Vinia Mattioli’s research while affiliated with European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and other places

Validation activities are critical to ensure the quality, credibility, and integrity of Earth observation data. With the deployment of advanced active remote sensors in space, a clear need arises for establishing best practices in the field of cloud, aerosol and precipitation profile validation. This publication fulfills this need, by proposing common practices, capturing lessons learned from earlier missions. The ACPPV publication is a result of an international collaboration of 97 scientists from 58 Institutions and Space Agencies, reviewed and accepted by the CEOS Working Group for Calibration and Validation (action item CV-22-01). The approaches and recommendations included, cover a range of issues, including correlative site and instrument selection, data processing and quality control, campaign criteria, configurations, scenarios, collocation methods, suggestions on issues concerning scene representativeness, and intercomparison methodologies. Moreover, guidance on the statistical validation through intercomparison between satellite-based remote sensing observations, and on the near-real time validation through monitoring in an NWP data assimilation system are included. To this end, the scientific communities involved in past missions have reviewed lessons learned and identified areas where convergence on similar approaches is beneficial. Finally, existing gaps in our Cal/Val knowledge are summarized. Given the complexity and diversity of geophysical scenarios and retrievals of aerosol, cloud, and precipitation regimes, the ACPPV document is aimed at knowledge exchange and conveying lessons learned, rather than definitions on single and strict protocols that have been agreed upon in some other domains with fewer degrees of freedom.

The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS-SG) will provide novel measurements of ice hydrometeors. ICI is a passive conically scanning radiometer that will operate within a frequency range of 183 to 664 GHz, helping to cover the present wavelength gap between microwave and infrared observations. Reliable global data will be produced on a daily basis. This paper presents the retrieval database to be used operationally and performs a final pre-launch assessment of ICI retrievals. Simulations are performed within atmospheric states that are consistent with radar reflectivities and represent the three-dimensional (3D) variability of clouds. The radiative transfer calculations use empirically based hydrometeor models. Azimuthal orientation of particles is mimicked, allowing for the consideration of polarisation. The degrees of freedom (DoFs) of the ICI retrieval database are shown to vary according to cloud type. The simulations are considered to be the most detailed performed to this date. Simulated radiances are shown to be statistically consistent with real observations. Machine learning is applied to perform inversions of the simulated ICI observations. The method used allows for the estimation of non-Gaussian uncertainties for each retrieved case. Retrievals of ice water path (IWP), mean mass height (Zm), and mean mass diameter (Dm) are presented. Distributions and zonal means of both database and retrieved IWP show agreement with DARDAR. Retrieval tests indicate that ICI will be sensitive to IWP between 10-2 and 101kgm-2. Retrieval performance is shown to vary with climatic region and surface type, with the best performance achieved over tropical regions and over ocean. As a consequence of this study, retrievals from real observations will be possible from day one of the ICI operational phase.

Accurate gas absorption models at millimetre and sub-millimetre wavelengths are required to make best use of observations from instruments on board the next generation of EUMETSAT polar-orbiting weather satellites, including the Ice Cloud Imager (ICI), which measures at frequencies up to 664 GHz. In this study, airborne observations of clear-sky scenes between 89 and 664 GHz are used to perform radiative closure calculations for both upward- and downward-looking viewing directions in order to evaluate two state-of-the-art absorption models, both of which are integrated into the Atmospheric Radiative Transfer Simulator (ARTS). Differences of 20 K are seen in some individual comparisons, with the largest discrepancies occurring where the brightness temperature is highly sensitive to the atmospheric water vapour profile. However, these differences are within the expected uncertainty due to the observed water vapour variability, highlighting the importance of understanding the spatial and temporal distribution of water vapour when performing such comparisons. The errors can be significantly reduced by averaging across multiple flights, which reduces the impact of uncertainties in individual atmospheric profiles. For upward-looking views, which have the greatest sensitivity to the absorption model, the mean differences between observed and simulated brightness temperatures are generally close to, or within, the estimated spectroscopic uncertainty. For downward-looking views, which more closely match the satellite viewing geometry, the mean differences were generally less than 1.5 K, with the exception of window channels at 89 and 157 GHz, which are significantly influenced by surface properties. These results suggest that both of the absorption models considered are sufficiently accurate for use with ICI.

Calibration of satellite observations is crucial for ensuring the quality of retrieved products essential for meteorological and climate applications. Calibration is obtained and monitored through a cascade of stages, including postlaunch vicarious calibration/validation activities through comparison with independent reference measurements. Here, the vicarious calibration method using radiative transfer simulations based on reference radiosondes is considered in the framework of the calibration/validation activities for the Microwave Imager (MWI) and the Ice Cloud Imager (ICI) to be launched with the Second Generation of the EUMETSAT Polar System. This paper presents an overview of the uncertainty characterizing the vicarious calibration of MWI and ICI using radiosondes as performed within the EUMETSAT-funded VICIRS study. The uncertainty characterization is pursued following a metrological approach, providing a preliminary estimation of all the identified sources. The same approach is used to develop a rigorous method for estimating the number of comparison pairs (i.e., observations vs. simulations) needed to reach a certain level of accuracy in bias determination.

Atmospheric radiative transfer models are extensively used in Earth observation to simulate radiative processes occurring in the atmosphere and to provide both upwelling and downwelling synthetic brightness temperatures for ground-based, airborne, and satellite radiometric sensors. For a meaningful comparison between simulated and observed radiances, it is crucial to characterize the uncertainty in such models. The purpose of this work is to quantify the uncertainty in radiative transfer models due to uncertainty in the associated spectroscopic parameters and to compute simulated brightness temperature uncertainties for millimeter- and submillimeter-wave channels of downward-looking satellite radiometric sensors (MicroWave Imager, MWI; Ice Cloud Imager, ICI; MicroWave Sounder, MWS; and Advanced Technology Microwave Sounder, ATMS) as well as upward-looking airborne radiometers (International Submillimetre Airborne Radiometer, ISMAR, and Microwave Airborne Radiometer Scanning System, MARSS). The approach adopted here is firstly to study the sensitivity of brightness temperature calculations to each spectroscopic parameter separately, then to identify the dominant parameters and investigate their uncertainty covariance, and finally to compute the total brightness temperature uncertainty due to the full uncertainty covariance matrix for the identified set of relevant spectroscopic parameters. The approach is applied to a recent version of the Millimeter-wave Propagation Model, taking into account water vapor, oxygen, and ozone spectroscopic parameters, though the approach is general and can be applied to any radiative transfer code. A set of 135 spectroscopic parameters were identified as dominant for the uncertainty in simulated brightness temperatures (26 for water vapor, 109 for oxygen, none for ozone). The uncertainty in simulated brightness temperatures is computed for six climatology conditions (ranging from sub-Arctic winter to tropical) and all instrument channels. Uncertainty is found to be up to few kelvins [K] in the millimeter-wave range, whereas it is considerably lower in the submillimeter-wave range (less than 1K).

Calibration of satellite observations is crucial to ensure the quality of retrieved products essential for meteorological and climate applications. Calibration is obtained and monitored through a cascade of stages, including post-launch vicarious calibration/validation activities through comparison with independent reference measurements. Here, the vicarious calibration method using radiative transfer simulations based on reference radiosondes is considered in the framework of the calibration/validation activities for the Microwave Imager (MWI) and the Ice Cloud Imager (ICI) to be launched with the Second Generation of EUMETSAT Polar System. This paper presents an overview of the uncertainty characterization of the vicarious calibration of MWI and ICI using radiosondes as performed within the EUMETSAT-funded VICIRS study.

The Ice Cloud Imager (ICI) aboard the Second Generation of the EUMETSAT Polar System (EPS-SG) will provide novel measurements of ice hydrometeors. ICI is a passive conically scanning radiometer that will operate within a frequency range of 183 GHz to 664 GHz, helping to cover the present wavelength gap between microwave and infrared observations. Reliable global data will be produced on a daily basis. This paper presents the retrieval database to be used operationally and performs a final pre-launch assessment of ICI retrievals. Simulations are performed within atmospheric states that are consistent with radar reflectivities and represent the three-dimensional variability of clouds. The radiative transfer calculations use empirically-based hydrometeor models. Azimuthal orientation of particles is mimicked, allowing for polarisation to be considered. The degrees of freedom of the ICI retrieval database are shown to vary according to cloud type. The simulations are considered to be the most detailed performed to this date. Simulated radiances are shown to be statistically consistent with real observations. Machine learning is applied to perform inversions of the simulated ICI observations. The method used allows for the estimation of non-Gaussian uncertainties for each retrieved case. Retrievals of ice water path (IWP), mean mass height (Zm) and mean mass diameter (Dm) are presented. Distributions and zonal means of both database and retrieved IWP show agreement with DARDAR. Retrieval tests indicate that ICI will be sensitive to IWP between 10-2 and 101 kg m-2. Retrieval performance is shown to vary with climatic region and surface type, with the best performance achieved over tropical regions and over ocean. As a consequence of this study, retrievals on real observations will be possible from day one of the ICI operational phase.

Atmospheric radiative transfer models are extensively used in Earth observation to simulate radiative processes occurring in the atmosphere and to provide both upwelling and downwelling synthetic brightness temperatures for ground-based, airborne, and satellite radiometric sensors. For a meaningful comparison between simulated and observed radiances, it is crucial to characterise the uncertainty of such models. The purpose of this work is to quantify the uncertainty in radiative transfer models due to uncertainty in the associated spectroscopic parameters, and to compute simulated brightness temperature uncertainties for millimeter- and submillimeter-wave channels of downward-looking satellite radiometric sensors (MWI, ICI, MWS and ATMS) as well as upward looking airborne radiometers (ISMAR and MARSS). The approach adopted here is firstly to study the sensitivity of brightness temperature calculations to each spectroscopic parameter separately, then to identify the dominant parameters and investigate their uncertainty covariance, and finally to compute the total brightness temperature uncertainty due to the full uncertainty covariance matrix for the identified set of relevant spectroscopic parameters. The approach is applied to a recent version of the Millimiter-Wave propagation model, taking into account water vapor, oxygen, and ozone spectroscopic parameters, though it is general and can be applied to any radiative transfer code. A set of 135 spectroscopic parameters were identified as dominant for the uncertainty of simulated brightness temperatures (26 for water vapor, 109 for oxygen, none for ozone). The uncertainty of simulated brightness temperatures is computed for six climatology conditions (ranging from sub-Arctic winter to Tropical) and all instrument channels. Uncertainty is found to be up to few kelvin [K] in the millimeter-wave range, whereas it is considerably lower in the submillimeter-wave range (less than 1 K).

Accurate gas absorption models at millimetre and sub-millimetre wavelengths are required to make best use of observations from instruments on board the next generation of EUMETSAT polar-orbiting weather satellites, including the Ice Cloud Imager (ICI), which measures at frequencies up to 664 GHz. In this study, airborne observations of clear-sky scenes between 89 and 664 GHz are used to evaluate two state-of-the-art absorption models by performing radiative closure calculations. Observed brightness temperatures are compared to simulated values from the Atmospheric Radiative Transfer Simulator (ARTS) for both upward and downward-looking viewing directions. It is shown that uncertainties in the atmospheric water vapour profile can have a significant impact on individual comparisons, but these errors can be reduced by averaging across multiple flights. For upward looking views, which have the greatest sensitivity to the absorption model, the mean differences between observed and simulated brightness temperatures are generally close to, or within, the estimated spectroscopic uncertainty. For downward-looking views, which more closely match the satellite viewing geometry, the mean differences were generally less than 1.5 K, with the exception of window channels at 89 and 157 GHz, which are significantly influenced by surface properties. These results suggest that both of the absorption models considered are sufficiently accurate for use with ICI.