Clémence Pierangelo

Probabilistic global maps of the CO2 column at daily and monthly scales from sparse satellite measurements

The column-average dry air-mole fraction of carbon dioxide in the atmosphere (XCO2) is measured by scattered satellite measurements like those from the Orbiting Carbon Observatory (OCO-2). We show that global continuous maps of XCO2 (corresponding to level 3 of the satellite data) at daily or coarser temporal resolution can be inferred from these data with a Kalman filter built on a model of persistence. Our application of this approach on two years of OCO-2 retrievals indicates that the filter provides better information than a climatology of XCO2 at both daily and monthly scales. Provided the assigned observation uncertainty statistics are tuned in each grid cell of the XCO2 maps from an objective method (based on consistency diagnostics), the errors predicted by the filter at daily and monthly scales represent the true error statistics reasonably well, except for a bias in the high latitudes of the winter hemisphere and a lack of resolution (i.e. a too small discrimination skill) of the predicted error standard deviations. Due to the sparse satellite sampling, the broad scale patterns of XCO2 described by the filter seem to lag behind the real signals by a few weeks. Finally, the filter offers interesting insights into the quality of the retrievals, both in terms of random and systematic errors.

Longwave passive remote sensing

The longwave spectral domain dealt with here, also called the “thermal infrared” domain, roughly covers the 3–15 μm spectral range. The main radiation source is not the sun but the Earth system, that is, the Earth’s surface (land and ocean) and the atmosphere. The infrared emission from bodies is directly linked to their temperature as described by Plank’s law: the hotter the bodies are, the higher their emission. The Earth and its atmosphere are heated by the fraction of sunlight they absorb. Their increase in temperature results in increased infrared emission to space, thus ensuring the energy balance of the Earth system.

Error budget of the MEthane Remote LIdar missioN (MERLIN) and its impact on the uncertainties of the global methane budget.

MEthane Remote LIdar missioN (MERLIN) is a German-French space mission, scheduled for launch in 2024 and built around an innovative light detecting and ranging instrument that will retrieve methane atmospheric weighted columns. MERLIN products will be assimilated into chemistry transport models to infer methane emissions and sinks. Here the expected performance of MERLIN to reduce uncertainties on methane emissions is estimated. A first complete error budget of the mission is proposed based on an analysis of the plausible causes of random and systematic errors. Systematic errors are spatially and temporally distributed on geophysical variables and then aggregated into an ensemble of 32 scenarios. Observing System Simulation Experiments are conducted, originally carrying both random and systematic errors. Although relatively small (±2.9 ppb), systematic errors are found to have a larger influence on MERLIN performances than random errors. The expected global mean uncertainty reduction on methane emissions compared to the prior knowledge is found to be 32%, limited by the impact of systematic errors. The uncertainty reduction over land reaches 60% when the largest desert regions are removed. At the latitudinal scale, the largest uncertainty reductions are achieved for temperate regions (84%) and then tropics (56%) and high latitudes (53%). Similar Observing System Simulation Experiments based on error scenarios for Greenhouse Gases Observing SATellite reveal that MERLIN should perform better than Greenhouse Gases Observing SATellite for most continental regions. The integration of error scenarios for MERLIN in another inversion system suggests similar results, albeit more optimistic in terms of uncertainty reduction.

Averaging Bias Correction for the Future Space-borne MethaneIPDA Lidar Mission MERLIN

The CNES (French Space Agency) and DLR (German Space Agency) project MERLIN is a future integrated path differential absorption (IPDA) lidar satellite mission that aims at measuring methane dry-air mixing ratio columns (XCH4) in order to improve surface flux estimates of this key greenhouse gas. To reach a 1 % relative random error on XCH4 measurements, MERLIN signal processing performs an averaging of data over 50 km along the satellite trajectory. This article discusses how to process this horizontal averaging in order to avoid the bias caused by the non-linearity of the measurement equation and measurements affected by random noise and horizontal geophysical variability. Three averaging schemes are presented: averaging of columns of XCH4 , averaging of columns of differential absorption optical depth (DAOD) and averaging of signals. The three schemes are affected both by statistical and geophysical biases that are discussed and compared, and correction algorithms are developed for the three schemes. These algorithms are tested and their biases are compared on modelled scenes from real satellite data. To achieve the accuracy requirements that are limited to 0.2 % relative systematic error (for a reference value of 1780 ppb), we recommend performing the averaging of signals corrected from the statistical bias due to the measurement noise and from the geophysical bias mainly due to variations of methane optical depth and surface reflectivity along the averaging track. The proposed method is compliant with the mission relative systematic error requirements dedicated to averaging algorithms of 0.06 % (±1 ppb for XCH4 = 1780ppb) for all tested scenes and all tested ground reflectivity values.

Progress in static fourier transform infrared spectroscopy: assessment of sifti preliminary performances

The concept of static Fourier transform interferometry at thermal infrared wavelengths is well suited in the case of narrow spectral bands that are looked at for targeted molecular species as CO and O3 for pollution and air quality monitoring, or H20 and CO2 for weather forecast, down to the troposphere. It permits a high spectral resolution and a very good radiometric performance, with the advantage of a static interferometer, including no moving part. Along with other molecules sounded in the UV-VIS domain, as for instance in the TRAQ mission, SIFTI will provide scientists with a complete set for pollution measurements and air quality survey. Our paper presents the principles of static Fourier transform spectrometry, the work led on the instrument performance model and our study of the SIFTI instrument. We describe the instrument, its main dimensions and characteristics, and its architecture and major subsystems. We eventually make a preliminary survey of the SIFTI performance budget items. As a conclusion, we introduce the future CNES phase A study of this instrument that is started in 2006

From the Concept to the Definition of the SIFTI Instrument: Static Infrared Fourier Transform Interferometer

SIFTI, a static interferometer using a pair of crossed staircase fixed mirrors, will provide high quality TIR spectra of O3and CO. At phase A mid-term, we review main technical choices, preliminary budgets and performances.