23 research outputs found
Optimization of the photon path length probability density function-simultaneous (PPDF-S) method and evaluation of CO 2 retrieval performance under dense aerosol conditions
The photon path length probability density function-simultaneous (PPDF-S) algorithm is effective for retrieving column-averaged concentrations of carbon dioxide (XCO 2 ) and methane (XCH 4 ) from Greenhouse gases Observing Satellite (GOSAT) spectra in Short Wavelength InfraRed (SWIR). Using this method, light-path modification attributable to light reflection/scattering by atmospheric clouds/aerosols is represented by the modification of atmospheric transmittance according to PPDF parameters. We optimized PPDF parameters for a more accurate XCO 2 retrieval under aerosol dense conditions based on simulation studies for various aerosol types and surface albedos. We found a more appropriate value of PPDF parameters referring to the vertical profile of CO 2 concentration as a measure of a stable solution. The results show that the constraint condition of a PPDF parameter that represents the light reflectance effect by aerosols is sufficiently weak to affect XCO 2 adversely. By optimizing the constraint, it was possible to obtain a stable solution of XCO 2 . The new optimization was applied to retrieval analysis of the GOSAT data measured in Western Siberia. First, we assumed clear sky conditions and retrieved XCO 2 from GOSAT data obtained near Yekaterinburg in the target area. The retrieved XCO 2 was validated through a comparison with ground-based Fourier Transform Spectrometer (FTS) measurements made at the Yekaterinburg observation site. The validation results showed that the retrieval accuracy was reasonable. Next, we applied the optimized method to dense aerosol conditions when biomass burning was active. The results demonstrated that optimization enabled retrieval, even under smoky conditions, and that the total number of retrieved data increased by about 70%. Furthermore, the results of the simulation studies and the GOSAT data analysis suggest that atmospheric aerosol types that affected CO 2 analysis are identifiable by the PPDF parameter value. We expect that we will be able to suggest a further improved algorithm after the atmospheric aerosol types are identified. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.Russian Science Foundation: 18-11-00024Acknowledgments: The v3.0 ACOS/OCO-2 absorption coefficient (ABSCO) tables, used for the calculation of gas absorption coefficients, were provided by NASA and the ACOS/OCO-2 project. Vyacheslav Zakharov, Konstantin Gribanov, and Nikita Rokotyan thank the Russian Science Foundation for support of their research under the framework of grant 18-11-00024
Latitudinal distribution of the deuterium to hydrogen ratio in the atmospheric water vapor retrieved from IMG/ADEOS data
A posteriori calculation of delta O-18 and delta D in atmospheric water vapour from ground-based near-infrared FTIR retrievals of (H2O)-O-16, H2O)-O-18, and (HDO)-O-16
A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 2: Optimization scheme and identical twin experiment of atmospheric CO<sub>2</sub> inversion
A four-dimensional variational method (4D-Var) is a popular technique for
source/sink inversions of atmospheric constituents, but it is not without
problems. Using an icosahedral grid transport model and the 4D-Var method, a
new atmospheric greenhouse gas (GHG) inversion system has been developed. The
system combines offline forward and adjoint models with a quasi-Newton
optimization scheme. The new approach is then used to conduct identical twin
experiments to investigate optimal system settings for an atmospheric
CO2 inversion problem, and to demonstrate the validity of the new
inversion system. In this paper, the inversion problem is simplified by
assuming the prior flux errors to be reasonably well known and by designing
the prior error correlations with a simple function as a first step. It is
found that a system of forward and adjoint models with smaller model errors
but with nonlinearity has comparable optimization performance to that of
another system that conserves linearity with an exact adjoint relationship.
Furthermore, the effectiveness of the prior error correlations is
demonstrated, as the global error is reduced by about 15 % by adding
prior error correlations that are simply designed when 65 weekly flask
sampling observations at ground-based stations are used. With the optimal
setting, the new inversion system successfully reproduces the spatiotemporal
variations of the surface fluxes, from regional (such as biomass burning) to
global scales. The optimization algorithm introduced in the new system does
not require decomposition of a matrix that establishes the correlation among
the prior flux errors. This enables us to design the prior error covariance
matrix more freely
Algorithm update of the GOSAT/TANSO-FTS thermal infrared CO<sub>2</sub> product (version 1) and validation of the UTLS CO<sub>2</sub> data using CONTRAIL measurements
The Thermal and Near Infrared Sensor for Carbon Observation (TANSO)–Fourier
Transform Spectrometer (FTS) on board the Greenhouse Gases Observing
Satellite (GOSAT) has been observing carbon dioxide (CO<sub>2</sub>)
concentrations in several atmospheric layers in the thermal infrared (TIR)
band since its launch. This study compared TANSO-FTS TIR version 1 (V1) CO<sub>2</sub> data
and CO<sub>2</sub> data obtained in the Comprehensive Observation Network for
TRace gases by AIrLiner (CONTRAIL) project in the upper troposphere and
lower stratosphere (UTLS), where the TIR band of TANSO-FTS is most sensitive
to CO<sub>2</sub> concentrations, to validate the quality of the TIR V1 UTLS
CO<sub>2</sub> data from 287 to 162 hPa. We first evaluated the impact of
considering TIR CO<sub>2</sub> averaging kernel functions on CO<sub>2</sub>
concentrations using CO<sub>2</sub> profile data obtained by the CONTRAIL
Continuous CO<sub>2</sub> Measuring Equipment (CME), and found that the impact at
around the CME level flight altitudes (∼ 11 km) was on average
less than 0.5 ppm at low latitudes and less than 1 ppm at middle and high
latitudes. From a comparison made during flights between Tokyo and Sydney,
the averages of the TIR upper-atmospheric CO<sub>2</sub> data were within 0.1 %
of the averages of the CONTRAIL CME CO<sub>2</sub> data with and without TIR
CO<sub>2</sub> averaging kernels for all seasons in the Southern Hemisphere. The
results of comparisons for all of the eight airline routes showed that the
agreements of TIR and CME CO<sub>2</sub> data were worse in spring and summer than
in fall and winter in the Northern Hemisphere in the upper troposphere.
While the differences between TIR and CME CO<sub>2</sub> data were on average
within 1 ppm in fall and winter, TIR CO<sub>2</sub> data had a negative bias up to
2.4 ppm against CME CO<sub>2</sub> data with TIR CO<sub>2</sub> averaging kernels at the
northern low and middle latitudes in spring and summer. The negative bias at
the northern middle latitudes resulted in the maximum of TIR CO<sub>2</sub>
concentrations being lower than that of CME CO<sub>2</sub> concentrations, which
led to an underestimate of the amplitude of CO<sub>2</sub> seasonal variation
Bias assessment of lower and middle tropospheric CO<sub>2</sub> concentrations of GOSAT/TANSO-FTS TIR version 1 product
CO2 observations in the free troposphere can be useful for constraining
CO2 source and sink estimates at the surface since they represent
CO2 concentrations away from point source emissions. The thermal
infrared (TIR) band of the Thermal and Near Infrared Sensor for Carbon
Observation (TANSO) Fourier transform spectrometer (FTS) on board the
Greenhouse Gases Observing Satellite (GOSAT) has been observing global
CO2 concentrations in the free troposphere for about 8 years and thus
could provide a dataset with which to evaluate the vertical transport of
CO2 from the surface to the upper atmosphere. This study evaluated
biases in the TIR version 1 (V1) CO2 product in the lower troposphere
(LT) and the middle troposphere
(MT) (736–287 hPa), on the basis of
comparisons with CO2 profiles obtained over airports using Continuous
CO2 Measuring Equipment (CME) in the Comprehensive Observation Network
for Trace gases by AIrLiner (CONTRAIL) project. Bias-correction values are
presented for TIR CO2 data for each pressure layer in the LT and MT
regions during each season and in each latitude band: 40–20° S, 20° S–20° N, 20–40° N, and 40–60° N.
TIR V1 CO2
data had consistent negative biases of 1–1.5 % compared with CME CO2
data in the LT and MT regions, with the largest negative biases at 541–398 hPa, partly due to the use of 10 µm CO2 absorption band in
conjunction with 15 and 9 µm absorption bands in the V1
retrieval algorithm. Global comparisons between TIR CO2 data to which
the bias-correction values were applied and CO2 data simulated by
a transport model based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM-TM) confirmed the validity of the bias-correction values evaluated over
airports in limited areas. In low latitudes in the upper MT region (398–287 hPa), however, TIR CO2 data in northern summer were overcorrected by
these bias-correction values; this is because the bias-correction values were
determined using comparisons mainly over airports in Southeast Asia, where
CO2 concentrations in the upper atmosphere display relatively large
variations due to strong updrafts
Imposing strong constraints on tropical terrestrial CO2 fluxes using passenger aircraft based measurements
A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 1: Offline forward and adjoint transport models
A
four-dimensional variational (4D-Var) method is a popular algorithm for
inverting atmospheric greenhouse gas (GHG) measurements. In order to meet the
computationally intense 4D-Var iterative calculation, offline forward and
adjoint transport models are developed based on the Nonhydrostatic
ICosahedral Atmospheric Model (NICAM). By introducing flexibility into the
temporal resolution of the input meteorological data, the forward model
developed in this study is not only computationally efficient, it is also
found to nearly match the transport performance of the online model. In a
transport simulation of atmospheric carbon dioxide (CO2), the
data-thinning error (error resulting from reduction in the time resolution of
the meteorological data used to drive the offline transport model) is
minimized by employing high temporal resolution data of the vertical
diffusion coefficient; with a low 6-hourly temporal resolution, significant
concentration biases near the surface are introduced. The new adjoint model
can be run in discrete or continuous adjoint mode for the advection process.
The discrete adjoint is characterized by perfect adjoint relationship with
the forward model that switches off the flux limiter, while the continuous
adjoint is characterized by an imperfect but reasonable adjoint relationship
with its corresponding forward model. In the latter case, both the forward
and adjoint models use the flux limiter to ensure the monotonicity of tracer
concentrations and sensitivities. Trajectory analysis for high CO2
concentration events are performed to test adjoint sensitivities. We also
demonstrate the potential usefulness of our adjoint model for diagnosing
tracer transport. Both the offline forward and adjoint models have
computational efficiency about 10 times higher than the online model. A
description of our new 4D-Var system that includes an optimization method,
along with its application in an atmospheric CO2 inversion and the effects
of using either the discrete or continuous adjoint method, is presented in an
accompanying paper Niwa et al.(2016)
A posteriori calculation of delta18O and deltaD in atmospheric water vapour from ground-based near-infrared FTIR retrievals of H2 16 O, H 2 18 O, and HD 16 O
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