scholarly journals An Efficient Algorithm for Retrieving CO2 in the Atmosphere From Hyperspectral Measurements of Satellites: Application of NLS-4DVar Data Assimilation Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhe Jin ◽  
Xiangjun Tian ◽  
Minzheng Duan ◽  
Rui Han
Keyword(s):  
Data Assimilation ◽  
Weighting Function ◽  
Nonlinear Least Squares ◽  
Large Data ◽  
Optimization Method ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Target Mode ◽  
The Mean

A novel and efficient inverse method, named Nonlinear least squares four-dimensional variational data Assimilation (NLS-4DVar)-based CO2 Retrieval Algorithm (NARA), is proposed for retrieving atmospheric CO2 from the satellite hyperspectral measurements, in which the NLS-4DVar method is used as the optimization method. As the NLS-4DVar method works independently of the tangent linear model and adjoint model, the time-consuming calculation of the weighting function matrix is unnecessary, and the computation complexity is tremendously reduced while maintaining the retrieval accuracy. This is extremely important for space-based CO2 retrievals with large data volumes. Observing system simulation experiments (OSSEs) over four different sites around the world showed that the NARA algorithm could retrieve XCO2 and CO2 profiles effectively. To further evaluate the NARA algorithm, it was used for real CO2 retrievals from target-mode observations of Orbiting Carbon Observatory-2 (OCO-2) over Lamont, Oklahoma, and Darwin, Australia. The results were compared with that of ground measurements of Total Carbon Column Observing Network (TCCON). The mean difference of XCO2 between NARA and TCCON over Lamont, from 180 observations, was −0.15 ppmv with a standard deviation (SD) of 0.76 ppmv. Over Darwin, the mean difference, from 180 observations (90 points over land and 90 points over the ocean), is −0.17 ppmv (SD: 1.26 ppmv). The preliminary results showed that the efficient NLS-4DVar-based algorithm could provide great help for satellite remote sensing of CO2, and it may be used as an operational procedure after further and extensive evaluations.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (7) ◽  
pp. 2961-2980 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT–SCIAMACHY (linear correlation coefficient r=0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT–TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY–TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

scholarly journals The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO<sub>2</sub> and CH<sub>4</sub> retrieval algorithm products with measurements from the TCCON

2014 ◽  
Vol 7 (6) ◽  
pp. 1723-1744 ◽  
Author(s):  
B. Dils ◽  
M. Buchwitz ◽  
M. Reuter ◽  
O. Schneising ◽  
H. Boesch ◽  
...  
Keyword(s):  
Climate Change ◽  
Fourier Transform ◽  
Greenhouse Gases ◽  
Greenhouse Gas ◽  
Weighting Function ◽  
Total Carbon ◽  
Inverse Modelling ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Change Initiative

Abstract. Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO2, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For XCH4, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH4 precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively.

scholarly journals Surface solar irradiance from SCIAMACHY measurements: algorithm and validation

2011 ◽  
Vol 4 (5) ◽  
pp. 875-891 ◽  
Author(s):  
P. Wang ◽  
P. Stammes ◽  
R. Mueller
Keyword(s):  
Standard Deviation ◽  
Mean Difference ◽  
Surface Radiation ◽  
Mean Bias Error ◽  
Retrieval Algorithm ◽  
Bias Error ◽  
Retrieval Scheme ◽  
The Mean ◽  
Scanning Imaging ◽  
One Year

Abstract. Broadband surface solar irradiances (SSI) are, for the first time, derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) satellite measurements. The retrieval algorithm, called FRESCO (Fast REtrieval Scheme for Clouds from the Oxygen A band) SSI, is similar to the Heliosat method. In contrast to the standard Heliosat method, the cloud index is replaced by the effective cloud fraction derived from the FRESCO cloud algorithm. The MAGIC (Mesoscale Atmospheric Global Irradiance Code) algorithm is used to calculate clear-sky SSI. The SCIAMACHY SSI product is validated against globally distributed BSRN (Baseline Surface Radiation Network) measurements and compared with ISCCP-FD (International Satellite Cloud Climatology Project Flux Dataset) surface shortwave downwelling fluxes (SDF). For one year of data in 2008, the mean difference between the instantaneous SCIAMACHY SSI and the hourly mean BSRN global irradiances is −4 W m−2 (−1 %) with a standard deviation of 101 W m−2 (20 %). The mean difference between the globally monthly mean SCIAMACHY SSI and ISCCP-FD SDF is less than −12 W m−2 (−2 %) for every month in 2006 and the standard deviation is 62 W m−2 (12 %). The correlation coefficient is 0.93 between SCIAMACHY SSI and BSRN global irradiances and is greater than 0.96 between SCIAMACHY SSI and ISCCP-FD SDF. The evaluation results suggest that the SCIAMACHY SSI product achieves similar mean bias error and root mean square error as the surface solar irradiances derived from polar orbiting satellites with higher spatial resolution.

scholarly journals Surface solar irradiance from SCIAMACHY measurements: algorithm and validation

2011 ◽  
Vol 4 (1) ◽  
pp. 873-912 ◽  
Author(s):  
P. Wang ◽  
P. Stammes ◽  
R. Mueller
Keyword(s):  
Standard Deviation ◽  
Mean Difference ◽  
Surface Radiation ◽  
Mean Bias Error ◽  
Retrieval Algorithm ◽  
Bias Error ◽  
Retrieval Scheme ◽  
The Mean ◽  
Scanning Imaging ◽  
One Year

Abstract. Broadband surface solar irradiances (SSI) are, for the first time, derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) satellite measurements. The retrieval algorithm, called FRESCO (Fast REtrieval Scheme for Clouds from Oxygen A band) SSI, is similar to the Heliosat method. In contrast to the standard Heliosat method, the cloud index is replaced by the effective cloud fraction derived from the FRESCO cloud algorithm. The MAGIC (Mesoscale Atmospheric Global Irradiance Code) algorithm is used to calculate clear-sky SSI. The SCIAMACHY SSI product is validated against the globally distributed BSRN (Baseline Surface Radiation Network) measurements and compared with the ISCCP-FD (International Satellite Cloud Climatology Project Flux Dataset) surface shortwave downwelling fluxes (SDF). For one year of data in 2008, the mean difference between the instantaneous SCIAMACHY SSI and the hourly mean BSRN global irradiances is −4 W m−2(−1%) with a standard deviation of 101 W m−2 (20%). The mean difference between the globally monthly mean SCIAMACHY SSI and ISCCP-FD SDF is less than −12 W m−2 (−2%) for every month in 2006 and the standard deviation is 62 W m−2 (12%). The correlation coefficient is 0.93 between SCIAMACHY SSI and BSRN global irradiances and is greater than 0.96 between SCIAMACHY SSI and ISCCP-FD SDF. The evaluation results suggest that the SCIAMACHY SSI product achieves similar mean bias error and root mean square error as the surface solar irradiances derived from polar orbiting satellites with higher spatial resolution.

scholarly journals Retrieval and Validation of XCO2 from TanSat Target Mode Observations in Beijing

2020 ◽  
Vol 12 (18) ◽  
pp. 3063
Author(s):  
Zhengyi Bao ◽  
Xingying Zhang ◽  
Tianxiang Yue ◽  
Lili Zhang ◽  
Zong Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Atmospheric Carbon Dioxide ◽  
Absolute Error ◽  
Satellite Observation ◽  
Mean Difference ◽  
Satellite Mission ◽  
Target Mode ◽  
Environmental Informatics ◽  
The Mean ◽  
Carbon Dioxide Co2

Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan’s Greenhouse Gases Observing SATellite (GOSAT), the USA’s Orbiting Carbon Observatory-2 (OCO-2), and China’s Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO2 measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO2) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China’s State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO2 bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC2 results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO2 effectively. In addition, the DEEI-retrieved XCO2 results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS.

scholarly journals Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements

2020 ◽  
Vol 12 (14) ◽  
pp. 2204 ◽  
Author(s):  
Shupeng Wang ◽  
Ronald van der A ◽  
Piet Stammes ◽  
Weihe Wang ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Northeast China ◽  
Goodness Of Fit ◽  
Total Carbon ◽  
Positive Bias ◽  
A Posteriori ◽  
Spectral Bands ◽  
Target Mode ◽  
Aerial Vehicle ◽  
The Mean

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the TanSat observations using the ACOS (Atmospheric CO2 Observations from Space) algorithm. The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for 2 years of TanSat data from 2017 to 2018. Based on the correlation of the XCO2 error over land with goodness of fit in three spectral bands at 0.76, 1.61 and 2.06 μm, we applied an a posteriori bias correction to TanSat retrievals. For overpass averaged results, XCO2 retrievals show a standard deviation (SD) of ~2.45 ppm and a positive bias of ~0.27 ppm compared to collocated TCCON sites. The validation also shows a relatively higher positive bias and variance against TCCON over high-latitude regions. Three cases to evaluate TanSat target mode retrievals are investigated, including one field campaign at Dunhuang with measurements by a greenhouse gas analyzer deployed on an unmanned aerial vehicle and two cases with measurements by a ground-based Fourier-transform spectrometer in Beijing. The results show the retrievals of all footprints, except footprint-6, have relatively low bias (within ~2 ppm). In addition, the orbital XCO2 distributions over Australia and Northeast China between TanSat and the second Orbiting Carbon Observatory (OCO-2) on 20 April 2017 are compared. It shows that the mean XCO2 from TanSat is slightly lower than that of OCO-2 with an average difference of ~0.85 ppm. A reasonable agreement in XCO2 distribution is found over Australia and Northeast China between TanSat and OCO-2.

scholarly journals The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO<sub>2</sub> and CH<sub>4</sub> retrieval algorithm products with measurements from the TCCON network

2013 ◽  
Vol 6 (5) ◽  
pp. 8679-8741 ◽  
Author(s):  
B. Dils ◽  
M. Buchwitz ◽  
M. Reuter ◽  
O. Schneising ◽  
H. Boesch ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Weighting Function ◽  
Optimal Estimation ◽  
Total Carbon ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Change Initiative ◽  
The University

Abstract. Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS and SCIAMACHY instruments on board GOSAT and ENVISAT using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier Transform Spectrometers (FTS) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the Weighting Function Modified Differential Optical Absorption Spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen Optimal Estimation DOAS algorithm (BESD, University of Bremen), the Iterative Maximum A Posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called Round Robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "Greenhouse Gases" (GHG). For CO2, all algorithms reach the precision requirements for inverse modelling (< 8 ppb), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For CH4, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fail to meet the < 34 ppb threshold, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH4 precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 ppb and 10.5 ppb respectively.

scholarly journals Do we need to improve the reporting of evidence in tendinopathy management? A critical appraisal of systematic reviews with recommendations on strength of evidence assessment

2021 ◽  
Vol 7 (1) ◽  
pp. e000920
Author(s):  
Dimitris Challoumas ◽  
Neal L Millar
Keyword(s):  
Systematic Reviews ◽  
Assessment Tool ◽  
Data Extraction ◽  
Cochrane Collaboration ◽  
Mean Difference ◽  
Strength Of Evidence ◽  
The Mean ◽  
Randomised Controlled ◽  
Meta Analyses ◽  
The Cochrane Collaboration

ObjectiveTo critically appraise the quality of published systematic reviews (SRs) of randomised controlled trials (RCTs) in tendinopathy with regard to handling and reporting of results with special emphasis on strength of evidence assessment.Data sourcesMedline from inception to June 2020.Study eligibilityAll SRs of RCTs assessing the effectiveness of any intervention(s) on any location of tendinopathy.Data extraction and synthesisIncluded SRs were appraised with the use of a 12-item tool devised by the authors arising from the Preferred Reporting Items in Systematic Reviews and Meta-Analyses statement and other relevant guidance. Subgroup analyses were performed based on impact factor (IF) of publishing journals and date of publication.ResultsA total of 57 SRs were included published in 38 journals between 2006 and 2020. The most commonly used risk-of-bias (RoB) assessment tool and strength of evidence assessment tool were the Cochrane Collaboration RoB tool and the Cochrane Collaboration Back Review Group tool, respectively. The mean score on the appraisal tool was 46.5% (range 0%–100%). SRs published in higher IF journals (>4.7) were associated with a higher mean score than those in lower IF journals (mean difference 26.4%±8.8%, p=0.004). The mean score of the 10 most recently published SRs was similar to that of the first 10 published SRs (mean difference 8.3%±13.7%, p=0.54). Only 23 SRs (40%) used the results of their RoB assessment in data synthesis and more than half (n=30; 50%) did not assess the strength of evidence of their results. Only 12 SRs (21%) assessed their strength of evidence appropriately.ConclusionsIn light of the poor presentation of evidence identified by our review, we provide recommendations to increase transparency and reproducibility in future SRs.

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