Vote for your favorite mineral in #MinCup25! - Silver vs. Baryte
Are you ready for beautiful utility as sparkling silver competes against hefty baryte?
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Seto, Rie, Koike, Toshio, Rasmy, Mohamed (2016) Heavy rainfall prediction applying satellite-based cloud data assimilation over land. Journal of Geophysical Research: Atmospheres, 121 (16). 9737-9755 doi:10.1002/2016jd025291

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleHeavy rainfall prediction applying satellite-based cloud data assimilation over land
JournalJournal of Geophysical Research: Atmospheres
AuthorsSeto, RieAuthor
Koike, ToshioAuthor
Rasmy, MohamedAuthor
Year2016 (August 27)Volume121
Issue16
PublisherAmerican Geophysical Union (AGU)
DOIdoi:10.1002/2016jd025291Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID4610012Long-form Identifiermindat:1:5:4610012:6
GUID0
Full ReferenceSeto, Rie, Koike, Toshio, Rasmy, Mohamed (2016) Heavy rainfall prediction applying satellite-based cloud data assimilation over land. Journal of Geophysical Research: Atmospheres, 121 (16). 9737-9755 doi:10.1002/2016jd025291
Plain TextSeto, Rie, Koike, Toshio, Rasmy, Mohamed (2016) Heavy rainfall prediction applying satellite-based cloud data assimilation over land. Journal of Geophysical Research: Atmospheres, 121 (16). 9737-9755 doi:10.1002/2016jd025291
In(2016, August) Journal of Geophysical Research: Atmospheres Vol. 121 (16) American Geophysical Union (AGU)

See Also

These are possibly similar items as determined by title/reference text matching only.

Seto, Rie, Koike, Toshio, Rasmy, Mohamed (2013) Analysis of the vertical structure of the atmospheric heating process and its seasonal variation over the Tibetan Plateau using a land data assimilation system. Journal of Geophysical Research: Atmospheres, 118 (22). 12 doi:10.1002/2013jd020072
Seto, R., Koike, T., Kanae, S. (2018) Representing Cloud Water Content of Extensive Cloud Systems Over Land Using Satelliteā€Based Passive Microwave Observations With a Coupled Land and Atmosphere Assimilation Method. Journal of Geophysical Research: Atmospheres, 123 (22). 12829-12856 doi:10.1029/2018jd028864
Sawada, Yohei, Koike, Toshio (2016) Towards ecohydrological drought monitoring and prediction using a land data assimilation system: A case study on the Horn of Africa drought (2010-2011). Journal of Geophysical Research: Atmospheres, 121 (14). 8229-8242 doi:10.1002/2015jd024705
Seto, Rie, Aida, Kentaro, Koike, Toshio, Kanae, Shinjiro (2021) Radiative Characteristics at 89 and 36 GHz for Satellite-Based Cloud Water Estimation Over Land. IEEE Transactions on Geoscience and Remote Sensing, 59 (2) 1355-1368 doi:10.1109/tgrs.2020.2996239
Seto, Rie, Koike, Toshio, Kachi, Misako (2024) Feasibility of Liquid Water Path Estimation of Over Land Using Satellite-Based Ka-Band Passive Microwave Data. IEEE Transactions on Geoscience and Remote Sensing, 62. 1-20 doi:10.1109/tgrs.2023.3340264
Sawada, Yohei, Koike, Toshio, Walker, Jeffrey P. (2015) A land data assimilation system for simultaneous simulation of soil moisture and vegetation dynamics. Journal of Geophysical Research: Atmospheres, 120 (12). 5910-5930 doi:10.1002/2014jd022895
Sawada, Yohei, Tsutsui, Hiroyuki, Koike, Toshio, Rasmy, Mohamed, Seto, Rie, Fujii, Hideyuki (2016) A Field Verification of an Algorithm for Retrieving Vegetation Water Content From Passive Microwave Observations. IEEE Transactions on Geoscience and Remote Sensing, 54 (4) 2082-2095 doi:10.1109/tgrs.2015.2495365
Rasmy, Mohamed, Koike, Toshio, Kuria, David, Mirza, Cyrus Raza, Li, Xin, Yang, Kun (2012) Development of the Coupled Atmosphere and Land Data Assimilation System (CALDAS) and Its Application Over the Tibetan Plateau. IEEE Transactions on Geoscience and Remote Sensing, 50 (11) 4227-4242 doi:10.1109/tgrs.2012.2190517
Rasmy, Mohamed, Koike, Toshio, Li, Xin (2014) Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa. Remote Sensing, 6 (6). doi:10.3390/rs6065306
Lu, Hui, Koike, Toshio, Yang, Kun, Hu, Zeyong, Xu, Xiangde, Rasmy, Mohamed, Kuria, David, Tamagawa, Katsunori (2012) Improving land surface soil moisture and energy flux simulations over the Tibetan plateau by the assimilation of the microwave remote sensing data and the GCM output into a land surface model. International Journal of Applied Earth Observation and Geoinformation, 17. 43-54 doi:10.1016/j.jag.2011.09.006
Zhang, Yong-Fei, Yang, Zong-Liang (2016) Estimating uncertainties in the newly developed multi-source land snow data assimilation system. Journal of Geophysical Research: Atmospheres, 121 (14). 8254-8268 doi:10.1002/2015jd024248
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Ā© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833ā€“844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 5, 2025 11:43:45
Go to top of page