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Quality Control of Geostationary Lightning Mapper Observations for Tropical Cyclone ApplicationsAbstract
The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, “bar” effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC was not applied beforehand.
Significance Statement
The Geostationary Lightning Mapper (GLM) provides total lightning observations in tropical cyclones that can benefit forecasts of intensity change. However, nonlightning artifacts in GLM observations make interpreting lightning observations challenging for automated techniques to predict intensity change. Quality control procedures have been developed to aid the TC community in using GLM observations for statistical and pattern-matching techniques.
Abstract
The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, “bar” effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC was not applied beforehand.
Significance Statement
The Geostationary Lightning Mapper (GLM) provides total lightning observations in tropical cyclones that can benefit forecasts of intensity change. However, nonlightning artifacts in GLM observations make interpreting lightning observations challenging for automated techniques to predict intensity change. Quality control procedures have been developed to aid the TC community in using GLM observations for statistical and pattern-matching techniques.
A Postlaunch Update on the Effects of Instrumental Measurement Errors on SWOT Estimates of Sea Surface Height, Velocity, and VorticityAbstract
The Ka-band radar interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite that was launched in December 2022 is providing the first two-dimensional altimetric views of sea surface height (SSH). Measurements are made across two parallel swaths of 50-km width separated by a 20-km gap. In the data product that will be used for most oceanographic applications, SSH estimates with a footprint diameter of about 3 km are provided on a 2 km × 2 km grid. Early analyses of in-flight KaRIn data conclude that the instrumental noise for this footprint diameter has a standard deviation less than σ 3km = 0.40 cm for conditions of 2-m significant wave height. This is a factor of 2.3 better than the prelaunch expectation based on the science requirement specification. The SSH fields measured by KaRIn allow the first satellite estimates of essentially instantaneous surface current velocity and vorticity computed geostrophically from SSH. The effects of instrumental noise on smoothed estimates of velocity and vorticity based on early postlaunch assessments are quantified here as functions of the half-power filter cutoff wavelength of the smoothing. Signal-to-noise ratios for smoothed estimates of velocity and vorticity are determined from simulated noisy KaRIn data derived from a high-resolution numerical model of the California Current System. The wavelength resolution capabilities for σ 3km = 0.40 cm are found to be about 17 and 35 km for velocity and vorticity, respectively, which correspond to feature diameters of about 8.5 and 17.5 km, and are better than the prelaunch expectations by about 45% and 35%.
Abstract
The Ka-band radar interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite that was launched in December 2022 is providing the first two-dimensional altimetric views of sea surface height (SSH). Measurements are made across two parallel swaths of 50-km width separated by a 20-km gap. In the data product that will be used for most oceanographic applications, SSH estimates with a footprint diameter of about 3 km are provided on a 2 km × 2 km grid. Early analyses of in-flight KaRIn data conclude that the instrumental noise for this footprint diameter has a standard deviation less than σ 3km = 0.40 cm for conditions of 2-m significant wave height. This is a factor of 2.3 better than the prelaunch expectation based on the science requirement specification. The SSH fields measured by KaRIn allow the first satellite estimates of essentially instantaneous surface current velocity and vorticity computed geostrophically from SSH. The effects of instrumental noise on smoothed estimates of velocity and vorticity based on early postlaunch assessments are quantified here as functions of the half-power filter cutoff wavelength of the smoothing. Signal-to-noise ratios for smoothed estimates of velocity and vorticity are determined from simulated noisy KaRIn data derived from a high-resolution numerical model of the California Current System. The wavelength resolution capabilities for σ 3km = 0.40 cm are found to be about 17 and 35 km for velocity and vorticity, respectively, which correspond to feature diameters of about 8.5 and 17.5 km, and are better than the prelaunch expectations by about 45% and 35%.
Abstract
In remote sensing imaging systems, stripe noise is a pervasive issue primarily caused by the inconsistent response of multiple detectors. Stripe noise not only affects image quality but also severely hinders subsequent quantitative derived products and applications. Therefore, it is crucial to eliminate stripe noise while preserving detailed structure information in order to enhance image quality. Although existing destriping methods have achieved certain effects to some extent, they still face problems such as loss of image details, image blur, and ringing artifacts. To address these issues, this study proposes an image stripe correction algorithm based on weighted block sparse representation. This research applies techniques such as differential low-rank constraint and edge weight factor to remove stripe noise while retaining image detail information. The algorithm also uses the alternating direction method of multipliers (ADMM) to solve the minimax concave penalty (MCP)-regularized least squares optimization problem model, improving the processing efficiency of the model. The results of this study have been applied and validated in imager data from the Medium Resolution Spectral Imager (MERSI-II) onboard Fengyun-3D satellite, the multichannel scanning radiometer [Advanced Geosynchronous Radiation Imager (AGRI)] onboard Fengyun-4A satellite, and precipitation microwave radiometer [Microwave Radiation Imager-Rainfall Mission (MWRI-RM)] onboard Fengyun-3G. Compared to typical stripe correction methods, the proposed method achieves better stripe removal while preserving image detail information. The destriped image data can be used to generate high-quality quantitative products for various applications. Overall, by combining insights from prior research and innovative techniques, this study provides a more effective and robust solution to the stripe noise problem in remote sensing and weather forecast.
Significance Statement
Stripe noise is a persistent problem in remote sensing imaging systems, hindering image quality and subsequent analysis. This study introduces a novel algorithm based on weighted block sparse representation to remove stripe noise while preserving image details. By incorporating techniques like differential low-rank constraint and edge weight factor, our method achieves superior stripe removal. The proposed approach was validated using data from MERSI-II and AGRI satellites, showing its effectiveness in enhancing image quality. This research provides a more robust solution to the stripe noise issue, benefiting various applications in remote sensing and weather forecast.
Abstract
In remote sensing imaging systems, stripe noise is a pervasive issue primarily caused by the inconsistent response of multiple detectors. Stripe noise not only affects image quality but also severely hinders subsequent quantitative derived products and applications. Therefore, it is crucial to eliminate stripe noise while preserving detailed structure information in order to enhance image quality. Although existing destriping methods have achieved certain effects to some extent, they still face problems such as loss of image details, image blur, and ringing artifacts. To address these issues, this study proposes an image stripe correction algorithm based on weighted block sparse representation. This research applies techniques such as differential low-rank constraint and edge weight factor to remove stripe noise while retaining image detail information. The algorithm also uses the alternating direction method of multipliers (ADMM) to solve the minimax concave penalty (MCP)-regularized least squares optimization problem model, improving the processing efficiency of the model. The results of this study have been applied and validated in imager data from the Medium Resolution Spectral Imager (MERSI-II) onboard Fengyun-3D satellite, the multichannel scanning radiometer [Advanced Geosynchronous Radiation Imager (AGRI)] onboard Fengyun-4A satellite, and precipitation microwave radiometer [Microwave Radiation Imager-Rainfall Mission (MWRI-RM)] onboard Fengyun-3G. Compared to typical stripe correction methods, the proposed method achieves better stripe removal while preserving image detail information. The destriped image data can be used to generate high-quality quantitative products for various applications. Overall, by combining insights from prior research and innovative techniques, this study provides a more effective and robust solution to the stripe noise problem in remote sensing and weather forecast.
Significance Statement
Stripe noise is a persistent problem in remote sensing imaging systems, hindering image quality and subsequent analysis. This study introduces a novel algorithm based on weighted block sparse representation to remove stripe noise while preserving image details. By incorporating techniques like differential low-rank constraint and edge weight factor, our method achieves superior stripe removal. The proposed approach was validated using data from MERSI-II and AGRI satellites, showing its effectiveness in enhancing image quality. This research provides a more robust solution to the stripe noise issue, benefiting various applications in remote sensing and weather forecast.
Abstract
This study investigated trends in satellite-based chlorophyll-a (Chl-a; 1998–2022), sea surface temperature (SST; 1982–2022), and sea level anomaly (SLA; 1993–2021) from the European Space Agency’s Climate Change Initiative records, integrating time series decomposition and spectral analysis. Trends in parameters signify prolonged increases, decreases, or no changes over time. These are time series in the same space as original parameters, excluding seasonalities and noise, and can exhibit nonlinearity. Trend rates approximate the pace of change per time unit. We quantified trends using conventional linear-fit and three incrementally advancing methods for time series decomposition: simple moving average (SMA), seasonal-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL (MSTL), across the global ocean, the Bay of Bengal, and the Chesapeake Bay. Challenges in decomposition include specifying accurate seasonal periods that are derived here by combining Fourier and Wavelet Transforms. Globally, SST and SLA trend upwards, and Chl-a has no significant change, yet regional variations are notable. We highlight the advantage of extracting multiple periods with MSTL and, more broadly, decomposition’s role in disentangling time-series components (seasonality, trend, noise) without resorting to monotonic functions, thereby preventing overlooking episodic events. Illustrations include extreme events temporarily counteracting background trends, e.g., the 2010–2011 SLA drop due to La Niña-induced rainfall over land. The continuous analysis clarifies the warming hiatus debate, affirming sustained warming. Decadal trend rates per grid cell are also mapped. These are ubiquitously significant for SST and SLA, whereas Chl-a trend rates are globally low but extreme across coasts and boundary currents.
Abstract
This study investigated trends in satellite-based chlorophyll-a (Chl-a; 1998–2022), sea surface temperature (SST; 1982–2022), and sea level anomaly (SLA; 1993–2021) from the European Space Agency’s Climate Change Initiative records, integrating time series decomposition and spectral analysis. Trends in parameters signify prolonged increases, decreases, or no changes over time. These are time series in the same space as original parameters, excluding seasonalities and noise, and can exhibit nonlinearity. Trend rates approximate the pace of change per time unit. We quantified trends using conventional linear-fit and three incrementally advancing methods for time series decomposition: simple moving average (SMA), seasonal-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL (MSTL), across the global ocean, the Bay of Bengal, and the Chesapeake Bay. Challenges in decomposition include specifying accurate seasonal periods that are derived here by combining Fourier and Wavelet Transforms. Globally, SST and SLA trend upwards, and Chl-a has no significant change, yet regional variations are notable. We highlight the advantage of extracting multiple periods with MSTL and, more broadly, decomposition’s role in disentangling time-series components (seasonality, trend, noise) without resorting to monotonic functions, thereby preventing overlooking episodic events. Illustrations include extreme events temporarily counteracting background trends, e.g., the 2010–2011 SLA drop due to La Niña-induced rainfall over land. The continuous analysis clarifies the warming hiatus debate, affirming sustained warming. Decadal trend rates per grid cell are also mapped. These are ubiquitously significant for SST and SLA, whereas Chl-a trend rates are globally low but extreme across coasts and boundary currents.
Abstract
Earth and planetary radiometry requires spectrally dependent observations spanning an expansive range in signal flux due to variability in celestial illumination, spectral albedo, and attenuation. Insufficient dynamic range inhibits contemporaneous measurements of dissimilar signal levels and restricts potential environments, time periods, target types, or spectral ranges that instruments observe. Next-generation (NG) advances in temporal, spectral, and spatial resolution also require further increases in detector sensitivity and dynamic range corresponding to increased sampling rate and decreased field-of-view (FOV), both of which capture greater intrapixel variability (i.e., variability within the spatial and temporal integration of a pixel observation). Optical detectors typically must support expansive linear radiometric responsivity, while simultaneously enduring the inherent stressors of field, airborne, or satellite deployment. Rationales for significantly improving radiometric observations of nominally dark targets are described herein, along with demonstrations of state-of-the-art (SOTA) capabilities and NG strategies for advancing SOTA. An evaluation of linear dynamic range and efficacy of optical data products is presented based on representative sampling scenarios. Low-illumination (twilight or total lunar eclipse) observations are demonstrated using a SOTA prototype. Finally, a ruggedized and miniaturized commercial-off-the-shelf (COTS) NG capability to obtain absolute radiometric observations spanning an expanded range in target brightness and illumination is presented. The presented NG technology combines a Multi-Pixel Photon Counter (MPPC) with a silicon photodetector (SiPD) to form a dyad optical sensing component supporting expansive dynamic range sensing, i.e., exceeding a nominal 10 decades in usable dynamic range documented for SOTA instruments.
Abstract
Earth and planetary radiometry requires spectrally dependent observations spanning an expansive range in signal flux due to variability in celestial illumination, spectral albedo, and attenuation. Insufficient dynamic range inhibits contemporaneous measurements of dissimilar signal levels and restricts potential environments, time periods, target types, or spectral ranges that instruments observe. Next-generation (NG) advances in temporal, spectral, and spatial resolution also require further increases in detector sensitivity and dynamic range corresponding to increased sampling rate and decreased field-of-view (FOV), both of which capture greater intrapixel variability (i.e., variability within the spatial and temporal integration of a pixel observation). Optical detectors typically must support expansive linear radiometric responsivity, while simultaneously enduring the inherent stressors of field, airborne, or satellite deployment. Rationales for significantly improving radiometric observations of nominally dark targets are described herein, along with demonstrations of state-of-the-art (SOTA) capabilities and NG strategies for advancing SOTA. An evaluation of linear dynamic range and efficacy of optical data products is presented based on representative sampling scenarios. Low-illumination (twilight or total lunar eclipse) observations are demonstrated using a SOTA prototype. Finally, a ruggedized and miniaturized commercial-off-the-shelf (COTS) NG capability to obtain absolute radiometric observations spanning an expanded range in target brightness and illumination is presented. The presented NG technology combines a Multi-Pixel Photon Counter (MPPC) with a silicon photodetector (SiPD) to form a dyad optical sensing component supporting expansive dynamic range sensing, i.e., exceeding a nominal 10 decades in usable dynamic range documented for SOTA instruments.
Abstract
Tides are often non-stationary due to non-astronomical influences. Investigating variable tidal properties implies a tradeoff between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a Matlab code that: a) uses CWT linearity (via the “Response Coefficient Method”) to implement super-resolution (Munk and Hasselman 1964); b) provides a Munk-Hasselman constituent-selection criterion; and c) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time-scales of days and multiple constituents per species with fortnightly filters. It outputs astronomical phase-lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s) and reconstruction(s), confidence limits, and signal-to-noise ratios. Artificial data and water-levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time-resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K1, S1, and P1) on fortnightly time scales, quantifying impacts of diel power-peaking (with a 24-hour period, like S1) on water levels in the LCRE. CWT_Multi also helps quantify impacts of high flows and a salt-barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to non-stationary data.
Abstract
Tides are often non-stationary due to non-astronomical influences. Investigating variable tidal properties implies a tradeoff between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a Matlab code that: a) uses CWT linearity (via the “Response Coefficient Method”) to implement super-resolution (Munk and Hasselman 1964); b) provides a Munk-Hasselman constituent-selection criterion; and c) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time-scales of days and multiple constituents per species with fortnightly filters. It outputs astronomical phase-lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s) and reconstruction(s), confidence limits, and signal-to-noise ratios. Artificial data and water-levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time-resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K1, S1, and P1) on fortnightly time scales, quantifying impacts of diel power-peaking (with a 24-hour period, like S1) on water levels in the LCRE. CWT_Multi also helps quantify impacts of high flows and a salt-barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to non-stationary data.
Abstract
Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler-frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in-situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method.
Abstract
Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler-frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in-situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method.
