scholarly journals Observation system simulation experiments for a global wind observing sounder

2012 ◽  
Vol 39 (17) ◽  
pp. n/a-n/a ◽  
Author(s):  
Lars Peter Riishojgaard ◽  
Zaizhong Ma ◽  
Michiko Masutani ◽  
John S. Woollen ◽  
George D. Emmitt ◽  
...  
Keyword(s):  
System Simulation ◽  
Observation System ◽  
Simulation Experiments

scholarly journals Observation system simulation experiments using water vapor isotope information

2014 ◽  
Vol 119 (13) ◽  
pp. 7842-7862 ◽  
Author(s):  
Kei Yoshimura ◽  
Takemasa Miyoshi ◽  
Masao Kanamitsu
Keyword(s):  
Water Vapor ◽  
System Simulation ◽  
Observation System ◽  
Simulation Experiments

scholarly journals How well can inverse analyses of high-resolution satellite data resolve heterogeneous methane fluxes? Observation System Simulation Experiments with the GEOS-Chem adjoint model (v35)

2021 ◽  
Author(s):  
Xueying Yu ◽  
Dylan B. Millet ◽  
Daven K. Henze
Keyword(s):  
High Resolution ◽  
System Simulation ◽  
Bias Reduction ◽  
Observation System ◽  
Adjoint Model ◽  
Simulation Experiments ◽  
Standard Scale ◽  
Methane Fluxes ◽  
Focus Analysis ◽  
Uniform Background

Abstract. We perform Observation System Simulation Experiments (OSSEs) with the GEOS-Chem adjoint model to test how well methane emissions over North America can be resolved using measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and similar high-resolution satellite sensors. We focus analysis on the impacts of i) spatial errors in the prior emissions, and ii) model transport errors. Along with a standard scale-factor (SF) optimization we conduct a set of inversions using alternative formalisms that aim to overcome limitations in the SF-based approach that arise for missing sources. We show that 4D-Var analysis of the TROPOMI data can improve monthly emission estimates at 25 km even with a spatially biased prior or model transport errors (42–93 % domain-wide bias reduction; R increases from 0.51 up to 0.73). However, when both errors are present, no single inversion framework can successfully improve both the overall bias and spatial distribution of fluxes relative to the prior on the 25 km model grid. In that case, the ensemble-mean optimized fluxes have a domain-wide bias of 77 Gg/d (comparable to that in the prior), with spurious source adjustments compensating for the transport errors. Increasing observational coverage through longer-timeframe inversions does not significantly change this picture. An inversion formalism that optimizes emission enhancements rather than scale factors exhibits the best performance for identifying missing sources, while an approach combining a uniform background emission with the prior inventory yields the best performance in terms of overall spatial fidelity—even in the presence of model transport errors. However, the standard SF optimization outperforms both of these for the magnitude of the domain-wide flux. For the common scenario in which prior errors are non-random, approximate posterior error reduction calculations for the inversions reflect the sensitivity to observations but have no spatial correlation with the actual emission improvements. This demonstrates that such information content analysis can be used for general observing system characterization but does not describe the spatial accuracy of the posterior emissions or of the actual emission improvements. Findings here highlight the need for careful evaluation of potential missing sources in prior emission datasets and for robust accounting of model transport errors in inverse analyses of the methane budget.

scholarly journals Design and evaluation of CO<sub>2</sub> observation network to optimize surface CO<sub>2</sub> fluxes in Asia using observation system simulation experiments

2019 ◽  
Author(s):  
Jun Park ◽  
Hyun Mee Kim
Keyword(s):  
Carbon Flux ◽  
System Simulation ◽  
Observation System ◽  
In Situ Observation ◽  
Simulation Experiments ◽  
Influence Matrix ◽  
Surface Carbon ◽  
Observation Network ◽  
Simulated Surface

Abstract. Continuous efforts have been made to monitor atmospheric CO2 as it is one of the most influential greenhouse gases in Earth's atmosphere. Inverse modeling, which is one of the methods to carry out such monitoring, derives estimated CO2 mole fractions in the air from calculated surface carbon fluxes using model and observed CO2 mole fraction data. Although observation data is crucial for successful modeling, comparatively fewer in-situ observation sites are located in Asia compared to Europe or North America. Based on the importance of the terrestrial ecosystem of Asia for global carbon exchanges, more observation stations and an effective observation network design are required. In this paper, several observation network experiments were conducted to optimize the surface carbon flux of Asia using CarbonTracker and observation system simulation experiments (OSSE). The impacts of the redistribution of and additions to the existing observation network of Asia were evaluated using hypothetical in-situ observation sites. In the case of the addition experiments, 10 observation stations, which is a practical number for real implementation, were added through three strategies: random addition, the influence matrix (i.e., self-sensitivity), and ecoregion information within the model. The simulated surface carbon flux in Asia in summer can be improved by redistributing the existing observation network. The addition experiments revealed that considering both the distribution of normalized self-sensitivity and ecoregion information can yield better simulated surface carbon fluxes compared to random addition, regardless of the season. This study provides a diagnosis of the existing observation network and useful information for future observation network design in Asia to estimate the surface carbon flux, and also suggests the use of an influence matrix for designing carbon observation networks. Unlike other previous observation network studies with many numerical experiments for optimization, comparatively fewer experiments were required in this study. Thus, the methodology used in this study may be used for designing observation networks for monitoring greenhouse gases at both continental and global scales.

scholarly journals Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 2: Observation system simulation experiments

2010 ◽  
Vol 10 (14) ◽  
pp. 6699-6709 ◽  
Author(s):  
D. Huang ◽  
A. Gasiewski ◽  
W. Wiscombe
Keyword(s):  
Liquid Water ◽  
System Simulation ◽  
Microwave Radiometer ◽  
Retrieval Algorithm ◽  
Observation System ◽  
Total Variation Regularization ◽  
L1 Norm ◽  
Simulation Experiments ◽  
Cloud Liquid Water ◽  
L2 Norm

Abstract. Part 1 of this research concluded that many conditions of the 2003 Wakasa Bay experiment were not optimal for the purpose of tomographic retrieval. Part 2 (this paper) then aims to find possible improvements to the mobile cloud tomography method using observation system simulation experiments. We demonstrate that the incorporation of the L1 norm total variation regularization in the tomographic retrieval algorithm better reproduces discontinuous structures than the widely used L2 norm Tikhonov regularization. The simulation experiments reveal that a typical ground-based mobile setup substantially outperforms an airborne one because the ground-based setup usually moves slower and has greater contrast in microwave brightness between clouds and the background. It is shown that, as expected, the error in the cloud tomography retrievals increases monotonically with both the radiometer noise level and the uncertainty in the estimate of background brightness temperature. It is also revealed that a lower speed of platform motion or a faster scanning radiometer results in more scan cycles and more overlap between the swaths of successive scan cycles, both of which help to improve the retrieval accuracy. The last factor examined is aircraft height. It is found that the optimal aircraft height is 0.5 to 1.0 km above the cloud top. To summarize, this research demonstrates the feasibility of tomographically retrieving the spatial structure of cloud liquid water using current microwave radiometric technology and provides several general guidelines to improve future field-based studies of cloud tomography.

scholarly journals Observation System Simulation Experiments of Water Vapor Profiles Observed by Raman Lidar Using LETKF System

SOLA ◽  
2020 ◽  
Vol 16 (0) ◽  
pp. 43-50 ◽  
Author(s):  
Satoru Yoshida ◽  
Sho Yokota ◽  
Hiromu Seko ◽  
Tetsu Sakai ◽  
Tomohiro Nagai
Keyword(s):  
Water Vapor ◽  
System Simulation ◽  
Observation System ◽  
Raman Lidar ◽  
Simulation Experiments

scholarly journals Observation system simulation experiments in the Atlantic Ocean for enhanced surface ocean <i>p</i>CO<sub>2</sub> reconstructions

Ocean Science ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 1011-1030
Author(s):  
Anna Denvil-Sommer ◽  
Marion Gehlen ◽  
Mathieu Vrac
Keyword(s):  
Atlantic Ocean ◽  
System Simulation ◽  
Ocean Surface ◽  
Ocean Model ◽  
Global Ocean ◽  
Observation System ◽  
Simulation Experiments ◽  
Atlantic Sector ◽  
Argo Floats ◽  
Surface Ocean

Abstract. To derive an optimal observation system for surface ocean pCO2 in the Atlantic Ocean and the Atlantic sector of the Southern Ocean, 11 observation system simulation experiments (OSSEs) were completed. Each OSSE is a feedforward neural network (FFNN) that is based on a different data distribution and provides ocean surface pCO2 for the period 2008–2010 with a 5 d time interval. Based on the geographical and time positions from three observational platforms, volunteering observing ships, Argo floats and OceanSITES moorings, pseudo-observations were constructed using the outputs from an online-coupled physical–biogeochemical global ocean model with 0.25∘ nominal resolution. The aim of this work was to find an optimal spatial distribution of observations to supplement the widely used Surface Ocean CO2 Atlas (SOCAT) and to improve the accuracy of ocean surface pCO2 reconstructions. OSSEs showed that the additional data from mooring stations and an improved coverage of the Southern Hemisphere with biogeochemical ARGO floats corresponding to least 25 % of the density of active floats (2008–2010) (OSSE 10) would significantly improve the pCO2 reconstruction and reduce the bias of derived estimates of sea–air CO2 fluxes by 74 % compared to ocean model outputs.

Observation System Simulation Experiments for surface ocean pCO2 reconstructions in the Atlantic Ocean 

2021 ◽  
Author(s):  
Anna Denvil-Sommer ◽  
Marion Gehlen ◽  
Mathieu Vrac
Keyword(s):  
Standard Deviation ◽  
Atlantic Ocean ◽  
Correlation Coefficient ◽  
System Simulation ◽  
Ocean Model ◽  
Observation System ◽  
Simulation Experiments ◽  
Model Reference ◽  
Surface Ocean ◽  
Global Estimates

&lt;p&gt;Global estimates of the ocean carbon sink are released with a yearly frequency as part of the global carbon budget. However, these global estimates hide important spatial and temporal variabilities that can only partly be resolved by direct in situ observations. In this work we explore options for future observational network design combining data streams from various platforms. Our objective is to identify an optimal observational network for surface ocean pCO&lt;sub&gt;2&lt;/sub&gt; in the Atlantic Ocean and the Atlantic sector of the Southern Ocean. For this purpose, eleven Observation System Simulation Experiments (OSSEs) were performed. Each OSSE is a Feed-Forward Neural Network (FFNN) that is based on different data distributions and provides ocean surface pCO&lt;sub&gt;2&lt;/sub&gt; for the period 2008-2010 with a 5-day time interval. Based on the geographical and time positions from three observational platforms, volunteering observing ships (VOS), Argo floats and OceanSITES moorings, pseudo-observations were constructed using the outputs from an online-coupled physical-biogeochemical global ocean model with a 0.25&amp;#186; nominal spatial resolution. The aim of this work was to find an optimal spatial distribution of observations to supplement the widely used Surface Ocean CO&lt;sub&gt;2&lt;/sub&gt; Atlas (SOCAT) and to improve the accuracy of ocean surface pCO&lt;sub&gt;2&lt;/sub&gt; reconstructions. OSSEs showed that the additional data from mooring stations and an improved coverage of the southern Hemisphere with biogeochemical ARGO floats corresponding to at least 25% of the density of active floats (2008-2010) would significantly improve the pCO&lt;sub&gt;2&lt;/sub&gt; reconstruction and reduce the bias of derived estimates of sea-air CO&lt;sub&gt;2&lt;/sub&gt; fluxes by 77%. The use of only SOCAT data results in a correlation coefficient of 0.67 compared to the ocean model output and a 26.08 &amp;#120583;atm standard deviation (25.34 &amp;#120583;atm for the model reference) over the chosen regions. While the best OSSE has a correlation coefficient of 0.85 and 24.89 &amp;#120583;atm for standard deviation. These results are close to the unrealistic benchmark case with total and only Argo float distribution over 2008-2010: 0.87 and 23.79&amp;#120583;atm. The reconstructed average pCO&lt;sub&gt;2&lt;/sub&gt; over the whole region is also close to the model reference, ~370 &amp;#120583;atm and ~371 &amp;#120583;atm, respectively. The integrated air-sea fluxes &lt;em&gt;f&lt;/em&gt;CO&lt;sub&gt;2&lt;/sub&gt; are about -0.83 Pg/yr (best OSSE) and -0.76 Pg/yr (model reference).&amp;#160;&lt;/p&gt;

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