# Estimating Atmospheric Motion Winds from Satellite Image Data using Space-time Drift Models

## TL;DR

This paper introduces a statistical space-time drift model to estimate atmospheric winds from satellite images, providing uncertainty measures and improving prediction accuracy over existing methods.

## Contribution

It proposes a novel covariance-based statistical model for wind estimation that includes uncertainty quantification and spatial smoothing, advancing beyond traditional feature-tracking algorithms.

## Key findings

- The method accurately estimates wind speed and direction.
- It reduces brightness temperature prediction error compared to DMW.
- Provides standard errors for wind estimates.

## Abstract

Geostationary satellites collect high-resolution weather data comprising a series of images which can be used to estimate wind speed and direction at different altitudes. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in images taken by the GOES-R series of the NOAA geostationary meteorological satellites. However, the wind estimates from the DMW Algorithm are sparse and do not come with uncertainty measures. This motivates us to statistically model wind motions as a spatial process drifting in time. We propose 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 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.