:Tmrows>2.0.Co;2
- Rosenzweig, C., J. Elliott, D. Deryng, A. C. Ruane, C. Muller, A. Arneth, K. J. Boote, C. Folberth, M. Glotter, and N. Khabarov, 2014: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences 111(9), 3268-3273. https://doi.org/10.1073/pnas.1222463110
- Shin, D., G. Baigorria, Y. Lim, S. Cocke, T. LaRow, J. J. O'brien, and J. W. Jones, 2009: Assessing crop yield simulations with various seasonal climate data, Science and Technology Infusion Climate Bulletin. NOAA's National Weather Service. 7th NOAA Annual Climate Prediction Application Science Workshop, Norman, OK, 24-27.
- Shin, D., G. Baigorria, Y. Lim, S. Cocke, T. LaRow, J. J. O'Brien, and J. W. Jones, 2010: Assessing maize and peanut yield simulations with various seasonal climate data in the southeastern United States. Journal of Applied Meteorology and Climatology 49(4), 592-603. https://doi.org/10.1175/2009JAMC2293.1
- Shin, S., S.-J. Lee, I. Noh, S.-H. Kim, Y.-Y. So, S. Lee, B. H. Min, and K. R. Kim, 2020: Temperature and solar radiation prediction performance of high-resolution KMAPP model in agricultural areas: Clear sky case studies in Cheorwon and Jeonbuk Province. Korean Journal of Agricultural and Forest Meteorology 22(4), 312-326. https://doi.org/10.5532/KJAFM.2020.22.4.312
- Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2008: A description of the Advanced Research WRF version 3. Tech Rep. No. NCAR/TN-468+STR, National Center for Atmospheric Research, 88pp. [Available online at https://opensky.ucar.edu/islandora/object/technotes:500]
- Song, C.-Y., S.-H. Kim, and J.-B. Ahn, 2021: Improvement in seasonal prediction of precipitation and drought over the United States based on regional climate model using empirical quantile mapping. Atmosphere 31(5), 637-656. https://doi.org/10.14191/Atmos.2021.31.5.1 (in Korean with English abstract)
- Tachikawa, T., M. Hato, M. Kaku, and A. Iwasaki, 2011: Characteristics of ASTER GDEM version 2. International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, IEEE, 3657-3660. https://doi.org/10.1109/IGARSS.2011.6050017
- Togliatti, K., S. V. Archontoulis, R. Dietzel, L. Puntel, and A. VanLoocke, 2017: How does inclusion of weather forecasting impact in-season crop model predictions? Field Crops Research 214, 261-272. https://doi.org/10.1016/j.fcr.2017.09.008
- Wang, J., R. M. Fonseca, K. Rutledge, J. MartinTorres, and J. Yu, 2019: Weather simulation uncertainty estimation using Bayesian hierarchical models. Journal of Applied Meteorology and Climatology 58(3), 585-603. https://doi.org/10.1175/JAMC-D-18-0018.1
- Wang, Z., L. Lu, and A. C. Bovik, 2004: Video quality assessment based on structural distortion measurement. Signal Processing: Image Communication 19(2), 121-132. https://doi.org/10.1016/S0923-5965(03)00076-6
- Yoo, B. H., J. Kim, B.-W. Lee, G. Hoogenboom, and K. S. Kim, 2020: A surrogate weighted mean ensemble method to reduce the uncertainty at a regional scale for the calculation of potential evapotranspiration. Scientific Reports 10(1).
- Yoo, B. H., and K. S. Kim, 2017: Development of a gridded climate data tool for the COordinated Regional climate Downscaling EXperiment data. Computers and Electronics in Agriculture 133, 128-140. https://doi.org/10.1016/j.compag.2016.12.001
- Yoo, B. H., K. S. Kim, and H.-Y. Ban, 2018: Development of a gridded crop growth simulation system for the DSSAT model using script languages. Korean Journal of Agricultural and Forest Meteorology 20(3), 243-251. https://doi.org/10.5532/KJAFM.2018.20.3.243
- Zhao, G., S. Siebert, A. Enders, E. E. Rezaei, C. Yan, and F. Ewert, 2015: Demand for multi-scale weather data for regional crop modeling. Agricultural and Forest Meteorology 200, 156-171. https://doi.org/10.1016/j.agrformet.2014.09.026
- Zhou, W., K. Guan, B. Peng, Z. Wang, R. Fu, B. Li, E. A. Ainsworth, E. DeLucia, L. Zhao, and Z. Chen, 2021: A generic risk assessment framework to evaluate historical and future climate-induced risk for rainfed corn and soybean yield in the U.S. Midwest. Weather and Climate Extremes 33.