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The second FORUM and friends early careers workshop will be held online on 24th September 2024. The aim of workshop is to give early career researchers working on the FORUM mission and related Earth Observation topics an opportunity to present their work to each other and the wider community. The format of the event will be an online workshop comprising keynote presentations and talks from early career researchers.
We welcome abstracts from early career researchers working on the FORUM mission or related topics including, but not limited to, spectroscopy, radiative transfer, retrievals, NWP, and climate modelling. We also encourage talks on the subject of related missions such as PREFIRE, TICFIRE and IASI/IASI-NG. To submit an abstract please email your abstract to laura.warwick@esa.int and cristina.sgattoni@cnr.it. The deadline for submissions is Friday 6th September.
The workshop will be held as a Microsoft Teams Webinar. Please follow this link to register for the webinar: https://events.teams.microsoft.com/event/c09ad19d-8e6b-479b-ae04-1f45b93d7251@9a5cacd0-2bef-4dd7-ac5c-7ebe1f54f495
The σ-FORUM is a fast radiative transfer code designed to calculate the Earth’s spectrum radiance in the infrared range and the associated Jacobian matrices. Primarily developed by the University of Basilicata for the IASI (Infrared Atmospheric Sounder Interferometer) instrument, it has recently been extended to deal with clouds presence and cover a wider spectral interval that includes the far infrared band observed by FORUM (Far-Infrared Outgoing Radiation Understanding and Monitoring). The σ model spans the spectral range of 5 to 3000 cm-1 and it uses a fixed pressure grid containing 60 levels that extend from the ground level to the top of the atmosphere. The code parameterizes gas optical depths as a low-order polynomial of temperature. For water vapor, it uses a different approach to account for gas concentration effects like self-broadening of spectral lines. The radiance calculations in cloudy sky conditions are based on an implementation of the scaling method that allows for multiple scattering effects.
As part of the FIT-FORUM project, the code has been completely reengineered compared to the previous σ version. Structural changes have been made to the management of internal operations, along with the addition of new functionalities. Tools have also been introduced to improve the interface and implement error handling, which was absent in the original version. Several new user-selectable features have been added.
FIT-FORUM (Forward and Inverse Tool for FORUM) is a research project aimed at supporting the Italian scientific activities relating to the FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission onboard ESA’s ninth Earth Explorer satellite. In the preparatory phase of the mission, there is the need for accurate and versatile codes to compute the spectrally resolved Earth radiation escaping to space, target of the FORUM measurements. The main objective of the FIT FORUM project consists in the definition and development of innovative tools dealing with every aspect of radiative transfer for the simulation, analysis and interpretation of the FORUM sensor measurements, both in clear weather conditions and in the presence of clouds and aerosols.
In the context of the FORUM mission, a further research work was carried out to implement scattering within the KLIMA (Kyoto protocol-informed management of adaptation) model to simulate all sky conditions. This effort was supported thanks to the contribution of activities carried out in the context of the Earth-Moon-Mars (EMM) project, led by INAF in partnership with ASI and CNR, under the National Recovery and Resilience Plan (NRRP), funded by the European Union. To this aim, we carried out a study on 80 atmospheric scenarios, comparing the cloudy radiances simulated by two different codes, KLIMA and SIGMA-IASI, which are based on different radiative transfer models.
SIGMA-IASI is a forward model for all sky radiative transfer calculations in the spectral range 10 to 2760 cm−1. It allows calculating all-sky radiances by means of original parametrization of the optical depth of atmospheric gases and clouds. The cloud parametrization relies on suitable scaling laws, which make the radiative transfer equations for a cloudy atmosphere identical to those for a clear atmosphere.
The KLIMA code is a self-standing algorithm, developed in FORTRAN, able to simulate and analyze the atmospheric spectral radiance acquired by remote sensing measurements in clear sky conditions. It implements the computation of spectral radiance derivatives with respect to atmospheric parameters and therefore it is suitable to be used in retrieval codes. KLIMA has been extensively validated comparing its radiances to those generated by the widely used line-by-line radiative transfer model (LBLRTM) code.
The aim of this study is to extend the application of KLIMA to cloudy sky scenarios, a significant improvement in order to exploit the KLIMA key features in all sky conditions. The comparison between SIGMA-IASI and KLIMA results allowed for an evaluation of KLIMA's performance in cloudy conditions, presented here.
Dinelli, B. M., Del Bianco, S., Castelli, E., Di Roma, A., Lorenzi, G., Premuda, M., ... & Di Natale, G. (2023). GBB-Nadir and KLIMA: Two Full Physics Codes for the Computation of the Infrared Spectrum of the Planetary Radiation Escaping to Space. Remote Sensing, 15(10), 2532.
Martinazzo, M., Magurno, D., Cossich, W., Serio, C., Masiello, G., & Maestri, T. (2021). Assessment of the accuracy of scaling methods for radiance simulations at far and mid infrared wavelengths. Journal of Quantitative Spectroscopy and Radiative Transfer, 271, 107739.
Masiello, Guido, et al. "The new σ-IASI code for all sky radiative transfer calculations in the spectral range 10 to 2760 cm-1: σ-IASI/F2N." Journal of Quantitative Spectroscopy and Radiative Transfer 312 (2024): 108814.
Palchetti, L., et al. "unique far-infrared satellite observations to better understand how Earth radiates energy to space." Bulletin of the American meteorological society 101.12 (2020): E2030-E2046.
σ-FORUM is a powerful and multipurpose radiative transfer code, developed for studying Earth's atmosphere in the longwave radiation. It manages to produce high-resolution spectra while remaining a fast code by using parametrized optical depths. It allows the computation of fast analytical derivatives of the radiance with respect to atmospheric properties, thus being suitable for the application in fast retrieval of IR instruments.
In the context of the “Earth-Moon-Mars” project by INAF, ASI, and CNR, this work aims to expand our knowledge of the Martian atmosphere and, in a comparative planetology approach, also Earth's, extending the application of the σ-FORUM code to the atmosphere of Mars. Exploiting its flexibility and capabilities, in particular speed and accuracy, σ-FORUM will be a powerful tool for the simulation of the Martian spectrum and retrieval of its atmospheric quantities.
The work, that will converge in a PhD thesis is at its first stages, where the optical depths for the Martian atmosphere are being computed by means of the Planetary Spectrum Generator (PSG), a line-by-line powerful radiative transfer model suite for simulating planetary spectra for a broad range of wavelengths, different geometry and observatories.
A new methodology (called MAMA) for the computation of spectrally resolved upwelling radiances in the presence of atmospheric diffusive layers is presented. The algorithm is developed as a new analysis tool for application to interferometric data for the study of the atmospheric components. It offers fast and accurate radiance simulations of the Earth’s entire longwave emission spectrum, particularly excelling in scenarios involving optically thin scattering layers like cirrus clouds or aerosols. The solution is obtained through a simplification of the multiple scattering term in the general equation of the radiative transfer assuming the plane parallel approximation.
A new property of the layer is introduced, named angular back-scattering coefficient, which describes the fraction of radiation coming from a hemisphere and back-scattered into a specific direction (the observer in our case). This property, easily derived from the phase function of the particle size distribution, can be calculated from any generic single scattering properties database which allows for simple upgrades of the reference optical properties within the code. This work presents the solutions for mean upward and downward ambient radiance assuming a general plane-parallel geometry. To assess the algorithm performances, results obtained with the MAMA solution are compared with those derived with a reference discrete-ordinate radiative transfer model (DISORT) and with other scaling methodologies. The results highlight that, for liquid water clouds, the code accuracy is mostly within 0.4 mW/(m2cm−1) with respect to the reference code both at far and mid infrared wavelengths. The presence of ice clouds is accurately simulated at mid infrared for all realistic cloud cases, which makes this algorithm suitable for the analysis of any spectral measurements of current satellite infrared sounders. At far infrared, the MAMA accuracy is excellent when ice clouds with optical depth less than 2 are considered, which is particularly valuable since cirrus clouds are one of the main targets of the future mission FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) of the European Space Agency. Unusual cases which account for ice clouds with small particle sizes (effective radius mostly less than 15 um) and large optical depths (larger than 3) provides discrepancies that with respect the full physics code can be larger.
Cloud detection is essential for operational satellite work, particularly in data analysis and atmospheric reconstruction. This study focuses on identifying clouds using machine learning techniques with only satellite radiance observations, specifically utilizing Support Vector Machines (SVM) on spectral radiances from the Infrared Atmospheric Sounding Interferometer (IASI) - Level 1C dataset, covering the wavenumber range from 645 cm−1 to 1600 cm−1.
The study conducted three different analyses, each dividing the training and test sets in different ways. All analyses used the Soil Type index from the ERA5 database, the fifth generation of ECMWF reanalysis, for division. However, they differed in the global divisions used: the first considered satellite observations of the entire Earth’s surface without further subdivisions, the second divided it into five climatic zones, and the third additionally divided it into three longitudinal zones.
The findings of this study are particularly noteworthy. They demonstrate a remarkably high cloud detection accuracy using only radiance information, without the need for incorporating physical models or prior domain-specific knowledge. This result underscores the potential of machine learning algorithms, particularly SVM, in simplifying and improving cloud detection processes in satellite meteorology. Although the research is still ongoing, the preliminary results are promising and represent a significant step forward in using machine learning for atmospheric studies. The current presentation reflects the progress made thus far and lays the groundwork for further refining and applying these techniques.
Aerosol pollution in urban areas poses a serious threat to human health (Brunekreef and Holgate 2002). In the last decades, the possibility of estimating the aerosol mass concentration at ground level (Particulate Matter, PM) from satellite observations of optical properties (Aerosol Optical Depth, AOD) was thoroughly investigated (eg. Hoff and Christopher 2009; Sorek-Hamer, Chatfield, and Liu 2020) with the aim of enhancing the monitoring of aerosol levels and assist health studies about the effects of aerosol exposure. A variety of methodologies have been validated and are available, such as statistical methods (Ma et al. 2022) and machine learning (Unik et al. 2023). These approaches are useful to resolve issues such as missing satellite retrievals and different spatio-temporal resolution between ground-based and satellite observations, but require large datasets to be trained and the physical interpretation of the results is usually not straightforward.
A physical approach stems from the theoretical relationship between the aerosol mass concentration and the aerosol optical properties and is able to take into account specific factors such as the aerosol vertical profile over urban areas, their average chemical composition and relative optical properties. In particular, aerosols are characterized by different hygroscopic properties (i.e., particle growth due to ambient humidity) depending on their chemical composition.
The first part of this study shows experimental evidences that linearly correlate satellite AOD at high spatial resolution and ground-based PM2.5 measurements, by using a semi-empirical methodology. PM measurements were carried out in the city of Bologna.
The Po Valley, where Bologna is located, is recognized as one of the most polluted areas in Europe due to the high pollutant emissions combined with morphological and local orographic characteristics that favour atmospheric stability, and has already been identified as a target of interest for this type of studies (e.g. Arvani et al. 2016; Ferrero et al. 2019). In the second part of this study, we attempt to assess the sensitivity of the theoretical equations to specific variables, such as the Planetary Boundary Layer, the Relative Humidity at ground-level, the Effective Radius of the aerosols, and the aerosol optical properties.
References:
Arvani, Barbara, R. Bradley Pierce, Alexei I. Lyapustin, Yujie Wang, Grazia Ghermandi, and Sergio Teggi. 2016. ‘Seasonal Monitoring and Estimation of Regional Aerosol Distribution over Po Valley, Northern Italy, Using a High-Resolution MAIAC Product’. Atmospheric Environment 141 (September):106–21. https://doi.org/10.1016/j.atmosenv.2016.06.037.
Brunekreef, Bert, and Stephen T. Holgate. 2002. ‘Air Pollution and Health’. The Lancet 360 (9341): 1233–42. https://doi.org/10.1016/S0140-6736(02)11274-8.
Ferrero, L., A. Riccio, B. S. Ferrini, L. D’Angelo, G. Rovelli, M. Casati, F. Angelini, et al. 2019. ‘Satellite AOD Conversion into Ground PM10, PM2.5 and PM1 over the Po Valley (Milan, Italy) Exploiting Information on Aerosol Vertical Profiles, Chemistry, Hygroscopicity and Meteorology’. Atmospheric Pollution Research 10 (6): 1895–1912. https://doi.org/10.1016/j.apr.2019.08.003.
Hoff, Raymond M., and Sundar A. Christopher. 2009. ‘Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?’ Journal of the Air & Waste Management Association 59 (6): 645–75. https://doi.org/10.3155/1047-3289.59.6.645.
Ma, Zongwei, Sagnik Dey, Sundar Christopher, Riyang Liu, Jun Bi, Palak Balyan, and Yang Liu. 2022. ‘A Review of Statistical Methods Used for Developing Large-Scale and Long-Term PM2.5 Models from Satellite Data’. Remote Sensing of Environment 269 (February):112827. https://doi.org/10.1016/j.rse.2021.112827.
Sorek-Hamer, Meytar, Robert Chatfield, and Yang Liu. 2020. ‘Review: Strategies for Using Satellite-Based Products in Modeling PM2.5 and Short-Term Pollution Episodes’. Environment International 144 (November):106057. https://doi.org/10.1016/j.envint.2020.106057.
Unik, Mitra, Imas Sitanggang, Lailan Syaufina, and I Nengah Jaya. 2023. ‘PM2.5 Estimation Using Machine Learning Models and Satellite Data: A Literature Review’. International Journal of Computer Science and Applications 14 (June):2023.
https://doi.org/10.14569/IJACSA.2023.0140538.
The Complete Data Fusion (CDF) technique combines measurements from multiple instruments or data sources into a unified dataset, leveraging the unique strengths of each to create a more accurate and comprehensive representation of the target phenomenon. Observational instruments provide complementary information due to differences in sensing capabilities, spectral coverage, vertical sensitivity (technical requirements), and spatial-temporal resolution (geophysical requirements). CDF integrates these complementary datasets, maximizing information content while minimizing uncertainties.
This project applies the CDF method to data from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS-Envisat) and the Infrared Atmospheric Sounding Interferometer (IASI-MetOP) to detect Stratospheric Intrusions (SI) of ozone-rich air into the troposphere, focusing on the Himalayan region. SI events impact the troposphere by introducing ozone, a regulated pollutant and greenhouse gas, affecting atmospheric processes. Recent studies show an increase in stratospheric ozone intrusion, linked globally to the strengthening of the Brewer-Dobson Circulation (BDC). At a regional level, other factors may also play a role. The unique meteorological conditions in the Himalayas make this region a hotspot of ozone intrusions.
Understanding these events is critical for improving tropospheric ozone climatology, which is currently measured using a combination of in-situ and remote sensing techniques. However, biases between model simulations and observations persist, requiring better vertical resolution and broader spatial-temporal coverage, particularly in the Upper Troposphere-Lower Stratosphere (UTLS) region. Using advanced satellite instruments and innovative data exploitation methods, this project aims to reduce uncertainties and enhance our understanding of ozone dynamics.
Knowledge of the emissivity of the Earth’s surface is vital for understanding the Earth’s radiation budget. However, there is a lack of emissivity measurements in the far-infrared (wavenumbers less than 667 cm-1 or wavelengths greater than 15 μm) despite studies showing that the surface emissivity in these regions can have a discernible impact on the outgoing longwave radiation. In-situ measurements of surface emissivity in the far-infrared are also needed to support the current and upcoming far-infrared satellite missions; the Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) developed by NASA and launched in May 2024, and the European Space Agency’s Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission due to launch in 2027.
To fill this observational gap, the Far INfrarEd Spectrometer for Surface Emissivity (FINESSE) was developed at Imperial College London. FINESSE has a spectral range of 400 to 1600 cm-1 (6.25 to 25μm) and is designed for in-situ measurements of surface emissivity, particularly in cold climates.
In this presentation we present observations from the first deployment of FINESSE to the ALOMAR observatory in Northern Norway (69°16' N, 16° 0’ E). We describe the campaign, the radiance measurements made by FINESSE and the auxiliary data taken. We then outline the method used to determine the surface temperature and emissivity and finally present the retrieved emissivity of the ice and snow surfaces.
The spectral dimension in climate applications is crucial for studying the evolution of key climate variables, characterizing relevant driving climate mechanisms, and identifying biases in climate model simulations. Today, this type of studies is made possible by the long time series of spectrally resolved radiances collected from instruments operating since the early 2000s, such as AIRS (2002-present), IASI (2006-present) and CrIS (2011-present). These instruments provide hyperspectral observations in the mid-infrared (MIR) range (667 to 2750 cm⁻¹) of the Earth emission spectrum, capturing essential data on key climate variables such as temperature, water vapor, and clouds. This spectral range also includes absorption lines from a variety of gases (CO₂, CH₄, O₃, N₂O, CO, SO₂, etc.). Additionally, the forthcoming FORUM mission, scheduled for launch in 2027, will offer unique spectrally resolved measurements extending into the far-infrared (FIR) region (100 to 667 cm-1). The long-term sequence of observations from IASI and FORUM will enable further exploration of spectral climate feedback, their representation in climate models across inter-annual and decadal timescales and provide additional constraints for climate model evaluation.
In this work, 12 years (2008-2019) of IASI Metop-A measurements are compared to simulated spectral radiances provided by the EC-Earth GCM (ECE, version 3.3.3) based on the atmospheric and surface fields predicted in all-sky conditions by the model. An innovative strategy is adopted to consider the cloud variability within the large model grid cell (roughly 80-km grid spacing near the equator) and to optimally compare the climate model outputs with the higher spatial resolution (about 15 km of diameter) observations performed by the instrument. The spectral radiances are simulated online every 3 hours by the σ-IASI radiative transfer model, that has been previously embedded in the climate model through the Cloud Feedback Model Intercomparison Project (COSP) module.
Spectral biases in the climate model are identified by comparing the trends of spectral radiances across different wavenumbers. Additionally, the impact of key climate variables on the temporal evolution of observed and simulated radiances is evaluated using precomputed spectral kernels.
The longwave feedback λ characterizes how Earth’s outgoing longwave radiation changes with surface temperature Ts, directly impacting climate sensitivity. Compared to λ, its spectrally resolved counterpart λν offers deeper insights into the underlying physical processes. Both λ and λν vary with Ts, but this Ts dependence has so far only been investigated using models. Here, we derive the clear-sky λν for Ts between 210K and 310K based on observations of the AIRS instrument. We disentangle the radiative signatures of the atmospheric general circulation by simulating λν based on a single-column model with different degrees of idealization. We find that at low Ts, the observed λν is dominated by the surface response and sensitive to biases in Earth’s skin temperature. At higher Ts, changes in the vertical distributions of atmospheric temperature and relative humidity play an important role in shaping λν. These changes impact both the absorption of surface emission in the atmospheric window and the atmospheric emission in the water vapor and CO2 absorption bands. Our results demonstrate that we can fully understand the observed λν at a wide range of Ts using a simple model of Earth’s atmosphere, lending further support to estimates of the clear-sky longwave λ, Earth’s most fundamental climate feedback. They also highlight the effect of different assumptions about Earth’s atmosphere on λ. Similar approaches can be used to better constrain changes in relative humidity and temperature with warming using satellite observations, as well as for paleoclimate and exoplanet studies.
Outgoing longwave radiation in the far-infrared (FIR) spectral region is responsible for over half of the Earth’s emissions to space, with CO2 and H2O being the two principal absorbers. The upcoming ESA FORUM mission will be the first to measure in the FIR at high spectral resolution. The interpretation of the FORUM measurements is reliant on our ability to perform accurate radiative transfer calculations in the FIR. The aim of this work is to improve the accuracy of the available spectroscopic linelists required for the FORUM mission.
We report here our first measurements of high resolution (0.00096 cm-1) spectra of pure CO2 in the FIR region, taken using synchrotron radiation at the Canadian Light Source. We derive preliminary intensities and line positions. We also report preliminary results of line positions and intensities for H2O in the FIR using spectra recorded previously at SOLEIL in France.
The Earth Energy Budget (EEB) at the Top Of the Atmosphere (TOA) is the net amount of energy that enters or leaves the Earth climate system. It is driven by the balance between the incoming solar radiation (in the shortwave) and the radiation leaving the climate system, the reflected solar radiation and the thermal radiation emitted by the Earth-atmosphere system (in the longwave, also known as Outgoing Longwave Radiation (OLR)). The EEB is a key property to fully understand how the Earth climate system works. It contains the signature of processes (such as natural or anthropogenic forcings) related to climate change, in addition to the ones related to internal climate variability. The El Niño - Southern Oscillation (ENSO) is one of the most important internal variability modes of the Earth climate system. ENSO perturbs the OLR by modifying Sea Surface Temperatures (SSTs) and shifting tropical convective zones. Studying the mechanisms behind this response is key to understand the evolution of the OLR in observations and climate models.
The aim of this work is to investigate how climate models participating to the Coupled Model Intercomparison Project phase 6 (CMIP6) (Eyring et al. (2016)) reproduce ENSO driven variability. The relationship between clear-sky OLR in the tropical ocean and ENSO is examined. Climate models are evaluated against the Clouds and Earth's Radiant Energy System (CERES) (Loeb et al. (2018)). Moreover, in anticipation of the real data from the Far-infared Outgoing Radiation Understanding and Monitoring (FORUM) mission, a clear-sky fluxes climatology of the Infrared Atmospheric Sounder Interferometer (IASI) (Whitburn et al. (2020)) is also used to study the spectrally resolved OLR response to ENSO.
References:
- LOEB, Norman G., et al. Clouds and the earth’s radiant energy system (CERES) energy balanced and filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. Journal of Climate, 2018, 31.2: 895-918.
- WHITBURN, Simon, et al. Spectrally resolved fluxes from IASI data: Retrieval algorithm for clear-sky measurements. Journal of Climate, 2020, 33.16: 6971-6988.
- EYRING, Veronika, et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 2016, 9.5: 1937-1958.