Browse

Comparison and Evaluation of Turbulent Heat Fluxes from Global Reanalysis Products Based on Buoy Observation in the East-Asian Marginal Seas

DC Field Value Language
dc.contributor.advisor장경일-
dc.contributor.author정욱재-
dc.date.accessioned2017-07-19T08:57:21Z-
dc.date.available2017-07-19T08:57:21Z-
dc.date.issued2013-02-
dc.identifier.other000000009253-
dc.identifier.urihttps://hdl.handle.net/10371/131449-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 해양학과, 2013. 2. 장경일.-
dc.description.abstractDetermination of accurate quantitative description of mean and variability of ocean surface turbulent heat fluxes is pivotal for understanding and modeling air-sea interactions. Oceanic reanalyses have been very useful in evaluating models, but the quality of these products has often been questioned for specific type of applications. Meteorological variables measured from 6 ocean buoys are used to calculate the latent and sensible heat fluxes with Coupled Ocean-Atmosphere Response Experiment (COARE) flux algorithm 3.0 (Fairall et al., 1996) over the East Asian Marginal Seas. The buoy-derived latent and sensible heat fluxes are compared with those from five atmospheric reanalyses (NCEP1, NCEP2, CFSR, ERA Interim, and MERRA) and objectively analyzed data (OAflux) for evaluation of the products. There exist significant mean biases of the products over the coastal region but relatively small biases in offshore region. NCEP significantly overestimates turbulent heat fluxes all over the region, otherwise MERRA always has the lowest mean values compared to other products, so slightly overestimate or even underestimate the fluxes at which other products show significant overestimation. All products simulate well day to day variation (correlation is higher than 0.83), but the amplitude of variation (in terms of the standard deviation) is better in an order of MERRA-OAflux-ERA Interim-CFSF-NCEP1-NCEP2. This order of performance is nearly same at all buoy stations. In general, heat flux bias comes from two sources-
dc.description.abstractone is algorithm-caused bias and the other is variable-caused bias. This study focuses on the variable-caused bias by applying the COARE 3.0 flux algorithm to bulk variables from all the reanalysis products and performing tests to identify the sensitivity of biases to a single variable or pairs of variables The sensitivity tests suggest that the mean biases of latent and sensible heat fluxes can be improved within an acceptable error range (<10 W/m2) by using high-quality SST observations in the East Asian Marginal Seas.-
dc.description.tableofcontentsContents

1. Introduction
2. Data and processing.
2.1 Reanalysis
2.1 Averaging issues and Matchup Procedure
3. Results
3.1 Comparisons with buoy
3.1.1 Long term mean bias
3.1.2 Variable Caused bias
3.1.3 Individual bulk Variable bias
3.2 Error characteristic
3.2.1 Vertical temperature and moisture gradient
3.2.2 Variable-caused errors in turbulent heat fluxes
3.2.3 Temporal characteristics of errors
3.2.4 Sensitive test
4. Summary
5. Reference
-
dc.formatapplication/pdf-
dc.format.extent2632884 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectheat flux-
dc.subjectreanalysis-
dc.subjectin-situ-
dc.subject.ddc551-
dc.titleComparison and Evaluation of Turbulent Heat Fluxes from Global Reanalysis Products Based on Buoy Observation in the East-Asian Marginal Seas-
dc.typeThesis-
dc.description.degreeMaster-
dc.citation.pages48-
dc.contributor.affiliation자연과학대학 해양학과-
dc.date.awarded2013-02-
Appears in Collections:
College of Natural Sciences (자연과학대학)Dept. of Earth and Environmental Sciences (지구환경과학부)Theses (Master's Degree_지구환경과학부)
Files in This Item:
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse