Correction: Balsamo, G., et al. Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review. Remote Sensing 2018, 10, 2038

Balsamo, Gianpaolo; Agustı́-Panareda, Anna; Albergel, Clément; Arduini, Gabriele; Beljaars, Anton; Bidlot, Jean‐Raymond; Blyth, Eleanor; Bousserez, Nicolas; Boussetta, Souhail; Brown, Andy; Buizza, Roberto; Buontempo, Carlo; Chevallier, F.; Choulga, Margarita; Cloke, Hannah; Cronin, Meghan F.; Dahoui, Mohamed; Rosnay, Patricia de; Dirmeyer, Paul A.; Drusch, Matthias; Dutra, Emanuel; Ek, Michael; Gentine, Pierre; Hewitt, Helene T.; Keeley, Sarah; Kerr, Yann H.; Kumar, Sujay V.; Lupu, Cristina; Mahfouf, J.‐F.; McNorton, Joe; Mecklenburg, Susanne; Mogensen, Kristian; Muñoz‐Sabater, Joaquín; Orth, René; Rabier, Florence; Reichle, Rolf H.; Ruston, Benjamin; Pappenberger, Florian; Sandu, Irina; Seneviratne, Sonia I.; Tietsche, Steffen; Trigo, Isabel F.; Uijlenhoet, R.; Wedi, Nils; Woolway, R. Iestyn; Zeng, Xubin

doi:10.3390/rs11080941

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, observation of snow cover is carried out by a person on the ground or by measurement equipment (Dong 2018). However, this ground observation is performed at sites that are distant from each other, and thus inevitably creates a large observational gap in space (Balsamo et al 2018). Satellite data, which are remote sensing data with excellent spatial resolution, can be used as a solution.…”

Section: Introductionmentioning

confidence: 99%

Ground- and Satellite-Based Evaluation of WRF Snowfall Prediction

Lee¹,

Lee²,

Lee³

2022

SOLA

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Ground- and Satellite-Based Evaluation of WRF Snowfall Prediction

Lee¹,

Lee²,

Lee³

2022

SOLA

View full text Add to dashboard Cite

show abstract

“…clouds), and data gaps that jeopardize the use of the data set in 44 practical applications, as discussed in recent publications on analysis-readiness of EO data (Baumann, 45 2024). Furthermore, when a long time-series of moderate and high resolution EO data is collected, the 46 size of the dataset can exceed the petabyte (PB), making data storage, access, and elaboration prohibitive 47 for most potential users (Balsamo et al, 2018). Solutions to these problems include data imputation (often 48 named "gap-filling"), smoothing, outlier removal, space and/or time aggregation, decomposition, and 49 image compression.…”

mentioning

confidence: 99%

A computational framework for processing time-series of Earth Observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution

Consoli,

Parente,

Simoes

et al. 2024

Preprint

View full text Add to dashboard Cite

Processing extremely large collections of Earth Observation (EO) time-series, often petabyte-sized, such as NASA's Landsat and ESA's Sentinel missions, can be computationally prohibitive and costly. Despite their name, even the Analysis Ready Data (ARD) versions of such collections can rarely be used as direct input for modeling and require additional time-series processing. Existing solutions for readily using these data are not openly available, are poor in performance, or lack flexibility. Addressing this issue, we developed SIRCLE (Signal Imputation and Refinement with Convolution Leaded Engine), a computational framework that can be used to apply diverse time-series processing techniques by simply adjusting the convolution kernel. Together with SIRCLE, this paper presents SWAG (Seasonally Weighted Average Generalization), a method for EO time-series reconstruction integrated in the framework. SWAG can be used as an imputation method to reconstruct EO images affected by the presence of clouds. Compared to a benchmark dataset, SWAG consistently outperformed the reference methods, reducing reconstruction errors by at least 15%. As the first large-scale application, SIRCLE and SWAG were employed to process the entire Global Land Analysis and Discovery (GLAD) ARD-2 Landsat archive, producing a cloud-free bi-monthly aggregated product. This process, covering seven Landsat bands globally from 1997 to 2022, with more than two trillion pixels and for each one a time-series of 156 samples in the aggregated product, required approximately 28 hours of computation using 1248 Intel(R) Xeon(R) Gold 6248R CPUs. The resulting reconstructed images can be used for machine learning models or to map biophysical indices. With the hosting of about 20 TB per band/index for an entire 30 m resolution, bi-monthly historical time-series, stored as Cloud-Optimized GeoTIFFs (COG) and distributed as open data, the product enables seamless, fast, and affordable access to the Landsat archive for environmental monitoring and analysis applications.

show abstract

Representation of Boundary-Layer Processes in Numerical Weather Prediction and Climate Models

Edwards

Beljaars

Holtslag

et al. 2020

Boundary-Layer Meteorol

View full text Add to dashboard Cite

Correction: Balsamo, G., et al. Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review. Remote Sensing 2018, 10, 2038

Abstract: The authors wish to make the following corrections to this paper [...]

Cited by 4 publications

References 2 publications

Ground- and Satellite-Based Evaluation of WRF Snowfall Prediction

Ground- and Satellite-Based Evaluation of WRF Snowfall Prediction

A computational framework for processing time-series of Earth Observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution

Representation of Boundary-Layer Processes in Numerical Weather Prediction and Climate Models

Contact Info

Product

Resources

About