scholarly journals Technical note: an interannual inversion method forcontinuous CO<sub>2</sub> data

2004 ◽  
Vol 4 (2) ◽  
pp. 477-484 ◽  
Author(s):  
R. M. Law
Keyword(s):  
Computational Cost ◽  
Technical Note ◽  
Inversion Method ◽  
Seasonal Cycles ◽  
Concentration Data ◽  
Sequential Method ◽  
Monthly Data ◽  
Hourly Data ◽  
Atmospheric Data ◽  
Comparable Quality

Abstract. A sequential synthesis inversion method is described to estimate CO2 sources from continuous atmospheric data. The sequential method makes the problem computationally feasible. The method is assessed using four-hourly synthetic concentration data generated from known sources. Multi-year mean sources and seasonal cycles are estimated with comparable quality as those from a traditional inversion of monthly mean data. Interannual variations in the estimated sources are closer to those of the known sources using the four-hourly data rather than monthly data. The computational cost of the basis function simulations can be reduced by generating responses that are only six months long. This does not significantly degrade the inversion results compared to using responses that are 12 months in length.

scholarly journals Technical note: an interannual inversion method for continuous CO<sub>2</sub> data

2003 ◽  
Vol 3 (6) ◽  
pp. 5977-6000
Author(s):  
R. M. Law
Keyword(s):  
Computational Cost ◽  
Technical Note ◽  
Inversion Method ◽  
Seasonal Cycles ◽  
Concentration Data ◽  
Sequential Method ◽  
Monthly Data ◽  
Hourly Data ◽  
Atmospheric Data ◽  
Comparable Quality

Abstract. A sequential synthesis inversion method is described to estimate CO2 sources from continuous atmospheric data. The sequential method makes the problem computationally feasible. The method is assessed using four-hourly synthetic concentration data generated from known sources. Multi-year mean sources and seasonal cycles are estimated with comparable quality as those from a traditional inversion of monthly mean data. Interannual variations in the estimated sources are closer to those of the known sources using the four-hourly data rather than monthly data. The computational cost of the basis function simulations can be reduced by generating responses that are only six months long. This does not significantly degrade the inversion results compared to using responses that are 12 months in length.

GRACE/GRACE-FO mascons and satellite laser ranging: Updates from GSFC

2020 ◽  
Author(s):  
Bryant Loomis ◽  
Michael Croteau ◽  
Terry Sabaka ◽  
Scott Luthcke ◽  
Kenny Rachlin
Keyword(s):  
Computational Cost ◽  
Technical Note ◽  
Satellite Laser Ranging ◽  
Laser Ranging ◽  
Normal Equations ◽  
Low Degree ◽  
Damping Parameter ◽  
Variable Gravity ◽  
Trade Study ◽  
Comparable Quality

&lt;p&gt;We present a summary of our recent work on time-variable gravity estimates derived from GRACE/GRACE-FO and satellite laser ranging (SLR). We show the latest results of our monthly mascon solution, with special attention paid to the selection and impact of the damping parameter applied to the regularization matrices. Additionally, we present a new method of regularized mascon estimation from spherical harmonics. Provided that the full normal equations for these coefficients are available, this method allows for a mathematically equivalent mascon estimate to those determined from a single iteration solution from Level-1B observations, but at a fraction of the computational cost. For this we use the ITSG-Grace2018 solution and full normal equations to produce a new mascon solution of comparable quality to current mascon products, and we are in the process of using this rapid approach to perform a trade study of various regularization strategies. Lastly, we present low degree SLR solutions that form the basis of the C20 and C30 solutions provided in Technical Note 14, and present preliminary results of large SLR-derived mascons from 1993 &amp;#8211; Present.&lt;/p&gt;

scholarly journals Technical note: PMR – a proxy metric to assess hydrological model robustness in a changing climate

2021 ◽  
Author(s):  
Paul Royer-Gaspard ◽  
Vazken Andréassian ◽  
Guillaume Thirel
Keyword(s):  
Hydrological Model ◽  
Evaluation Studies ◽  
Computational Cost ◽  
Climatic Conditions ◽  
Technical Note ◽  
Performance Metric ◽  
Reliable Assessment ◽  
Sample Test ◽  
The Impact ◽  
Model Robustness

Abstract. The ability of hydrological models to perform in climatic conditions different from those encountered in calibration is crucial to ensure a reliable assessment of the impact of climate change in water management sectors. However, most evaluation studies based on the Differential Split-Sample Test (DSST) endorsed the consensus that rainfall-runoff models lack climatic robustness. Models typically exhibit substantial errors on streamflow volumes applied under climatologically different conditions. In this technical note, we propose a new performance metric to evaluate model robustness without applying the DSST and which performs with a single hydrological model calibration. The Proxy for Model Robustness (PMR) is based on the systematic computation of model error on sliding sub-periods of the whole streamflow time series. We demonstrate that the metric shows patterns similar to those obtained with the DSST for a conceptual model on a set of 377 French catchments. An analysis of sensitivity to the length of the sub-periods shows that this length influences the values of the PMR and its adequation with DSST biases. We recommend a range of a few years for the choice of sub-period lengths, although this should be context-dependent. Our work makes it possible to evaluate the temporal transferability of any hydrological model, including uncalibrated models, at a very low computational cost.

scholarly journals Evaluation of a Straight-Ray Forward Model for Bayesian Inversion of Crosshole Ground Penetrating Radar Data

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 630 ◽  
Author(s):  
Hui Qin ◽  
Xiongyao Xie ◽  
Yu Tang
Keyword(s):  
Ground Penetrating Radar ◽  
Computational Cost ◽  
Measurement Data ◽  
Forward Model ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Model Parameters ◽  
Model Structure ◽  
Modeling Error ◽  
Ground Penetrating

Bayesian inversion of crosshole ground penetrating radar (GPR) data is capable of characterizing the subsurface dielectric properties and qualifying the associated uncertainties. Markov chain Monte Carlo (MCMC) simulations within the Bayesian inversion usually require thousands to millions of forward model evaluations for the parameters to hit their posterior distributions. Therefore, the CPU cost of the forward model is a key issue that influences the efficiency of the Bayesian inversion method. In this paper we implement a widely used straight-ray forward model within our Bayesian inversion framework. Based on a synthetic unit square relative permittivity model, we simulate the crosshole GPR first-arrival traveltime data using the finite-difference time-domain (FDTD) and straight-ray solver, respectively, and find that the straight-ray simulator runs 450 times faster than its FDTD counterpart, yet suffers from a modeling error that is more than 7 times larger. We also perform a series of numerical experiments to evaluate the performance of the straight-ray model within the Bayesian inversion framework. With modeling error disregarded, the inverted posterior models fit the measurement data nicely, yet converge to the wrong set of parameters at the expense of unreasonably large number of iterations. When the modeling error is accounted for, with a quarter of the computational burden, the main features of the true model can be identified from the posterior realizations although there still exist some unwanted artifacts. Finally, a smooth constraint on the model structure improves the inversion results considerably, to the extent that it enhances the inversion accuracy approximating to those of the FDTD model, and further reduces the CPU demand. Our results demonstrate that the use of the straight-ray forward model in the Bayesian inversion saves computational cost tremendously, and the modeling error correction together with the model structure constraint are the necessary amendments that ensure that the model parameters converge correctly.

scholarly journals A Comparison of Splitting Techniques for 3D Complex Padé Fourier Finite Difference Migration

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Jessé C. Costa ◽  
Débora Mondini ◽  
Jörg Schleicher ◽  
Amélia Novais
Keyword(s):  
Wave Equation ◽  
Finite Difference ◽  
Computational Cost ◽  
Three Dimensional ◽  
Depth Level ◽  
Numerical Examples ◽  
The Cost ◽  
Comparable Quality ◽  
3D Problem ◽  
Dimensional Wave

Three-dimensional wave-equation migration techniques are still quite expensive because of the huge matrices that need to be inverted. Several techniques have been proposed to reduce this cost by splitting the full 3D problem into a sequence of 2D problems. We compare the performance of splitting techniques for stable 3D Fourier finite-difference (FFD) migration techniques in terms of image quality and computational cost. The FFD methods are complex Padé FFD and FFD plus interpolation, and the compared splitting techniques are two- and four-way splitting as well as alternating four-way splitting, that is, splitting into the coordinate directions at one depth and the diagonal directions at the next depth level. From numerical examples in homogeneous and inhomogeneous media, we conclude that, though theoretically less accurate, alternate four-way splitting yields results of comparable quality as full four-way splitting at the cost of two-way splitting.

scholarly journals An intercomparison of total column-averaged nitrous oxide between ground-based FTIR TCCON and NDACC measurements at seven sites and comparisons with the GEOS-Chem model

2019 ◽  
Vol 12 (2) ◽  
pp. 1393-1408 ◽  
Author(s):  
Minqiang Zhou ◽  
Bavo Langerock ◽  
Kelley C. Wells ◽  
Dylan B. Millet ◽  
Corinne Vigouroux ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Southern Hemisphere ◽  
Near Infrared ◽  
A Priori ◽  
Polar Vortex ◽  
Inversion Method ◽  
Seasonal Cycles ◽  
A Posteriori ◽  
Model Simulations ◽  
N2o Fluxes

Abstract. Nitrous oxide (N2O) is an important greenhouse gas and it can also generate nitric oxide, which depletes ozone in the stratosphere. It is a common target species of ground-based Fourier transform infrared (FTIR) near-infrared (TCCON) and mid-infrared (NDACC) measurements. Both TCCON and NDACC networks provide a long-term global distribution of atmospheric N2O mole fraction. In this study, the dry-air column-averaged mole fractions of N2O (XN2O) from the TCCON and NDACC measurements are compared against each other at seven sites around the world (Ny-Ålesund, Sodankylä, Bremen, Izaña, Réunion, Wollongong, Lauder) in the time period of 2007–2017. The mean differences in XN2O between TCCON and NDACC (NDACC–TCCON) at these sites are between −3.32 and 1.37 ppb (−1.1 %–0.5 %) with standard deviations between 1.69 and 5.01 ppb (0.5 %–1.6 %), which are within the uncertainties of the two datasets. The NDACC N2O retrieval has good sensitivity throughout the troposphere and stratosphere, while the TCCON retrieval underestimates a deviation from the a priori in the troposphere and overestimates it in the stratosphere. As a result, the TCCON XN2O measurement is strongly affected by its a priori profile. Trends and seasonal cycles of XN2O are derived from the TCCON and NDACC measurements and the nearby surface flask sample measurements and compared with the results from GEOS-Chem model a priori and a posteriori simulations. The trends and seasonal cycles from FTIR measurement at Ny-Ålesund and Sodankylä are strongly affected by the polar winter and the polar vortex. The a posteriori N2O fluxes in the model are optimized based on surface N2O measurements with a 4D-Var inversion method. The XN2O trends from the GEOS-Chem a posteriori simulation (0.97±0.02 (1σ) ppb yr−1) are close to those from the NDACC (0.93±0.04 ppb yr−1) and the surface flask sample measurements (0.93±0.02 ppb yr−1). The XN2O trend from the TCCON measurements is slightly lower (0.81±0.04 ppb yr−1) due to the underestimation of the trend in TCCON a priori simulation. The XN2O trends from the GEOS-Chem a priori simulation are about 1.25 ppb yr−1, and our study confirms that the N2O fluxes from the a priori inventories are overestimated. The seasonal cycles of XN2O from the FTIR measurements and the model simulations are close to each other in the Northern Hemisphere with a maximum in August–October and a minimum in February–April. However, in the Southern Hemisphere, the modeled XN2O values show a minimum in February–April while the FTIR XN2O retrievals show different patterns. By comparing the partial column-averaged N2O from the model and NDACC for three vertical ranges (surface–8, 8–17, 17–50 km), we find that the discrepancy in the XN2O seasonal cycle between the model simulations and the FTIR measurements in the Southern Hemisphere is mainly due to their stratospheric differences.

scholarly journals Sensitivity of isoprene emissions estimated using MEGAN to the time resolution of input climate data

2010 ◽  
Vol 10 (3) ◽  
pp. 1193-1201 ◽  
Author(s):  
K. Ashworth ◽  
O. Wild ◽  
C. N. Hewitt
Keyword(s):  
Temporal Resolution ◽  
Input Data ◽  
Climate Models ◽  
Computational Cost ◽  
Earth System ◽  
Climate Data ◽  
Isoprene Emission ◽  
Monthly Average ◽  
Hourly Data ◽  
The Impact

Abstract. We evaluate the effect of varying the temporal resolution of the input climate data on isoprene emission estimates generated by the community emissions model MEGAN (Model of Emissions of Gases and Aerosols from Nature). The estimated total global annual emissions of isoprene is reduced from 766 Tg y−1 when using hourly input data to 746 Tg y−1 (a reduction of 3%) for daily average input data and 711 Tg y−1 (down 7%) for monthly average input data. The impact on a local scale can be more significant with reductions of up to 55% at some locations when using monthly average data compared with using hourly data. If the daily and monthly average temperature data are used without the imposition of a diurnal cycle the global emissions estimates fall by 27–32%, and local annual emissions by up to 77%. A similar pattern emerges if hourly isoprene fluxes are considered. In order to better simulate and predict isoprene emission rates using MEGAN, we show it is necessary to use temperature and radiation data resolved to one hour. Given the importance of land-atmosphere interactions in the Earth system and the low computational cost of the MEGAN algorithms, we recommend that chemistry-climate models and the new generation of Earth system models input biogenic emissions at the highest temporal resolution possible.

scholarly journals Optimization of the seasonal cycles of simulated CO<sub>2</sub> flux by fitting simulated atmospheric CO<sub>2</sub> to observed vertical profiles

2009 ◽  
Vol 6 (12) ◽  
pp. 2733-2741 ◽  
Author(s):  
Y. Nakatsuka ◽  
S. Maksyutov
Keyword(s):  
Net Primary Productivity ◽  
Transport Model ◽  
Vegetation Type ◽  
Vertical Mixing ◽  
Terrestrial Ecosystem ◽  
Co2 Concentration ◽  
Seasonal Cycles ◽  
Concentration Data ◽  
Near Surface ◽  
Terrestrial Ecosystem Model

Abstract. An inverse of a combination of atmospheric transport and flux models was used to optimize the Carnegie-Ames-Stanford Approach (CASA) terrestrial ecosystem model properties such as light use efficiency and temperature dependence of the heterotrophic respiration separately for each vegetation type. The method employed in the present study is based on minimizing the differences between the simulated and observed seasonal cycles of CO2 concentrations. In order to compensate for possible vertical mixing biases in a transport model we use airborne observations of CO2 vertical profile aggregated to a partial column instead of surface observations used predominantly in other parameter optimization studies. Effect of the vertical mixing on optimized net ecosystem production (NEP) was evaluated by carrying out 2 sets of inverse calculations: one with partial-column concentration data from 15 locations and another with near-surface CO2 concentration data from the same locations. We confirmed that the simulated growing season net flux (GSNF) and net primary productivity (NPP) are about 14% higher for northern extra-tropical land when optimized with partial column data as compared to the case with near-surface data.

scholarly journals Inferring particle dynamics in the Mediterranean Sea from in-situ pump POC and chloropigment data using Bayesian statistics

2018 ◽  
Author(s):  
Weilei Wang ◽  
Cindy Lee ◽  
Francois Primeau
Keyword(s):  
Mediterranean Sea ◽  
Rate Constants ◽  
Particle Aggregation ◽  
Inversion Method ◽  
Particle Dynamic ◽  
Concentration Data ◽  
Data Set ◽  
Aggregation And Disaggregation ◽  
The Mediterranean

Abstract. Chloropigment and particulate organic carbon (POC) concentration data collected using in-situ large-volume pumps during the MedFlux project in the Mediterranean Sea in May 2005 provided an opportunity to estimate rate constants that control the fate of particles and specifically chloropigments in the water column. Additionally, comparisons to thorium and chloropigment data from settling-velocity (SV) sediment traps at the same site enabled us to distinguish between the influence of the sampling method used vs. the tracer used on particle dynamic rate constants. Here we introduce a Bayesian statistical inversion method that combines the data with a new box model and has the capacity to infer rate constants for POC respiration/dissolution, chlorophyll and phaeopigment degradation, and particle aggregation and disaggregation. The estimated small-particle (1–70 μm) POC respiration rate constant was 1.25+0.55−0.38 yr−1 (0.80 yr). For this data set, the rate constants for chlorophyll (Chl) degradation to phaeopigments and phaeopigment respiration were not well constrained. The estimated aggregation and disaggregation rate constants were 7.65+3.35−2.33 (0.13 yr) and 106.09+39.13−28.59 yr−1 (0.01 yr), respectively, which indicates that particle aggregation and disaggregation were extensive at the studied depths (125–750 m) in May after the spring bloom had ended and flux was low.

scholarly journals Optimization of the seasonal cycles of simulated CO<sub>2</sub> flux by fitting simulated atmospheric CO<sub>2</sub> to observed vertical profiles

2009 ◽  
Vol 6 (3) ◽  
pp. 5933-5957 ◽  
Author(s):  
Y. Nakatsuka ◽  
S. Maksyutov
Keyword(s):  
Net Primary Productivity ◽  
Transport Model ◽  
Vertical Mixing ◽  
Terrestrial Ecosystem ◽  
Co2 Concentration ◽  
Model Parameters ◽  
Seasonal Cycles ◽  
Concentration Data ◽  
Near Surface ◽  
Terrestrial Ecosystem Model

Abstract. An inverse of a combination of atmospheric transport and flux models was used to optimize model parameters of the Carnegie-Ames-Stanford Approach (CASA) terrestrial ecosystem model. The method employed in the present study is based on minimizing an appropriate cost function (i.e. the weighted differences between the simulated and observed seasonal cycles of CO2 concentrations). We tried to reduce impacts that the inaccuracy of a vertical mixing in a transport model has on the simulated amplitudes of seasonal cycles of carbon flux by using airborne observations of CO2 vertical profile aggregated to a partial column. Effect of the vertical mixing on optimized NEP was evaluated by carrying out 2 sets of inverse calculations: one with partial-column concentration data from 15 locations and another with near-surface CO2 concentration data from the same 15 locations. We found that the values of simulated growing season net flux (GSNF) and net primary productivity (NPP) are affected by the rate of vertical mixing in a transport model used in the optimization. Optimized GSNF and NPP are higher when optimized with partial column data as compared to the case with near-surface data only due to the weak vertical mixing in the transport model used in this study.

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