Data Assimilation and Applications in Forecasting

The work presented here spans two projects which are connected by data assimilationand specifically the ensemble Kalman filter (EnKF). The first explores how spatial localization, an important method commonly used in the EnKF, can be extended to multiscale problems. Rather than using a single length scale when localizing, we construct a localized covariance matrix through the estimation of eigenvectors. Specifically, we estimate the leading large-scale eigenvectors from a sample covari- ance matrix calculated from a spatially smoothed ensemble with spatial localization applied with a long localization distance. We then create projection matrices from these eigenvectors which allows us to calculate the space orthogonal to these initial large scales. This process can then be repeated for multiple scales if required. We present numerical experiments using this localization method using both simplified examples in which the correct covariance matrix is known and cycling experiments with the Lorenz Model III. The second project explores an application of the EnKF. We use the EnKF as part of a system to forecast cloud cover. Cloud cover forecasts are useful when forecasting solar power generation because clouds are the primary driver of reducing irradiance and therefore solar power generation. Our method uses satellite images, optical flow, and numerical weather prediction (NWP) in conjunction with an EnKF to estimate cloud motion vectors (CMVs) which are then used to advect cloud index (CI) fields using a 2-D advection scheme. This system produces an ensemble forecast which can be used to produce deterministic forecasts. We explore the effectiveness of these forecasts over Tucson, AZ.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Graduate College

Grantor dc:publisher

The University of Arizona.

Year dc:date.issued

2021

Author dc:creator

• Harty, Travis Michael

Advisor dc:contributor.advisor

• Morzfeld, Matthias

Committee members dc:contributor.committeemember

• Snyder, Chris
• Venkataramani, Shankar C.
• Arellano, Avelino F.

Statement dc:rights

• Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10150/650873

OAI identifier oai:identifier

oai:repository.arizona.edu:10150/650873