Processing National CO2 Inventory Emissions Data and their Total Uncertainty Estimates

2007 ◽  
pp. 93-107 ◽  
Author(s):  
Zbigniew Nahorski ◽  
Waldemar Jęda
Keyword(s):  
Total Uncertainty ◽  
Uncertainty Estimates

scholarly journals Calculating uncertainty for the RICE ice core continuous flow analysis water isotope record

2018 ◽  
Vol 11 (8) ◽  
pp. 4725-4736 ◽  
Author(s):  
Elizabeth D. Keller ◽  
W. Troy Baisden ◽  
Nancy A. N. Bertler ◽  
B. Daniel Emanuelsson ◽  
Silvia Canessa ◽  
...  
Keyword(s):  
High Resolution ◽  
Continuous Flow ◽  
Ice Core ◽  
Flow Analysis ◽  
Calibration Error ◽  
Data Set ◽  
Total Uncertainty ◽  
Continuous Flow Analysis ◽  
Uncertainty Estimates ◽  
Water Isotope

Abstract. We describe a systematic approach to the calibration and uncertainty estimation of a high-resolution continuous flow analysis (CFA) water isotope (δ2H, δ18O) record from the Roosevelt Island Climate Evolution (RICE) Antarctic ice core. Our method establishes robust uncertainty estimates for CFA δ2H and δ18O measurements, comparable to those reported for discrete sample δ2H and δ18O analysis. Data were calibrated using a time-weighted two-point linear calibration with two standards measured both before and after continuously melting 3 or 4 m of ice core. The error at each data point was calculated as the quadrature sum of three factors: Allan variance error, scatter over our averaging interval (error of the variance) and calibration error (error of the mean). Final mean total uncertainty for the entire record is δ2H=0.74 ‰ and δ18O=0.21 ‰. Uncertainties vary through the data set and were exacerbated by a range of factors, which typically could not be isolated due to the requirements of the multi-instrument CFA campaign. These factors likely occurred in combination and included ice quality, ice breaks, upstream equipment failure, contamination with drill fluid and leaks or valve degradation. We demonstrate that our methodology for documenting uncertainty was effective across periods of uneven system performance and delivered a significant achievement in the precision of high-resolution CFA water isotope measurements.

scholarly journals Uncertainty information in climate data records from Earth observation

2017 ◽  
Author(s):  
Christopher J. Merchant ◽  
Frank Paul ◽  
Thomas Popp ◽  
Michael Ablain ◽  
Sophie Bontemps ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Earth Observation ◽  
Satellite Measurement ◽  
Climate Modelling ◽  
Climate Data ◽  
Total Uncertainty ◽  
Sources Of Uncertainty ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Uncertainty Information

Abstract. Climate data records (CDRs) derived from Earth observation (EO) should include rigorous uncertainty information, to support application of the data in policy, climate modelling and numerical weather prediction reanalysis. Uncertainty, error and quality are distinct concepts, and CDR products should follow international norms for presenting quantified uncertainty. Ideally, uncertainty should be quantified per datum in a CDR, and the uncertainty estimates should be able to discriminate more and less certain data with confidence. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence held in the uncertainty estimate provided, or indicators of conditions violating retrieval assumptions). Errors have many sources and some are correlated across a wide range of time and space scales. Error effects that contribute negligibly to the total uncertainty in a single satellite measurement can be the dominant sources of uncertainty in a CDR on large space and long time scales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. Characterisation of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation, where possible. These principles are quite general, but the form of uncertainty information appropriate to different essential climate variables (ECVs) is highly variable, as confirmed by a quick review of the different approaches to uncertainty taken across different ECVs in the European Space Agency’s Climate Change Initiative. User requirements for uncertainty information can conflict with each other, and again a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

scholarly journals Collocation mismatch uncertainties in satellite aerosol retrieval validation

2018 ◽  
Vol 11 (2) ◽  
pp. 925-938 ◽  
Author(s):  
Timo H. Virtanen ◽  
Pekka Kolmonen ◽  
Larisa Sogacheva ◽  
Edith Rodríguez ◽  
Giulia Saponaro ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Satellite Data ◽  
Reference Data ◽  
Measurement Data ◽  
Spatiotemporal Variability ◽  
Small Samples ◽  
Total Uncertainty ◽  
Uncertainty Estimates ◽  
Retrieval Errors ◽  
Temporal Sample

Abstract. Satellite-based aerosol products are routinely validated against ground-based reference data, usually obtained from sun photometer networks such as AERONET (AEROsol RObotic NETwork). In a typical validation exercise a spatial sample of the instantaneous satellite data is compared against a temporal sample of the point-like ground-based data. The observations do not correspond to exactly the same column of the atmosphere at the same time, and the representativeness of the reference data depends on the spatiotemporal variability of the aerosol properties in the samples. The associated uncertainty is known as the collocation mismatch uncertainty (CMU). The validation results depend on the sampling parameters. While small samples involve less variability, they are more sensitive to the inevitable noise in the measurement data. In this paper we study systematically the effect of the sampling parameters in the validation of AATSR (Advanced Along-Track Scanning Radiometer) aerosol optical depth (AOD) product against AERONET data and the associated collocation mismatch uncertainty. To this end, we study the spatial AOD variability in the satellite data, compare it against the corresponding values obtained from densely located AERONET sites, and assess the possible reasons for observed differences. We find that the spatial AOD variability in the satellite data is approximately 2 times larger than in the ground-based data, and the spatial variability correlates only weakly with that of AERONET for short distances. We interpreted that only half of the variability in the satellite data is due to the natural variability in the AOD, and the rest is noise due to retrieval errors. However, for larger distances (∼ 0.5∘) the correlation is improved as the noise is averaged out, and the day-to-day changes in regional AOD variability are well captured. Furthermore, we assess the usefulness of the spatial variability of the satellite AOD data as an estimate of CMU by comparing the retrieval errors to the total uncertainty estimates including the CMU in the validation. We find that accounting for CMU increases the fraction of consistent observations.

scholarly journals Estimating global landslide susceptibility and its uncertainty through ensemble modelling

2021 ◽  
Author(s):  
Anne Felsberg ◽  
Jean Poesen ◽  
Michel Bechtold ◽  
Matthias Vanmaercke ◽  
Gabriëlle J. M. De Lannoy
Keyword(s):  
Soil Moisture ◽  
Landslide Susceptibility ◽  
Land Surface ◽  
Large Scale ◽  
Random Effect ◽  
Ensemble Average ◽  
Scale Model ◽  
Predictor Variables ◽  
Total Uncertainty ◽  
Uncertainty Estimates

Abstract. This study assesses global landslide susceptibility (LSS) at the coarse 36-km spatial resolution of global satellite soil moisture observations, to prepare for a subsequent combination of a global LSS map with dynamic soil moisture estimates for landslide modelling. Global LSS estimation intrinsically contains uncertainty, arising from errors in the underlying data, the spatial mismatch between landslide events and predictor information, and large-scale model generalizations. For a reliable uncertainty assessment, this study combines methods from the landslide community with common practices in meteorological modelling to create an ensemble of global LSS maps. The predictive LSS models are obtained from a mixed effects logistic regression, associating hydrologically triggered landslide data from the Global Landslide Catalog (GLC) with predictor variables from the Catchment land surface modeling system (incl. input parameters of soil (hydrological) properties and resulting climatological statistics of water budget estimates), geomorphological and lithological data. Road network density is introduced as a random effect to mitigate potential landslide inventory bias. We use a blocked random cross validation to assess the model uncertainty that propagates into the LSS maps. To account for other uncertainty sources, such as input uncertainty, we also perturb the predictor variables and obtain an ensemble of LSS maps. The perturbations are optimized so that the total predicted uncertainty fits the observed discrepancy between the ensemble average LSS and the landslide presence or absence from the GLC. We find that the most reliable total uncertainty estimates are obtained through the inclusion of a topography-dependent perturbation between 15 % and 20 % to the predictor variables. The areas with the largest LSS uncertainty coincide with moderate ensemble average LSS. The spatial patterns of the average LSS agree well with previous global studies and yield areas under the Receiver Operation Characteristic between 0.63 and 0.9 for independent regional to continental landslide inventories.

scholarly journals Collocation Mismatch Uncertainties in Satellite Aerosol Retrieval Validation

2017 ◽  
Author(s):  
Timo H. Virtanen ◽  
Pekka Kolmonen ◽  
Larisa Sogacheva ◽  
Edith Rodríguez ◽  
Giulia Saponaro ◽  
...  
Keyword(s):  
Satellite Data ◽  
Reference Data ◽  
Measurement Data ◽  
Spatiotemporal Variability ◽  
Small Samples ◽  
Total Uncertainty ◽  
Uncertainty Estimates ◽  
Retrieval Errors ◽  
Temporal Sample ◽  
Along Track

Abstract. Satellite based aerosol products are routinely validated against ground based reference data, usually obtained from sunphotometer networks such as AERONET (AEROsol Robotic Network). In a typical validation exercise a spatial sample of the instantaneous satellite data is compared against a temporal sample of the point-like ground based data. The observations do not correspond to exactly the same column of the atmosphere at the same time, and the representativiness of the reference data depends on the spatiotemporal variability of the aerosol properties in the samples. The associated uncertainty is known as the collocation mismatch uncertainty (CMU). The validation results depend on the sampling parameters. While small samples involve less variability, they are more sensitive to the inevitable noise in the measurement data. In this paper we study systematically the effect of the sampling parameters in the validation of AATSR (Advanced Along Track Scanning Radiometer) aerosol optical depth (AOD) product against AERONET data and the associated collocation mismatch uncertainty. To this end, we study the spatial AOD variability in the satellite data, compare it against the corresponding values obtained from densely located AERONET sites, and assess the possible reasons for observed differences. We find that the spatial AOD variability in the satellite data is approximately two times larger than in the ground based data, and the local AOD variability values correlate only weakly for short distances. We interprete that only half of the variability in the satellite data is due to the natural variability in the AOD, and the rest is noise due to retrieval errors. However, for larger distances (∼ 0.5°) the correlation is improved as the noise is averaged out, and the day to day changes in regional AOD variability are well captured. Furthermore, we assess the usefulness of the spatial variability of the satellite AOD data as an estimate of CMU by comparing the retrieval errors to the total uncertainty estimates in the validation. We find that accounting for CMU increases the fraction of consistent observations.

scholarly journals Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends

2018 ◽  
Vol 11 (7) ◽  
pp. 4033-4058 ◽  
Author(s):  
Marina Zara ◽  
K. Folkert Boersma ◽  
Isabelle De Smedt ◽  
Andreas Richter ◽  
Enno Peters ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Column Density ◽  
Trend Detection ◽  
Optical Absorption Spectroscopy ◽  
Statistical Uncertainty ◽  
Random Component ◽  
Climate Variables ◽  
Total Uncertainty ◽  
Uncertainty Estimates ◽  
Calculation Step

Abstract. Nitrogen dioxide (NO2) and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone (O3) and aerosol essential climate variables, and it is essential to quantify and characterise their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from four different research groups (BIRA-IASB, IUP Bremen, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in differential optical absorption spectroscopy (DOAS) fitting techniques and by the desire to provide a fully traceable uncertainty budget for the climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement but with substantial differences in the reported uncertainties between groups and instruments. To check the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8  ×  1015 molec. cm−2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8–12  ×  1015 molec. cm−2), but the closeness between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite's level 1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels with high measurement signal-to-noise ratios. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1–2 % year−1, which is related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase  <  1 % year−1) and this is related to the use of Earth radiance reference spectra which reduces stripes. For GOME-2A, NO2 and HCHO SCD uncertainties increased by 7–9 and 11–15 % year−1 respectively up until September 2009, when heating of the instrument markedly reduced further throughput loss, stabilising the degradation of SCD uncertainty to  <  3 % year−1 for 2009–2015. Our work suggests that the NO2 SCD uncertainty largely consists of a random component ( ∼  65 % of the total uncertainty) as a result of the propagation of measurement noise but also of a substantial systematic component ( ∼  35 % of the total uncertainty) mainly from stripe effects. Averaging over multiple pixels in space and/or time can significantly reduce the SCD uncertainties. This suggests that trend detection in OMI, GOME-2 NO2, and HCHO time series is not limited by the spectral fitting but rather by the adequacy of assumptions on the atmospheric state in the later air mass factor (AMF) calculation step.

scholarly journals Optimizing CO<sub>2</sub> observing networks in the presence of model error: results from TransCom 3

2003 ◽  
Vol 3 (5) ◽  
pp. 5185-5204
Author(s):  
P. J. Rayner
Keyword(s):  
Co2 Concentration ◽  
Model Error ◽  
Control Case ◽  
Transport Models ◽  
Total Uncertainty ◽  
Optimal Network ◽  
Uncertainty Estimates ◽  
Set Up ◽  
Optimal Networks

Abstract. We use a genetic algorithm to construct optimal observing networks of atmospheric CO2 concentration for inverse determination of net sources. Optimal networks are those that produce a minimum in average posterior uncertainty plus a term representing the divergence among source estimates for different transport models. The addition of this last term modifies the choice of observing sites, leading to larger networks than would be chosen under the traditional estimated variance metric. Model-model differences behave like sub-grid heterogeneity and optimal networks try to average over some of this. The optimization does not, however, necessarily reject apparently difficult sites to model. Although the results are so conditioned on the experimental set-up that the specific networks chosen are unlikely to be the best choices in the real world, the counter-intuitive behaviour of the optimization suggests the model error contribution should be taken into account when designing observing networks. Finally we compare the flux and total uncertainty estimates from the optimal network with those from the TransCom 3 control case. The comparison suggests that the TransCom 3 control case is robust.

scholarly journals Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

2020 ◽  
Vol 12 (19) ◽  
pp. 3122
Author(s):  
Yuan Zhao ◽  
Xiaoqiu Chen ◽  
Thomas Luke Smallman ◽  
Sophie Flack-Prain ◽  
David T. Milodowski ◽  
...  
Keyword(s):  
Leaf Area ◽  
Spatial Resolution ◽  
Southern China ◽  
Temporal Frequency ◽  
In Situ Measurements ◽  
Retrieval Algorithm ◽  
Area Index ◽  
Total Uncertainty ◽  
Uncertainty Estimates

Leaf area is a key parameter underpinning ecosystem carbon, water and energy exchanges via photosynthesis, transpiration and absorption of radiation, from local to global scales. Satellite-based Earth Observation (EO) can provide estimates of leaf area index (LAI) with global coverage and high temporal frequency. However, the error and bias contained within these EO products and their variation in time and across spatial resolutions remain poorly understood. Here, we used nearly 8000 in situ measurements of LAI from six forest environments in southern China to evaluate the magnitude, uncertainty, and dynamics of three widely used EO LAI products. The finer spatial resolution GEOV3 PROBA-V 300 m LAI product best estimates the observed LAI from a multi-site dataset (R2 = 0.45, bias = −0.54 m2 m−2, RMSE = 1.21 m2 m−2) and importantly captures canopy dynamics well, including the amplitude and phase. The GEOV2 PROBA-V 1 km LAI product performed the next best (R2 = 0.36, bias = −2.04 m2 m−2, RMSE = 2.32 m2 m−2) followed by MODIS 500 m LAI (R2 = 0.20, bias = −1.47 m2 m−2, RMSE = 2.29 m2 m−2). The MODIS 500 m product did not capture the temporal dynamics observed in situ across southern China. The uncertainties estimated by each of the EO products are substantially smaller (3–5 times) than the observed bias for EO products against in situ measurements. Thus, reported product uncertainties are substantially underestimated and do not fully account for their total uncertainty. Overall, our analysis indicates that both the retrieval algorithm and spatial resolution play an important role in accurately estimating LAI for the dense canopy forests in Southern China. When constraining models of the carbon cycle and other ecosystem processes are run, studies should assume that current EO product LAI uncertainty estimates underestimate their true uncertainty value.

scholarly journals Intercomparison and evaluation of ground- and satellite-based stratospheric ozone and temperature profiles above Observatoire de Haute-Provence during the Lidar Validation NDACC Experiment (LAVANDE)

2020 ◽  
Vol 13 (10) ◽  
pp. 5621-5642
Author(s):  
Robin Wing ◽  
Wolfgang Steinbrecht ◽  
Sophie Godin-Beekmann ◽  
Thomas J. McGee ◽  
John T. Sullivan ◽  
...  
Keyword(s):  
Stratospheric Ozone ◽  
The Other ◽  
Atmospheric Composition ◽  
Temperature Profiles ◽  
Composition Change ◽  
Total Uncertainty ◽  
Broadband Emission ◽  
Uncertainty Estimates ◽  
532 Nm ◽  
Good Agreement

Abstract. A two-part intercomparison campaign was conducted at Observatoire de Haute-Provence (OHP) for the validation of lidar ozone and temperature profiles using the mobile NASA Stratospheric Ozone Lidar (NASA STROZ), satellite overpasses from the Microwave Limb Sounder (MLS), the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER), meteorological radiosondes launched from Nîmes, and locally launched ozonesondes. All the data were submitted and compared “blind”, before the group could see results from the other instruments. There was good agreement between all ozone measurements between 20 and 40 km, with differences of generally less than 5 % throughout this region. Below 20 km, SABER and MLS measured significantly more ozone than the lidars or ozonesondes. Temperatures for all lidars were in good agreement between 30 and 60 km, with differences on the order of ±1 to 3 K. Below 30 km, the OHP lidar operating at 532 nm has a significant cool bias due to contamination by aerosols. Systematic, altitude-varying bias up to ±5 K compared to the lidars was found for MLS at many altitudes. SABER temperature profiles are generally closer to the lidar profiles, with up 3 K negative bias near 50 km. Total uncertainty estimates for ozone and temperature appear to be realistic for nearly all systems. However, it does seem that the very low estimated uncertainties of lidars between 30 and 50 km, between 0.1 and 1 K, are not achieved during Lidar Validation Network for the Detection of Atmospheric Composition Change (NDACC) Experiment (LAVANDE). These estimates might have to be increased to 1 to 2 K.

scholarly journals Uncertainty information in climate data records from Earth observation

2017 ◽  
Vol 9 (2) ◽  
pp. 511-527 ◽  
Author(s):  
Christopher J. Merchant ◽  
Frank Paul ◽  
Thomas Popp ◽  
Michael Ablain ◽  
Sophie Bontemps ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Good Practice ◽  
Earth Observation ◽  
Climate Modelling ◽  
Climate Data ◽  
Total Uncertainty ◽  
Sources Of Uncertainty ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Uncertainty Information

Abstract. The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

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