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This content will become publicly available on May 17, 2017

Title: Spectral characteristics of background error covariance and multiscale data assimilation

The steady increase of the spatial resolutions of numerical atmospheric and oceanic circulation models has occurred over the past decades. Horizontal grid spacing down to the order of 1 km is now often used to resolve cloud systems in the atmosphere and sub-mesoscale circulation systems in the ocean. These fine resolution models encompass a wide range of temporal and spatial scales, across which dynamical and statistical properties vary. In particular, dynamic flow systems at small scales can be spatially localized and temporarily intermittent. Difficulties of current data assimilation algorithms for such fine resolution models are numerically and theoretically examined. Our analysis shows that the background error correlation length scale is larger than 75 km for streamfunctions and is larger than 25 km for water vapor mixing ratios, even for a 2-km resolution model. A theoretical analysis suggests that such correlation length scales prevent the currently used data assimilation schemes from constraining spatial scales smaller than 150 km for streamfunctions and 50 km for water vapor mixing ratios. Moreover, our results highlight the need to fundamentally modify currently used data assimilation algorithms for assimilating high-resolution observations into the aforementioned fine resolution models. Lastly, within the framework of four-dimensional variational data assimilation,more » a multiscale methodology based on scale decomposition is suggested and challenges are discussed.« less
 [1] ;  [2] ;  [3] ;  [4]
  1. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  2. Univ. of California, Los Angeles, CA (United States)
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  4. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0271-2091; R&D Project: 2016-BNL-EE630EECA-Budg; KP1701000
Grant/Contract Number:
Accepted Manuscript
Journal Name:
International Journal for Numerical Methods in Fluids
Additional Journal Information:
Journal Name: International Journal for Numerical Methods in Fluids; Journal ID: ISSN 0271-2091
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
54 ENVIRONMENTAL SCIENCES variational data as similation; Kalman Filter; atmospheric and oceanic models; multiscale algorithm; background error covariance; spectral power density