東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Numerical weather prediction error o...

Hacker, Joshua P.

FindBook

Google Book

Amazon

博客來

Numerical weather prediction error over the North Pacific and western North America: An investigation with short-range ensemble techniques.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Numerical weather prediction error over the North Pacific and western North America: An investigation with short-range ensemble techniques./
作者:	Hacker, Joshua P.
面頁冊數:	126 p.
附註:	Source: Dissertation Abstracts International, Volume: 63-10, Section: B, page: 4725.
Contained By:	Dissertation Abstracts International63-10B.
標題:	Physics, Atmospheric Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ73172
ISBN:	0612731723

Numerical weather prediction error over the North Pacific and western North America: An investigation with short-range ensemble techniques.
Hacker, Joshua P.

Numerical weather prediction error over the North Pacific and western North America: An investigation with short-range ensemble techniques. - 126 p.

Source: Dissertation Abstracts International, Volume: 63-10, Section: B, page: 4725.

Thesis (Ph.D.)--The University of British Columbia (Canada), 2002.

Numerical weather prediction models, which are discretized approximations to the physical equations of the atmosphere, are a critical part of weather forecasting. They can project an observed state of the atmosphere into the future, but forecasts have many error sources. To counter this, several forecasts made from slightly different initial states or models can be made over the same domain and time period. This approach, called ensemble forecasting, can forecast uncertainty, and produce a better average forecast.

ISBN: 0612731723Subjects--Topical Terms:

1019431
Physics, Atmospheric Science.

Numerical weather prediction error over the North Pacific and western North America: An investigation with short-range ensemble techniques.
LDR:03574nmm 2200337 4500 001 1810283
005 20040126131514.5
008 130610s2002 eng d
020 $a 0612731723
035 $a (UnM)AAINQ73172
035 $a AAINQ73172
040 $a UnM $c UnM
100 1 $a Hacker, Joshua P. $3 1899901
245 1 0 $a Numerical weather prediction error over the North Pacific and western North America: An investigation with short-range ensemble techniques.
300 $a 126 p.
500 $a Source: Dissertation Abstracts International, Volume: 63-10, Section: B, page: 4725.
500 $a Adviser: Roland B. Stull.
502 $a Thesis (Ph.D.)--The University of British Columbia (Canada), 2002.
520 $a Numerical weather prediction models, which are discretized approximations to the physical equations of the atmosphere, are a critical part of weather forecasting. They can project an observed state of the atmosphere into the future, but forecasts have many error sources. To counter this, several forecasts made from slightly different initial states or models can be made over the same domain and time period. This approach, called ensemble forecasting, can forecast uncertainty, and produce a better average forecast.
520 $a In this study, ensemble forecasts are generated and analyzed to understand the nature of initial condition (IC) and model error over the North Pacific. A poorly-predicted storm event (a bust) in Feb. 1999 is a useful case study period. To approximate different aspects of IC uncertainty, three IC-perturbation methods are used: (1) ranked perturbations that target coherent structures in the analyses; (2) perturbations that simulate differences between operational analyses from major forecast centers; and (3) random perturbations. An ensemble of different model configurations approximates model uncertainty.
520 $a Ensembles are verified several ways to separate model and IC uncertainty, and evaluate ensemble performance. It is found that during the period surrounding the bust, IC error is greater than model error. But for one critical forecast, model error is unusually high while error from ICs is unusually small.
520 $a Comparison with rawinsonde observations shows that differences between operational analyses cannot account for analysis error. Ensembles generated with this information show that analysis differences contain some spatial information about analysis uncertainty, but the magnitude is too small. To account for total forecast error, model uncertainty must be included with IC uncertainty.
520 $a Comparing different ensembles reveals that a scaled ranked-perturbation ensembles has the best characteristics, including perturbation magnitude, uncertainty growth vs. error growth, spread-error correlation, and shape of the variance spectrum. Its properties are verified by running an independent case study, and only minor differences are found.
520 $a Contributions to the field of weather prediction include a new ranked-perturbation method that results in improved short-range ensemble-mean forecasts, an understanding of the relationship between analysis error and analysis differences, and the confirmation that model uncertainty is necessary to account for short-range forecast error.
590 $a School code: 2500.
650 4 $a Physics, Atmospheric Science. $3 1019431
650 4 $a Geophysics. $3 535228
690 $a 0608
690 $a 0373
710 2 0 $a The University of British Columbia (Canada). $3 626643
773 0 $t Dissertation Abstracts International $g 63-10B.
790 1 0 $a Stull, Roland B., $e advisor
790 $a 2500
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ73172