scholarly journals How long do satellites need to overlap? Evaluation of climate data stability from overlapping satellite records

2017 ◽  
Vol 17 (24) ◽  
pp. 15069-15093 ◽  
Author(s):  
Elizabeth C. Weatherhead ◽  
Jerald Harder ◽  
Eduardo A. Araujo-Pradere ◽  
Greg Bodeker ◽  
Jason M. English ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Spectral Irradiance ◽  
Data Sets ◽  
Climate Data ◽  
Data Set ◽  
Long Term Stability ◽  
Earth Observations ◽  
Earth’S Climate ◽  
The Stability

Abstract. Sensors on satellites provide unprecedented understanding of the Earth's climate system by measuring incoming solar radiation, as well as both passive and active observations of the entire Earth with outstanding spatial and temporal coverage. A common challenge with satellite observations is to quantify their ability to provide well-calibrated, long-term, stable records of the parameters they measure. Ground-based intercomparisons offer some insight, while reference observations and internal calibrations give further assistance for understanding long-term stability. A valuable tool for evaluating and developing long-term records from satellites is the examination of data from overlapping satellite missions. This paper addresses how the length of overlap affects the ability to identify an offset or a drift in the overlap of data between two sensors. Ozone and temperature data sets are used as examples showing that overlap data can differ by latitude and can change over time. New results are presented for the general case of sensor overlap by using Solar Radiation and Climate Experiment (SORCE) Spectral Irradiance Monitor (SIM) and Solar Stellar Irradiance Comparison Experiment (SOLSTICE) solar irradiance data as an example. To achieve a 1 % uncertainty in estimating the offset for these two instruments' measurement of the Mg II core (280 nm) requires approximately 5 months of overlap. For relative drift to be identified within 0.1 % yr−1 uncertainty (0.00008 W m−2 nm−1 yr−1), the overlap for these two satellites would need to be 2.5 years. Additional overlap of satellite measurements is needed if, as is the case for solar monitoring, unexpected jumps occur adding uncertainty to both offsets and drifts; the additional length of time needed to account for a single jump in the overlap data may be as large as 50 % of the original overlap period in order to achieve the same desired confidence in the stability of the merged data set. Results presented here are directly applicable to satellite Earth observations. Approaches for Earth observations offer additional challenges due to the complexity of the observations, but Earth observations may also benefit from ancillary observations taken from ground-based and in situ sources. Difficult choices need to be made when monitoring approaches are considered; we outline some attempts at optimizing networks based on economic principles. The careful evaluation of monitoring overlap is important to the appropriate application of observational resources and to the usefulness of current and future observations.

scholarly journals How long do satellites need to overlap? Evaluation of climate data stability from overlapping satellite records

2017 ◽  
Author(s):  
Elizabeth C. Weatherhead ◽  
Jerald Harder ◽  
Eduardo A. Araujo-Pradere ◽  
Jason M. English ◽  
Lawrence E. Flynn ◽  
...  
Keyword(s):  
Climate Data ◽  
Long Term Stability ◽  
Earth Observations ◽  
Satellite Technology ◽  
Irradiance Data ◽  
Earth’S Climate ◽  
Added Uncertainty ◽  
The Stability

Abstract. Sensors on satellites provide unprecedented understanding of the Earth’s climate system by measuring incoming solar radiation, as well as both passive and active observations of the entire Earth with outstanding spatial and temporal coverage that would be currently impossible without satellite technology. A common challenge with satellite observations is to quantify their ability to provide well-calibrated, long-term, stable records of the parameters they measure. Ground-based intercomparisons offer some insight, while reference observations and internal calibrations give further assistance for understanding long-term stability. A valuable tool for evaluating and developing long-term records from satellites is the examination of data from overlapping satellites. Prior papers have used overlap periods to identify the offset between data from two satellites and estimate the added uncertainty to long-term records. This paper addresses the length of overlap needed to identify an offset or a drift in the offsets of data between two sensors. The results are presented for the general case of sensor overlap by using the case of overlap of the SORCE SIM and SOLSTICE solar irradiance data as an example. To achieve a 1 % uncertainty in estimating the offset for these two instruments’ measurement of the Mg II core (280 nm) requires approximately 5 months of overlap. For relative drift to be identified within 0.1 % yr−1 uncertainty, the overlap for these two satellites would need to be 2.6 years. Additional overlap of satellite measurements is needed if, as is the case for solar monitoring, unexpected jumps may occur because these jumps add to the uncertainty of both offsets and drifts; the additional length of time needed to account for a single jump in the overlap data may be as large as 50 % of the original overlap period in order to achieve the same desired confidence in the stability of the merged dataset. Extension of the results presented here are directly applicable to satellite Earth observations. Approaches for Earth observations may be challenged by the complexity of those observations but may also benefit from ancillary observations taken from ground-based and in situ sources. Difficult choices need to be made when monitoring approaches are considered; we outline some attempts at optimizing networks based on economic principles. The careful evaluation of monitoring overlap is important to the appropriate application of observational resources and to the usefulness of current and future observations.

scholarly journals Assessment of Odin-OSIRIS ozone measurements from 2001 to the present using MLS, GOMOS, and ozone sondes

2013 ◽  
Vol 6 (2) ◽  
pp. 3819-3857 ◽  
Author(s):  
C. Adams ◽  
A. E. Bourassa ◽  
V. Sofieva ◽  
L. Froidevaux ◽  
C. A. McLinden ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Imaging System ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Long Term Stability ◽  
Ozone Data ◽  
High Bias ◽  
The Stability

Abstract. The Optical Spectrograph and InfraRed Imaging System (OSIRIS) was launched aboard the Odin satellite in 2001 and is continuing to take limb-scattered sunlight measurements of the atmosphere. This work aims to characterize and assess the stability of the OSIRIS 11 yr v5.0x ozone data set. Three validation data sets were used: the v2.2 Microwave Limb Sounder (MLS) and v6 Global Ozone Monitoring of Occultation on Stars (GOMOS) satellite data records, and ozone sonde measurements. Global mean percent differences between coincident OSIRIS and validation measurements are within 5% of zero at all altitude layers above 18.5 km for MLS, above 21.5 km for GOMOS, and above 17.5 km for ozone sondes. Below 17.5 km, OSIRIS measurements agree with ozone sondes within 5% and are well-correlated (R > 0.75) with them. For low OSIRIS optics temperatures (< 16 °C), OSIRIS ozone measurements are biased low by up 6% compared with the validation data sets for 25.5–40.5 km. Biases between OSIRIS ascending and descending node measurements were investigated and were found to be related to aerosol retrievals below 27.5 km. Above 30 km, agreement between OSIRIS and the validation data sets was related to the OSIRIS retrieved albedo, which measures apparent upwelling, with a high bias for in OSIRIS data with large albedos. In order to assess the long-term stability of OSIRIS measurements, global average drifts relative to the validation data sets were calculated and were found to be < 3% per decade for comparisons against MLS for 19.5–36.5 km, GOMOS for 18.5–54.5 km, and ozone sondes for 12.5–22.5 km, and within error of 3% per decade at most altitudes. Above 36.5 km, the relative drift for OSIRIS versus MLS ranged from ~ 0–6%, depending on the data set used to convert MLS data to the OSIRIS altitude versus number density grid. Overall, this work demonstrates that the OSIRIS 11 yr ozone data set from 2001 to the present is suitable for trend studies.

scholarly journals Assessment of Odin-OSIRIS ozone measurements from 2001 to the present using MLS, GOMOS, and ozonesondes

2014 ◽  
Vol 7 (1) ◽  
pp. 49-64 ◽  
Author(s):  
C. Adams ◽  
A. E. Bourassa ◽  
V. Sofieva ◽  
L. Froidevaux ◽  
C. A. McLinden ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Imaging System ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Long Term Stability ◽  
Ozone Data ◽  
The Stability ◽  
Global Mean

Abstract. The Optical Spectrograph and InfraRed Imaging System (OSIRIS) was launched aboard the Odin satellite in 2001 and is continuing to take limb-scattered sunlight measurements of the atmosphere. This work aims to characterize and assess the stability of the OSIRIS 11 yr v5.0x ozone data set. Three validation data sets were used: the v2.2 Microwave Limb Sounder (MLS) and v6 Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite data records, and ozonesonde measurements. Global mean percent differences between coincident OSIRIS and validation measurements are within 5% at all altitudes above 18.5 km for MLS, above 21.5 km for GOMOS, and above 17.5 km for ozonesondes. Below 17.5 km, OSIRIS measurements agree with ozonesondes within 5% and are well-correlated (R > 0.75) with them. For low OSIRIS optics temperatures (< 16 °C), OSIRIS ozone measurements have a negative bias of 1–6% compared with the validation data sets for 25.5–40.5 km. Biases between OSIRIS ascending and descending node measurements were investigated and found to be related to aerosol retrievals below 27.5 km. Above 30 km, agreement between OSIRIS and the validation data sets was related to the OSIRIS retrieved albedo, which measures apparent upwelling, with a positive bias in OSIRIS data with large albedos. In order to assess the long-term stability of OSIRIS measurements, global average drifts relative to the validation data sets were calculated and were found to be < 3% per decade for comparisons with MLS for 19.5–36.5 km, GOMOS for 18.5–54.5 km, and ozonesondes for 12.5–22.5 km. Above 36.5 km, the relative drift for OSIRIS versus MLS ranged from ~ 0 to 6% per decade, depending on the data set used to convert MLS data to the OSIRIS altitude versus number density grid. Overall, this work demonstrates that the OSIRIS 11 yr ozone data set from 2001 to the present is suitable for trend studies.

scholarly journals Yearly changes in surface solar radiation in New Caledonia

2015 ◽  
Vol 12 (1) ◽  
pp. 1-4 ◽  
Author(s):  
P. Blanc ◽  
C. Coulaud ◽  
L. Wald
Keyword(s):  
Solar Radiation ◽  
New Caledonia ◽  
Solar Irradiation ◽  
Data Sets ◽  
Climate Data ◽  
Surface Solar Radiation ◽  
Data Set ◽  
The Mean ◽  
Energy Plants

Abstract. New Caledonia experiences a decrease in surface solar irradiation since 2004. It is of order of 4% of the mean yearly irradiation over the 10 years period: 2004–2013, and amounts to −9 W m−2. The preeminent roles of the changes in cloud cover and to a lesser extent, those in aerosol optical depth on the decrease in yearly irradiation are evidenced. The study highlights the role of data sets offering a worldwide coverage in understanding changes in solar radiation and planning large solar energy plants such as the ICOADS (International Comprehensive Ocean-Atmosphere Data Set) of the NOAA and MACC (Monitoring Atmosphere Composition and Climate) data sets combined with the McClear model.

scholarly journals Relative drifts and stability of satellite and ground-based stratospheric ozone profiles at NDACC lidar stations

2012 ◽  
Vol 5 (6) ◽  
pp. 1301-1318 ◽  
Author(s):  
P. J. Nair ◽  
S. Godin-Beekmann ◽  
L. Froidevaux ◽  
L. E. Flynn ◽  
J. M. Zawodny ◽  
...  
Keyword(s):  
Stratospheric Ozone ◽  
Measurement Techniques ◽  
Data Sets ◽  
Data Set ◽  
Meteorological Observatory ◽  
The Stability ◽  
Combined Time Series ◽  
Relative Differences ◽  
Term Data

Abstract. The long-term evolution of stratospheric ozone at different stations in the low and mid-latitudes is investigated. The analysis is performed by comparing the collocated profiles of ozone lidars, at the northern mid-latitudes (Meteorological Observatory Hohenpeißenberg, Haute-Provence Observatory, Tsukuba and Table Mountain Facility), tropics (Mauna Loa Observatory) and southern mid-latitudes (Lauder), with ozonesondes and space-borne sensors (SBUV(/2), SAGE II, HALOE, UARS MLS and Aura MLS), extracted around the stations. Relative differences are calculated to find biases and temporal drifts in the measurements. All measurement techniques show their best agreement with respect to the lidar at 20–40 km, where the differences and drifts are generally within ±5% and ±0.5% yr−1, respectively, at most stations. In addition, the stability of the long-term ozone observations (lidar, SBUV(/2), SAGE II and HALOE) is evaluated by the cross-comparison of each data set. In general, all lidars and SBUV(/2) exhibit near-zero drifts and the comparison between SAGE II and HALOE shows larger, but insignificant drifts. The RMS of the drifts of lidar and SBUV(/2) is 0.22 and 0.27% yr−1, respectively at 20–40 km. The average drifts of the long-term data sets, derived from various comparisons, are less than ±0.3% yr−1 in the 20–40 km altitude at all stations. A combined time series of the relative differences between SAGE II, HALOE and Aura MLS with respect to lidar data at six sites is constructed, to obtain long-term data sets lasting up to 27 years. The relative drifts derived from these combined data are very small, within ±0.2% yr−1.

scholarly journals Building the COllaborative Carbon Column Observing Network (COCCON): long-term stability and ensemble performance of the EM27/SUN Fourier transform spectrometer

2019 ◽  
Vol 12 (3) ◽  
pp. 1513-1530 ◽  
Author(s):  
Matthias Frey ◽  
Mahesh K. Sha ◽  
Frank Hase ◽  
Matthäus Kiel ◽  
Thomas Blumenstock ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Total Carbon ◽  
Data Sets ◽  
Term Study ◽  
Data Set ◽  
Term Stability ◽  
Long Term Stability ◽  
Long Term Study ◽  
Empirical Calibration

Abstract. In a 3.5-year long study, the long-term performance of a mobile, solar absorption Bruker EM27/SUN spectrometer, used for greenhouse gas observations, is checked with respect to a co-located reference Bruker IFS 125HR spectrometer, which is part of the Total Carbon Column Observing Network (TCCON). We find that the EM27/SUN is stable on timescales of several years; the drift per year between the EM27/SUN and the official TCCON product is 0.02 ppmv for XCO2 and 0.9 ppbv for XCH4, which is within the 1σ precision of the comparison, 0.6 ppmv for XCO2 and 4.3 ppbv for XCH4. The bias between the two data sets is 3.9 ppmv for XCO2 and 13.0 ppbv for XCH4. In order to avoid sensitivity-dependent artifacts, the EM27/SUN is also compared to a truncated IFS 125HR data set derived from full-resolution TCCON interferograms. The drift is 0.02 ppmv for XCO2 and 0.2 ppbv for XCH4 per year, with 1σ precisions of 0.4 ppmv for XCO2 and 1.4 ppbv for XCH4, respectively. The bias between the two data sets is 0.6 ppmv for XCO2 and 0.5 ppbv for XCH4. With the presented long-term stability, the EM27/SUN qualifies as an useful supplement to the existing TCCON network in remote areas. To achieve consistent performance, such an extension requires careful testing of any spectrometers involved by application of common quality assurance measures. One major aim of the COllaborative Carbon Column Observing Network (COCCON) infrastructure is to provide these services to all EM27/SUN operators. In the framework of COCCON development, the performance of an ensemble of 30 EM27/SUN spectrometers was tested and found to be very uniform, enhanced by the centralized inspection performed at the Karlsruhe Institute of Technology prior to deployment. Taking into account measured instrumental line shape parameters for each spectrometer, the resulting average bias across the ensemble with respect to the reference EM27/SUN used in the long-term study in XCO2 is 0.20 ppmv, while it is 0.8 ppbv for XCH4. The average standard deviation of the ensemble is 0.13 ppmv for XCO2 and 0.6 ppbv for XCH4. In addition to the robust metric based on absolute differences, we calculate the standard deviation among the empirical calibration factors. The resulting 2σ uncertainty is 0.6 ppmv for XCO2 and 2.2 ppbv for XCH4. As indicated by the executed long-term study on one device presented here, the remaining empirical calibration factor deduced for each individual instrument can be assumed constant over time. Therefore the application of these empirical factors is expected to further improve the EM27/SUN network conformity beyond the scatter among the empirical calibration factors reported above.

scholarly journals Relative drifts and stability of satellite and ground-based stratospheric ozone profiles at NDACC lidar stations

2012 ◽  
Vol 5 (1) ◽  
pp. 471-516
Author(s):  
P. J. Nair ◽  
S. Godin-Beekmann ◽  
L. Froidevaux ◽  
L. E. Flynn ◽  
J. M. Zawodny ◽  
...  
Keyword(s):  
Stratospheric Ozone ◽  
Measurement Techniques ◽  
Data Sets ◽  
Data Set ◽  
Meteorological Observatory ◽  
The Stability ◽  
Combined Time Series ◽  
Relative Differences ◽  
Term Data

Abstract. The long-term evolution of stratospheric ozone at different stations in the low and mid-latitudes is investigated. The analysis is performed by comparing the collocated profiles of ozone lidars, at the northern mid-latitudes (Meteorological Observatory Hohenpeißenberg, Haute-Provence Observatory, Tsukuba and Table Mountain Facility), tropics (Mauna Loa Observatory) and southern mid-latitudes (Lauder), with ozonesondes and space-borne sensors (SBUV(/2), SAGE II, HALOE, UARS MLS and Aura MLS), extracted around the stations. Relative differences are calculated to find biases and temporal drifts in the measurements. All measurement techniques show their best agreement with respect to the lidar at 20–40 km, where the differences are within ±3% and drifts are less than ±0.3% yr−1 at all stations. In addition, the stability of the long-term ozone observations (lidar, SBUV(/2), SAGE II and HALOE) is evaluated by the cross-comparison of each data set. In general, all lidars and SBUV(/2) exhibit near zero drifts and the comparison between SAGE II and HALOE shows larger, but insignificant drifts. The RMS of the drifts of lidar and SBUV(/2) is 0.22 and 0.27% yr−1, respectively. The average drifts of the long-term data sets, derived from various comparisons, are less than ±0.3% yr−1 in 20–40 km at all stations. A combined time series of the relative differences between SAGE II, HALOE and Aura MLS with respect to lidar data at six sites is constructed, to obtain long-term data sets lasting up to 27 yr. The relative drifts derived from these combined data are very small, within ±0.2% yr−1.

The Stability of the WHO Reference Thromboplastin NIBS & C 67/40

1979 ◽  
Vol 42 (04) ◽  
pp. 1135-1140 ◽  
Author(s):  
G I C Ingram
Keyword(s):  
Human Brain ◽  
Collaborative Study ◽  
Calibration Constant ◽  
Long Term Stability ◽  
Freeze Dried ◽  
Show Evidence ◽  
Human Material ◽  
Reference Preparation ◽  
The Stability

SummaryThe International Reference Preparation of human brain thromboplastin coded 67/40 has been thought to show evidence of instability. The evidence is discussed and is not thought to be strong; but it is suggested that it would be wise to replace 67/40 with a new preparation of human brain, both for this reason and because 67/40 is in a form (like Thrombotest) in which few workers seem to use human brain. A �plain� preparation would be more appropriate; and a freeze-dried sample of BCT is recommended as the successor preparation. The opportunity should be taken also to replace the corresponding ox and rabbit preparations. In the collaborative study which would be required it would then be desirable to test in parallel the three old and the three new preparations. The relative sensitivities of the old preparations could be compared with those found in earlier studies to obtain further evidence on the stability of 67/40; if stability were confirmed, the new preparations should be calibrated against it, but if not, the new human material should receive a calibration constant of 1.0 and the new ox and rabbit materials calibrated against that.The types of evidence available for monitoring the long-term stability of a thromboplastin are discussed.

Sparse reflectivity inversion for nonstationary seismic data with surface-related multiples: Numerical and field-data experiments

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. R199-R217 ◽  
Author(s):  
Xintao Chai ◽  
Shangxu Wang ◽  
Genyang Tang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Anelastic Attenuation ◽  
Seismic Resolution ◽  
Text Filtering ◽  
The Stability ◽  
Reflectivity Inversion

Seismic data are nonstationary due to subsurface anelastic attenuation and dispersion effects. These effects, also referred to as the earth’s [Formula: see text]-filtering effects, can diminish seismic resolution. We previously developed a method of nonstationary sparse reflectivity inversion (NSRI) for resolution enhancement, which avoids the intrinsic instability associated with inverse [Formula: see text] filtering and generates superior [Formula: see text] compensation results. Applying NSRI to data sets that contain multiples (addressing surface-related multiples only) requires a demultiple preprocessing step because NSRI cannot distinguish primaries from multiples and will treat them as interference convolved with incorrect [Formula: see text] values. However, multiples contain information about subsurface properties. To use information carried by multiples, with the feedback model and NSRI theory, we adapt NSRI to the context of nonstationary seismic data with surface-related multiples. Consequently, not only are the benefits of NSRI (e.g., circumventing the intrinsic instability associated with inverse [Formula: see text] filtering) extended, but also multiples are considered. Our method is limited to be a 1D implementation. Theoretical and numerical analyses verify that given a wavelet, the input [Formula: see text] values primarily affect the inverted reflectivities and exert little effect on the estimated multiples; i.e., multiple estimation need not consider [Formula: see text] filtering effects explicitly. However, there are benefits for NSRI considering multiples. The periodicity and amplitude of the multiples imply the position of the reflectivities and amplitude of the wavelet. Multiples assist in overcoming scaling and shifting ambiguities of conventional problems in which multiples are not considered. Experiments using a 1D algorithm on a synthetic data set, the publicly available Pluto 1.5 data set, and a marine data set support the aforementioned findings and reveal the stability, capabilities, and limitations of the proposed method.

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