1 |
Sun, Q., C. Miao, Q. Duan, H. Ashouri, S. Sorooshian, and K.L. Hsu, 2018. A review of global precipitation data sets: Data sources, estimation, and intercomparisons, Reviews of Geophysics, 56(1): 79-107.
DOI
|
2 |
Vicente, G.A., R.A. Scofield, and W.P. Menzel, 1998. The operational GOES infrared rainfall estimation technique, Bulletin of the American Meteorological Society, 79(9): 1883-1898.
DOI
|
3 |
Yoon, S., 2013. statistical of extreme rainfall events and applications of radar rainfall estimates for reducing flood risk in Gyeongnam area, Kyungsang University, Jinju, KR (in Korean with English abstract).
|
4 |
Hou, A.Y., R.K. Kakar, S. Neeck, A.A. Azarbarzin, C.D. Kummerow, M. Kojima, R. Oki, K. Nakamura, and T. Iguchi, 2014. The global precipitation measurement mission, Bulletin of the American Meteorological Society, 95(5): 701-722.
DOI
|
5 |
Tao, Y., K. Hsu, A. Ihler, X. Gao, and S. Sorooshian, 2018. A two-stage deep neural network framework for precipitation estimation from bispectral satellite information, Journal of Hydrometeorology, 19(2): 393-408.
DOI
|
6 |
Chung, S.-R., M.-H. Ahn, K.-S. Han, K.-T. Lee, and D.-B. Shin, 2020. Meteorological Products of Geo-KOMPSAT 2A (GK2A) Satellite, Asia-Pacific Journal of Atmospheric Sciences, 56: 185.
DOI
|
7 |
Hong, Y., K.-L. Hsu, S. Sorooshian, and X. Gao, 2004. Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system, Journal of Applied Meteorology, 43(12): 1834-1853.
DOI
|
8 |
Breiman, L., 2001. Random forests, Machine Learning, 45(1): 5-32.
DOI
|
9 |
Miao, C., H. Ashouri, K.-L. Hsu, S. Sorooshian, and Q. Duan, 2015. Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China, Journal of Hydrometeorology, 16(3): 1387-1396.
DOI
|
10 |
Huffman, G.J., R.F. Adler, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. Rudolf, and U. Schneider, 1997. The global precipitation climatology project (GPCP) combined precipitation dataset, Bulletin of the American Meteorological Society, 78(1): 5-20.
DOI
|
11 |
Jang, E., Y.J. Kim, J. Im, and Y.-G. Park, 2021. Improvement of SMAP sea surface salinity in river-dominated oceans using machine learning approaches, GIScience and Remote Sensing, 58(1): 138-160.
DOI
|
12 |
Kidd, C. and G. Huffman, 2011. Global precipitation measurement, Meteorological Applications, 18(3): 334-353.
DOI
|
13 |
Sadeghi, M., A.A. Asanjan, M. Faridzad, P. Nguyen, K. Hsu, S. Sorooshian, and D. Braithwaite, 2019. PERSIANN-CNN: Precipitation estimation from remotely sensed information using artificial neural networks-convolutional neural networks, Journal of Hydrometeorology, 20(12): 2273-2289.
DOI
|
14 |
Han, D., J. Lee, J. Im, S. Sim, S. Lee, and H. Han, 2019. A novel framework of detecting convective initiation combining automated sampling, machine learning, and repeated model tuning from geostationary satellite data, Remote Sensing, 11(12): 1454.
DOI
|
15 |
Kim, M., J. Lee, and J. Im, 2018. Deep learning-based monitoring of overshooting cloud tops from geostationary satellite data, GIScience and Remote Sensing, 55(5): 763-792.
DOI
|
16 |
Marshall, J.S., 1948. The distribution of raindrops with size, Quarterly Journal of the Royal Meteorological Society, 76(327): 165-166.
|
17 |
Meisner, B.N., and P.A. Arkin, 1987. Spatial and annual variations in the diurnal cycle of large-scale tropical convective cloudiness and precipitation, Monthly Weather Review, 115(9): 2009-2032.
DOI
|
18 |
Prigent, C., 2010. Precipitation retrieval from space: An overview, Comptes Rendus Geoscience, 342(4-5): 380-389.
DOI
|
19 |
Shin, J.-Y., Y. Ro, J.-W. Cha, K.-R. Kim, and J.-C. Ha, 2019. Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018, Advances in Meteorology, 2019.
|
20 |
Sakolnakhon, K. and S. Nuntakamolwaree, 2016. The estimation rainfall using infrared (IR) band of Himawari-8 satellite over Thailand, Engineering: Naresuan University, 39: 236-248.
|
21 |
Tsay, J.-D., K. Kao, C.-C. Chao, and Y.-C. Chang, 2020. Deep learning for satellite rainfall retrieval using Himawari-8 multiple spectral channels, Preprints, 2020100648.
|
22 |
Marcos, C. and A. Rodriguez, 2013. Validation report for "convective rainfall rate"(CRR-PGE05 v4.0). NWC-CDOP2-GEO-AEMET-SCI-ATBD-Precipitation_v1.1, 15p. Available online at https://www.nwcsaf.org/documents/20182/30785/NWC-CDOP2-GEOAEMET-SCI-ATBD-Precipitation_v1.1.pdf/091e7abe-518d-4b55-a744-cccd12d9f2b9, Accessed on Oct. 19, 2021.
|
23 |
Sorooshian, S., K.-L. Hsu, X. Gao, H.V. Gupta, B. Imam, and D. Braithwaite, 2000. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall, Bulletin of the American Meteorological Society, 81(9): 2035-2046.
DOI
|
24 |
Mishra, K.V., A. Gharanjik, M.B. Shankar, and B. Ottersten, 2018. Deep learning framework for precipitation retrievals from communication satellites, Proc. of 10th European Conference on Radar in Meteorology and Hydrology, Wageningen, NL, Jul. 1-6, pp.1-9.
|
25 |
Saltikoff, E., K. Friedrich, J. Soderholm, K. Lengfeld, B. Nelson, A. Becker, R. Hollmann, B. Urban, M. Heistermann, and C. Tassone, 2019. An overview of using weather radar for climatological studies: Successes, challenges, and potential, Bulletin of the American Meteorological Society, 100(9): 1739-1752.
DOI
|
26 |
Song, H.-J. and B.-J. Sohn, 2015. Two heavy rainfall types over the Korean peninsula in the humid East Asian summer environment: A satellite observation study, Monthly Weather Review, 143(1): 363-382.
DOI
|
27 |
Wu, H., Q. Yang, J. Liu, and G. Wang, 2020. A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China, Journal of Hydrology, 584: 124664.
DOI
|
28 |
Yoo, C., J. Im, S. Park, and D. Cho, 2017. Thermal characteristics of Daegu using land cover data and satellite-derived surface temperature downscaled based on machine learning, Korean Journal of Remote Sensing, 33(6-2): 1101-1118 (in Korean with English abstract).
DOI
|
29 |
Yu, P.-S., T.-C. Yang, S.-Y. Chen, C.-M. Kuo, and H.-W. Tseng, 2017. Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting, Journal of Hydrology, 552: 92-104.
DOI
|
30 |
Ahn, S.-H., K.-J. Park, J.-Y. Kim, and B.-J. Kim, 2015. The characteristics of the frequency and damage for meteorological disasters in Korea, Journal of Korean Society of Hazard Mitigation, 15(2): 133-144 (in Korean with English abstract).
DOI
|
31 |
Ashouri, H., K.-L. Hsu, S. Sorooshian, D.K. Braithwaite, K.R. Knapp, L.D. Cecil, B.R. Nelson, and O.P. Prat, 2015. PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies, Bulletin of the American Meteorological Society, 96(1): 69-83.
DOI
|
32 |
Griffith, C.G., W.L. Woodley, P.G. Grube, D.W. Martin, J. Stout, and D.N. Sikdar, 1978. Rain estimation from geosynchronous satellite imagery-Visible and infrared studies, Monthly Weather Review, 106(8): 1153-1171.
DOI
|
33 |
Cho, D., C. Yoo, J. Im, Y. Lee, and J. Lee, 2020. Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique, GIScience & Remote Sensing, 57(5): 633-649.
DOI
|
34 |
Choi, H., J.-J. Seo, J. Bae, S. Kim, and K.-M. Lee, 2018. Improvement of Non-linear Estimation Equation of Rainfall Intensity over the Korean Peninsula by using the Brightness Temperature of Satellite and Radar Reflectivity Data, Journal of the Korean Earth Science Society, 39(2): 131-138 (in Korean with English abstract).
DOI
|
35 |
Germann, U., G. Galli, M. Boscacci, and M. Bolliger, 2006. Radar precipitation measurement in a mountainous region, Quarterly Journal of the Royal Meteorological Society: A Journal of the Atmospheric Sciences, Applied Meteorology and Physical Oceanography, 132(618): 1669-1692.
DOI
|
36 |
Joss, J., A. Waldvogel, and C. Collier, 1990. Precipitation measurement and hydrology, Radar in Meteorology, 1990: 577-606.
|
37 |
Hong, Y., D. Gochis, J.-T. Cheng, K.-l. Hsu, and S. Sorooshian, 2007. Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network, Journal of Hydrometeorology, 8(3): 469-482.
DOI
|
38 |
Hsu, K.-L., X. Gao, S. Sorooshian, and H.V. Gupta, 1997. Precipitation estimation from remotely sensed information using artificial neural networks, Journal of Applied Meteorology, 36(9): 1176-1190.
DOI
|