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Enhancing Computational Efficiency for Climate Reanalysis Data Analysis

This project explores the challenges of analyzing NCEP Reanalysis data, focusing on improving computation time while working with multiple parameters. Using R scripts, the current method faces limitations due to long processing times and a lack of automated techniques. The dataset, which includes 17 pressure levels and approximately 10,000 grid points, necessitates chunk loading by longitude for better management. Future work will involve migrating the code to MATLAB, employing distributed computing strategies, and optimizing the analysis for individual levels to combine results efficiently.

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Enhancing Computational Efficiency for Climate Reanalysis Data Analysis

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  1. Computation Time Analysis - Climate ReanalysisData DipanwitaDasgupta Graduate Operating Systems

  2. Overview • Problem Description • Dataset • Results • Proposed course of work

  3. Problem • Cannot increase the number of parameters • Code in R script • Code using direct computational techniques • Data to be saved in the machine • Data has to be loaded in chunks in terms of longitude to get a better computation time • Very high computation time • No automated technique for computation

  4. Data • NCEP Reanalysis Data • Composed of data at 17 pressure levels • Total of approximately 10000 grid points • Available at ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.derived/pressure/hgt.mon.mean.nc

  5. Results • Time increases with number oflevels • Represents time per block of data • Only one parameter used • Only one measure of dispersion

  6. Course of Work • Transfer the code to Matlab • Use distributed computing to reduce computation time • Compute for each level separately • Combine the results from each level • Increase the number of parameters

  7. Questions??

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