scholarly journals Observing System Simulation Experiment: Development of the system and preliminary results

2011 ◽  
Vol 116 (D13) ◽  
Author(s):  
Shu-Hua Chen ◽  
Jhih-Ying Chen ◽  
Wei-Yu Chang ◽  
Pay-Liam Lin ◽  
Po-Hsiung Lin ◽  
...  
Keyword(s):  
Simulation Experiment ◽  
System Simulation ◽  
Preliminary Results ◽  
Observing System Simulation Experiment

scholarly journals Monitoring the Northern Current in the Gulf of Lions with an observing system simulation experiment

2012 ◽  
Vol 76 (3) ◽  
pp. 441-453 ◽  
Author(s):  
Aurélie Duchez ◽  
Jacques Verron ◽  
Jean-Michel Brankart ◽  
Yann Ourmières ◽  
Philippe Fraunié
Keyword(s):  
Simulation Experiment ◽  
System Simulation ◽  
Gulf Of Lions ◽  
Observing System Simulation Experiment ◽  
Northern Current

scholarly journals Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1

2019 ◽  
Vol 12 (7) ◽  
pp. 2899-2914
Author(s):  
Yun Liu ◽  
Eugenia Kalnay ◽  
Ning Zeng ◽  
Ghassem Asrar ◽  
Zhaohui Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Simulation Experiment ◽  
System Simulation ◽  
Carbon Fluxes ◽  
Observation Window ◽  
Surface Carbon ◽  
Ensemble Transform Kalman Filter ◽  
Observing System Simulation Experiment ◽  
Ensemble Transform ◽  
Assimilation System

Abstract. We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model, version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place” (RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation (Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated. This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models, especially in order to make greater use of observations in conjunction with models.

Quantifying Snow Mass Mission Concept Trade-Offs Using an Observing System Simulation Experiment

2019 ◽  
Vol 20 (1) ◽  
pp. 155-173 ◽  
Author(s):  
Camille Garnaud ◽  
Stéphane Bélair ◽  
Marco L. Carrera ◽  
Chris Derksen ◽  
Bernard Bilodeau ◽  
...  
Keyword(s):  
Land Surface ◽  
Simulation Experiment ◽  
Snow Water Equivalent ◽  
System Simulation ◽  
Surface Model ◽  
Trade Offs ◽  
Wet Snow ◽  
Observing System Simulation Experiment ◽  
The Impact ◽  
Snow Mass

Abstract Because of its location, Canada is particularly affected by snow processes and their impact on the atmosphere and hydrosphere. Yet, snow mass observations that are ongoing, global, frequent (1–5 days), and at high enough spatial resolution (kilometer scale) for assimilation within operational prediction systems are presently not available. Recently, Environment and Climate Change Canada (ECCC) partnered with the Canadian Space Agency (CSA) to initiate a radar-focused snow mission concept study to define spaceborne technological solutions to this observational gap. In this context, an Observing System Simulation Experiment (OSSE) was performed to determine the impact of sensor configuration, snow water equivalent (SWE) retrieval performance, and snow wet/dry state on snow analyses from the Canadian Land Data Assimilation System (CaLDAS). The synthetic experiment shows that snow analyses are strongly sensitive to revisit frequency since more frequent assimilation leads to a more constrained land surface model. The greatest reduction in spatial (temporal) bias is from a 1-day revisit frequency with a 91% (93%) improvement. Temporal standard deviation of the error (STDE) is mostly reduced by a greater retrieval accuracy with a 65% improvement, while a 1-day revisit reduces the temporal STDE by 66%. The inability to detect SWE under wet snow conditions is particularly impactful during the spring meltdown, with an increase in spatial RMSE of up to 50 mm. Wet snow does not affect the domain-wide annual maximum SWE nor the timing of end-of-season snowmelt timing in this case, indicating that radar measurements, although uncertain during melting events, are very useful in adding skill to snow analyses.

scholarly journals An Observing System Simulation Experiment for the Calibration and Validation of the Surface Water Ocean Topography Sea Surface Height Measurement Using In Situ Platforms

2018 ◽  
Vol 35 (2) ◽  
pp. 281-297 ◽  
Author(s):  
Jinbo Wang ◽  
Lee-Lueng Fu ◽  
Bo Qiu ◽  
Dimitris Menemenlis ◽  
J. Thomas Farrar ◽  
...  
Keyword(s):  
Surface Water ◽  
Simulation Experiment ◽  
System Simulation ◽  
Sea Surface ◽  
Station Keeping ◽  
Calibration And Validation ◽  
Wavenumber Spectrum ◽  
Ocean Topography ◽  
Observing System Simulation Experiment

AbstractThe wavenumber spectrum of sea surface height (SSH) is an important indicator of the dynamics of the ocean interior. While the SSH wavenumber spectrum has been well studied at mesoscale wavelengths and longer, using both in situ oceanographic measurements and satellite altimetry, it remains largely unknown for wavelengths less than ~70 km. The Surface Water Ocean Topography (SWOT) satellite mission aims to resolve the SSH wavenumber spectrum at 15–150-km wavelengths, which is specified as one of the mission requirements. The mission calibration and validation (CalVal) requires the ground truth of a synoptic SSH field to resolve the targeted wavelengths, but no existing observational network is able to fulfill the task. A high-resolution global ocean simulation is used to conduct an observing system simulation experiment (OSSE) to identify the suitable oceanographic in situ measurements for SWOT SSH CalVal. After fixing 20 measuring locations (the minimum number for resolving 15–150-km wavelengths) along the SWOT swath, four instrument platforms were tested: pressure-sensor-equipped inverted echo sounders (PIES), underway conductivity–temperature–depth (UCTD) sensors, instrumented moorings, and underwater gliders. In the context of the OSSE, PIES was found to be an unsuitable tool for the target region and for SSH scales 15–70 km; the slowness of a single UCTD leads to significant aliasing by high-frequency motions at short wavelengths below ~30 km; an array of station-keeping gliders may meet the requirement; and an array of moorings is the most effective system among the four tested instruments for meeting the mission’s requirement. The results shown here warrant a prelaunch field campaign to further test the performance of station-keeping gliders.

scholarly journals Assimilation of Surface-Based Boundary Layer Profiler Observations during a Cool-Season Weather Event Using an Observing System Simulation Experiment. Part I: Analysis Impact

2011 ◽  
Vol 139 (8) ◽  
pp. 2309-2326 ◽  
Author(s):  
Jason A. Otkin ◽  
Daniel C. Hartung ◽  
David D. Turner ◽  
Ralph A. Petersen ◽  
Wayne F. Feltz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Simulation Experiment ◽  
System Simulation ◽  
Microwave Radiometer ◽  
Lower Troposphere ◽  
Moisture Profile ◽  
Wind Profiles ◽  
Observing System Simulation Experiment ◽  
The Impact

AbstractIn this study, an Observing System Simulation Experiment was used to examine how the assimilation of temperature, water vapor, and wind profiles from a potential array of ground-based remote sensing boundary layer profiling instruments impacts the accuracy of atmospheric analyses when using an ensemble Kalman filter data assimilation system. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). The case study tracked the evolution of several extratropical weather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results demonstrate that using networks of high-quality temperature, wind, and moisture profile observations of the lower troposphere has the potential to improve the accuracy of wintertime atmospheric analyses over land. The impact of each profiling system was greatest in the lower and middle troposphere on the variables observed or retrieved by that instrument; however, some minor improvements also occurred in the unobserved variables and in the upper troposphere, particularly when RAM observations were assimilated. The best analysis overall was achieved when DWL wind profiles and temperature and moisture observations from the RAM, AERI, or MWR were assimilated simultaneously, which illustrates that both mass and momentum observations are necessary to improve the analysis accuracy.

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