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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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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•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.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2022.112988</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>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</subject><ispartof>Remote sensing of environment, 2022-06, Vol.274, p.112988, Article 112988</ispartof><rights>2022</rights><rights>Copyright Elsevier BV Jun 1, 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3168-aefd90f45a0701483ff57fc3c13f168a15296ef80317fade6bbfc37f7705b6b93</citedby><cites>FETCH-LOGICAL-c3168-aefd90f45a0701483ff57fc3c13f168a15296ef80317fade6bbfc37f7705b6b93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S003442572200102X$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids></links><search><creatorcontrib>Mortimer, C.</creatorcontrib><creatorcontrib>Mudryk, L.</creatorcontrib><creatorcontrib>Derksen, C.</creatorcontrib><creatorcontrib>Brady, M.</creatorcontrib><creatorcontrib>Luojus, K.</creatorcontrib><creatorcontrib>Venäläinen, P.</creatorcontrib><creatorcontrib>Moisander, M.</creatorcontrib><creatorcontrib>Lemmetyinen, J.</creatorcontrib><creatorcontrib>Takala, M.</creatorcontrib><creatorcontrib>Tanis, C.</creatorcontrib><creatorcontrib>Pulliainen, J.</creatorcontrib><title>Benchmarking algorithm changes to the Snow CCI+ snow water equivalent product</title><title>Remote sensing of environment</title><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.</description><subject>Algorithms</subject><subject>Climate change</subject><subject>Daily precipitation</subject><subject>data collection</subject><subject>Datasets</subject><subject>Density</subject><subject>environment</subject><subject>Equivalence</subject><subject>Northern Hemisphere</subject><subject>Parameterization</subject><subject>Product development</subject><subject>Snow</subject><subject>Snow accumulation</subject><subject>Snow courses</subject><subject>Snow cover</subject><subject>Snow density</subject><subject>Snow depth</subject><subject>Snow-water equivalent</subject><subject>snowpack</subject><subject>Special Sensor Microwave Imager</subject><subject>Time series</subject><subject>time series analysis</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAQhi0EEqXwA9gssSChBDtfdsQEER-VihiA2XKcc-OQJq2dtOLf4ypMDEw-6Z73fPcgdElJSAnNbpvQOggjEkUhpVHO-RGaUc7ygDCSHKMZIXESJFHKTtGZcw0hNOWMztDrA3SqXkv7ZboVlu2qt2ao11jVsluBw0OPhxrwe9fvcVEsbrA7VHs5gMWwHc1OttANeGP7alTDOTrRsnVw8fvO0efT40fxEizfnhfF_TJQMc14IEFXOdFJKv12NOGx1inTKlY01r4vaRrlGWhOYsq0rCArS99lmjGSllmZx3N0Pc31_25HcINYG6egbWUH_ehElCWcJylj1KNXf9CmH23nt_NUyiPuNRBP0YlStnfOghYba7yVb0GJOAgWjfCCxUGwmAT7zN2UAX_pzoAVThlvEypjQQ2i6s0_6R9zGYJf</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Mortimer, C.</creator><creator>Mudryk, L.</creator><creator>Derksen, C.</creator><creator>Brady, M.</creator><creator>Luojus, K.</creator><creator>Venäläinen, P.</creator><creator>Moisander, M.</creator><creator>Lemmetyinen, J.</creator><creator>Takala, M.</creator><creator>Tanis, C.</creator><creator>Pulliainen, J.</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20220601</creationdate><title>Benchmarking algorithm changes to the Snow CCI+ snow water equivalent product</title><author>Mortimer, C. ; Mudryk, L. ; Derksen, C. ; Brady, M. ; Luojus, K. ; Venäläinen, P. ; Moisander, M. ; Lemmetyinen, J. ; Takala, M. ; Tanis, C. ; Pulliainen, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3168-aefd90f45a0701483ff57fc3c13f168a15296ef80317fade6bbfc37f7705b6b93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Climate change</topic><topic>Daily precipitation</topic><topic>data collection</topic><topic>Datasets</topic><topic>Density</topic><topic>environment</topic><topic>Equivalence</topic><topic>Northern Hemisphere</topic><topic>Parameterization</topic><topic>Product development</topic><topic>Snow</topic><topic>Snow accumulation</topic><topic>Snow courses</topic><topic>Snow cover</topic><topic>Snow density</topic><topic>Snow depth</topic><topic>Snow-water equivalent</topic><topic>snowpack</topic><topic>Special Sensor Microwave Imager</topic><topic>Time series</topic><topic>time series analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mortimer, C.</creatorcontrib><creatorcontrib>Mudryk, L.</creatorcontrib><creatorcontrib>Derksen, C.</creatorcontrib><creatorcontrib>Brady, M.</creatorcontrib><creatorcontrib>Luojus, K.</creatorcontrib><creatorcontrib>Venäläinen, P.</creatorcontrib><creatorcontrib>Moisander, M.</creatorcontrib><creatorcontrib>Lemmetyinen, J.</creatorcontrib><creatorcontrib>Takala, M.</creatorcontrib><creatorcontrib>Tanis, C.</creatorcontrib><creatorcontrib>Pulliainen, J.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mortimer, C.</au><au>Mudryk, L.</au><au>Derksen, C.</au><au>Brady, M.</au><au>Luojus, K.</au><au>Venäläinen, P.</au><au>Moisander, M.</au><au>Lemmetyinen, J.</au><au>Takala, M.</au><au>Tanis, C.</au><au>Pulliainen, J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Benchmarking algorithm changes to the Snow CCI+ snow water equivalent product</atitle><jtitle>Remote sensing of environment</jtitle><date>2022-06-01</date><risdate>2022</risdate><volume>274</volume><spage>112988</spage><pages>112988-</pages><artnum>112988</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>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.</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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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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