Detection and Height Measurement of Tenuous Clouds and Blowing Snow in ICESat‐2 ATLAS Data

Herzfeld, U. C.; Hayes, Adam; Palm, Stephen P.; Hancock, D. W.; Vaughan, Mark; Barbieri, K. A.

doi:10.1029/2021gl093473

Cited by 13 publications

(6 citation statements)

References 42 publications

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“…1] is the aerosol and cloud extinction profile, which can therefore be independently retrieved from the aerosol and cloud backscatter (β M (z)) profile. This ability to perform independent retrievals of the backscatter and extinction profiles is the major improvement with respect to that from an elastic backscatter lidar like CALIPSO (Vaughan et al, 2009) or ICESaT-2 (Herzfeld et al, 2021) for which an extinction-to-backscatter ratio has to be assigned for each pixel.…”

Section: Algorithm Backgroundmentioning

confidence: 99%

“…Another new approach has been created for the NASA ICESat-2 mission, which carries the space-borne lidar system ATLAS (Markus et al, 2017) operating at 532 nm. The aim of this mask (Herzfeld et al, 2021) is to detect layers in the ICESat-2 data during complex atmospheric situations, specifically aiming at the detection of blowing snow and thin cirrus clouds. The method adopts a Gaussian radial data aggregation function with an auto-adaptive threshold determination.…”

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confidence: 99%

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Detection of aerosol and cloud features for the EarthCARE lidar ATLID: the A-FM product

Zadelhoff

Donovan

Wang

2023

Preprint

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Abstract. The EarthCARE satellite mission’s objective is to retrieve profiles of the aerosol and water cloud physical properties using the combination of cloud-profiling radar (CPR), high spectral resolution UV lidar (ATLID), and passive multi-spectral spectral imager (MSI) data. Based on synergistic retrievals using data from these instruments, the 3D atmospheric cloud/aerosol state is estimated, which then are used to forward modelled radiative properties, which may then be compared to co-incident broad band radiometer (BBR) measurements. A high spectral resolution lidar enables the independent retrieval of extinction and backscatter but, being space-based, suffers from relatively high signal-to-noise levels. The ATLID FeatureMask (A-FM) product provides a probability mask for the existence of atmospheric features within the lidar profiles. Next to this, it also identifies those regions where the lidar beam has been fully attenuated and when the surface has impacted the measured lidar backscatter signals. From the pixels assigned as clear sky with no features present above, the clear sky averaged profiles for the three ATLID channels, the co-polar Mie channel, the total cross channel and the co-polar Rayleigh channel, are created. These ‘feature-free’ or ‘clear-sky’ profiles are useful for e.g. the quality of the ATLID l1 attenuated backscatters. The scientific goals of the A-FM product is to guide smoothing strategies within the ATLID profile retrieval algorithm which is one step further in the EarthCARE L2 processing chain. As a secondary product a frame-by-frame evaluation of the ATLID L1b cross talk calibration can be preformed by comparing the retrieved clear sky profiles to the expected channel profiles. The A-FM algorithm has been evaluated thoroughly using the synthetic test scenes. The A-FM product has been applied to both synthetic data from the EarthCARE end-to-end simulator (ECSIM) ass well as ALADIN L1 data from the Aeolus wind-lidar mission. Comparisons against the ECSIM model truth indicate A-FM has a percentage correctness > 0.9 and is capable of reliably detecting aerosol and cloud regions with extinctions > 1E-5 m−1.

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Section: Algorithm Backgroundmentioning

confidence: 99%

mentioning

confidence: 99%

Detection of aerosol and cloud features for the EarthCARE lidar ATLID: the A-FM product

Zadelhoff

Donovan

Wang

2023

Preprint

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“…The DDA is a family of fully automated algorithms designed to track complex surfaces in micro-pulse photon-counting lidar altimeter data, such as ICESat-2 (Herzfeld et al, 2017(Herzfeld et al, , 2021a. The DDA-bifurcate-seaice algorithm was designed to track height in complex sea ice topography and has the ability to simultaneously track two diverging surfaces.…”

Section: Density Dimension Algorithm -Bifurcate -Seaicementioning

confidence: 99%

Observing the Evolution of Summer Melt on Multiyear Sea Ice with ICESat-2 and Sentinel-2

Buckley

Farrell

Herzfeld

et al. 2023

Preprint

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Abstract. We investigate sea ice conditions during the 2020 melt season, when warm air temperature anomalies in Spring led to early melt onset, an extended melt season and the second-lowest September minimum Arctic ice extent observed. We focus on the region of the most persistent ice cover and examine melt pond depth retrieved from ICESat-2 using two distinct algorithms in concert with a time series of melt pond fraction and ice concentration derived from Sentinel-2 imagery to obtain insights about the melting ice surface in three dimensions. We find melt pond fraction derived from Sentinel-2 in the study region increased rapidly in June, with the mean melt pond fraction peaking at 16 % +/- 6 % on 24 June 2020, followed by a slow decrease to 8 % +/- 6 % by 3 July, and remained below 10 % for the remainder of the season through 15 September. Sea ice concentration was consistently high (>95 %) at the beginning of the melt season until 4 July, and as floes disintegrated, decreased to a minimum of 70 % on July 30, then became more variable ranging from 75 % to 90 % for the remainder of the melt season. Pond depth increased steadily from a median depth of 0.40 m +/- 0.17 m in early June, peaked at 0.97 m +/- 0.51 m on 16 July, even as melt pond fraction had already started to decrease. Our results demonstrate that by combining high-resolution passive and active remote sensing we now have the ability to track evolving melt conditions and observe changes in the sea ice cover throughout the summer season.

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“…The Density-Dimension Algorithm for bifurcating reflectors (DDA-bif) is part of the density-dimension algorithm family. The density-dimension algorithms have been developed for the analysis of micro-pulse photon-counting lidar altimeter data, especially data collected with the ICESat-2 ATLAS instrument and its airborne predecessors such as the SIGMA, MABEL and SIMPL instruments, and include algorithms for ice surface data (the DDA-ice) , [Herzfeld et al, 2021c], , for vegetation data (the DDAsigma-veg) [Herzfeld et al, 2014] and for atmospheric layers (the DDA-atmos) , . Common to all density-dimension algorithms is the ability to retrieve surfaces and other reflectors in situations of complex spatial data distributions and mathematically difficult signal-to-noise relationships.…”

Section: A the Family Of Density-dimension Algorithmsmentioning

confidence: 99%

“…The occurrence of melt ponds is by no means specific to sea ice, as melt ponding occurs over glaciers, ice sheets and snow fields as well [Fricker et al, 2020]. There are, however, challenges specific to melt-pond detection over sea ice, which warrant the development of a specific sea-ice algorithm.…”

Section: Challenges Specific To Melt-pond Detection Over Seaicementioning

confidence: 99%

Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice)

Herzfeld

Trantow

Han

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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As climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover is imminent, understanding melt ponding is central to understanding changes in the new Arctic. NASA's Ice, Cloud and land Elevation Satellite (ICESat-2) has the capacity to provide measurements and monitoring of the onset of melt in the Arctic and on melt progression. Yet ponds are currently not reported on the ICESat-2 standard seaice products because of the low resolution of the products, in which only a single surface is determined.The objective of this paper is to introduce a mathematical algorithm that facilitates automated detection of melt ponds in ICESat-2 ATLAS data, retrieval of two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds. With the Advanced Topographic Laser Altimeter System (ATLAS), ICESat-2 carries the first space-borne multibeam micro-pulse photon-counting laser altimeter system, operating at 532 nm frequency. ATLAS data are recorded as clouds of discrete photon points. The Density-Dimension Algorithm for bifurcating sea-ice reflectors (DDA-bifurcate-seaice) is an autoadaptive algorithm that solves the problem of pond detection near the 0.7m nominal alongtrack resolution of ATLAS data, utilizing the radial basis function for calculation of a density field and a threshold function that automatically adapts to changes in background, apparent surface reflectance and some instrument effects. The DDA-bifurcate-seaice is applied to large ICESat-2 data sets from the 2019 and 2020 melt seasons in the multi-year Arctic sea-ice region. Results are evaluated by comparison to those from a manually forced algorithm.

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Detection and Height Measurement of Tenuous Clouds and Blowing Snow in ICESat‐2 ATLAS Data

Cited by 13 publications

References 42 publications

Detection of aerosol and cloud features for the EarthCARE lidar ATLID: the A-FM product

Detection of aerosol and cloud features for the EarthCARE lidar ATLID: the A-FM product

Observing the Evolution of Summer Melt on Multiyear Sea Ice with ICESat-2 and Sentinel-2

Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice)

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