Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Joon Jin Song"'
Publikováno v:
Atmosphere, Vol 15, Iss 8, p 1018 (2024)
Accurately generating high-resolution surface grid datasets often involves merging multiple weather observation networks and addressing the challenge of network heterogeneity. This study aims to tackle the problem of accurately interpolating temperat
Externí odkaz:
https://doaj.org/article/e636fb85c1f749cc892be6e9dfe077d3
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 1, Pp n/a-n/a (2024)
Abstract Retrieving raindrop size distribution (DSD) is essential to understanding precipitation processes. Conventional approaches based on polarimetric radar (e.g., polynomial regression) struggle to accurately capture the inherent nonlinearity bet
Externí odkaz:
https://doaj.org/article/2e0811bfe2db4d84835083c47184a97c
Publikováno v:
Remote Sensing, Vol 14, Iss 15, p 3820 (2022)
An accurate classification of the precipitation type is important for forecasters, particularly in the winter season. We explored the capability of three supervised machine learning (ML) methods (decision tree, random forest, and support vector machi
Externí odkaz:
https://doaj.org/article/58199e7c6338470c84e937389703928c
Publikováno v:
Remote Sensing, Vol 13, Iss 11, p 2039 (2021)
Estimating precipitation area is important for weather forecasting as well as real-time application. This paper aims to develop an analytical framework for efficient precipitation area estimation using S-band dual-polarization radar measurements. Sev
Externí odkaz:
https://doaj.org/article/90fb894f34d0416598b5e31fab48f3fc
Publikováno v:
Remote Sensing, Vol 13, Iss 4, p 694 (2021)
Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Zh, R–(Zh, ZDR), etc. One of the biggest advantages of machine learning algorithms
Externí odkaz:
https://doaj.org/article/0a75ba9e208943609f76584532751f25
Publikováno v:
Mathematics, Vol 8, Iss 10, p 1678 (2020)
Recently, the area of sea ice is rapidly decreasing due to global warming, and since the Arctic sea ice has a great impact on climate change, interest in this is increasing very much all over the world. In fact, the area of sea ice reached a record l
Externí odkaz:
https://doaj.org/article/c828cbe4044f45fbbe5df0ee32ec63a9
Publikováno v:
Journal of the Korean Data And Information Science Society. 33:299-309
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc85505a1773ee9fe3dd0cba35ff7832
https://doi.org/10.7465/jkdi.2022.33.2.299
https://doi.org/10.7465/jkdi.2022.33.2.299
Publikováno v:
Technometrics. 64:65-79
Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA’s Orbiting Carbon Observatory-2 (OCO-...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::854e3538f455033dc7286699756b37a2
https://doi.org/10.1080/00401706.2021.1895890
https://doi.org/10.1080/00401706.2021.1895890
Autor:
Nimrod Carmon, Alexander Berk, Niklas Bohn, Phillip G. Brodrick, Jeff Dozier, Margaret Johnson, Charles E. Miller, David R. Thompson, Michael Turmon, Charles M. Bachmann, Robert O. Green, Regina Eckert, Elliott Liggett, Hai Nguyen, Francisco Ochoa, Gregory S. Okin, Rory Samuels, David Schimel, Joon Jin Song, Jouni Susiluoto
Publikováno v:
Remote Sensing of Environment. 288:113497
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c4da0b11d73fa06ef21f075284485c6
https://doi.org/10.1016/j.rse.2023.113497
https://doi.org/10.1016/j.rse.2023.113497
Publikováno v:
Asia-Pacific Journal of Atmospheric Sciences. 57:331-345
In this work, we suggest new spatial precipitation interpolation schemes using compressed sensing (CS), which is a new framework for signal acquisition and smart sensor design. Using CS, the precipitation maps are recovered in high resolution by obta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe9bcf92df8b38b6dd02e8eb2c8b1f9d
https://doi.org/10.1007/s13143-020-00200-7
https://doi.org/10.1007/s13143-020-00200-7