Spatio-Temporal Validation of AIRS CO2 Observations Using GAW, HIPPO and TCCON

Carbon dioxide (CO2) is a significant atmospheric greenhouse gas and its concentrations can be observed by in situ surface stations, aircraft flights and satellite sensors. This paper investigated the ability of the CO2 satellite observations to monitor, analyze and predict the horizontal and vertical distribution of atmospheric CO2 concentration at global scales. CO2 observations retrieved by an Atmospheric Infrared Sounder (AIRS) were inter-compared with the Global Atmosphere Watch Program (GAW) and HIAPER Pole-to-Pole Observations (HIPPOs), with reference to the measurements obtained using high-resolution ground-based Fourier Transform Spectrometers (FTS) in the Total Carbon Column Observing Network (TCCON) from near-surface level to the mid-to-high troposphere. After vertically integrating the AIRS-retrieved values with the column averaging kernels of TCCON measurements, the AIRS observations are spatio-temporally compared with HIPPO-integrated profiles in the mid-to-high troposphere. Five selected GAW stations are used for comparisons with TCCON sites near the surface of the Earth. The results of AIRS, TCCON (5–6 km), GAW and TCCON (1 km) CO2 measurements from 2007 to 2013 are compared, analyzed and discussed at their respective altitudes. The outcomes indicate that the difference of about 3.0 ppmv between AIRS and GAW or other highly accurate in situ surface measurements is mainly due to the different vertical altitudes, rather than the errors in the AIRS. The study reported here also explores the potential of AIRS satellite observations for analyzing the spatial distribution and seasonal variation of CO2 concentration at global scales.

Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the TanSat observations using the ACOS (Atmospheric CO2 Observations from Space) algorithm. The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for 2 years of TanSat data from 2017 to 2018. Based on the correlation of the XCO2 error over land with goodness of fit in three spectral bands at 0.76, 1.61 and 2.06 μm, we applied an a posteriori bias correction to TanSat retrievals. For overpass averaged results, XCO2 retrievals show a standard deviation (SD) of ~2.45 ppm and a positive bias of ~0.27 ppm compared to collocated TCCON sites. The validation also shows a relatively higher positive bias and variance against TCCON over high-latitude regions. Three cases to evaluate TanSat target mode retrievals are investigated, including one field campaign at Dunhuang with measurements by a greenhouse gas analyzer deployed on an unmanned aerial vehicle and two cases with measurements by a ground-based Fourier-transform spectrometer in Beijing. The results show the retrievals of all footprints, except footprint-6, have relatively low bias (within ~2 ppm). In addition, the orbital XCO2 distributions over Australia and Northeast China between TanSat and the second Orbiting Carbon Observatory (OCO-2) on 20 April 2017 are compared. It shows that the mean XCO2 from TanSat is slightly lower than that of OCO-2 with an average difference of ~0.85 ppm. A reasonable agreement in XCO2 distribution is found over Australia and Northeast China between TanSat and OCO-2.

Advanced Ultraviolet Radiation and Ozone Retrieval for Applications (AURORA): A Project Overview

With the launch of the Sentinel-5 Precursor (S-5P, lifted-off on 13 October 2017), Sentinel-4 (S-4) and Sentinel-5 (S-5)(from 2021 and 2023 onwards, respectively) operational missions of the ESA/EU Copernicus program, a massive amount of atmospheric composition data with unprecedented quality will become available from geostationary (GEO) and low Earth orbit (LEO) observations. Enhanced observational capabilities are expected to foster deeper insight than ever before on key issues relevant for air quality, stratospheric ozone, solar radiation, and climate. A major potential strength of the Sentinel observations lies in the exploitation of complementary information that originates from simultaneous and independent satellite measurements of the same air mass. The core purpose of the AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project is to investigate this exploitation from a novel approach for merging data acquired in different spectral regions from on board the GEO and LEO platforms. A data processing chain is implemented and tested on synthetic observations. A new data algorithm combines the ultraviolet, visible and thermal infrared ozone products into S-4 and S-5(P) fused profiles. These fused products are then ingested into state-of-the-art data assimilation systems to obtain a unique ozone profile in analyses and forecasts mode. A comparative evaluation and validation of fused products assimilation versus the assimilation of the operational products will seek to demonstrate the improvements achieved by the proposed approach. This contribution provides a first general overview of the project, and discusses both the challenges of developing a technological infrastructure for implementing the AURORA concept, and the potential for applications of AURORA derived products, such as tropospheric ozone and UV surface radiation, in sectors such as air quality monitoring and health.

Spectral modelling near the 1.6 μm window for satellite based estimation of CO2

Measurements of inter annual CO2 variability are important inputs for modelling global carbon cycle. Satellite observations play important role in quantification and modelling of CO2 fluxes in the atmosphere, where observed radiances in narrow spectral channels are used to estimate the trace gas concentration using spectroscopic principles. The 1.6 μm spectral window is important for CO2 detection and study of the two CO2 bands in this region is performed at different spectral resolutions. In order to select the optimum spectral resolution and wavelength positions, suitable for CO2 estimation from satellite platform, sensitivities of different spectral lines to changes in CO2 concentration are studied. Analysis is carried out using a line by line FASCOD radiative transfer model in tropical atmospheric and rural aerosol conditions. The CO2 concentration is varied from 200 to 1000 ppmv and spectral resolution is varied from 0.025 nm to 10 nm. It is observed that atmospheric transmittances reduce sharply with increase in CO2 concentration. With decrease in resolution initially the sensitivity steeply reduces but at resolutions lower than 0.15 nm the sensitivity remains nearly constant. The Continuum Interpolated Band Ratio method is used for inverse concentration retrieval. Based on the study it is evaluated that 0.2 nm is the optimum limit for resolution.

Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring

The Greenhouse Gases Observing Satellite (GOSAT) monitors carbon dioxide (CO(2)) and methane (CH(4)) globally from space using two instruments. The Thermal and Near Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) detects gas absorption spectra of the solar short wave infrared (SWIR) reflected on the Earth's surface as well as of the thermal infrared radiated from the ground and the atmosphere. TANSO-FTS is capable of detecting three narrow bands (0.76, 1.6, and 2.0 microm) and a wide band (5.5-14.3 microm) with 0.2 cm(-1) spectral resolution (interval). The TANSO Cloud and Aerosol Imager (TANSO-CAI) is an ultraviolet (UV), visible, near infrared, and SWIR radiometer designed to detect cloud and aerosol interference and to provide the data for their correction. GOSAT is placed in a sun-synchronous orbit 666 km at 13:00 local time, with an inclination angle of 98 degrees . A brief overview of the GOSAT project, scientific requirements, instrument designs, hardware performance, on-orbit operation, and data processing is provided.

An inversion technique to retrieve the source of a tracer with an application to synthetic satellite measurements

An increasing number of satellites are being launched to observe the atmospheric concentrations of a variety of trace species. They cover a wide area at once and are expected to provide more extensive information than the rare ground-based concentration measurements. The paper introduces an adjoint technique to retrieve the emissions based on a recent concept of renormalization. This technique is used with a set of synthetic column-averaged measurements for an idealized satellite corresponding to a prescribed ground-level source. The Indian region is considered with two contrast meteorological conditions in the months of January and July, corresponding to winter and monsoon season. Since it is not feasible to handle a large volume of satellite data in the inversion due to the time involved in the computation of the matrices, a preprocessing is suggested to extract the manageable data set as a representative of the whole data.

Considering a limited number of observations, it is shown that the emissions are underestimated without and with the renormalization procedure. The degree of underestimation is relatively more with non-renormalized estimates. The non-renormalized estimate is degraded further by a refined resolution of the model, whereas the renormalized estimate is not altered appreciably. The preprocessing based on aggregation of data is found to retrieve the prescribed emissions up to 75% in the month of January and 90% in the month of July. The relatively computationally expensive renormalization may be avoided except in the case of partial visibility of the area of interest, due to cloud cover or a technical constraint. A simple criterion for the optimum design of a monitoring network is suggested.