Simulating the precipitation in the data-scarce Tianshan Mountains, Northwest China based on the Earth system data products

Abstract. Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

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Earth system data cubes unravel global multivariate dynamics

10.5194/esd-2019-62 ◽

2019 ◽

Cited By ~ 2

Author(s):

Miguel D. Mahecha ◽

Fabian Gans ◽

Gunnar Brandt ◽

Rune Christiansen ◽

Sarah E. Cornell ◽

...

Keyword(s):

Data Streams ◽

Multiple Time Scales ◽

Earth System ◽

Model Data ◽

Data Cubes ◽

Intensive Research ◽

Data Intensive ◽

The Earth ◽

System Data ◽

Multiple Variables

Abstract. Understanding Earth system dynamics in the light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing inter-disciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple time-scales; and (3) data-model integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. Latest developments in machine learning, causal inference, and model data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.

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Earth system data cubes unravel global multivariate dynamics

Earth System Dynamics ◽

10.5194/esd-11-201-2020 ◽

2020 ◽

Vol 11 (1) ◽

pp. 201-234 ◽

Cited By ~ 6

Author(s):

Miguel D. Mahecha ◽

Fabian Gans ◽

Gunnar Brandt ◽

Rune Christiansen ◽

Sarah E. Cornell ◽

...

Keyword(s):

Data Integration ◽

Data Streams ◽

Scale Model ◽

Earth System ◽

Model Data ◽

Data Cubes ◽

Intensive Research ◽

Data Intensive ◽

The Earth ◽

System Data

Abstract. Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model–data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model–data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model–data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.

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Overview of NASA's MODIS and VIIRS Snow-Cover Earth System Data Records

10.5194/essd-2017-25 ◽

2017 ◽

Cited By ~ 1

Author(s):

George A. Riggs ◽

Dorothy K. Hall ◽

Miguel O. Román

Keyword(s):

Snow Cover ◽

Information Content ◽

Infrared Imaging ◽

Global Climate ◽

Detection Accuracy ◽

Earth System ◽

Detection Algorithms ◽

Snow Cover Extent ◽

Data Products ◽

System Data

Abstract. Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth’s climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally-applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow-detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. DOIs of the referenced datasets: MODIS Collection 5 doi: http://dx.doi.org/10.5067/ACYTYZB9BEOS doi: http://dx.doi.org/10.5067/R90VAMI75N22 doi: http://dx.doi.org/10.5067/63NQASRDPDB0 doi: http://dx.doi.org/10.5067/ZFAEMQGSR4XD doi: http://dx.doi.org/10.5067/EI5HGLM2NNHN doi: http://dx.doi.org/10.5067/EW53FPU9NAS6 MODIS Collection 6 doi: http://dx.doi.org/10.5067/MODIS/MOD10_L2.006 doi: http://dx.doi.org/10.5067/MODIS/MYD10_L2.006 doi: http://dx.doi.org/10.5067/MODIS/MOD10A1.006 doi: http://dx.doi.org/10.5067/MODIS/MYD10A1.006 doi: http://dx.doi.org/10.5067/MODIS/MOD10C1.006 doi: http://dx.doi.org/10.5067/MODIS/MYD10C1.006 VIIRS Collection 1 doi:10.5067/VIIRS/VNP10.001

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