scholarly journals Objective evaluation of surface- and satellite-driven CO<sub>2</sub> atmospheric inversions

2019 ◽  
Author(s):  
Frédéric Chevallier ◽  
Marine Remaud ◽  
Christopher W. O'Dell ◽  
David Baker ◽  
Philippe Peylin ◽  
...  
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Spatial Scales ◽  
Objective Evaluation ◽  
Annual Growth Rate ◽  
Marine Surface ◽  
Surface Measurements ◽  
Surface Network ◽  
Aircraft Data ◽  
Atmospheric Inversions

Abstract. We study an ensemble of six multi-year global Bayesian CO2 atmospheric inversions that vary in terms of assimilated observations (either column retrievals from one of two satellites or surface air sample measurements) and transport model. The time series of inferred annual fluxes are first compared with each other at various spatial scales. We then objectively evaluate the small inversion ensemble based on a large dataset of accurate aircraft measurements in the free troposphere over the globe, that are independent from all assimilated data. The measured variables are connected with the inferred fluxes through mass-conserving transport in the global atmosphere and are part of the inversion results. Large-scale annual fluxes estimated from the bias-corrected land retrievals of the second Orbiting Carbon Observatory (OCO-2) differ from the prior fluxes much, but are similar to the fluxes estimated from the surface network within the uncertainty of these surface-based estimates. The OCO-2- and surface-based inversions have similar performance when projected in the space of the aircraft data, but relative strengths and weaknesses of the two flux estimates vary within the Northern and Tropical parts of the continents. The verification data also suggests that the more complex and more recent transport model does not improve the inversion skill. In contrast, the inversion using bias-corrected retrievals from the Greenhouse Gases Observing Satellite (GOSAT) or, to a larger extent, a non-Bayesian inversion that simply adjusts a recent bottom-up flux estimate with the annual growth rate diagnosed from marine surface measurements, estimate much different fluxes and fit the aircraft data less. Our study highlights a way to rate global atmospheric inversions. It suggests that some satellite retrievals can now provide inversion results that are, despite their uncertainty, comparable in credibility to traditional inversions using the accurate but sparse surface network and that are therefore complementary for studies of the global carbon budget.

scholarly journals Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions

2019 ◽  
Vol 19 (22) ◽  
pp. 14233-14251 ◽  
Author(s):  
Frédéric Chevallier ◽  
Marine Remaud ◽  
Christopher W. O'Dell ◽  
David Baker ◽  
Philippe Peylin ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Large Scale ◽  
Transport Model ◽  
Spatial Scales ◽  
Objective Evaluation ◽  
Annual Growth Rate ◽  
Surface Network ◽  
Carbon Dioxide Co2 ◽  
Aircraft Data ◽  
Atmospheric Inversions

Abstract. We study an ensemble of six multi-year global Bayesian carbon dioxide (CO2) atmospheric inversions that vary in terms of assimilated observations (either column retrievals from one of two satellites or surface air sample measurements) and transport model. The time series of inferred annual fluxes are first compared with each other at various spatial scales. We then objectively evaluate the small inversion ensemble based on a large dataset of accurate aircraft measurements in the free troposphere over the globe, which are independent of all assimilated data. The measured variables are connected with the inferred fluxes through mass-conserving transport in the global atmosphere and are part of the inversion results. Large-scale annual fluxes estimated from the bias-corrected land retrievals of the second Orbiting Carbon Observatory (OCO-2) differ greatly from the prior fluxes, but are similar to the fluxes estimated from the surface network within the uncertainty of these surface-based estimates. The OCO-2-based and surface-based inversions have similar performance when projected in the space of the aircraft data, but the relative strengths and weaknesses of the two flux estimates vary within the northern and tropical parts of the continents. The verification data also suggest that the more complex and more recent transport model does not improve the inversion skill. In contrast, the inversion using bias-corrected retrievals from the Greenhouse Gases Observing Satellite (GOSAT) or, to a larger extent, a non-Bayesian inversion that simply adjusts a recent bottom-up flux estimate with the annual growth rate diagnosed from marine surface measurements both estimate much different fluxes and fit the aircraft data less. Our study highlights a way to rate global atmospheric inversions. Without any general claim regarding the usefulness of all OCO-2 retrieval datasets vs. all GOSAT retrieval datasets, it still suggests that some satellite retrievals can now provide inversion results that are, despite their uncertainty, comparable with respect to credibility to traditional inversions using the accurate but sparse surface network and that are therefore complementary for studies of the global carbon budget.

scholarly journals On the parallelization of atmospheric inversions of CO<sub>2</sub> surface fluxes within a variational framework

2013 ◽  
Vol 6 (3) ◽  
pp. 783-790 ◽  
Author(s):  
F. Chevallier
Keyword(s):  
Transport Model ◽  
Spatial Scales ◽  
Computational Cost ◽  
Surface Fluxes ◽  
Atmospheric Measurements ◽  
Transport Modelling ◽  
Time Space ◽  
Variational Minimization ◽  
Analytical Approaches ◽  
Atmospheric Inversions

Abstract. The variational formulation of Bayes' theorem allows inferring CO2 sources and sinks from atmospheric concentrations at much higher time–space resolution than the ensemble or analytical approaches. However, it usually exhibits limited scalable parallelism. This limitation hinders global atmospheric inversions operated on decadal time scales and regional ones with kilometric spatial scales because of the computational cost of the underlying transport model that has to be run at each iteration of the variational minimization. Here, we introduce a physical parallelization (PP) of variational atmospheric inversions. In the PP, the inversion still manages a single physically and statistically consistent window, but the transport model is run in parallel overlapping sub-segments in order to massively reduce the computation wall-clock time of the inversion. For global inversions, a simplification of transport modelling is described to connect the output of all segments. We demonstrate the performance of the approach on a global inversion for CO2 with a 32 yr inversion window (1979–2010) with atmospheric measurements from 81 sites of the NOAA global cooperative air sampling network. In this case, we show that the duration of the inversion is reduced by a seven-fold factor (from months to days), while still processing the three decades consistently and with improved numerical stability.

scholarly journals Optimizing global CO emission estimates using a four-dimensional variational data assimilation system and surface network observations

2011 ◽  
Vol 11 (10) ◽  
pp. 4705-4723 ◽  
Author(s):  
P. B. Hooghiemstra ◽  
M. C. Krol ◽  
J. F. Meirink ◽  
P. Bergamaschi ◽  
G. R. van der Werf ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Transport Model ◽  
Model Simulation ◽  
Solution Space ◽  
Large Solution ◽  
Surface Network ◽  
Posterior Simulation ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We apply a four-dimensional variational (4D-VAR) data assimilation system to optimize carbon monoxide (CO) emissions for 2003 and 2004 and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. The system is designed to assimilate large (satellite) datasets, but in the current study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-VAR system. By design, the system is capable to adjust the emissions in such a way that the posterior simulation reproduces background CO mixing ratios and large-scale pollution events at background stations. Uncertainty reduction up to 60 % in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies for 2004. However, with the limited amount of data from the surface network, the system becomes data sparse resulting in a large solution space. Sensitivity studies have shown that model uncertainties (e.g., vertical distribution of biomass burning emissions and the OH field) and the prior inventories used, influence the inferred emission estimates. Also, since the observations only constrain total CO emissions, the 4D-VAR system has difficulties in separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument version 4 (V4) shows a slight improved agreement over the well-constrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10 %. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes.

scholarly journals On the parallelization of atmospheric inversions of CO<sub>2</sub> surface fluxes within a variational framework

2013 ◽  
Vol 6 (1) ◽  
pp. 37-57 ◽  
Author(s):  
F. Chevallier
Keyword(s):  
Transport Model ◽  
Spatial Scales ◽  
Computational Cost ◽  
Surface Fluxes ◽  
Atmospheric Measurements ◽  
Transport Modelling ◽  
Sources And Sinks ◽  
Variational Framework ◽  
Variational Minimization ◽  
Atmospheric Inversions

Abstract. The variational formulation of Bayes' theorem allows inferring CO2 sources and sinks from atmospheric concentrations at much higher space-time resolution than the ensemble approach or the analytical one. However, it usually exhibits limited scalable parallelism. This limitation hinders global atmospheric inversions operated on decadal time scales and regional ones with kilometric spatial scales, because of the computational cost of the underlying transport model that has to be run at each iteration of the variational minimization. Here, we introduce a Physical Parallelisation (PP) of variational atmospheric inversions. In the PP, the inversion still manages a single physically and statistically consistent window, but the transport model is run in parallel overlapping sub-segments in order to massively reduce the computation wall clock time of the inversion. For global inversions, a simplification of transport modelling is described to connect the output of all segments. We demonstrate the performance of the approach on a global inversion for CO2 with a 32-yr inversion window (1979–2010) with atmospheric measurements from 81 sites of the NOAA global cooperative air sampling network. In this case, we show that the duration of the inversion is reduced by a seven-fold factor (from months to days) while still processing the three decades consistently and with improved numerical stability.

The spatial correlation between track roughness and ground-penetrating radar inferred ballast degradation

2018 ◽  
Vol 232 (7) ◽  
pp. 1917-1931
Author(s):  
Kirk M Scanlan ◽  
Michael T Hendry ◽  
C Derek Martin ◽  
Douglas R Schmitt
Keyword(s):  
Ground Penetrating Radar ◽  
Large Scale ◽  
Spatial Scales ◽  
Radar Data ◽  
Detection Tool ◽  
Heavy Haul ◽  
Surface Measurements ◽  
Radar Measurements ◽  
Ground Penetrating ◽  
Heavy Haul Railway

Ballast degradation is considered to be a primary factor that contributes to the development of track roughness, and as such it is important to develop efficient techniques to assess the condition of the ballast. Ground-penetrating radar is one method that has been applied in a variety of railway foundation studies including those attempting to non-destructively assess ballast degradation. However, there has yet to be a large-scale study that attempts to correlate the ground-penetrating radar-based estimates of ballast degradation with the observed track roughness. This study investigates this correlation along a 335 km-long heavy-haul railway subdivision in Alberta, Canada. Track roughness is quantified from repeated track alignment and surface measurements spanning 15 months prior to the ground-penetrating radar data acquisition. Three sets of 400 MHz ground-penetrating radar measurements were performed in August 2012, one along each ballast shoulder and one along the track centreline. The results of this study reveal that significant correlations between the observed track roughness and the ground-penetrating radar-based interpretation of ballast degradation are rare and only exist when the data are compared at very small spatial scales. The absence of significant correlations between track roughness and the estimates of ballast degradation is primarily interpreted as being the result of ambiguous ground-penetrating radar data caused by local-scale variations in the track foundation unrelated to ballast degradation. To address these issues, potential improvements in the application of ground-penetrating radar as a ballast degradation detection tool are proposed.

scholarly journals Optimizing global CO emissions using a four-dimensional variational data assimilation system and surface network observations

2011 ◽  
Vol 11 (1) ◽  
pp. 341-386 ◽  
Author(s):  
P. B. Hooghiemstra ◽  
M. C. Krol ◽  
J. F. Meirink ◽  
P. Bergamaschi ◽  
G. R. van der Werf ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Transport Model ◽  
Solution Space ◽  
Large Solution ◽  
Mixing Ratios ◽  
Surface Network ◽  
Posterior Simulation ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We apply a four-dimensional variational (4D-VAR) data assimilation system to optimize carbon monoxide (CO) emissions for 2003 and 2004 and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. The system is designed to assimilate large (satellite) datasets, but in the current study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-VAR system. By design, the system is capable to adjust the emissions in such a way that the posterior simulation reproduces background CO mixing ratios and large-scale pollution events at background stations. Uncertainty reduction up to 60% in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies. However, with the limited amount of data from the surface network, the system becomes data sparse. This results in a large solution space and the 4D-VAR system has difficulties in separating anthropogenic and biogenic sources in particular. In addition we show that uncertainties in the model such as biomass burning injection height and the OH distribution largely influence the inversion results. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument version 4 (V4) shows only a slight improved agreement over the well-constrained Northern Hemisphere. However, the model with optimized emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 40%. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes.

scholarly journals Stratospheric impact on tropospheric ozone variability and trends: 1990–2009

2013 ◽  
Vol 13 (2) ◽  
pp. 649-674 ◽  
Author(s):  
P. G. Hess ◽  
R. Zbinden
Keyword(s):  
Tropospheric Ozone ◽  
Large Scale ◽  
Transport Model ◽  
Spatial Scales ◽  
Stratospheric Ozone ◽  
Atmospheric Model ◽  
Chemical Transport Model ◽  
Ozone Trends ◽  
Environmental Prediction

Abstract. The influence of stratospheric ozone on the interannual variability and trends in tropospheric ozone is evaluated between 30 and 90° N from 1990–2009 using ozone measurements and a global chemical transport model, the Community Atmospheric Model with chemistry (CAM-chem). Long-term measurements from ozonesondes, at 150 and 500 hPa, and the Measurements of OZone and water vapour by in-service Airbus aircraft programme (MOZAIC), at 500 hPa, are analyzed over Japan, Canada, the Eastern US and Northern and Central Europe. The measurements generally emphasize northern latitudes, although the simulation suggests that measurements over the Canadian, Northern and Central European regions are representative of the large-scale interannual ozone variability from 30 to 90° N at 500 hPa. CAM-chem is run with input meteorology from the National Center for Environmental Prediction; a tagging methodology is used to identify the stratospheric contribution to tropospheric ozone concentrations. A variant of the synthetic ozone tracer (synoz) is used to represent stratospheric ozone. Both the model and measurements indicate that on large spatial scales stratospheric interannual ozone variability drives significant tropospheric variability at 500 hPa and the surface. In particular, the simulation and the measurements suggest large stratospheric influence at the surface sites of Mace Head (Ireland) and Jungfraujoch (Switzerland) as well as many 500 hPa measurement locations. Both the measurements and simulation suggest the stratosphere has contributed to tropospheric ozone trends. In many locations between 30–90° N 500 hPa ozone significantly increased from 1990–2000, but has leveled off since (from 2000–2009). The simulated global ozone budget suggests global stratosphere-troposphere exchange increased in 1998–1999 in association with a global ozone anomaly. Discrepancies between the simulated and measured ozone budget include a large underestimation of measured ozone variability and discrepancies in long-term stratospheric ozone trends. This suggests the need for more sophisticated simulations including better representations of stratospheric chemistry and circulation.

scholarly journals Analysis of 220 Years of Floodplain Population Dynamics in the US at Different Spatial Scales

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 141
Author(s):  
Firoza Akhter ◽  
Maurizio Mazzoleni ◽  
Luigia Brandimarte
Keyword(s):  
Population Dynamics ◽  
Large Scale ◽  
Spatial Scales ◽  
National Level ◽  
State Level ◽  
Economic Activities ◽  
Protection Measures ◽  
Management Planning ◽  
The Us ◽  
Over Time

In this study, we explore the long-term trends of floodplain population dynamics at different spatial scales in the contiguous United States (U.S.). We exploit different types of datasets from 1790–2010—i.e., decadal spatial distribution for the population density in the US, global floodplains dataset, large-scale data of flood occurrence and damage, and structural and nonstructural flood protection measures for the US. At the national level, we found that the population initially settled down within the floodplains and then spread across its territory over time. At the state level, we observed that flood damages and national protection measures might have contributed to a learning effect, which in turn, shaped the floodplain population dynamics over time. Finally, at the county level, other socio-economic factors such as local flood insurances, economic activities, and socio-political context may predominantly influence the dynamics. Our study shows that different influencing factors affect floodplain population dynamics at different spatial scales. These facts are crucial for a reliable development and implementation of flood risk management planning.

scholarly journals Insectivorous songbirds as early indicators of future defoliation by spruce budworm

2021 ◽  
Author(s):  
Marion Germain ◽  
Daniel Kneeshaw ◽  
Louis De Grandpré ◽  
Mélanie Desrochers ◽  
Patrick M. A. James ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Spruce Budworm ◽  
Detection Methods ◽  
Maximum Probability ◽  
Point Count ◽  
Probability Of Presence ◽  
Time Lagged ◽  
One Year ◽  
Local Sampling

Abstract Context Although the spatiotemporal dynamics of spruce budworm outbreaks have been intensively studied, forecasting outbreaks remains challenging. During outbreaks, budworm-linked warblers (Tennessee, Cape May, and bay-breasted warbler) show a strong positive response to increases in spruce budworm, but little is known about the relative timing of these responses. Objectives We hypothesized that these warblers could be used as sentinels of future defoliation of budworm host trees. We examined the timing and magnitude of the relationships between defoliation by spruce budworm and changes in the probability of presence of warblers to determine whether they responded to budworm infestation before local defoliation being observed by standard detection methods. Methods We modelled this relationship using large-scale point count surveys of songbirds and maps of cumulative time-lagged defoliation over multiple spatial scales (2–30 km radius around sampling points) in Quebec, Canada. Results All three warbler species responded positively to defoliation at each spatial scale considered, but the timing of their response differed. Maximum probability of presence of Tennessee and Cape May warbler coincided with observations of local defoliation, or provided a one year warning, making them of little use to guide early interventions. In contrast, the probability of presence of bay-breasted warbler consistently increased 3–4 years before defoliation was detectable. Conclusions Early detection is a critical step in the management of spruce budworm outbreaks and rapid increases in the probability of presence of bay-breasted warbler could be used to identify future epicenters and target ground-based local sampling of spruce budworm.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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