In this work we enable the data assimilation algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) to estimate global neutral winds. EMPIRE corrects the ion drifts and neutral winds from background models using electron density in the F region derived mainly from total electron content measurements. The new EMPIRE basis functions for the neutral winds are vector spherical harmonics, enforcing the field to be smooth and continuous globally. Global basis functions allow us to estimate the horizontal wind vector from time‐varying plasma densities. The geomagnetic storm on 25 October 2011 is studied to investigate the global implementation of neutral wind estimation. During this storm, the estimates are also compared to those of the old method of estimation using power series and to Fabry‐Perot interferometer (FPI) measurements for validation at three different sites: Pisgah, Cariri, and Nasca. The new global estimation is 10%–20% closer to the FPI measurements than the model and than the old method for most of the viewing directions of the sites, in terms of root‐mean‐square residuals. In the northward direction at Pisgah the estimates using either the old or new implementation disagree with the model, and might be related to an insufficiently small temporal scale used in EMPIRE.
This content will become publicly available on December 1, 2024
Geostationary weather satellites collect high‐resolution data comprising a series of images. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in the images. However, the wind estimates from the DMW Algorithm are often missing and do not come with uncertainty measures. Also, the DMW Algorithm estimates can only be half‐integers, since the algorithm requires the original and shifted data to be at the same locations, in order to calculate the displacement vector between them. This motivates us to statistically model wind motions as a spatial process drifting in time. Using a covariance function that depends on spatial and temporal lags and a drift parameter to capture the wind speed and wind direction, we estimate the parameters by local maximum likelihood. Our method allows us to compute standard errors of the local estimates, enabling spatial smoothing of the estimates using a Gaussian kernel weighted by the inverses of the estimated variances. We conduct extensive simulation studies to determine the situations where our method performs well. The proposed method is applied to the GOES‐15 brightness temperature data over Colorado and reduces prediction error of brightness temperature compared to the DMW Algorithm.
more » « less- Award ID(s):
- 1953088
- NSF-PAR ID:
- 10485003
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Environmetrics
- Volume:
- 34
- Issue:
- 8
- ISSN:
- 1180-4009
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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