A Fourier Approach to Cloud Motion Estimation

Arking, Albert; Lo, R. C.; Rosenfeld, Azriel

doi:10.1175/1520-0450(1978)017<0735:afatcm>2.0.co;2

Cited by 17 publications

(4 citation statements)

References 7 publications

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“…[20] Automated methods for cloud tracking have a long history in the meteorological community [Clark et al, 1968;Leese et al, 1971;Arking et al, 1978]. Different classes of algorithms exist for tracking apparent motion in satellite images (see discussion in Velden et al [2005] for details).…”

Section: Methodsmentioning

confidence: 99%

Velocities in the plume of the 2010 Eyjafjallajökull eruption

et al. 2013

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[1] The eruption of the Icelandic volcano Eyjafjallajökull in the spring of 2010 lasted for 39 days with an explosive phase (14-18 April), an effusive phase (18 April to 4 May) and a phase with renewed explosive activity (5-17 May). Images every 5 s from a camera mounted 34 km from the volcano are available for most of the eruption. Applying the maximum cross-correlation method (MCC) on these images, the velocity structure of the eruption cloud has been mapped in detail for four time intervals covering the three phases of the eruption. The results show that on average there are updrafts in one part of the cloud and lateral motion or downdrafts in another. Even within the updraft part, there are alternating motions of strong updrafts, weak updrafts, and downward motion. These results show a highly variable plume driven by intermittent explosions. The results are discussed in the context of integral plume models and in terms of elementary parcel theory.

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Section: Methodsmentioning

confidence: 99%

Velocities in the plume of the 2010 Eyjafjallajökull eruption

et al. 2013

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“…The first step is to calculate the bulk motion using Fourier transform [1]. Let us define: This means that f 2 (x, y) ≈ f 1 (x−∆x, y−∆y).…”

Section: Cloud Motion -Bulk Motionmentioning

confidence: 99%

Stereo 3D cloud motion from tandem satellites

Zhou,

Abrahams,

Mendoza

et al. 2024

Preprint

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In this study, we introduce a software framework developed during the SWI 2023 Study Group for accurate geolocation and 3D reconstruction of cloud observations. Jointly proposed by the European Space Agency (ESA) and the Royal Netherlands Meteorological Institute (KNMI), this project extracts and transforms cloud observation data into actionable insights. Implemented primarily in Python3 with libraries such as numpy, matplotlib, and scipy, the framework provides a robust pipeline for processing netCDF data, including cloud observations and locations. It handles three main tasks: reconstruction, motion extraction, and shape analysis. Key procedures include 3D reconstruction, bulk motion extraction, shape analysis, cloud selection and smoothing, and optical flow. The focus is on accurately extracting cloud latitude and longitude from observed positions, considering the true height of the cloud surface.

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“…For longer forecast time horizons, non-linearities in atmospheric motion and cloud formation and evaporation cause Numerical Weather Prediction (NWP) models to outperform satellite-based CMV forecasts . Arking et al (1978) applied Fourier phase difference technique which allows motion estimates to be made for individual spatial frequencies related to cloud pattern dimensions. However in the presence of mixtures of motions, changes in cloud shape and edge effects, the cross-correlation scheme yields a more reliable estimate of cloud motion than the phase difference technique.…”

Section: Cloud Motion Estimationmentioning

confidence: 99%

Robust cloud motion estimation by spatio-temporal correlation analysis of irradiance data

Jamaly

Kleissl

2018

Solar Energy

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The main contributor to spatio-temporal variability in the solar resource is clouds passing over photovoltaic (PV) modules. Cloud velocity is a principal input to many short-term forecast and variability models. In this paper spatio-temporal correlations of irradiance data are analyzed to estimate cloud motion. The analysis is performed using two spatially and temporally resolved simulated irradiance datasets generated from large eddy simulation. Cloud motion is estimated using two different methods; the cross-correlation method (CCM) applied to two or a few consecutive time steps and cross-spectral analysis (CSA) where the cloud speed and direction are estimated by cross-spectral analysis of a longer time series. CSA is modified to estimate the cloud motion direction as the case with least variation for all the velocities in the cloud motion direction. To ensure reliable cloud motion estimation, quality control (QC) is added to the CSA and CCM analyses. The results show 33% (52°) and 21% (6°) improvement in the cloud motion speed (direction) estimation using the modified CSA and CCM over the original methods (without QC), respectively. In general, CCM results are accurate for all the different cloud cover fractions with average relative mean bias error (rMBE) of cloud speed and mean absolute error of cloud direction equal to 3% and 3°, respectively. For low cloud cover fractions, CSA estimates the cloud motion speed and direction with rMBE and mean absolute error equal to 10% and 11°, respectively. However, for high cloud cover fractions and unsteady cloud speed, CSA results are not reliable for 3-4 h time series; however, splitting the whole time series into shorter time intervals reduces the rMBE and mean absolute error to 15% and 16° respectively.

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A Fourier Approach to Cloud Motion Estimation

Cited by 17 publications

References 7 publications

Velocities in the plume of the 2010 Eyjafjallajökull eruption

Velocities in the plume of the 2010 Eyjafjallajökull eruption

Stereo 3D cloud motion from tandem satellites

Robust cloud motion estimation by spatio-temporal correlation analysis of irradiance data

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