64 research outputs found
Insights into Tikhonov regularization: application to trace gas column retrieval and the efficient calculation of total column averaging kernels
Insights are given into Tikhonov regularization and its application
to the retrieval of vertical column densities of atmospheric trace
gases from remote sensing measurements. The study builds upon the
equivalence of the least-squares profile-scaling approach and
Tikhonov regularization method of the first kind with an infinite
regularization strength. Here, the vertical profile is expressed
relative to a reference profile. On the basis of this, we propose a
new algorithm as an extension of the least-squares profile scaling
which permits the calculation of total column averaging kernels on
arbitrary vertical grids using an analytic expression. Moreover, we
discuss the effective null space of the retrieval, which comprises
those parts of a vertical trace gas distribution which cannot be
inferred from the measurements.
Numerically the algorithm
can be implemented in a robust and efficient manner. In particular
for operational data processing with challenging demands on
processing time, the proposed inversion method in combination with
highly efficient forward models is an asset. For demonstration
purposes, we apply the algorithm to CO column retrieval from
simulated measurements in the 2.3 μm spectral region and
to O<sub>3</sub> column retrieval from the UV. These represent ideal
measurements of a series of spaceborne spectrometers such as
SCIAMACHY, TROPOMI, GOME, and GOME-2. For both spectral ranges, we
consider clear-sky and cloudy scenes where clouds are modelled as an
elevated Lambertian surface. Here, the smoothing error for the
clear-sky and cloudy atmosphere is significant and reaches several
percent, depending on the reference profile which is used for
scaling. This underlines the importance of the column averaging
kernel for a proper interpretation of retrieved column densities.
Furthermore, we show that the smoothing due to regularization can be
underestimated by calculating the column averaging kernel on a too
coarse vertical grid. For both retrievals, this effect becomes
negligible for a vertical grid with 20–40 equally thick layers
between 0 and 50 km
Sensitivity of PARASOL multi-angle photopolarimetric aerosol retrievals to cloud contamination
Estimation of aerosol water and chemical composition from AERONET Sun–sky radiometer measurements at Cabauw, the Netherlands
Remote sensing of aerosols provides important information on atmospheric
aerosol abundance. However, due to the hygroscopic nature of aerosol
particles observed aerosol optical properties are influenced by atmospheric
humidity, and the measurements do not unambiguously characterize the aerosol
dry mass and composition, which complicates the comparison with aerosol
models. In this study we derive aerosol water and chemical composition by a
modeling approach that combines individual measurements of remotely sensed
aerosol properties (e.g., optical thickness, single-scattering albedo,
refractive index and size distribution) from an AERONET (Aerosol Robotic
Network) Sun–sky radiometer with radiosonde measurements of relative
humidity. The model simulates water uptake by aerosols based on the chemical
composition (e.g., sulfates, ammonium, nitrate, organic matter and black
carbon) and size distribution. A minimization method is used to calculate
aerosol composition and concentration, which are then compared to in situ
measurements from the Intensive Measurement Campaign At the Cabauw Tower
(IMPACT, May 2008, the Netherlands). Computed concentrations show good
agreement with campaign-average (i.e., 1–14 May) surface observations (mean
bias is 3% for PM<sub>10</sub> and 4–25% for the individual compounds). They
follow the day-to-day (synoptic) variability in the observations and are in
reasonable agreement for daily average concentrations (i.e., mean bias is
5% for PM<sub>10</sub> and black carbon, 10% for the inorganic salts and
18% for organic matter; root-mean-squared deviations are 26% for
PM<sub>10</sub> and 35–45% for the individual compounds). The modeled water
volume fraction is highly variable and strongly dependent on composition.
During this campaign we find that it is >0.5 at approximately 80% relative humidity
(RH) when the aerosol composition is dominated by hygroscopic inorganic salts, and
<0.1 when RH is below 40%, especially when the composition is
dominated by less hygroscopic compounds such as organic matter. The
scattering enhancement factor (f(RH), the ratio of the scattering coefficient
at 85% RH and its dry value at 676 nm) during 1–14 May is
2.6 ± 0.5. The uncertainty in AERONET (real) refractive index
(0.025–0.05) is the largest source of uncertainty in the modeled aerosol
composition and leads to an uncertainty of 0.1–0.25 (50–100%) in aerosol
water volume fraction. Our methodology performs relatively well at Cabauw,
but a better performance may be expected for regions with higher aerosol
loading where the uncertainties in the AERONET inversions are smaller
Опухоли с невыявленным первичным очагом: современные подходы к лечению
Представлены современные методы и схемы лечения разных видов рака с невыясненным очагом и получаемые результаты.Contemporary methods of treatment of various types of cancer with unrevealed focus as well as the obtained results are described
The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO₂ and CH₄ retrieval algorithm products with measurements from the TCCON
Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs).
For XCO₂, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold.
For XCH₄, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH₄ precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively
Toward accurate CO_2 and CH_4 observations from GOSAT
The column-average dry air mole fractions of atmospheric carbon dioxide and methane (X_(CO_2) and X_(CH_4)) are inferred from observations of backscattered sunlight conducted by the Greenhouse gases Observing SATellite (GOSAT). Comparing the first year of GOSAT retrievals over land with colocated ground-based observations of the Total Carbon Column Observing Network (TCCON), we find an average difference (bias) of −0.05% and −0.30% for X_(CO_2) and X_(CH_4) with a station-to-station variability (standard deviation of the bias) of 0.37% and 0.26% among the 6 considered TCCON sites. The root-mean square deviation of the bias-corrected satellite retrievals from colocated TCCON observations amounts to 2.8 ppm for X_(CO_2) and 0.015 ppm for X_(CH_4). Without any data averaging, the GOSAT records reproduce general source/sink patterns such as the seasonal cycle of X_(CO_2) suggesting the use of the satellite retrievals for constraining surface fluxes
The impact of spectral resolution on satellite retrieval accuracy of CO_2 and CH_4
The Fourier-transform spectrometer on board the Japanese GOSAT (Greenhouse gases Observing SATellite) satellite offers an excellent opportunity to study the impact of instrument resolution on retrieval accuracy of CO_2 and CH_4. This is relevant to further improve retrieval accuracy and to optimize the cost–benefit ratio of future satellite missions for the remote sensing of greenhouse gases. To address this question, we degrade GOSAT measurements with a spectral resolution of ≈ 0.24 cm^(−1) step by step to a resolution of 1.5 cm^(−1). We examine the results by comparing relative differences at various resolutions, by referring the results to reference values from the Total Carbon Column Observing Network (TCCON), and by calculating and inverting synthetic spectra for which the true CO_2 and CH_4 columns are known. The main impacts of degrading the spectral resolution are reproduced for all approaches based on GOSAT measurements; pure forward model errors identified with simulated measurements are much smaller.
For GOSAT spectra, the most notable effect on CO_2 retrieval accuracy is the increase of the standard deviation of retrieval errors from 0.7 to 1.0% when the spectral resolution is reduced by a factor of six. The retrieval biases against atmospheric water abundance and air mass become stronger with decreasing resolution. The error scatter increase for CH_4 columns is less pronounced. The selective degradation of single spectral windows demonstrates that the retrieval accuracy of CO_2 and CH_4 is dominated by the spectral range where the absorption lines of the target molecule are located. For both GOSAT and synthetic measurements, retrieval accuracy decreases with lower spectral resolution for a given signal-to-noise ratio, suggesting increasing interference errors
Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach
This paper describes a neural network algorithm for the estimation of liquid
water cloud optical properties from the Polarization and Directionality of
Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization &
Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations
from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic
multi-angle, multi-wavelength measurements of reflectance and polarization
and has been applied to the processing of 1 year of POLDER-3 data.
Comparisons of the retrieved cloud properties with Moderate Resolution
Imaging Spectroradiometer (MODIS) products show that the neural network
algorithm has a low bias of around 2 in cloud optical thickness (COT) and
between 1 and 2 µm in the cloud effective radius. Comparisons with
existing POLDER-3 datasets suggest that the proposed scheme may have enhanced
capabilities for cloud effective radius retrieval, at least over land. An
additional feature of the presented algorithm is that it provides COT and
effective radius retrievals at the native POLDER-3 Level 1B pixel level.</p
Inverse modelling of CH4 emissions for 2010-2011 using different satellite retrieval products from GOSAT and SCIAMACHY
At the beginning of 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent methane (CH) retrievals were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on board the ENVironmental SATellite (ENVISAT). The GOSAT and SCIAMACHY XCH retrievals can be compared during the period of overlap. We estimate monthly average CH emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modelling system. In addition to satellite data, high-accuracy measurements from the Cooperative Air Sampling Network of the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) are used, providing strong constraints on the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC “Full- Physics” (FP) XCH retrievals available from SRON/KIT. The GOSAT-based inversions show significant reductions in the root mean square (rms) difference between retrieved and modelled XCH, and require much smaller bias corrections compared to the inversion using SCIAMACHY retrievals, reflecting the higher precision and relative accuracy of the GOSAT XCH. Despite the large differences between the GOSAT and SCIAMACHY retrievals, 2-year average emission maps show overall good agreement among all satellitebased inversions, with consistent flux adjustment patterns, particularly across equatorial Africa and North America. Over North America, the satellite inversions result in a significant redistribution of CH emissions from North-East to South-Central United States. This result is consistent with recent independent studies suggesting a systematic underestimation of CH emissions from North American fossil fuel sources in bottom-up inventories, likely related to natural gas production facilities. Furthermore, all four satellite inversions yield lower CH fluxes across the Congo basin compared to the NOAA-only scenario, but higher emissions across tropical East Africa. The GOSAT and SCIAMACHY inversions show similar performance when validated against independent shipboard and aircraft observations, and XCH retrievals available from the Total Carbon Column Observing Network (TCCON)
Mapping carbon monoxide pollution from space down to city scales with daily global coverage
On 13 October 2017, the European Space Agency (ESA) successfully
launched the Sentinel-5 Precursor satellite with the Tropospheric
Monitoring Instrument (TROPOMI) as its single payload. TROPOMI is
the first of ESA's atmospheric composition Sentinel missions, which
will provide complete long-term records of atmospheric trace gases
for the coming 30 years as a contribution to the European Union's
Earth Observing program Copernicus. One of TROPOMI's primary
products is atmospheric carbon monoxide (CO). It is observed with daily global
coverage and a high spatial resolution of 7×7 km2.
The moderate atmospheric resistance time and the low background
concentration leads to localized pollution hotspots of CO and allows
the tracking of the atmospheric transport of pollution on regional to global
scales. In this contribution, we
demonstrate the groundbreaking performance of the TROPOMI CO product, sensing
CO enhancements above cities and industrial areas and tracking, with
daily coverage, the atmospheric transport of pollution from biomass
burning regions. The CO data product is validated with two months
of Fourier-transform spectroscopy (FTS) measurements at nine
ground-based stations operated by the Total Carbon Column Observing
Network (TCCON). We found a good agreement between both datasets with a mean bias
of 6 ppb (average of individual station biases) for both clear-sky and
cloudy TROPOMI CO retrievals. Together with the corresponding
standard deviation of the individual station biases of 3.8 ppb for
clear-sky and 4.0 ppb for cloudy sky, it indicates that the CO data
product is already well within the mission requirement.</p
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