scholarly journals Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Perfect Observations Simulation Validation

2018 ◽  
Vol 35 (1) ◽  
pp. 207-226 ◽  
Author(s):  
Sid-Ahmed Boukabara ◽  
Kayo Ide ◽  
Narges Shahroudi ◽  
Yan Zhou ◽  
Tong Zhu ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Simulation Experiment ◽  
System Simulation ◽  
Statistical Characteristics ◽  
Bending Angle ◽  
Simulation Validation ◽  
Starting Point ◽  
Regression Relationship ◽  
Sensor Channel ◽  
Observing System Simulation Experiment

AbstractThe simulation of observations—a critical Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) component—is validated first by a comparison of error-free simulated observations for the first 24 h at the start of the nature run (NR) to the real observations for those sensors that operated during that period. Sample results of this validation are presented here for existing low-Earth-orbiting (LEO) infrared (IR) and microwave (MW) brightness temperature (BT) observations, for radio occultation (RO) bending angle observations, and for various types of conventional observations. For sensors not operating at the start of the NR, a qualitative validation is obtained by comparing geographic and statistical characteristics of observations over the initial day for such a sensor and an existing similar sensor. The comparisons agree, with no significant unexplained bias, and to within the uncertainties caused by real observation errors, time and space collocation differences, radiative transfer uncertainties, and differences between the NR and reality. To validate channels of a proposed future MW sensor with no equivalent existing spaceborne sensor channel, multiple linear regression is used to relate these channels to existing similar channels. The validation then compares observations simulated from the NR to observations predicted by the regression relationship applied to actual real observations of the existing channels. Overall, the CGOP simulations of error-free observations from conventional and satellite platforms that make up the global observing system are found to be reasonably accurate and suitable as a starting point for creating realistic simulated observations for OSSEs. These findings complete a critical step in the CGOP validation, thereby reducing the caveats required when interpreting the OSSE results.

scholarly journals An Observing System Simulation Experiment with a Constellation of Radio Occultation Satellites

2018 ◽  
Vol 146 (12) ◽  
pp. 4247-4259 ◽  
Author(s):  
L. Cucurull ◽  
R. Atlas ◽  
R. Li ◽  
M. J. Mueller ◽  
R. N. Hoffman
Keyword(s):  
Radio Occultation ◽  
Simulation Experiment ◽  
System Simulation ◽  
Weather Prediction ◽  
Satellite System ◽  
Prediction Systems ◽  
Global Navigation Satellite ◽  
The Tropics ◽  
Observing System Simulation Experiment ◽  
The Impact

Abstract Experiments with a global observing system simulation experiment (OSSE) system based on the recent 7-km-resolution NASA nature run (G5NR) were conducted to determine the potential value of proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellations in current operational numerical weather prediction systems. The RO observations were simulated with the geographic sampling expected from the original planned Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) system, with six equatorial (total of ~6000 soundings per day) and six polar (total of ~6000 soundings per day) receiver satellites. The experiments also accounted for the expected improved vertical coverage provided by the Jet Propulsion Laboratory RO receivers on board COSMIC-2. Except that RO observations were simulated and assimilated as refractivities, the 2015 version of the NCEP’s operational data assimilation system was used to run the OSSEs. The OSSEs quantified the impact of RO observations on global weather analyses and forecasts and the impact of adding explicit errors to the simulation of perfect RO profiles. The inclusion or exclusion of explicit errors had small, statistically insignificant impacts on results. The impact of RO observations was found to increase the length of the useful forecasts. In experiments with explicit errors, these increases were found to be 0.6 h in the Northern Hemisphere extratropics (a 0.4% improvement), 5.9 h in the Southern Hemisphere extratropics (a significant 4.0% improvement), and 12.1 h in the tropics (a very substantial 28.4% improvement).

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.

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