WP-1500.8 – Nadir and limb radiative transfer models for Earth, Mars and other Planetary atmospheres

Products:

New computation resource at ISAC (Bologna)
New hardware was acquired: 1 server 2 x Intel Xeon 32-Core 8358 2.6Ghz, 16 x DDR4 GB module, 24 x WD 20TB SAS III 7.200 RPM.
Fast radiative transfer code for Mars
Modeling and inversion techniques are fundamental in atmospheric science, providing the framework to interpret remote sensing data and understand atmospheric processes. Forward models compute the expected radiative signal given a known atmospheric state, including temperature, pressure, gas composition, and clouds or aerosols. Inversion techniques, on the other hand, start from observed measurements and aim to retrieve the underlying atmospheric properties, enabling the design of retrieval algorithms, validation of models, and extraction of meaningful physical information from measurements.
This project focuses on creating a fast radiative transfer code for Mars, named σ4Mars, designed for the fast and accurate production of Martian radiance spectra in the longwave infrared region (100-3000 cm-1) under nadir geometry. The final purpose is including the forward in an inversion algorithm to improve the analysis of current and future Martian datasets and enhance our understanding of the Martian atmosphere.
The σ4Mars model relies on the flexibility and capabilities of the recently developed forward model σ-FORUM (also known as σ-IASI/F2N) [1], a high-performance, multipurpose radiative transfer code, originally developed for studying Earth’s longwave atmospheric radiation. It generates high-resolution spectra while maintaining computational efficiency through the use of precomputed lookup tables for the computation of gas and clouds/aerosol optical depths. Multiple scattering effects are treated using scaling methods, specifically the Chou scaling approximation [2] and the Chou adjustment (Tang correction) [3]. In addition, the code allows the computation of fast analytical derivatives of the radiance with respect to atmospheric and cloud properties, thus being suitable for the application in fast retrieval of spectrally resolved infrared observations.

To adapt σ-FORUM for Martian studies, the following modifications and inputs were implemented:

The code has been tuned to simulate Mars-specific atmospheric characteristics.
New pressure levels were defined to discretize the atmosphere, accounting for Mars’s highly variable surface pressure.
High-resolution optical depths for the most abundant and radiatively active gases were computed using a line-by-line radiative transfer code, the Planetary Spectrum Generator (PSG) developed by NASA [4] and subsequently parametrized as a polynomial function of temperature.
Collision Induced Absorption [5] continuum effect for the self CO2 have been included
Dust properties have been parametrized for the simulation of dusty scenarios
The performance of the model has been evaluated using PSG as reference code by comparing gas transmittances and high-resolution radiance spectra.

References:

[1] Masiello G. et al. (2024) JQSRT, 312, 108814, DOI: 10.1016/j.jqsrt.2023.108814.
[2] Chou M.-D. et al. (1999) Journal of Cimate, 12 (1), pp.159-169, DOI: 10.1175/1520-0442-12.1.159
[3] Tang G. et al. (2018) Journal of Atmospheric Science, 75 (7), pp. 2217 – 2233, DOI: 10.1175/JAS-D-18-0014.1
[4] Villanueva G. L. et al. (2018) JQSRT, 217, pp 86-104, DOI: 10.1016/j.jqsrt.2018.05.023.
[5] Terragni J. et al (2025), JQSRT, 347, 109631, DOI: 10.1016/j.jqsrt.2025.109631.
[6] Guerlet S. et al. (2022), Journal of Geophysical Research: Planets, 127, DOI:10.1029/2021JE007062
[7] Edwards C.S. et al. (2021) Space Sci Rev, 217, 77 DOI: 10.1007/s11214-021-00848-1

WP-1500.10 – Optimization and upgrade of the KLIMA radiative transfer and inversion model

The KLIMA (Kyoto protocoL Informed Management of the Adaptation) [Dinelli et al. 2023] code is a self-standing algorithm developed in FORTRAN at the Research Unit of IFAC CNR of Firenze. KLIMA can be used to simulate and analyse the spectral radiance acquired by remote sensing measurements of the atmosphere in all-sky conditions [Butali et al. 2026 (submitted)]. KLIMA was born from the previous version of the code called MARC (Millimetre-wave Atmospheric-Retrieval Code) [Carli et al. 2007]. KLIMA can be applied to various viewing geometries (limb [Del Bianco et al. 2007][Dinelli et al. 2009], zenith [Belotti et al. 2022] and nadir [Del Bianco et al. 2013][Bianchini et al. 2008][Ceccherini et al. 2010][Tirelli et al. 2021][Sgheri et al. 2022] and spectral bands (from millimetre and sub- millimetre wave [Carli et al. 2007] to the near-infrared [Mazzoni et al. 2008]).

Data center and network infrastructure

Although KLIMA has been optimized both from run-time and from memory resources point of view, the program requires large computing resources. For this reason, 5 computing servers are operational in the IFAC computing center, each one with 2 CPUs AMD EPIC 9474@3.6GHz, provided with 768GB RAM DDR5, 43.5 TB of SSD raw storage and connectivity up to 100Gb Ethernet; a SSD storage server with total raw capacity of 153.6 TB with connectivity up to 100Gb Ethernet and a new 16 ports 100Gb Ethernet switch.
Furthermore, the CNR-Florence research area has a network infrastructure composed of a passive section of mono-modal optical fibers (type 96/125 OS2) connecting the EMM data-centers and laboratories, and an active section consisting of new fast network switches. The configuration is a star structure: the center is connected to Internet router; the data centers and laboratories of IFAC, INO, IBE, IAC are in the different branches of the star. The fast EMM network is well integrated within the network infrastructure of the Area.

Example of radiance measured at the limb geometry using KLIMA. The tangent altitudes are shown in the figure

Example of simulated radiance at the nadir geometry using KLIMA in Martian atmosphere.

Example of real radiance measured at the zenith geometry analyzed using KLIMA. The real measurement, the simulated measurement and the difference are shown in the figure (data from WHAFFFERS campaign: see https://www.forum-ee9.eu/transat-2024/).

Example of real radiance measured at the nadir geometry analyzed using KLIMA. The real measurement, the simulated measurement and the difference are shown in the figure (data from ATMOSFER campaign: see https://www.forum-ee9.eu/transat-2024/).

Example of atmospheric profiles obtained from real radiance measured at the nadir geometry analyzed using KLIMA. The temperature and water vapor profile are shown in the figure (data from ATMOSFER campaign: see https://www.forum-ee9.eu/transat-2024/).

WP-1500.11 – Complete Data Fusion (CDF) – Go to website

Complete Data Fusion (CDF) is a statistical method designed to optimally combine multiple independent atmospheric profile retrievals into a single, consistent, and information-rich product. It is particularly suited for satellite remote sensing applications, where different instruments provide complementary measurements of the same geophysical quantity (e.g., ozone, temperature, trace gases).

Core Principles and Evolution

CDF was first introduced by Ceccherini et al. (2015) and has since evolved through several key developments. It is based on optimal estimation theory (Rodgers, 2000) and operates on Level 2 retrieval products, including retrieved profiles, averaging kernel matrices (AKMs), noise covariance matrices (CMs), and a priori profiles with their associated covariance matrices. The method produces a fused profile with its own AKM and CM, ensuring statistical consistency and traceability to the inputs.

Formulations of CDF

Original CDF (2015): Introduced by Ceccherini et al. (2015), this version uses the inverse of the noise covariance matrices to weight the input profiles. It is effective but limited by the potential singularity of these matrices.

Extended CDF (2018): To handle profiles on different vertical grids and from non-coincident observations, Ceccherini et al. (2018) introduced interpolation and coincidence error models, enabling fusion across instruments and platforms.

Improved CDF (2022): Ceccherini, Zoppetti, and Carli (2022) proposed a formulation that replaces noise CMs with retrieval error CMs, which are always non-singular. This version eliminates the need for generalized inverses and improves numerical stability. It also rigorously incorporates interpolation and coincidence errors.

Applications and Benefits

CDF has been applied to ozone profile fusion from Sentinel-4 and Sentinel-5 simulations, and to multi-platform fusion of geostationary and low-Earth orbit satellite data. It enables reprocessing of retrievals with new a priori constraints and supports consistency checks and quality control of retrieval products.
A notable application is presented in Guidetti et al. (2025), where the improved CDF formulation was used to combine MIPAS (limb) and IASI (nadir) ozone profiles over the Himalayas. The fused dataset showed increased vertical information content in the troposphere, improved agreement with ozonesonde data, and reduced retrieval errors, especially in the UTLS region. The fusion successfully propagated MIPAS information to lower altitudes where it has limited sensitivity, demonstrating the method’s ability to enhance the quality of atmospheric datasets and support studies of stratosphere–troposphere exchange and air quality.

The Role of CDF within the EMM Research Infrastructure

Within the EMM (Earth Moon Mars) research infrastructure, the Complete Data Fusion (CDF) technique plays a central role in enabling advanced scientific studies based on real atmospheric data. EMM provides the necessary computational resources, specialized software tools, and expert knowledge to support the application of CDF to multi-instrument satellite observations. By integrating measurements from different instrumens, the CDF allows researchers to generate high-resolution, information-rich atmospheric profiles that are essential for investigating complex processes such as stratosphere–troposphere exchange and air quality dynamics. Through its dedicated CDF platform, EMM facilitates collaborative research and promotes the adoption of this methodology across the scientific community. For more information, please visit the dedicated section of the EMM website.

Key References

Rodgers, C. D. (2000) – Inverse Methods for Atmospheric Sounding: Theory and Practice
Ceccherini, S., Carli, B., & Raspollini, P. (2015) – Equivalence of data fusion and simultaneous retrieval
Ceccherini, S., Carli, B., et al. (2018) – Importance of interpolation and coincidence errors in data fusion
Ceccherini, S., Zoppetti, N., & Carli, B. (2022) – An improved formula for the complete data fusion
Tirelli, C., Ceccherini, S., Del Bianco, S., Funke, B., Höpfner, M., Cortesi, U., and Raspollini, P.: Extension of the Complete Data Fusion algorithm to tomographic retrieval products, Atmos. Meas. Tech., 18, 5619–5636, https://doi.org/10.5194/amt-18-5619-2025, 2025.
Guidetti, L., Brattich, E., Ceccherini, S., et al. (2025) – Development and Validation of a New Ozone Dataset Using Complete Data Fusion

WP-1500.13 – Experimental chamber for spectroscopic measurements of atmospheric gases

Scientific and infrastructural objectives The experimental chamber for spectroscopic measurements of atmospheric gases is an advanced experimental facility designed for high-resolution spectroscopic measurements of planetary atmospheric gases. Technical specifications summary

spectral range: 2000–25000 cm⁻¹ (0.4–5 μm);
maximum spectral resolution: 0.002 cm⁻¹;
temperature range: 100–550 K;
maximum pressure of the gas to be studied: up to 70 bar;
optical system entirely in vacuum (better than 30 µbar)
optical path length: 3.27 m;
remote control via dedicated software.

Applications Applications range from the study of the optical properties of atmospheric gases, to the measurement of the transmittance of complex mixtures, to experimental support for space missions dealing with planetary atmospheres.