Assimilation of Sparse Continuous Near‐Earth Weather Measurements by NECTAR Model Morphing. Issue 11 (3rd November 2020)

• Record Type: Journal Article
• Authors: Galkin, I. A.; Reinisch, B. W.; Vesnin, A. M.; Bilitza, D.; Fridman, S.; Habarulema, J. B.; Veliz, O.
• Is Part Of: Space weather. Volume 18:Issue 11(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-03
• Subjects: data assimilation -- diurnal harmonic analysis -- Hopfield networks -- model morphing -- spatial prediction -- weather nowcast; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2020SW002463
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: Non‐linear Error Compensation Technique with Associative Restoration (NECTAR) is a novel approach to the assimilation of fragmentary sensor data to produce a global nowcast of the near‐Earth space weather. NECTAR restores missing information by iteratively transforming ("morphing") an underlying global climatology model into agreement with currently available sensor data. The morphing procedure benefits from analysis of the inherent multiscale diurnal periodicity of the geosystems by processing 24‐hr time histories of the differences between measured and climate‐expected values at each sensor site. The 24‐hr deviation time series are used to compute and then globally interpolate the diurnal deviation harmonics. NECTAR therefore views the geosystem in terms of its periodic planetary‐scale basis to associate observed fragments of the activity with the grand‐scale weather processes of the matching variability scales. Such approach strengthens the restorative capability of the assimilation, specifically when only a limited number of observatories is available for the weather nowcast. Scenarios where the NECTAR concept works best are common in planetary‐scale near‐Earth weather applications, especially where sensor instrumentation is complex, expensive, and therefore scarce. To conduct the assimilation process, NECTAR employs a Hopfield feedback recurrent neural network commonly used in the associative memory architectures. Associative memories mimic human capability to restore full information from its initial fragments. When applied to the sparse spatial data, such a neural network becomes a nonlinear multiscale interpolator of missing information. Early tests of the NECTAR morphing reveal its enhanced capability to predict system dynamics over no‐data regions (spatial interpolation). Key Points: A novel technique is presented for assimilating fragmentary sensor data to produce a global‐scale space weather nowcast The technique iteratively transforms ("morphs") an underlying global climatology model into agreement with available sensor data The technique senses the inherent multiscale diurnal periodicity of geosystems to restore missing information over no‐data regions