scholarly journals A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0): 1. Off-line forward and adjoint transport models

2016 ◽  
Author(s):  
Yosuke Niwa ◽  
Hirofumi Tomita ◽  
Masaki Satoh ◽  
Ryoichi Imasu ◽  
Yousuke Sawa ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Meteorological Data ◽  
Forward Model ◽  
Adjoint Model ◽  
Transport Models ◽  
Line Model ◽  
Flux Limiter ◽  
On Line ◽  
Adjoint Transport ◽  
Continuous Adjoint

Abstract. A 4-dimensional variational (4D-Var) method is a popular algorithm for inverting atmospheric greenhouse gas (GHG) measurements. In order to meet the computationally intense 4D-Var iterative calculation, off-line forward and adjoint transport models are developed based on the Nonhydorstatic ICosahedral Atmospheric Model (NICAM). By introducing flexibility into the temporal resolution of the input meteorological data, the forward model developed in this study is not only computationally efficient but is also found to nearly match the transport performance of the on-line model. In a transport simulation of atmospheric carbon dioxide (CO2), the data-thinning error (error resulting from reduction in the time resolution of the meteorological data used to drive the off-line transport model) is minimized by employing high temporal resolution data of the vertical diffusion coefficient; with a lower temporal resolution, significant concentration biases near the surface are introduced. The new adjoint model can be run in discrete or continuous adjoint mode for the advection process. The discrete adjoint is characterized by perfect adjoint relationship with the forward model that switches off the flux limiter, while the continuous adjoint is characterized by imperfect but reasonable adjoint relationship with its corresponding forward model. In the latter case, both the forward and adjoint models use the flux limiter to ensure the monotonicity of tracer concentrations and sensitivities. Trajectory analysis for high-CO2 concentration events are performed to test adjoint sensitivities; we also demonstrate the potential usefulness of our adjoint model for diagnosing tracer transport. Both the off-line forward and adjoint models have computational efficiency about ten times more than that of the on-line model. A description of our new 4D-Var system that includes an optimization method, along with its application in an atmospheric CO2 inversion and the effects of using either the discrete or continuous adjoint method, is presented in an accompanying paper (Niwa et al., 2016).

scholarly journals A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 1: Offline forward and adjoint transport models

2017 ◽  
Vol 10 (3) ◽  
pp. 1157-1174 ◽  
Author(s):  
Yosuke Niwa ◽  
Hirofumi Tomita ◽  
Masaki Satoh ◽  
Ryoichi Imasu ◽  
Yousuke Sawa ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Meteorological Data ◽  
Forward Model ◽  
High Temporal Resolution ◽  
Tracer Transport ◽  
Adjoint Model ◽  
Transport Models ◽  
Flux Limiter ◽  
Adjoint Transport ◽  
Continuous Adjoint

Abstract. A four-dimensional variational (4D-Var) method is a popular algorithm for inverting atmospheric greenhouse gas (GHG) measurements. In order to meet the computationally intense 4D-Var iterative calculation, offline forward and adjoint transport models are developed based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). By introducing flexibility into the temporal resolution of the input meteorological data, the forward model developed in this study is not only computationally efficient, it is also found to nearly match the transport performance of the online model. In a transport simulation of atmospheric carbon dioxide (CO2), the data-thinning error (error resulting from reduction in the time resolution of the meteorological data used to drive the offline transport model) is minimized by employing high temporal resolution data of the vertical diffusion coefficient; with a low 6-hourly temporal resolution, significant concentration biases near the surface are introduced. The new adjoint model can be run in discrete or continuous adjoint mode for the advection process. The discrete adjoint is characterized by perfect adjoint relationship with the forward model that switches off the flux limiter, while the continuous adjoint is characterized by an imperfect but reasonable adjoint relationship with its corresponding forward model. In the latter case, both the forward and adjoint models use the flux limiter to ensure the monotonicity of tracer concentrations and sensitivities. Trajectory analysis for high CO2 concentration events are performed to test adjoint sensitivities. We also demonstrate the potential usefulness of our adjoint model for diagnosing tracer transport. Both the offline forward and adjoint models have computational efficiency about 10 times higher than the online model. A description of our new 4D-Var system that includes an optimization method, along with its application in an atmospheric CO2 inversion and the effects of using either the discrete or continuous adjoint method, is presented in an accompanying paper Niwa et al.(2016).

scholarly journals Development of a variational flux inversion system (INVICAT v1.0) within the TOMCAT chemical transport model

2013 ◽  
Vol 6 (4) ◽  
pp. 7117-7159 ◽  
Author(s):  
C. Wilson ◽  
M. P. Chipperfield ◽  
M. Gloor ◽  
F. Chevallier
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Chemical Transport ◽  
Forward Model ◽  
Chemical Transport Model ◽  
Adjoint Model ◽  
Inverse Models ◽  
Adjoint Transport ◽  
Inverse Transport ◽  
Atmospheric Trace Gas

Abstract. We present a new variational inverse transport model, named INVICAT (v1.0), which is based upon the global chemical transport model TOMCAT, and a new corresponding adjoint transport model, ATOMCAT. The adjoint model is constructed through manually derived discrete adjoint algorithms, and includes subroutines governing advection, convection and boundary layer mixing. We present extensive testing of the adjoint and inverse models, and also thoroughly assess the accuracy of the TOMCAT forward model's representation of atmospheric transport through comparison with observations of the atmospheric trace gas SF6. The forward model is shown to perform well in comparison with these observations, capturing the latitudinal gradient and seasonal cycle of SF6 to within acceptable tolerances. The adjoint model is shown, through numerical identity tests and novel transport reciprocity tests, to be extremely accurate in comparison with the forward model, with no error shown at the level of accuracy possible with our machines. The potential for the variational system as a tool for inverse modelling is investigated through an idealised test using simulated observations, and the system demonstrates an ability to retrieve known fluxes from a perturbed state accurately. Using basic off-line chemistry schemes, the inverse model is ready and available to perform inversions of trace gases with relatively simple chemical interactions, including CH4, CO2 and CO.

scholarly journals Development of a variational flux inversion system (INVICAT v1.0) using the TOMCAT chemical transport model

2014 ◽  
Vol 7 (5) ◽  
pp. 2485-2500 ◽  
Author(s):  
C. Wilson ◽  
M. P. Chipperfield ◽  
M. Gloor ◽  
F. Chevallier
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Chemical Transport ◽  
Forward Model ◽  
Chemical Transport Model ◽  
Adjoint Model ◽  
Inverse Models ◽  
Adjoint Transport ◽  
Inverse Transport ◽  
Atmospheric Trace Gas

Abstract. We present a new variational inverse transport model, named INVICAT (v1.0), which is based on the global chemical transport model TOMCAT, and a new corresponding adjoint transport model, ATOMCAT. The adjoint model is constructed through manually derived discrete adjoint algorithms, and includes subroutines governing advection, convection and boundary layer mixing, all of which are linear in the TOMCAT model. We present extensive testing of the adjoint and inverse models, and also thoroughly assess the accuracy of the TOMCAT forward model's representation of atmospheric transport through comparison with observations of the atmospheric trace gas SF6. The forward model is shown to perform well in comparison with these observations, capturing the latitudinal gradient and seasonal cycle of SF6 to within acceptable tolerances. The adjoint model is shown, through numerical identity tests and novel transport reciprocity tests, to be extremely accurate in comparison with the forward model, with no error shown at the level of accuracy possible with our machines. The potential for the variational system as a tool for inverse modelling is investigated through an idealised test using simulated observations, and the system demonstrates an ability to retrieve known fluxes from a perturbed state accurately. Using basic off-line chemistry schemes, the inverse model is ready and available to perform inversions of trace gases with relatively simple chemical interactions, including CH4, CO2 and CO.

scholarly journals The SPRINTARS version 3.80/4D-Var data assimilation system: development and inversion experiments based on the observing system simulation experiment framework

2013 ◽  
Vol 6 (2) ◽  
pp. 3427-3471
Author(s):  
K. Yumimoto ◽  
T. Takemura
Keyword(s):  
Data Assimilation ◽  
Simulation Experiment ◽  
Inverse Modelling ◽  
Adjoint Model ◽  
Line Model ◽  
On Line ◽  
Aerosol Emissions ◽  
Observing System Simulation Experiment ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present an aerosol data assimilation system based on a global aerosol climate model (SPRINTARS) and a four-dimensional variational data assimilation method (4D-Var). Its main purposes are to optimize emission estimates, improve composites, and obtain the best estimate of the radiative effects of aerosols in conjunction with observations. To reduce the huge computational cost caused by the iterative integrations in the models, we developed an off-line model and a corresponding adjoint model, which are driven by pre-calculated meteorological, land, and soil data. The off-line and adjoint model shortened the computational time of the inner loop by more than 30%. By comparing the results with a 1yr simulation from the original on-line model, the consistency of the off-line model was verified, with correlation coefficient R^2 > 0.97 and absolute value of normalized mean bias NMB < 7% for the natural aerosol emissions and aerosol optical thickness (AOT) of individual aerosol species. Deviations between the off-line and original on-line models are mainly associated with the time interpolation of the input meteorological variables in the off-line model; the smaller variability and difference in the wind velocity near the surface and relative humidity cause negative and positive biases in the wind-blown aerosol emissions and AOTs of hygroscopic aerosols, respectively. The feasibility and capability of the developed system for aerosol inverse modelling was demonstrated in several inversion experiments based on the observing system simulation experiment framework. In the experiments, we generated the simulated observation data sets of fine- and coarse-mode AOTs from sun-synchronous polar orbits to investigate the impact of the observational frequency (number of satellites) and coverage (land and ocean). Observations over land have a notably positive impact on the performance of inverse modelling comparing with observations over ocean, implying that reliable observational information over land is important for inverse modelling of land-born aerosols. The experimental results also indicate that aerosol type classification is crucial to inverse modelling over regions where various aerosol species co-exist (e.g. industrialized regions and areas downwind of them).

scholarly journals Model – TCCON comparisons of column-averaged methane with a focus on the stratosphere

2016 ◽  
Author(s):  
Andreas Ostler ◽  
Ralf Sussmann ◽  
Prabir K. Patra ◽  
Sander Houweling ◽  
Marko De Bruine ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Meteorological Data ◽  
Michelson Interferometer ◽  
Total Carbon ◽  
Data Sets ◽  
Transport Models ◽  
Improved Model ◽  
The Impact ◽  
Chemistry Experiment

Abstract. The distribution of methane (CH4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH4 mixing ratio (XCH4), which is being measured increasingly for the assessment of CH4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH4 accurately for estimating surface emissions from XCH4. Simulations from three CTMs are tested against XCH4 observations from the Total Carbon Column Network (TCCON). We analyze how the model-TCCON agreement in XCH4 depends on the model representation of stratospheric CH4 distributions. Model equivalents of TCCON XCH4 are computed with stratospheric CH4 fields from both the model simulations and from satellite-based CH4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. In comparison to simulated model fields we find an improved model-TCCON XCH4 agreement for all models with MIPAS-based stratospheric CH4 fields. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH4 bias is significantly reduced from 38.1 ppb to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 ppb to 4.3 ppb), and LMDz (Laboratoire de Météorologie Dynamique model with Zooming capability; from 6.8 ppb to 4.3 ppb), respectively. MIPAS stratospheric CH4 fields adjusted to ACE-FTS reduce the average XCH4 bias for ACTM (3.3 ppb), but increase the average XCH4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that the range of satellite-based stratospheric CH4 is insufficient to resolve a possible stratospheric contribution to differences in total column CH4 between TCCON and TM5 or LMDz. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH4 and, hence, its contribution to XCH4. This implies that there is a need to better understand the impact of individual model transport components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) on modeled stratospheric CH4.

On-Line Model Predictive Control for Energy Efficiency in Data Center

2021 ◽  
Vol 38 (12) ◽  
pp. 943-951
Author(s):  
Min Sik Chu ◽  
Hyun Ah Kim ◽  
Kyu Jong Lee ◽  
Ji Hoon Kang
Keyword(s):  
Energy Efficiency ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Data Center ◽  
Line Model ◽  
On Line
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