Observing System Simulation Experiment (OSSE) of future ALTIUS ozone profiles

2020 ◽  
Author(s):  
Quentin Errera ◽  
Jonas Debosscher ◽  
Emmanuel Dekemper ◽  
Philippe Demoulin ◽  
Didier Fussen ◽  
...  
Keyword(s):  
Simulation Experiment ◽  
System Simulation ◽  
Stratospheric Ozone ◽  
Reference Dataset ◽  
Polar Night ◽  
Satellite Mission ◽  
Polar Orbit ◽  
Atmospheric Sciences ◽  
Assimilation Experiment ◽  
Observing System Simulation Experiment

<p>ALTIUS (Atmospheric Limb Tracker for the Investigation of the Upcoming Stratosphere) is a satellite mission dedicated to continue Earth limb measurements for atmospheric sciences (Fussen et al., JQSRT, 2019). It is an element of the ESA Earth Watch programme and is expected to be launched in 2024 on a low earth polar orbit. The instrument is based on three spectral imagers that will measure in UV-vis-NIR wavelength range and will operate in different viewing geometry: limb scattering and occultation of the sun, the moon, the planets and the stars. ALTIUS will retrieve vertical profiles of ozone, nitrogen dioxide, aerosol extinction, among others.</p><p>In this study, we present an Observing System Simulation Experiment (OSSE) of ALTIUS ozone profiles that we have compared with the existing observations from Aura Microwave Limb Sounder (MLS). For this purpose, we have created a stratospheric ozone reference dataset between June and September 2008 based on the assimilation of MLS data with the Belgian Assimilation System for Chemical Observations (BASCOE). During the MLS assimilation experiment, the ozone state is saved in the space of ALTIUS previously determined with the ALTIUS orbit simulator, then perturbed according to the ALTIUS error budget, which creates ALTIUS synthetic observations. The assimilation of these ALTIUS ozone profiles agrees well with those of MLS. The assimilation of the different modes of ALTIUS reveals that all modes are necessary to constrain ozone during the polar night: solar and stellar occultations are the most constraining during the June-August period while limb scattering profiles are the most constraining from September onward.</p><div> </div>

scholarly journals On the capability of the future ALTIUS ultraviolet–visible–near-infrared limb sounder to constrain modelled stratospheric ozone

2021 ◽  
Vol 14 (6) ◽  
pp. 4737-4753
Author(s):  
Quentin Errera ◽  
Emmanuel Dekemper ◽  
Noel Baker ◽  
Jonas Debosscher ◽  
Philippe Demoulin ◽  
...  
Keyword(s):  
Near Infrared ◽  
Simulation Experiment ◽  
Stratospheric Ozone ◽  
Polar Night ◽  
Significant Information ◽  
Upper Troposphere ◽  
Reference Atmosphere ◽  
Observing System Simulation Experiment ◽  
Good Agreement ◽  
Tropical Upper Troposphere

Abstract. ALTIUS (Atmospheric Limb Tracker for the Investigation of the Upcoming Stratosphere) is the upcoming stratospheric ozone monitoring limb sounder from ESA's Earth Watch programme. Measuring in the ultraviolet–visible–near-infrared (UV–VIS–NIR) spectral regions, ALTIUS will retrieve vertical profiles of ozone, aerosol extinction coefficients, nitrogen dioxide and other trace gases from the upper troposphere to the mesosphere. In order to maximize the geographical coverage, the instrument will observe limb-scattered solar light during daytime (i.e. bright limb observations), solar occultations at the terminator and stellar/lunar/planetary occultations during nighttime. This paper evaluates the constraint of ALTIUS ozone profiles on modelled stratospheric ozone by means of an observing system simulation experiment (OSSE). In this effort, a reference atmosphere has been built and used to generate ALTIUS ozone profiles, along with an instrument simulator. These profiles are then assimilated to provide ozone analyses. A good agreement is found between the analyses and the reference atmosphere in the stratosphere and in the extra-tropical upper troposphere. In the tropical upper troposphere, although providing significant information in the analyses, the assimilation of ozone profiles does not completely eliminate the bias with respect to the reference atmosphere. The impacts of the different modes of observations have also been evaluated, showing that all of them are necessary to constrain ozone during polar winters where solar/stellar occultations are the most important during the polar night and bright limb data are the most important during the development of the ozone hole in the polar spring.

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 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 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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