Benchmarking algorithm changes to the Snow CCI+ snow water equivalent product

The European Space Agency (ESA) Snow Climate Change Initiative (CCI+) provides long-term, global time series of daily snow cover fraction and snow water equivalent (SWE). The Snow CCI+ SWE Version 1 (CCIv1) product is built on the GlobSnow algorithm, which combines passive microwave (PMW) data with...

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Veröffentlicht in:Remote sensing of environment 2022-06, Vol.274, p.112988, Article 112988
Hauptverfasser: Mortimer, C., Mudryk, L., Derksen, C., Brady, M., Luojus, K., Venäläinen, P., Moisander, M., Lemmetyinen, J., Takala, M., Tanis, C., Pulliainen, J.
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container_start_page 112988
container_title Remote sensing of environment
container_volume 274
creator Mortimer, C.
Mudryk, L.
Derksen, C.
Brady, M.
Luojus, K.
Venäläinen, P.
Moisander, M.
Lemmetyinen, J.
Takala, M.
Tanis, C.
Pulliainen, J.
description The European Space Agency (ESA) Snow Climate Change Initiative (CCI+) provides long-term, global time series of daily snow cover fraction and snow water equivalent (SWE). The Snow CCI+ SWE Version 1 (CCIv1) product is built on the GlobSnow algorithm, which combines passive microwave (PMW) data with in situ snow depth (SD) measurements to estimate SWE. While CCIv1 remains algorithmically similar to the most recent GlobSnow product (GlobSnow Version 3), Snow CCI+ SWE Version 2 (CCIv2) incorporates two notable differences. CCIv2 uses updated PMW data from the NASA MEaSUREs Calibrated Passive Microwave Daily EASE-Grid 2.0 Earth Science Data Record and is generated in EASE-Grid 2.0 with 12.5 km grid spacing. It also adjusts SWE retrievals in post-processing by incorporating spatially and temporally varying snow density information. Due to the phased product development framework CCI+ employs, proposed changes between CCIv1 and CCIv2 were implemented in a series of step-wise developmental datasets. Using these developmental datasets, we analyze how changes to input PMW and SD data and the snow density parameterization affect the resulting SWE product. Using in situ snow courses as reference data, we demonstrate that the correlation and RMSE of the CCIv2 developmental product improved 18% (0.10) and 12% (5 mm), respectively, relative to CCIv1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) to the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is also removed. •Development ESA Snow CCI+ Snow Water Equivalent (SWE) version 2 recently completed.•Reprocessed NASA MEaSUREs data rectifies SWE time series discontinuity in 2009.•New treatment of snow density applied in post-processing improved SWE estimates.
doi_str_mv 10.1016/j.rse.2022.112988
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The Snow CCI+ SWE Version 1 (CCIv1) product is built on the GlobSnow algorithm, which combines passive microwave (PMW) data with in situ snow depth (SD) measurements to estimate SWE. While CCIv1 remains algorithmically similar to the most recent GlobSnow product (GlobSnow Version 3), Snow CCI+ SWE Version 2 (CCIv2) incorporates two notable differences. CCIv2 uses updated PMW data from the NASA MEaSUREs Calibrated Passive Microwave Daily EASE-Grid 2.0 Earth Science Data Record and is generated in EASE-Grid 2.0 with 12.5 km grid spacing. It also adjusts SWE retrievals in post-processing by incorporating spatially and temporally varying snow density information. Due to the phased product development framework CCI+ employs, proposed changes between CCIv1 and CCIv2 were implemented in a series of step-wise developmental datasets. Using these developmental datasets, we analyze how changes to input PMW and SD data and the snow density parameterization affect the resulting SWE product. Using in situ snow courses as reference data, we demonstrate that the correlation and RMSE of the CCIv2 developmental product improved 18% (0.10) and 12% (5 mm), respectively, relative to CCIv1. 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The Snow CCI+ SWE Version 1 (CCIv1) product is built on the GlobSnow algorithm, which combines passive microwave (PMW) data with in situ snow depth (SD) measurements to estimate SWE. While CCIv1 remains algorithmically similar to the most recent GlobSnow product (GlobSnow Version 3), Snow CCI+ SWE Version 2 (CCIv2) incorporates two notable differences. CCIv2 uses updated PMW data from the NASA MEaSUREs Calibrated Passive Microwave Daily EASE-Grid 2.0 Earth Science Data Record and is generated in EASE-Grid 2.0 with 12.5 km grid spacing. It also adjusts SWE retrievals in post-processing by incorporating spatially and temporally varying snow density information. Due to the phased product development framework CCI+ employs, proposed changes between CCIv1 and CCIv2 were implemented in a series of step-wise developmental datasets. Using these developmental datasets, we analyze how changes to input PMW and SD data and the snow density parameterization affect the resulting SWE product. Using in situ snow courses as reference data, we demonstrate that the correlation and RMSE of the CCIv2 developmental product improved 18% (0.10) and 12% (5 mm), respectively, relative to CCIv1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) to the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is also removed. •Development ESA Snow CCI+ Snow Water Equivalent (SWE) version 2 recently completed.•Reprocessed NASA MEaSUREs data rectifies SWE time series discontinuity in 2009.•New treatment of snow density applied in post-processing improved SWE estimates.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2022.112988</doi><oa>free_for_read</oa></addata></record>
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source Elsevier ScienceDirect Journals
subjects Algorithms
Climate change
Daily precipitation
data collection
Datasets
Density
environment
Equivalence
Northern Hemisphere
Parameterization
Product development
Snow
Snow accumulation
Snow courses
Snow cover
Snow density
Snow depth
Snow-water equivalent
snowpack
Special Sensor Microwave Imager
Time series
time series analysis
title Benchmarking algorithm changes to the Snow CCI+ snow water equivalent product
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