Winds from ABI on the GOES-R Series

Daniels, Jaime; Bresky, Wayne; Bailey, Andrew; Allegrino, Americo; Velden, Christopher S.; Wanzong, Steve

doi:10.1016/b978-0-12-814327-8.00008-1

Cited by 5 publications

(15 citation statements)

References 25 publications

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“…They placed the age of the upper deoxidized zone as post-Tazewell or later Wisconsin. Daniels (1957) concluded that the outcrops of the loess weathering zones, the distribution of the soils, and the geomorphic surfaces were closely related. The percent base saturation, particle-size distribution, and free iron content of the soils studied were found to be related to the age of the surface and the chemical properties of the loess zones from which the profiles had developed.…”

Section: Geomorphic Surfaces and Weathering Zonesmentioning

confidence: 99%

Soils, weathering zones, and landscapes in the upland loess of Tama and Grundy counties, Iowa

Fenton

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Section: Geomorphic Surfaces and Weathering Zonesmentioning

confidence: 99%

Soils, weathering zones, and landscapes in the upland loess of Tama and Grundy counties, Iowa

Fenton

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“…The retrieval of atmospheric motion vectors (AMVs) has been performed since the earliest geostationary satellite observations (Menzel, 2001). AMVs provide information about the motion of clouds in the atmosphere, including DCCs (Bedka and Mecikalski, 2005), and are routinely generated for the majority of operational geostationary earth observation satellites, including GOES-16 (Daniels et al, 2008). However, although AMVs may provide useful information about the motion of DCCs, the non-geostrophic nature of wind fields in these conditions may result in the AMVs being calculated inaccurately or rejected by quality control checks (Bedka and Mecikalski, 2005).…”

Section: Estimation Of Cloud Motion Vectors Using Optical Flowmentioning

confidence: 99%

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

Jones¹,

Christensen²,

Stier³

2023

Atmos. Meas. Tech.

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Abstract. Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.

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“…For clear sky portions, the satellite records images of suitable indicators of atmospheric moisture content, such as specific humidity. Daniels et al (2010) provides a description of and the physical basis for the estimation of atmospheric winds from the images taken by geostationary satellites.…”

Section: Derived Motion Winds Algorithmmentioning

confidence: 99%

“…The Derived Motion Winds (DMW) Algorithm (Daniels et al, 2010) is a standard algorithm for estimating motion winds from satellite images. The DMW Algorithm takes as input brightness temperature images from the National Oceanic and Atmospheric Administration (NOAA) geostationary meteorological satellites and gives estimated wind fields as outputs.…”

Section: Introductionmentioning

confidence: 99%

“…Stein et al (2013) uses this idea to fit a space-time model. Instead of modeling the covariance function globally, we borrow the idea of Nested Tracking from Daniels et al (2010) by considering local data buffers and estimate wind vectors using maximum likelihood estimates under the assumption of local stationarity. Local likelihood estimation of covariance parameters has been studied extensively in Haas (1990); Haas (1995); Ver Hoef et al (2004); Risser and Calder (2015); Kuusela and Stein (2018); Lenzi et al (2020); Wiens et al (2020); Castro-Camilo and Huser (2020).…”

Section: Introductionmentioning

confidence: 99%

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

2023

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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.

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Winds from ABI on the GOES-R Series

Cited by 5 publications

References 25 publications

Soils, weathering zones, and landscapes in the upland loess of Tama and Grundy counties, Iowa

Soils, weathering zones, and landscapes in the upland loess of Tama and Grundy counties, Iowa

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

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

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