| LDR | | 01957nmm uu200373 4500 |
| 001 | | 000000331338 |
| 005 | | 20240805164203 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438136182 |
| 035 | |
▼a (MiAaPQ)AAI10903793 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 551 |
| 100 | 1 |
▼a Ying, Yue. |
| 245 | 10 |
▼a Ensemble Data Assimilation for the Analysis and Prediction of Multiscale Tropical Weather Systems. |
| 260 | |
▼a [S.l.] :
▼b The Pennsylvania State University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 195 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The Pennsylvania State University, 2018. |
| 520 | |
▼a Tropical weather systems are important components of the global circulation that span a wide range of spatial and temporal scales. On the large-scale end of the spectrum, the Madden-Julian Oscillation (MJO) is found to be the dominant mode. Atmo |
| 520 | |
▼a Using a convection-permitting numerical model, Weather Research and Forecasting (WRF), an MJO active phase during October 2011 is simulated. The practical predictability limit is estimated from an ensemble forecast with realistic initial and bou |
| 520 | |
▼a For ensemble filtering, covariance localization and inflation methods are required to account for sampling errors due to limited ensemble size and unrepresented model errors. Tuning the localization and inflation to achieve optimal filter perfor |
| 590 | |
▼a School code: 0176. |
| 650 | 4 |
▼a Meteorology. |
| 690 | |
▼a 0557 |
| 710 | 20 |
▼a The Pennsylvania State University.
▼b Meteorology. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0176 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000750
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |