•A new method to fill missing surface albedo data over the Arctic sea ice is presented.•Gradient boosting based albedo estimates outperform alternative estimates.•Microwave based data are shown to be ...a useful additional model input.
Surface albedo is a necessary parameter for climate studies and modeling. There is a need for a full spatial coverage of albedo data, but clouds and high solar zenith angle cause missing values to the optical satellite products, especially around the polar areas. Therefore, our motivation is to develop gap filling models. For that purpose, we will apply monthly gradient boosting (GB) based models to the Arctic sea ice area of the 34 years long albedo time series of the Satellite Application Facility on Climate Monitoring (CM SAF) project. We demonstrate the ability of the GB models to accurately fill missing data using albedo monthly mean, brightness temperature, and sea ice concentration as model inputs. Monthly GB models produce the most unbiased, precise, and robust estimates when compared to alternative estimates presented, such as monthly mean albedo values or estimates from monthly linear regression (LR) models. The mean relative differences between GB based estimates and original pentad values vary from −20% to 20% with RMSE being 0.048, compared to relative differences varying from −20% to over 60% with RMSE varying from 0.054 to 0.074 between other estimates and original pentad values. Pixelwise mean differences and standard deviations (std) over the whole Arctic sea ice area show that GB based estimates are more accurate (mean differences from −0.02 to 0.02) and more precise (std from 0.02 to 0.08) than other estimates (mean differences varying between −0.05 to over 0.05, and std varying from around 0.03 to over 0.1). Also, albedo of the melting sea ice is predicted better by the GB model, with negligible mean differences, compared to the LR model. Based on these results, we show that GB method is a useful technique to fill missing data, and the brightness temperature and sea ice concentration are useful additional model input data sources.
This article presents a method within a Bayesian framework for quantifying uncertainty in satellite aerosol remote sensing when retrieving aerosol optical depth (AOD). By using a Bayesian model ...averaging technique, we take into account uncertainty in aerosol optical model selection and also obtain a shared inference about AOD based on the best-fitting optical models. In particular, uncertainty caused by forward-model approximations has been taken into account in the AOD retrieval process to obtain a more realistic uncertainty estimate. We evaluated a model discrepancy, i.e., forward-model uncertainty, empirically by exploiting the residuals of model fits and using a Gaussian process to characterise the discrepancy. We illustrate the method with examples using observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite. We evaluated the results against ground-based remote sensing aerosol data from the Aerosol Robotic Network (AERONET).
China’s first carbon dioxide (CO
2
) measurement satellite mission, TanSat, was launched in December 2016. This paper introduces the first attempt to detect anthropogenic CO
2
emission signatures ...using CO
2
observations from TanSat and NO
2
measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S5P) satellite. We focus our analysis on two selected cases in Tangshan, China and Tokyo, Japan. We found that the TanSat XCO
2
measurements have the capability to capture the anthropogenic variations in the plume and have spatial patterns similar to that of the TROPOMI NO
2
observations. The linear fit between TanSat XCO
2
and TROPOMI NO
2
indicates the CO
2
-to-NO
2
ratio of 0.8 × 10
−16
ppm (molec cm
−2
)
−1
in Tangshan and 2.3 × 10
−16
ppm (molec cm
−2
)
−1
in Tokyo. Our results align with the CO
2
-to-NO
x
emission ratios obtained from the EDGAR v6 emission inventory.
Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high ...latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (XCH4) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH4 fluxes using the CarbonTracker Europe-CH4 (CTE-CH4) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research’s (SRON) operational and University of Bremen’s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH4 emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher CH4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates.
Atmospheric effects, especially aerosols, are a significant source of uncertainty for optical remote sensing of surface parameters, such as albedo. Also to achieve a homogeneous surface albedo time ...series, the atmospheric correction has to be homogeneous. However, a global homogeneous aerosol optical depth (AOD) time series covering several decades did not previously exist. Therefore, we have constructed an AOD time series 1982–2014 using aerosol index (AI) data from the satellite measurements of the Total Ozone Mapping Spectrometer (TOMS) and the Ozone Monitoring Instrument (OMI), together with the Solar zenith angle and land use classification data. It is used as input for the Simplified Method for Atmospheric Correction (SMAC) algorithm when processing the surface albedo time series CLARA-A2 SAL (the Surface ALbedo from the Satellite Application Facility on Climate Monitoring project cLoud, Albedo and RAdiation data record, the second release). The surface reflectance simulations using the SMAC algorithm for different sets of satellite-based AOD data show that the aerosol-effect correction using the constructed TOMS/OMI based AOD data is comparable to using other satellite-based AOD data available for a shorter time range. Moreover, using the constructed TOMS/OMI based AOD as input for the atmospheric correction typically produces surface reflectance -20values closer to those obtained using in situ AOD values than when using other satellite-based AOD data.
Monitoring Greenhouse Gases from Space Boesch, Hartmut; Liu, Yi; Tamminen, Johanna ...
Remote sensing (Basel, Switzerland),
07/2021, Volume:
13, Issue:
14
Journal Article
Peer reviewed
Open access
The increase in atmospheric greenhouse gas concentrations of CO2 and CH4, due to human activities, is the main driver of the observed increase in surface temperature by more than 1 °C since the ...pre-industrial era. At the 2015 United Nations Climate Change Conference held in Paris, most nations agreed to reduce greenhouse gas emissions to limit the increase in global surface temperature to 1.5 °C. Satellite remote sensing of CO2 and CH4 is now well established thanks to missions such as NASA’s OCO-2 and the Japanese GOSAT missions, which have allowed us to build a long-term record of atmospheric GHG concentrations from space. They also give us a first glimpse into CO2 and CH4 enhancements related to anthropogenic emission, which helps to pave the way towards the future missions aimed at a Monitoring & Verification Support (MVS) capacity for the global stock take of the Paris agreement. China plays an important role for the global carbon budget as the largest source of anthropogenic carbon emissions but also as a region of increased carbon sequestration as a result of several reforestation projects. Over the last 10 years, a series of projects on mitigation of carbon emission has been started in China, including the development of the first Chinese greenhouse gas monitoring satellite mission, TanSat, which was successfully launched on 22 December 2016. Here, we summarise the results of a collaborative project between European and Chinese teams under the framework of the Dragon-4 programme of ESA and the Ministry of Science and Technology (MOST) to characterize and evaluate the datasets from the TanSat mission by retrieval intercomparisons and ground-based validation and to apply model comparisons and surface flux inversion methods to TanSat and other CO2 missions, with a focus on China.
Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements. Previous ...research in comparing MCMC and Optimal Estimation (OE) for the OCO-2 retrieval has highlighted the issues of slow convergence of MCMC, and furthermore OE and MCMC not necessarily agreeing with the simulated ground truth. In this work, we exploit the inherent low information content of the OCO-2 measurement and use the Likelihood-Informed Subspace (LIS) dimension reduction to significantly speed up the convergence of MCMC. We demonstrate the strength of this analysis method by assessing the non-Gaussian shape of the retrieval’s posterior distribution, and the effect of operational OCO-2 prior covariance’s aerosol parameters on the retrieval. We further show that in our test cases we can use this analysis to improve the retrieval to retrieve the simulated true state significantly more accurately and to characterize the non-Gaussian form of the posterior distribution of the retrieval problem.
The TROPOMI surface UV algorithm Lindfors, Anders V; Kujanpää, Jukka; Kalakoski, Niilo ...
Atmospheric measurement techniques,
02/2018, Volume:
11, Issue:
2
Journal Article
Peer reviewed
Open access
The TROPOspheric Monitoring Instrument (TROPOMI) is the only payload of the
Sentinel-5 Precursor (S5P), which is a polar-orbiting satellite mission of
the European Space Agency (ESA). TROPOMI is ...a nadir-viewing spectrometer
measuring in the ultraviolet, visible, near-infrared, and the shortwave
infrared that provides near-global daily coverage. Among other things,
TROPOMI measurements will be used for calculating the UV radiation reaching
the Earth's surface. Thus, the TROPOMI surface UV product will contribute to
the monitoring of UV radiation by providing daily information on the
prevailing UV conditions over the globe. The TROPOMI UV algorithm builds on
the heritage of the Ozone Monitoring Instrument (OMI) and the Satellite
Application Facility for Atmospheric Composition and UV Radiation (AC SAF)
algorithms. This paper provides a description of the algorithm that will be
used for estimating surface UV radiation from TROPOMI observations. The
TROPOMI surface UV product includes the following UV quantities: the UV
irradiance at 305, 310, 324, and 380 nm; the erythemally weighted UV;
and the vitamin-D weighted UV. Each of these are available as (i) daily dose or
daily accumulated irradiance, (ii) overpass dose rate or irradiance, and
(iii) local noon dose rate or irradiance. In addition, all quantities are
available corresponding to actual cloud conditions and as clear-sky values,
which otherwise correspond to the same conditions but assume a cloud-free
atmosphere. This yields 36 UV parameters altogether. The TROPOMI UV algorithm
has been tested using input based on OMI and the Global Ozone Monitoring
Experiment-2 (GOME-2) satellite measurements. These preliminary results
indicate that the algorithm is functioning according to expectations.
Since the Paris Agreement was adopted in 2015, the role of space-based observations for monitoring anthropogenic greenhouse gas (GHG) emissions has increased. To meet the requirements for monitoring ...carbon dioxide (CO
2
) emissions, the European Copernicus programme is preparing a dedicated CO
2
Monitoring (CO2M) satellite constellation that will provide CO
2
and nitrogen dioxide (NO
2
) observations at 4 km
2
resolution along a 250 km wide swath. In this paper, we adapt the recently developed divergence method to derive both CO
2
and nitrogen oxide (NO
x
) emissions of cities and power plants from a CO2M satellite constellation by using synthetic observations from the COSMO-GHG model. Due to its long lifetime, the large CO
2
atmospheric background needs to be removed to highlight the anthropogenic enhancements before calculating the divergence. Since the CO
2
noise levels are large compared to the anthropogenic enhancements, we apply different denoising methods and compare the effect on the CO
2
emission estimates. The annual NO
x
and CO
2
emissions estimated from the divergence maps using the peak fitting approach are in agreement with the expected values, although with larger uncertainties for CO
2
. We also consider the possibility to use co-emitted NO
x
emission estimates for quantifying the CO
2
emissions, by using source-specific NO
x
-to-CO
2
emission ratios derived directly from satellite observations. In general, we find that the divergence method provides a promising tool for estimating CO
2
emissions, alternative to typical methods based on inverse modeling or on the analysis of individual CO
2
plumes.
PDF
