Fusing Numerical Weather Prediction Ensembles with Refractivity Inversions during Surface Ducting Conditions

Daniel P. Greenway aCoastal Carolina University, Conway, South Carolina

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Tracy Haack bNaval Research Laboratory, Monterey, California

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Erin E. Hackett aCoastal Carolina University, Conway, South Carolina

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Abstract

This study investigates the use of numerical weather prediction (NWP) ensembles to aid refractivity inversion problems during surface ducting conditions. Thirteen sets of measured thermodynamic atmospheric data from an instrumented helicopter during the Wallops Island field experiment are fit to a two-layer parametric surface duct model to characterize the duct. This modeled refractivity is considered “ground truth” for the environment and is used to generate the synthetic radar propagation loss field that then drives the inversion process. The inverse solution (refractivity derived from the synthetic radar data) is compared with this ground truth refractivity. For the inversion process, parameters of the two-layer model are iteratively estimated using genetic algorithms to determine which parameters likely produced the synthetic radar propagation field. Three numerical inversion experiments are conducted. The first experiment utilizes a randomized set of two-layer model parameters to initialize the inversion process, while the second experiment initializes the inversion using NWP ensembles, and the third experiment uses NWP ensembles to both initialize and restrict the parameter search intervals used in the inversion process. The results show that incorporation of NWP data benefits the accuracy and speed of the inversion result. However, in a few cases, an extended NWP ensemble forecast period was needed to encompass the ground truth parameters in the restricted search space. Furthermore, it is found that NWP ensemble populations with smaller spreads are more likely to hinder the inverse process than to aid it.

Haack: Retired.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Erin E. Hackett, ehackett@coastal.edu

Abstract

This study investigates the use of numerical weather prediction (NWP) ensembles to aid refractivity inversion problems during surface ducting conditions. Thirteen sets of measured thermodynamic atmospheric data from an instrumented helicopter during the Wallops Island field experiment are fit to a two-layer parametric surface duct model to characterize the duct. This modeled refractivity is considered “ground truth” for the environment and is used to generate the synthetic radar propagation loss field that then drives the inversion process. The inverse solution (refractivity derived from the synthetic radar data) is compared with this ground truth refractivity. For the inversion process, parameters of the two-layer model are iteratively estimated using genetic algorithms to determine which parameters likely produced the synthetic radar propagation field. Three numerical inversion experiments are conducted. The first experiment utilizes a randomized set of two-layer model parameters to initialize the inversion process, while the second experiment initializes the inversion using NWP ensembles, and the third experiment uses NWP ensembles to both initialize and restrict the parameter search intervals used in the inversion process. The results show that incorporation of NWP data benefits the accuracy and speed of the inversion result. However, in a few cases, an extended NWP ensemble forecast period was needed to encompass the ground truth parameters in the restricted search space. Furthermore, it is found that NWP ensemble populations with smaller spreads are more likely to hinder the inverse process than to aid it.

Haack: Retired.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Erin E. Hackett, ehackett@coastal.edu
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