ESA CCI and C3S Soil Moisture: latest product updates and climate assessments

2020 ◽  
Author(s):  
Wouter Dorigo ◽  
Wolfgang Preimesberger ◽  
Adam Pasik ◽  
Alexander Gruber ◽  
Leander Moesinger ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
European Space Agency ◽  
Climate Data ◽  
Change Initiative ◽  
Space Agency ◽  
Data Store ◽  
Climate Data Record ◽  
Level 2

<p>As part of the European Space Agency (ESA) Climate Change Initiative (CCI) a more than 40 year long climate data record (CDR) is produced by systematically combining Level-2 datasets from separate missions. Combining multiple level 2 datasets into a single consistent long-term product combines the advantages of individual missions and allows deriving a harmonised long-term record with optimal spatial and temporal coverage. The current version of ESA CCI Soil Moisture includes a PASSIVE (radiometer-based) dataset covering the period 1978 to 2019, an ACTIVE (scatterometer-based) product covering the period 1991-2019 and a COMBINED product (1978-2019). </p><p>The European Commission’s Copernicus Climate Changes Service (C3S) uses the ESA CCI soil moisture algorithm to produce similar climate data records from near-real-time Level-2 data streams.  These products are continuously extended within 10 days after data acquisition and instantaneously made available through the C3S Climate Data Store. In addition to a daily product, monthly aggregates as well as a dekadal (10-days) products are produced.</p><p>In this presentation we give an overview of the latest developments of the ESA CCI and C3S Soil Moisture datasets, which include the integration of SMAP and various algorithmic updates, and use the datasets to assess the hydrological conditions of 2019 with respect to a 30-year historical baseline.</p><p>The development of the ESA CCI products has been supported by ESA’s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB). The Copernicus Climate Change Service (C3S) soil moisture product is funded by the Copernicus Climate Change Service implemented by ECMWF through C3S 312b Lot 7 Soil Moisture service.</p>

ESA CCI and C3S Soil Moisture Products: Generation and Quality Assurance

2021 ◽  
Author(s):  
Tracy Scanlon ◽  
Wouter Dorigo ◽  
Wolfgang Preimesberger ◽  
Robin van der Schalie ◽  
Martin Hirschi ◽  
...  
Keyword(s):  
Climate Change ◽  
Quality Assurance ◽  
Soil Moisture ◽  
Land Surface ◽  
European Space Agency ◽  
Global Scale ◽  
Climate Data ◽  
Change Initiative ◽  
Data Product ◽  
Level 2

<p>Soil moisture Climate Data Records (CDRs) produced from active and passive microwave sensors are valuable for the study of the coupled water, energy and carbon cycles over land on a global scale. As part of the European Space Agency (ESA) Climate Change Initiative (CCI) a multi-decadal CDR is produced by systematically combining Level-2 datasets from separate missions. The combination of individual Level 2 datasets into a single product gives us the opportunity to profit from the advantages of individual missions, and to obtain homogenised CDRs with improved spatial and temporal coverage.<br>The most recent version of the ESA CCI product (v06) provides 3 products: (1978 – 2020), ACTIVE (1991 – 2020) and COMBINED (1978 – 2020). This latest version of the product includes several advances that result in the improved quality of the product. Improvements to the input datasets include updated passive (LPRM – Land Parameter Retrieval Model) data to improve inter-calibration and snow / frozen condition flagging as well as updated ASCAT data from the H-SAF project to improve vegetation correction. <br>Algorithmic improvements include the cross-flagging of snow / frozen conditions to take advantage of the flags provided for each input dataset across all sensors as well as the update of the Signal to Noise Ratio – Vegetation Optical Depth (SNR-VOD) regression used in gap-filling the SNR in locations where retrieval has failed. Additional data is also included through the use of the Global Precipitation Measurement (GPM) mission, the FengYun-3B (FY3B) mission and extending the Tropical Rainfall Measuring Mission (TRMM) dataset used to 2015.<br>An operational product based on the ESA CCI SM product continues to be provided through the EU Copernicus Climate Changes Services (C3S) Climate Data Store (CDS). This operational product provides daily data and decadal (10 daily) aggregates in near-real-time as well as monthly aggregates for the historical dataset. The anomalies derived from this dataset (with a base period of 1991 to 2010) can be seen on the TU Wien data viewer (https://dataviewer.geo.tuwien.ac.at/).<br>The accuracy of each data product is assessed through comparison to in-situ soil moisture observations from the International Soil Moisture Network (ISMN) as well as modelled data from Land Surface Models (LSMs). Such assessments are undertaken each time a new ESA CCI version is generated, and the results compared against previous versions to assess the evolution of the product quality over time. For transparency and traceability, an online portal is provided for the public to perform similar validations (Quality Assurance for Soil Moisture – www.qa4sm.eu). <br>In this study, an overview of the product generation and the updates provided at ESA CCI SM v06 is presented as well as examples of how the data product has been used. The associated quality assurance requirements, assessment procedures and results will also be presented.<br>The development of the ESA CCI products has been supported by ESA’s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB). Funded by Copernicus Climate Change Service implemented by ECMWF through C3S 312a Lot 7 Soil Moisture service.</p>

scholarly journals Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology

2019 ◽  
Vol 11 (2) ◽  
pp. 717-739 ◽  
Author(s):  
Alexander Gruber ◽  
Tracy Scanlon ◽  
Robin van der Schalie ◽  
Wolfgang Wagner ◽  
Wouter Dorigo
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Scientific Literature ◽  
Passive Microwave ◽  
Climate Data ◽  
Regular Grid ◽  
Change Initiative ◽  
Merging Algorithm ◽  
Merging Algorithms

Abstract. The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent quality-controlled long-term (1978–2018) climate data records for soil moisture, which serves thousands of scientists and data users worldwide. It harmonises and merges soil moisture retrievals from multiple satellites into (i) an active-microwave-based-only product, (ii) a passive-microwave-based-only product and (iii) a combined active–passive product, which are sampled to daily global images on a 0.25∘ regular grid. Since its first release in 2012 the algorithm has undergone substantial improvements which have so far not been thoroughly reported in the scientific literature. This paper fills this gap by reviewing and discussing the science behind the three major ESA CCI SM merging algorithms, versions 2 (https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c; Wagner et al., 2018), 3 (https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c; Dorigo et al., 2018) and 4 (https://doi.org/10.5285/dce27a397eaf47e797050c220972ca0e; Dorigo et al., 2019), and provides an outlook on the expected improvements planned for the next algorithm, version 5.

scholarly journals Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology

2019 ◽  
Author(s):  
Alexander Gruber ◽  
Tracy Scanlon ◽  
Robin van der Schalie ◽  
Wolfgang Wagner ◽  
Wouter Dorigo
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Scientific Literature ◽  
Passive Microwave ◽  
Climate Data ◽  
Regular Grid ◽  
Change Initiative ◽  
Merging Algorithm

Abstract. The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent quality-controlled long-term (1978–2018) Climate Data Records for soil moisture which serves thousands of scientists and data users worldwide. It harmonises and merges soil moisture retrievals from multiple satellites into (i) an active-microwave-based only, (ii) a passive-microwave-based only, and a combined active-passive product, which are sampled to daily global images on a 0.25 degree regular grid. Since its first release in 2012 the algorithm has undergone substantial improvements which have so far not been thoroughly reported in the scientific literature. This paper fills this gap by reviewing and discussing the science behind the three major ESA CCI SM merging algorithm versions 2 (https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c; Wagner et al., 2018), 3 (https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c; Dorigo et al., 2018a), and 4 (https://doi.org/10.5285/3a8a94c3fa464d68b6d70df291afd457; Dorigo et al., 2018b) and provides an outlook to the expected improvements planned for the next algorithm version 5.

scholarly journals The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables

2013 ◽  
Vol 94 (10) ◽  
pp. 1541-1552 ◽  
Author(s):  
R. Hollmann ◽  
C. J. Merchant ◽  
R. Saunders ◽  
C. Downy ◽  
M. Buchwitz ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Modeling ◽  
European Space Agency ◽  
Climate Science ◽  
Climate Variables ◽  
Climate Data ◽  
Change Initiative ◽  
System Perspective ◽  
Space Agency ◽  
Over 40 Years

Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

scholarly journals Merged SAGE II, Ozone_cci and OMPS ozone profile dataset and evaluation of ozone trends in the stratosphere

2017 ◽  
Vol 17 (20) ◽  
pp. 12533-12552 ◽  
Author(s):  
Viktoria F. Sofieva ◽  
Erkki Kyrölä ◽  
Marko Laine ◽  
Johanna Tamminen ◽  
Doug Degenstein ◽  
...  
Keyword(s):  
Climate Change ◽  
Stratospheric Ozone ◽  
European Space Agency ◽  
Space Agency ◽  
Ozone Profile ◽  
Ozone Trends ◽  
Stratospheric Cooling ◽  
The Individual ◽  
Good Agreement

Abstract. In this paper, we present a merged dataset of ozone profiles from several satellite instruments: SAGE II on ERBS, GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, ACE-FTS on SCISAT, and OMPS on Suomi-NPP. The merged dataset is created in the framework of the European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The datasets from the individual instruments have been extensively validated and intercompared; only those datasets which are in good agreement, and do not exhibit significant drifts with respect to collocated ground-based observations and with respect to each other, are used for merging. The long-term SAGE–CCI–OMPS dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The merged SAGE–CCI–OMPS dataset consists of deseasonalized anomalies of ozone in 10° latitude bands from 90° S to 90° N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to July 2016. This newly created dataset is used for evaluating ozone trends in the stratosphere through multiple linear regression. Negative ozone trends in the upper stratosphere are observed before 1997 and positive trends are found after 1997. The upper stratospheric trends are statistically significant at midlatitudes and indicate ozone recovery, as expected from the decrease of stratospheric halogens that started in the middle of the 1990s and stratospheric cooling.

scholarly journals Merged SAGE II, Ozone_cci and OMPS ozone profiles dataset and evaluation of ozone trends in the stratosphere

2017 ◽  
Author(s):  
Viktoria F. Sofieva ◽  
Erkki Kyrölä ◽  
Marko Laine ◽  
Johanna Tamminen ◽  
Doug Degenstein ◽  
...  
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stratospheric Ozone ◽  
European Space Agency ◽  
Space Agency ◽  
Ozone Trends ◽  
The Individual ◽  
Good Agreement

Abstract. In this paper, we present a merged dataset of ozone profiles from several satellite instruments: SAGE II on ERBS, GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, ACE-FTS on SCISAT, and OMPS on Suomi-NPP. The merged dataset is created in the framework of European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The datasets from the individual instruments have been extensively validated and inter-compared; only those datasets, which are in good agreement and do not exhibit significant drifts with respect to collocated ground-based observations and with respect to each other, are used for merging. The long-term SAGE-CCI-OMPS dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The merged SAGE-CCI-OMPS dataset consists of deseasonalized anomalies of ozone in 10° latitude bands from 90° S to 90° N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to July 2016. This newly created dataset is used for evaluating ozone trends in the stratosphere through multiple linear regression. Negative ozone trends in the upper stratosphere are observed before 1997 and positive trends are found after 1997. The upper stratospheric trends are statistically significant at mid-latitudes in the upper stratosphere and indicate ozone recovery, as expected from the decrease of stratospheric halogens that started in the middle of the 1990s.

scholarly journals An improved and homogeneous altimeter sea level record from the ESA Climate Change Initiative

2018 ◽  
Vol 10 (1) ◽  
pp. 281-301 ◽  
Author(s):  
Jean-François Legeais ◽  
Michaël Ablain ◽  
Lionel Zawadzki ◽  
Hao Zuo ◽  
Johnny A. Johannessen ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Level ◽  
Global Climate ◽  
European Space Agency ◽  
Bias Reduction ◽  
Second Phase ◽  
Change Initiative ◽  
Internal Validation ◽  
Spatial And Temporal Scales ◽  
Space Agency

Abstract. Sea level is a very sensitive index of climate change since it integrates the impacts of ocean warming and ice mass loss from glaciers and the ice sheets. Sea level has been listed as an essential climate variable (ECV) by the Global Climate Observing System (GCOS). During the past 25 years, the sea level ECV has been measured from space by different altimetry missions that have provided global and regional observations of sea level variations. As part of the Climate Change Initiative (CCI) program of the European Space Agency (ESA) (established in 2010), the Sea Level project (SL_cci) aimed to provide an accurate and homogeneous long-term satellite-based sea level record. At the end of the first phase of the project (2010–2013), an initial version (v1.1) of the sea level ECV was made available to users (Ablain et al., 2015). During the second phase of the project (2014–2017), improved altimeter standards were selected to produce new sea level products (called SL_cci v2.0) based on nine altimeter missions for the period 1993–2015 (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612; Legeais and the ESA SL_cci team, 2016c). Corresponding orbit solutions, geophysical corrections and altimeter standards used in this v2.0 dataset are described in detail in Quartly et al. (2017). The present paper focuses on the description of the SL_cci v2.0 ECV and associated uncertainty and discusses how it has been validated. Various approaches have been used for the quality assessment such as internal validation, comparisons with sea level records from other groups and with in situ measurements, sea level budget closure analyses and comparisons with model outputs. Compared with the previous version of the sea level ECV, we show that use of improved geophysical corrections, careful bias reduction between missions and inclusion of new altimeter missions lead to improved sea level products with reduced uncertainties on different spatial and temporal scales. However, there is still room for improvement since the uncertainties remain larger than the GCOS requirements (GCOS, 2011). Perspectives on subsequent evolution are also discussed.

scholarly journals Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records

2020 ◽  
Vol 12 (20) ◽  
pp. 3439
Author(s):  
Mendy van der Vliet ◽  
Robin van der Schalie ◽  
Nemesio Rodriguez-Fernandez ◽  
Andreas Colliander ◽  
Richard de Jeu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
European Space Agency ◽  
Data Availability ◽  
Climate Data ◽  
Mountainous Regions ◽  
Soil Moisture Data ◽  
Climate Records ◽  
The Individual ◽  
The Impact

Reliable soil moisture retrievals from passive microwave satellite sensors are limited during certain conditions, e.g., snow coverage, radio-frequency interference, and dense vegetation. In these cases, the retrievals can be masked using flagging algorithms. Currently available single- and multi-sensor soil moisture products utilize different flagging approaches. However, a clear overview and comparison of these approaches and their impact on soil moisture data are still lacking. For long-term climate records such as the soil moisture products of the European Space Agency (ESA) Climate Change Initiative (CCI), the effect of any flagging inconsistency resulting from combining multiple sensor datasets is not yet understood. Therefore, the first objective of this study is to review the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP). The SMOS and SMAP soil moisture flagging systems differ substantially in number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems were compared for the SMOS and SMAP soil moisture datasets. Major differences in data availability were observed globally, especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlight the importance of a consistent and well-performing approach that is applicable to all individual products used in long-term soil moisture data records. Consequently, the second objective of the present study is to design a consistent and model-independent flagging strategy to improve soil moisture climate records such as the ESA CCI products. As snow cover, ice, and frozen conditions were demonstrated to have the biggest impact on data availability, a uniform satellite driven flagging strategy was designed for these conditions and evaluated against two ground observation networks. The new flagging strategy demonstrated to be a robust flagging alternative when compared to the individual flagging strategies adopted by the SMOS and SMAP soil moisture datasets with a similar performance, but with the applicability to the entire ESA CCI time record without the use of modelled approximations.

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