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Initialization of clouds in the PSU/...

Cantrell, Louis Edward, Jr.

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Initialization of clouds in the PSU/NCAR mesoscale model using the Air Force's Real-Time Nephanalysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Initialization of clouds in the PSU/NCAR mesoscale model using the Air Force's Real-Time Nephanalysis./
作者:	Cantrell, Louis Edward, Jr.
面頁冊數:	240 p.
附註:	Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 1891.
Contained By:	Dissertation Abstracts International63-04B.
標題:	Physics, Atmospheric Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3050619
ISBN:	0493650296

Initialization of clouds in the PSU/NCAR mesoscale model using the Air Force's Real-Time Nephanalysis.
Cantrell, Louis Edward, Jr.

Initialization of clouds in the PSU/NCAR mesoscale model using the Air Force's Real-Time Nephanalysis. - 240 p.

Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 1891.

Thesis (Ph.D.)--Texas A&M University, 2002.

Modern operational mesoscale numerical weather prediction models have the potential to forecast cloud structure and distribution more accurately through cloud physical initialization than through simple cloud cover estimation with synoptic-scale data or through dynamic initialization. In an attempt to produce a better cloud forecast and to reduce model spin-up time, a technique is developed that converts the Air Force's Real-Time Nephanalysis (RTNEPH) into cloud species mixing ratios that are used to initialize the PSU/NCAR Fifth-Generation Mesoscale Model (MM5). The cloud analysis and the model are chosen to simulate the operational modeling environment at Air Force Weather Agency (AFWA). MM5 is used to forecast clouds evolving around a stationary front along the Texas coast of the Gulf of Mexico from 13 September 2000 through 15 September 2000.

ISBN: 0493650296Subjects--Topical Terms:

1019431
Physics, Atmospheric Science.

Initialization of clouds in the PSU/NCAR mesoscale model using the Air Force's Real-Time Nephanalysis.
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502 $a Thesis (Ph.D.)--Texas A&M University, 2002.
520 $a Modern operational mesoscale numerical weather prediction models have the potential to forecast cloud structure and distribution more accurately through cloud physical initialization than through simple cloud cover estimation with synoptic-scale data or through dynamic initialization. In an attempt to produce a better cloud forecast and to reduce model spin-up time, a technique is developed that converts the Air Force's Real-Time Nephanalysis (RTNEPH) into cloud species mixing ratios that are used to initialize the PSU/NCAR Fifth-Generation Mesoscale Model (MM5). The cloud analysis and the model are chosen to simulate the operational modeling environment at Air Force Weather Agency (AFWA). MM5 is used to forecast clouds evolving around a stationary front along the Texas coast of the Gulf of Mexico from 13 September 2000 through 15 September 2000.
520 $a A cloud physical parameterization scheme currently in use in the Eta model provides the framework for converting RTNEPH clouds to data that can be used to initialize MM5. Modifications to this scheme make it purely diagnostic and account for the higher resolution grid to which it is applied. The technique used to initialize clouds is called the Cloud Initialization Scheme (CIS). Cloud variables analyzed by CIS are used to examine how sensitive MM5 forecast cloud distributions are to the initial distribution of clouds. Analyzed cloud is also, compared to MM5 forecast clouds to determine if cloud forecasts are improved using this technique, and to determine if model spin-up is reduced.
520 $a Results indicate dramatic improvement in reducing spin-up time but only slight improvement in forecast accuracy. Large differences exist between the distribution characteristics of the analysis and of the forecast. MM5's response to cloud insertion indicates cloud-scale vertical winds must be assimilated, clouds diagnosed in regions of synoptic-scale descent must be permitted to persist, and diffusion of cloud variables must be reduced to maintain the discrete characteristics of real clouds. Although this work was inconclusive in producing a better cloud forecast, it has demonstrated that improving cloud forecast accuracy is within reach by including cloud updrafts, by correctly assimilating the synoptic wind analysis with the nephanalysis, and by adjusting the model's cloud diffusion technique.
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