Consistency of Satellite Climate Data Records for Earth System Monitoring

Popp, Thomas; Hegglin, Michaela I.; Hollmann, Rainer; Ardhuin, Fabrice; Bartsch, Annett; Bastos, Ana; Bennett, Victoria; Boutin, Jacqueline; Brockmann, Carsten; Buchwitz, Michael; Chuvieco, Emilio; Ciais, Philippe; Dorigo, Wouter; Ghent, Darren; Jones, Richard F.; Lee, Thomas; Merchant, Christopher J.; Meyssignac, Benoît; Paul, Frank; Quegan, S.; Sathyendranath, Shubha; Scanlon, Tracy; Schröder, Marc; Simis, Stefan; Willén, Ulrika

doi:10.1175/bams-d-19-0127.1

Cited by 29 publications

(9 citation statements)

References 45 publications

(21 reference statements)

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“…Radiometer striping features sharp and non-periodic stripes with large fluctuations, found in radiance data (Jarnot et al 1996;Qin et al 2013;Kim et al 2014;Yang et al 2021), and affecting science products (Ma and Zou 2015) and data assimilation (Bormann et al 2013;Popp et al 2020). Striping appears directional in the cross-track direction.…”

Section: Radiometer Stripingmentioning

confidence: 99%

SatERR: A Community Error Inventory for Satellite Microwave Observation Error Representation and Uncertainty Quantification

Yang

You

Blackwell

et al. 2024

Bulletin of the American Meteorological Society

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Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly-developed algorithms. SatERR takes a bottom-up approach: Errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT.

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Section: Radiometer Stripingmentioning

confidence: 99%

SatERR: A Community Error Inventory for Satellite Microwave Observation Error Representation and Uncertainty Quantification

Yang

You

Blackwell

et al. 2024

Bulletin of the American Meteorological Society

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“…This is the step where the framework for the evaluation is defined. It is important to describe in this step the identified quality attribute or dimension, the evaluation method used, and the protocols, standards and workflows applied (e.g., Wu et al 2017;Zhou et al 2016;Lemieux et al 2017;Popp et al 2020). A well-documented quality evaluation helps to increase transparency, verifiability, reproducibility and resilience of the quality evaluation process.…”

Section: A Basic Elements To Consider When Curating Dataset Quality Informationmentioning

confidence: 99%

“…-ATMODAT metadata assessment checklists (Ganske et al 2020a;2020b); -Consistency of Satellite Climate Data Records for Earth System Monitoring (Popp et al 2020); -CORE-CLIMAX Product System Maturity Matrix (EUMETSAT 2013); -Data Operational Readiness Levels (Moe et al 2018; https://www.esipfed.org/orl); -JPSS Data Product Maturity Matrix (Zhou et al 2016); -Metadata checklist by LTER (O'Brien et al 2016); -NASA IMPACT ARC Metadata Quality Framework (Bugbee et al 2021); -NASA Technical Readiness Level (Mankins 1995;2009)…”

Section: Informationmentioning

confidence: 99%

International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets

Lacagnina

Peng

Ivánová

et al. 2021

Preprint

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“…The data selection determines whether and how the closure of the hydrological balance is ultimately achieved. Ideally, consistency between the selected data products would already be taken into account at the acquisition stage (Popp et al, 2020), but this is generally impossible due to the diversity of monitoring programs for the different components of the water cycle. Problematic elements include, for example, total evaporation from the catchment estimated by modelling or by analogy, estimates of the flow in unmeasured basins, or withdrawals from reservoirs estimated from observing changes in water levels (Pellet et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Detection of Changes in Evapotranspiration on a Catchment Scale Under Changing Climate Conditions in Selected River Basins of Slovakia

Keszeliová

Výleta

Danáčová

et al. 2022

Slovak Journal of Civil Engineering

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Potential changes in a hydrological regime caused by a changing climate represent a crucial source of uncertainty in water resources management. For example, in Slovakia, they may manifest themselves in a decrease in water resources, a change in the seasonality of runoff, and an increase in the extremes of floods and droughts. The research presented here focuses on using the hydrological balance equation to predict changes in the total catchment evapotranspiration under changing climate conditions. Using the TUW rainfall-runoff model and the KNMI and MPI climate change scenarios, the hydrological regime of eight selected basins in Slovakia was simulated for three thirty-year periods from 2010 until the year 2100. The results showed that the growth of total catchment evapotranspiration observed in recent decades is likely to increase further in the future.

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Consistency of Satellite Climate Data Records for Earth System Monitoring

Cited by 29 publications

References 45 publications

SatERR: A Community Error Inventory for Satellite Microwave Observation Error Representation and Uncertainty Quantification

SatERR: A Community Error Inventory for Satellite Microwave Observation Error Representation and Uncertainty Quantification

International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets

Detection of Changes in Evapotranspiration on a Catchment Scale Under Changing Climate Conditions in Selected River Basins of Slovakia

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