Estimating atmospheric motion winds from satellite image data using space‐time drift models

Abstract

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.

Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects

“…Furthermore, the employment of nonparametric methods for anomaly detection (Scagliarini et al, 2023) and quantile regression for clustering satellite time series data (Musau et al, 2022) share methodological similarities with our work. Additionally, investigations into spatial dependence (Shooter et al, 2021) and atmospheric motions modeling (Sahoo et al, 2023) from satellite data reflect the broader utility of statistical techniques and satellite imagery in environmental monitoring, aspects our research also engages with.…”