The effect of using limited scene-dependent averaging kernels approximations for the implementation of fast observing system simulation experiments targeted on lower tropospheric ozone

Abstract. Practical implementations of chemical OSSEs (Observing System Simulation Experiments) usually rely on approximations of the pseudo-observations by means of a predefined parametrization of the averaging kernels, which describe the sensitivity of the observing system to the target atmospheric species. This is intended to avoid the use of a computationally expensive pseudo-observations simulator, that relies on full radiative transfer calculations. Here we present an investigation on how no, or limited, scene dependent averaging kernels parametrizations may misrepresent the sensitivity of an observing system. We carried out the full radiative transfer calculation for a three-days period over Europe, to produce reference pseudo-observations of lower tropospheric ozone, as they would be observed by a concept geostationary observing system called MAGEAQ (Monitoring the Atmosphere from Geostationary orbit for European Air Quality). The selected spatio-temporal interval is characterised by an ozone pollution event. We then compared our reference with approximated pseudo-observations, following existing simulation exercises made for both the MAGEAQ and GEOstationary Coastal and Air Pollution Events (GEO-CAPE) missions. We found that approximated averaging kernels may fail to replicate the variability of the full radiative transfer calculations. In addition, we found that the approximations substantially overestimate the capability of MAGEAQ to follow the spatio-temporal variations of the lower tropospheric ozone in selected areas, during the mentioned pollution event. We conclude that such approximations may lead to false conclusions if used in an OSSE. Thus, we recommend to use comprehensive scene-dependent approximations of the averaging kernels, in cases where the full radiative transfer is computationally too costly for the OSSE being investigated.

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The effect of using limited scene-dependent averaging kernels approximations for the implementation of fast Observing System Simulation Experiments targeted on lower tropospheric ozone

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-2413-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 2413-2448 ◽

Cited By ~ 4

Author(s):

P. Sellitto ◽

G. Dufour ◽

M. Eremenko ◽

J. Cuesta ◽

V.-H. Peuch ◽

...

Keyword(s):

Radiative Transfer ◽

Tropospheric Ozone ◽

System Simulation ◽

Ozone Pollution ◽

Simulation Experiments ◽

Atmospheric Species ◽

Radiative Transfer Calculation ◽

Pollution Event ◽

Pollution Events ◽

Simulation Exercises

Abstract. Practical implementations of chemical OSSEs (Observing System Simulation Experiments) usually rely on approximations of the pseudo-observations by means of a prior parametrization of the averaging kernels, which describe the sensitivity of the observing systems to the target atmospheric species. This is intended to avoid the need for use of a computationally expensive pseudo-observations simulator that relies on full radiative transfer calculations. Here we present an investigation on how no, or limited, scene dependent averaging kernels parametrizations may misrepresent the sensitivity of an observing system, and thus possibly lead to inaccurate results of OSSEs. We carried out the full radiative transfer calculation for a three-days period over Europe, to produce reference pseudo-observations of lower tropospheric ozone, as they would be observed by a concept geostationary observing system called MAGEAQ (Monitoring the Atmosphere from Geostationary orbit for European Air Quality). The selected spatiotemporal interval is characterized by a peculiar ozone pollution event. We then compared our reference with approximated pseudo-observations, following existing simulation exercises made for both the MAGEAQ and GEOstationary Coastal and Air Pollution Events (GEO-CAPE) missions. We found that approximated averaging kernels may fail to replicate the variability of the full radiative transfer calculations. Then, we compared the full radiative transfer and the approximated pseudo-observations during a pollution event. We found that the approximations substantially overestimate the capability of the MAGEAQ to follow the spatiotemporal variations of the lower tropospheric ozone in selected areas. We conclude that such approximations may lead to false conclusions if used in an OSSE. Thus, we recommend to use comprehensive scene-dependent approximations of the averaging kernels, in cases where the full radiative transfer is computationally too costly for the OSSE being investigated.

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Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends

Elem Sci Anth ◽

10.1525/elementa.265 ◽

2018 ◽

Vol 6 ◽

Cited By ~ 62

Author(s):

P. J. Young ◽

V. Naik ◽

A. M. Fiore ◽

A. Gaudel ◽

J. Guo ◽

...

Keyword(s):

Atmospheric Chemistry ◽

Tropospheric Ozone ◽

Model Performance ◽

Temporal Distribution ◽

Temporal Variations ◽

Global Scale ◽

Scale Model ◽

Significant Information ◽

Assessment Report ◽

Spatio Temporal

The goal of the Tropospheric Ozone Assessment Report (TOAR) is to provide the research community with an up-to-date scientific assessment of tropospheric ozone, from the surface to the tropopause. While a suite of observations provides significant information on the spatial and temporal distribution of tropospheric ozone, observational gaps make it necessary to use global atmospheric chemistry models to synthesize our understanding of the processes and variables that control tropospheric ozone abundance and its variability. Models facilitate the interpretation of the observations and allow us to make projections of future tropospheric ozone and trace gas distributions for different anthropogenic or natural perturbations. This paper assesses the skill of current-generation global atmospheric chemistry models in simulating the observed present-day tropospheric ozone distribution, variability, and trends. Drawing upon the results of recent international multi-model intercomparisons and using a range of model evaluation techniques, we demonstrate that global chemistry models are broadly skillful in capturing the spatio-temporal variations of tropospheric ozone over the seasonal cycle, for extreme pollution episodes, and changes over interannual to decadal periods. However, models are consistently biased high in the northern hemisphere and biased low in the southern hemisphere, throughout the depth of the troposphere, and are unable to replicate particular metrics that define the longer term trends in tropospheric ozone as derived from some background sites. When the models compare unfavorably against observations, we discuss the potential causes of model biases and propose directions for future developments, including improved evaluations that may be able to better diagnose the root cause of the model-observation disparity. Overall, model results should be approached critically, including determining whether the model performance is acceptable for the problem being addressed, whether biases can be tolerated or corrected, whether the model is appropriately constituted, and whether there is a way to satisfactorily quantify the uncertainty.

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Satellite observation of lowermost tropospheric ozone by multispectral synergism of IASI thermal infrared and GOME-2 ultraviolet measurements over Europe

Atmospheric Chemistry and Physics ◽

10.5194/acp-13-9675-2013 ◽

2013 ◽

Vol 13 (19) ◽

pp. 9675-9693 ◽

Cited By ~ 49

Author(s):

J. Cuesta ◽

M. Eremenko ◽

X. Liu ◽

G. Dufour ◽

Z. Cai ◽

...

Keyword(s):

Radiative Transfer ◽

Tropospheric Ozone ◽

Degrees Of Freedom ◽

Transport Model ◽

Thermal Infrared ◽

Satellite Observation ◽

Ozone Pollution ◽

Pixel Resolution ◽

Uv Reflectance ◽

Global Coverage

Abstract. We present a new multispectral approach for observing lowermost tropospheric ozone from space by synergism of atmospheric radiances in the thermal infrared (TIR) observed by IASI (Infrared Atmospheric Sounding Interferometer) and earth reflectances in the ultraviolet (UV) measured by GOME-2 (Global Ozone Monitoring Experiment-2). Both instruments are onboard the series of MetOp satellites (in orbit since 2006 and expected until 2022) and their scanning capabilities offer global coverage every day, with a relatively fine ground pixel resolution (12 km-diameter pixels spaced by 25 km for IASI at nadir). Our technique uses altitude-dependent Tikhonov–Phillips-type constraints, which optimize sensitivity to lower tropospheric ozone. It integrates the VLIDORT (Vector Linearized Discrete Ordinate Radiative Transfer) and KOPRA (Karlsruhe Optimized and Precise Radiative transfer Algorithm) radiative transfer codes for simulating UV reflectance and TIR radiance, respectively. We have used our method to analyse real observations over Europe during an ozone pollution episode in the summer of 2009. The results show that the multispectral synergism of IASI (TIR) and GOME-2 (UV) enables the observation of the spatial distribution of ozone plumes in the lowermost troposphere (LMT, from the surface up to 3 km a.s.l., above sea level), in good agreement with the CHIMERE regional chemistry-transport model. In this case study, when high ozone concentrations extend vertically above 3 km a.s.l., they are similarly observed over land by both the multispectral and IASI retrievals. On the other hand, ozone plumes located below 3 km a.s.l. are only clearly depicted by the multispectral retrieval (both over land and over ocean). This is achieved by a clear enhancement of sensitivity to ozone in the lowest atmospheric layers. The multispectral sensitivity in the LMT peaks at 2 to 2.5 km a.s.l. over land, while sensitivity for IASI or GOME-2 only peaks at 3 to 4 km a.s.l. at lowest (above the LMT). The degrees of freedom for the multispectral retrieval increase by 0.1 (40% in relative terms) with respect to IASI only retrievals for the LMT. Validations with ozonesondes (over Europe during summer 2009) show that our synergetic approach for combining IASI (TIR) and GOME-2 (UV) measurements retrieves lowermost tropospheric ozone with a mean bias of 1% and a precision of 16%, when smoothing by the retrieval vertical sensitivity (1% mean bias and 21% precision for direct comparisons).

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Adaptive Observation Error Inflation for Assimilating All-Sky Satellite Radiance

Monthly Weather Review ◽

10.1175/mwr-d-16-0257.1 ◽

2017 ◽

Vol 145 (3) ◽

pp. 1063-1081 ◽

Cited By ~ 39

Author(s):

Masashi Minamide ◽

Fuqing Zhang

Keyword(s):

Radiative Transfer ◽

System Simulation ◽

Error Variance ◽

Radiative Transfer Model ◽

Transfer Model ◽

Observation Error ◽

Simulation Experiments ◽

Observation Operator ◽

Brightness Temperatures ◽

Adaptive Observation

An empirical flow-dependent adaptive observation error inflation (AOEI) method is proposed for assimilating all-sky satellite brightness temperatures through observing system simulation experiments with an ensemble Kalman filter. The AOEI method adaptively inflates the observation error when the absolute difference (innovation) between the observed and simulated brightness temperatures is greater than the square root of the combined variance of the uninflated observational error variance and ensemble-estimated background error variance. This adaptive method is designed to limit erroneous analysis increments where there are large representativeness errors, as is often the case for cloudy-affected radiances, even if the forecast model and the observation operator (the radiative transfer model) are perfect. The promising performance of this newly proposed AOEI method is demonstrated through observing system simulation experiments assimilating all-sky brightness temperatures from GOES-R (now GOES-16) in comparison with experiments using an alternative empirical observation error inflation method proposed by Geer and Bauer. It is found that both inflation methods perform similarly in the accuracy of the analysis and in the containment of potential representativeness errors; both outperform experiments using a constant observation error without inflation. Besides being easier to implement, the empirical AOEI method proposed here also shows some advantage over the Geer–Bauer method in better updating variables at large scales. Large representative errors are likely to be compounded by unavoidable uncertainties in the forecast system and/or nonlinear observation operator (as for the radiative transfer model), in particular in the areas of moist processes, as will be the case for real-data cloudy radiances, which will be further investigated in future studies.

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