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Measurements of the atmospheric HDO/H2O ratio help us to better understand the hydrological cycle and improve models to correctly simulate tropospheric humidity and therefore climate change. We present an updated version of the column-averaged HDO/H2O ratio data set from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The data set is extended with 2 additional years, now covering 2003-2007, and is validated against co-located ground-based total column δD measurements from Fourier transform spectrometers (FTS) of the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC, produced within the framework of the MUSICA project). Even though the time overlap among the available data is not yet ideal, we determined a mean negative bias in SCIAMACHY δD of -35±30∞ compared to TCCON and -69±15∞ compared to MUSICA (the uncertainty indicating the stationto- station standard deviation). The bias shows a latitudinal dependency, being largest (~-60 to -80 ∞) at the highest latitudes and smallest (~-20 to -30 ∞) at the lowest latitudes. We have tested the impact of an offset correction to the SCIAMACHY HDO and H2O columns. This correction leads to a humidity- and latitude-dependent shift in δD and an improvement of the bias by 27 ∞, although it does not lead to an improved correlation with the FTS measurements nor to a strong reduction of the latitudinal dependency of the bias. The correction might be an improvement for dry, high-altitude areas, such as the Tibetan Plateau and the Andes region. For these areas, however, validation is currently impossible due to a lack of ground stations. The mean standard deviation of single-sounding SCIAMACHY-FTS differences is ~115 ∞, which is reduced by a factor ~ 2 when we consider monthly means. When we relax the strict matching of individual measurements and focus on the mean seasonalities using all available FTS data, we find that the correlation coefficients between SCIAMACHY and the FTS networks improve from 0.2 to 0.7-0.8. Certain ground stations show a clear asymmetry in δD during the transition from the dry to the wet season and back, which is also detected by SCIAMACHY. This asymmetry points to a transition in the source region temperature or location of the water vapour and shows the added information that HDO/H2O measurements provide when used in combination with variations in humidity. [ABSTRACT FROM AUTHOR]

Global monitoring of sun-induced chlorophyll fluorescence (SIF) is improving our knowledge about the photosynthetic functioning of terrestrial ecosystems. The feasibility of SIF retrievals from spaceborne atmospheric spectrometers has been demonstrated by a number of studies in the last years. In this work, we investigate the potential of the upcoming TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite mission for SIF retrieval. TROPOMI will sample the 675-775 nm spectral window with a spectral resolution of 0.5 nm and a pixel size of 7 km×7 km. We use an extensive set of simulated TROPOMI data in order to assess the uncertainty of single SIF retrievals and subsequent spatio-temporal composites. Our results illustrate the enormous improvement in SIF monitoring achievable with TROPOMI with respect to comparable spectrometers currently in-flight, such as the Global Ozone Monitoring Experiment-2 (GOME-2) instrument. We find that TROPOMI can reduce global uncertainties in SIF mapping by more than a factor of 2 with respect to GOME-2, which comes together with an approximately 5-fold improvement in spatial sampling. Finally, we discuss the potential of TROPOMI to map other important vegetation parameters at a global scale with moderate spatial resolution and short revisit time. Those include leaf photosynthetic pigments and proxies for canopy structure, which will complement SIF retrievals for a self-contained description of vegetation condition and functioning. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory-2 (OCO-2), launched on 2 July 2014, is a NASA mission designed to measure the column-averaged CO2 dry air mole fraction, XCO2. Towards that goal, it will collect spectra of reflected sunlight in narrow spectral ranges centered at 0.76, 1.6 and 2.0 μm with a resolving power (λ/Δλ) of 20 000. These spectra will be used in an optimal estimation framework to retrieve XCO2. About 100000 cloud free soundings of XCO2 each day will allow estimates of net CO2 fluxes on regional to continental scales to be determined. Here, we evaluate the OCO-2 spectrometer performance using pre-launch data acquired during instrument thermal vacuum tests in April 2012. A heliostat and a diffuser plate were used to feed direct sunlight into the OCO-2 instrument and spectra were recorded. These spectra were compared to those collected concurrently from a nearby high-resolution Fourier Transform Spectrometer that was part of the Total Carbon Column Observing Network (TCCON). Using the launch-ready OCO-2 calibration and spectroscopic parameters, we performed total column scaling fits to all spectral bands and compared these to TCCON results. On 20 April, we detected a CO2 plume from the Los Angeles basin at the JPL site with strongly enhanced short-term variability on the order of 1 % (3-4 ppm). We also found good (< 0.5 ppm) inter-footprint consistency in retrieved XCO2. The variations in spectral fitting residuals are consistent with signal-to-noise estimates from instrument calibration, while average residuals are systematic and mostly attributable to remaining errors in our knowledge of the CO2 and O2 spectroscopic parameters. A few remaining inconsistencies observed during the tests may be attributable to the specific instrument setup on the ground and will be re-evaluated with in-orbit data. [ABSTRACT FROM AUTHOR]

Two quantitative retrieval techniques were evaluated to estimate methane (CH4) enhancement in concentrated plumes using high spatial and moderate spectral resolution data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). An iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) algorithm performed well for an ocean scene containing natural CH4 emissions from the Coal Oil Point (COP) seep field near Santa Barbara, California. IMAP-DOAS retrieval precision errors are expected to equal between 0.31 to 0.61 ppm CH4 over the lowest atmospheric layer (height up to 1.04 km), corresponding to about a 30 to 60 ppm error for a 10m thick plume. However, IMAP-DOAS results for a terrestrial scene were adversely influenced by the underlying land cover. A hybrid approach using singular value decomposition (SVD) was particularly effective for terrestrial surfaces because it could better account for spectral variability in surface reflectance. Using this approach, a CH4 plume was observed extending 0.1 km downwind of two hydrocarbon storage tanks at the Inglewood Oil Field in Los Angeles, California (USA) with a maximum near surface enhancement of 8.45 ppm above background. At COP, the distinct plume had a maximum enhancement of 2.85 ppm CH4 above background, and extended more than 1 km downwind of known seep locations. A sensitivity analysis also indicates CH4 sensitivity should be more than doubled for the next generation AVIRIS sensor (AVIRISng) due to improved spectral resolution and sampling. AVIRIS-like sensors offer the potential to better constrain emissions on local and regional scales, including sources of increasing concern like industrial point source emissions and fugitive CH4 from the oil and gas industry. [ABSTRACT FROM AUTHOR]

Many modern instruments generate more data than may be fully processed in a timely manner. For some atmospheric sounders, much of the raw data cannot be processed into meaningful observations due to suboptimal viewing conditions, such as the presence of clouds. Conventional solutions are quick, empirical-threshold filters hand-created by domain experts to weed out unlikely or unreasonable observations, coupled with randomized down sampling when the data volume is still too high. In this paper, we describe a method for the construction of a subsampling and ordering solution that maximizes the likelihood that a requested data subset will be usefully processed. The method can be used for any metadata-rich source and implicitly discerns informative vs. non-informative data features while still permitting user feedback into the final features selected for filter implementation. We demonstrate the method by creating a selector for the spectra of the Japanese GOSAT satellite designed to measure column averaged mixing ratios of greenhouse gases including carbon dioxide (CO2). This is done within the Atmospheric CO2 Measurements from Space (ACOS) NASA project with the intention of eventual use during the early Orbiting Carbon Observatory-2 (OCO-2) mission. OCO-2 will have a 1.5 orders of magnitude larger data volume than ACOS, requiring intelligent pre-filtration. [ABSTRACT FROM AUTHOR]

Remote sensing of the isotopic composition of water vapor can provide valuable information on the hydrological cycle. Here, we demonstrate the feasibility of retrievals of the relative abundance of HDO (the HDO/H2O ratio) from the Japanese GOSAT satellite. For this purpose, we use high spectral resolution nadir radiances around 6400 cm-1 (1.56 μm) to retrieve vertical column amounts of H2O and HDO. Retrievals of H2O correlate well with ECMWF (European Centre for Medium-Range Weather Forecasts) integrated profiles (r2 = 0.96). Typical precision errors in the retrieved column-averaged deuterium depletion (δD) are 20-40 ‰.We compare δD against a TCCON (Total Carbon Column Observing Network) ground-based station in Lamont, Oklahoma. Using retrievals in very dry areas over Antarctica, we detect a small systematic offset in retrieved H2O and HDO column amounts and take this into account for a bias correction of δD. Monthly averages of δD in the June 2009 to September 2011 time frame are well correlated with TCCON (r2 = 0.79) and exhibit a slope of 0.98 (1.23 if not bias corrected). We also compare seasonal averages on the global scale with results from the SCIAMACHY instrument in the 2003-2005 time frame. Despite the lack of temporal overlap, seasonal averages in general agree well, with spatial correlations (r2) ranging from 0.62 in September through November to 0.83 in June through August. However, we observe higher variability in GOSAT δD, indicated by fitted slopes between 1.2 and 1.46. The discrepancies are likely related to differences in vertical sensitivities but warrant further validation of both GOSAT and SCIAMACHY and an extension of the validation dataset. [ABSTRACT FROM AUTHOR]

Monitoring the global distribution and long-term variations of CO2 sources and sinks is required for characterizing the global carbon budget. Total column measurements are useful for estimating regional-scale fluxes; however, model transport remains a significant error source, particularly for quantifying local sources and sinks. To improve the capability of estimating regional fluxes, we estimate lower tropospheric CO2 concentrations from groundbased near-infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. The NIR measurements are obtained from the Total Carbon Column Observing Network (TCCON) of solar measurements, which provide an estimate of the total CO2 column amount. Estimates of tropospheric CO2 that are co-located with TCCON are obtained by assimilating Tropospheric Emission Spectrometer (TES) free tropospheric CO2 estimates into the GEOS-Chem model.We find that quantifying lower tropospheric CO2 by subtracting free tropospheric CO2 estimates from total column estimates is a linear problem, because the calculated random uncertainties in total column and lower tropospheric estimates are consistent with actual uncertainties as compared to aircraft data. For the total column estimates, the random uncertainty is about 0.55 ppm with a bias of -5.66 ppm, consistent with previously published results. After accounting for the total column bias, the bias in the lower tropospheric CO2 estimates is 0.26 ppm with a precision (one standard deviation) of 1.02 ppm. This precision is sufficient for capturing the winter to summer variability of approximately 12 ppm in the lower troposphere; double the variability of the total column. This work shows that a combination of NIR and TIR measurements can profile CO2 with the precision and accuracy needed to quantify lower tropospheric CO2 variability. [ABSTRACT FROM AUTHOR]

The article focuses on the application of remote sensing on chlorophyll fluorescence in scattering atmospheres. It says that the remote sensing was used to understand the effect of chlorophyll fluorescence to the retrieval of greenhouse gas and its interference with retrievals of atmospheric carbon dioxide (CO2). It states that retrieval of fluorescence could help prevent biases in the inverting of fluxes.

The article presents a study that uses multiple satellite viewing angles to investigate the combined aerosol and greenhouse gas retrieval. The study suggests that the method improves the ability to retrieve aerosol properties by 2 to 3 degrees of freedom. It also reveals that the improved capability to retrieve aerosol parameters significantly reduces interference errors introduced into retrieved CO2 and CH4 total column averages.

The article presents a study on column averaged carbon dioxide dry air mole fraction which were obtained from spectra recorded over land by the Greenhouse gases Observing Satellite. It discusses how the study was conducted which involves several retrieval methods developed for the U.S. National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory mission. It explores the study's findings including bias in surface pressure.

The article focuses on the changes to the vertical resolution and uncertainties of the tropospheric emission spectrometer (TES) version 5 retrieval algorithm of H2O, HDO, CH4 and nitrous oxide (N2O). It notes that the water estimates demonstrate enhanced vertical resolution in the lower troposphere and boundary layer. Moreover, it mentions that the new retrievals shows sensitivity to methane in the free troposphere.

The article presents a study which explores the Atmospheric CO2 Observations from Space (ACOS) X CO2 retrieval algorithm of the U.S. National Aeronautics and Space Administration (NASA). Realistic simulations were used to evaluate systematic and random errors in the retrieved ACOS algorithms, such as the impacts of realistic cloud screening and post-retrieval filters. The study suggests that post-retrieval filters are essential in eliminating poorest retrievals.