A Cloud Identification and Classification algorithm named CIC is illustrated. CIC is a machine learning method used for the classification of far and mid infrared radiances which allows to classify spectral observations by relying on small size training sets. The code is flexible meaning that can be easily set up and can be applied to diverse infrared spectral sensors on multiple platforms. Since its definition in 2019, the CIC has been applied to many observational geometries (airborne, satellite and ground-based) and is currently adopted as the scene classificator of the end-2-end simulator of the next ESA 9^th Earth Explorer, the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) which will spectrally observe the far infrared part of the spectrum with unprecedent accuracy. The algorithm has been recently improved to enhance its sensitivity to thin clouds (and also to surface features) and to increase the cloud hit rates in challenging conditions such as those characterizing the polar regions. The newly introduced metric is presented in details and the set-up procedures are discussed since they are critical for a correct application of the code. We illustrate the definition of the metric, the calibration process and the code optimization. The issues related to the definition of the reference training sets and to the classification of multiple classes are also presented.

The new σ-IASI/F2N radiative transfer model is an advancement of the σ-IASI model, introduced in 2002. It enables rapid simulations of Earth-emitted radiance and Jacobians under various sky conditions and geometries, covering the spectral range of 3-100μm. Successfully utilized in δ-IASI, the advanced Optimal Estimation tool tailored for the IASI MetOp interferometer, its extension to the Far Infrared (FIR) holds significance for the ESA Earth Explorer FORUM mission, necessitating precise cloud radiative effect treatment, crucial in regions with dense clouds and temperature gradients. The model's update, incorporating the "linear-in-T" correction, addresses these challenges, complementing the "linear-in-tau" approach. Demonstrations highlight its effectiveness in simulating cloud complexities, with the integration of the "linear-in-T" and Tang correction for the computation of cloud radiative effects. The results presented will show that the updated σ-IASI/F2N can treat the overall complexity of clouds effectively and completely, at the same time minimizing biases.

This study evaluates the effects of precipitation scavenging on aerosol loading and its subsequent impact on air quality in Shanghai, one of the world’s largest and fastest-growing megacities. The study employs advanced statistical techniques and machine learning models to assess which variables influence pollution levels, providing valuable information on periodic patterns and unexpected fluctuations in air quality. The use of Random Forest (RF) models demonstrated robust capabilities in predicting pollution trends over longer time scales, underscoring the importance of feature interpretability in environmental forecasting models. In addition, the study underscores the need to integrate data-driven approaches, such as machine learning, into environmental monitoring systems to improve predictive accuracy and policy effectiveness. The findings support the argument that the use of advanced computational models and large datasets can lead to more targeted interventions and better decision-making frameworks for urban planners and policy makers.

The Mediterranean basin is one of those areas where the impact of climate change is showing its most alarming consequences. Many regions in this area, both woodlands and croplands, have been suffering from droughts and water deficits due to the intense summer heatwaves of the last decades. Monitoring these phenomena is key to understanding how they are evolving and what could be done to mitigate their effects. Emissivity is a useful parameter in identifying the presence (or absence) of water. Surface and dew point temperatures are extremely useful not only in measuring the intensity of the heatwave but also in accounting for how much water content the surface is losing as humidity to the atmosphere. This paper presents a climatological study of Southern Italy’s water loss for the period 2015-2023 based on daily observations acquired by the Infrared Atmospheric Sounding Interferometer (IASI), mounted on top of EUMETSAT’s MetOp satellites. The Water Deficit Index (WDI) and the Emissivity Contrast Index (ECI) were estimated: monthly averages of each quantity were produced for the period of interest. Moreover, a validation with in situ measurements was conducted to better understand how these heatwave-induced droughts have been impacting the surface on different types of land covers.

2023 has replaced 2016 as the warmest year on record since 1850, bringing us closer to the 1.5°C limit set by the Paris Agreement. High temperatures increase the likelihood of extreme events, with heatwaves and drought being prominent among them. Climate change has led to a rise in the frequency of droughts, affecting countries that never experienced them. Assessing drought events is crucial and satellite data can provide significant assistance due to its large spatial coverage and continuous data supply. Based on the Infrared Atmospheric Sounder Interferometer (IASI), we designed a new Water Deficit Index (wdi) that we have already proven useful in detecting drought events. Unfortunately, infrared sensors such as IASI cannot penetrate thick cloud layers, so observations are blinded to surface emissions under cloudiness bringing sparse and not homogeneous distributed data over a given spatial region. To reconstruct a model of the field of interest for the entire surface on a regular grid mesh, interpolation techniques, and spatial statistics to deal with huge data sets are mandatory. In this paper, we exploited the capability of two machine learning algorithms, i.e. gradient boosting and random forest, in converting IASI L2 scattered data to a regular L3 grid. Specifically, we trained a model that can predict the wdi index over a 0.05° regular grid, using data from other sensors as a proxy, including vegetation indices (FVC, NDVI), soil moisture content, and geographic information (digital elevation models, land cover types) as covariates. We applied the methodology over the Po Valley region, which experienced an intense drought in the last three years causing high vegetation and soil water stress. Overall, the methods achieved a significant improvement in spatial resolution, with mean absolute error (MAE) and root mean square error (RMSE) values of 1.28°C and 1.68°C, respectively, enabling efficient regular grid conversion and downscaling.

Nitric acid is a key gas for understanding the processes leading to ozone depletion in Antarctica. The Antarctica ozone hole is a cyclic phenomenon that begins in early September when the region exits the polar night and fully develops in October-November. Nitric acid is the primary constituent of stratospheric aerosol. In the Antarctic region, when the temperature gets below 195K, it condenses from the gas phase to NAT or nitric acid trihydrate (HNO₃·3H₂O) in the form of ice crystals. Recently, we have developed a new forward/inverse model capable of computing the top-ofatmosphere infrared spectral radiance in all-sky conditions (clear/cloudy, day/night) and surface type (land or ocean). The new forward/inverse system has been applied to IASI (infrared Atmospheric Sounder interferometer) to retrieve O₃ and HNO₃ simultaneously. We have analyzed data for September 2021 and 2023, and HNO₃ column retrievals have been compared to those observed with the MLS (Microwave Limb Sounder) instrument to check the capability of IASI to estimate HNO₃. The paper also addresses the relationship between HNO₃ and O₃, especially at the onset of the ozone hole in the early spring. It will be shown that ozone depletion is paralleled by a consistent diminishing of HNO₃ from the gas phase.

The Arctic Observing Mission (AOM) is currently under mission concept study by the Government of Canada for potential implementation with prospective U.S. and European partners. It would use two satellites in a Highly Elliptical Orbit (HEO) to make geostationary-like observations of greenhouse gases (GHGs), air quality species of interest, meteorology, and space weather over northern regions. An imaging Fourier transform spectrometer (iFTS) has been selected as the technology for GHG observations. To elevate the technology readiness level of the iFTS, the Canadian Space Agency (CSA) has awarded contracts to Canadian industry and academia to advance the technology. An iFTS instrument suitable for sub-orbit demonstration has been built. Calibration techniques and software suites for processing the acquired iFTS image data have been developed. The CSA has flown the iFTS instrument in a stratospheric balloon campaign from its CSA-CNES Stratos Balloon Facility based in Timmins, Ontario, Canada to demonstrate its ability to measure GHGs over the boreal forest from an altitude of 37km. This paper briefly describes the development of the iFTS instrument and its adaptations to the stratospheric balloon platform for sub-orbital flight demonstration. The paper also reports instrument pre- and post-flight calibration, FTS image data processing techniques, retrieval of GHG data products and a brief analysis of these products. The balloon flight demonstration with a sub-optimal geometry and low cost iFTS prototype delivered GHG data products that met all expectations. This work not only elevated the technology readiness level of the iFTS technology, but also provided lessons that greatly benefit the development of the iFTS for the AOM mission.

This study demonstrates that the two-directional data of GCOM-C/SGLI (second-generation global imager) as a result of simultaneous polarized and un-polarized observations are useful to estimate the vertical information of biomass-burning aerosols (BBA). The vertical profiles of black carbon (BC) concentration simulated by the chemical transport model (CTM) are also useful for altitude information of the BBA. Comparison of the satellite products from SGLI observations with BC distribution from the CTM simulations reveals their mutual consistency. Using 3D visualization of the California forest fires as an example, this work discusses the effects of mountains and optical distance from satellites.

Intense wildfire and volcanic eruption emit a large quantity of aerosol in a form of plume that triggers various social and climatic consequences. Accurate simulation of such explosive emission events and the prediction of their consequences require a knowledge of the plume top height that could be estimated in several different approaches. This paper presents the application of stereoscopic height estimation technique to the data acquired by the Second-generation Global Imager (SGLI) aboard the Global change observation mission – C (GCOM-C) satellite that collected more than 6 years of global wide-swath (1200km) two-directional measurements. The SGLI captured 5 volcanic plumes among 10 most explosive volcanic eruptions in the 5-year period from 2018 to 2023. Along with an intense wildfire plume, we applied to observed plumes the stereoscopic plume top height estimation based on the normalized cross correlation (NCC) template matching. The estimated plume top height agrees with previous studies in most cases, while estimation was challenging for a volcanic plume case with low contrast and limited plume-top texture. The error estimation shows that the precision is about 700m and the accuracy is likely better than 1400m.

Atmospheric High Spectral Resolution Lidars (HSRLs) are unable to perform measurements close to the emission source because, apart from being blind in the first hundredths of meters (overlap problem), their spatiotemporal resolution is insufficient since they use low repetition rate and ultranarrow band (and thus long-pulse) lasers. In this work, we present the proof-of-concept of a compact short-range HSRL (SR-HSRL), closing the gap of aerosol characterization in the short-range. The system and laser were characterized, and the right balance between spectral performance (laser linewidth) and range resolution (pulse duration) was found. Then, the SR-HSRL concept was tested, measuring water droplets under controlled conditions. We demonstrate that it is feasible to implement the HSRL technique in the short range to characterize aerosols near the source without making assumptions.

Surface atmosphere has the small and steep spatiotemporal dynamics. It is influenced by the ground / sea-surface conditions, their up and down, air current, and so on. It greatly affects to the human living space. 265nm LED mini-lidar is developed to observe the surface atmosphere. As the transmitting light is in solar blind region, it can monitor near-range atmosphere up to 100m in daytime with the same signal-to-noise ratio in nighttime. The lidar echo detection was evaluated under the solar illumination. It is confirmed that the enough signal-to-noise ratio is obtained in daytime, which is matched with the theoretical estimation. The surface atmosphere observation is simulated with the artificial fog / rain facility “Light Tunnel”. Fog flow and rainfall were visualized and analyzed with time series lidar echoes. We will propose the easy-to-use method for dynamics monitoring of surface atmosphere.

Sensor performance plays a central role in the quality of weather data products produced by weather satellite observing systems. Next generation sensors harness commercial technologies and small satellite architectures that may include fewer spectral bands. The Aerospace Corporation is developing an end-to-end modeling and simulation testbed for performance analysis using CLAVR-x, a cloud detection algorithm made available by the Cooperative Institute of Meteorological Satellite Studies (CIMSS) at the University of Wisconsin. The current version of the testbed is being developed specifically for the U.S. Space Force’s EO/IR Weather System (EWS) mission to fill two top priority observational needs: cloud characterization and theater weather imagery. We present simulations of sensor performance based on MODIS data, characterizing the sensitivity of cloud products to sensor errors.

Traditional cloud detection algorithms for weather monitoring require radiometrically calibrated multispectral visible and infrared (IR) sensors such as those on dedicated weather satellites. In contrast, deep learning methods facilitate the use of proliferated earth observing constellations with uncalibrated sensors and fewer spectral bands for weather applications. This capability detects clouds and classifies types by recognizing spatial and spectral features using a deep neural network. In this study, we leverage existing U-Net architectures and train on several different satellite datasets. Following model development, we compare several ways to segment clouds in remote sensing images, including the number of spectral bands and separation of thin and thick clouds. The separation of thin and thick clouds is a first step in segmenting clouds by type. The capability developed in this work will facilitate the exploitation of rapidly growing data sources from the expanding market of proliferated commercial remote sensing systems.

Airborne and satellite observations have successfully extracted a wealth of information about clouds, aerosols, and the Earth’s surface. These observations can be significantly complemented by the long-term ground-based radiation data provided by the Multi-Filter Rotating Shadowband Radiometers (MFRSRs) supported by the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Program. Until recently, ARM-supported MFRSRs measured total irradiance and its direct and diffuse components at six wavelengths (415, 500, 615, 675, 870, and 940 nm). The limited number of wavelengths and the narrow spectral range of these MFRSRs prevent improved retrievals of aerosol, cloud, and surface characteristics. For example, spectrally resolved aerosol optical depth derived from the direct irradiance measured across a wide spectral range offer a valuable avenue for improved estimations of aerosol size distributions, especially for large particles. To address these limitations, ARM has supported the development of two successors to the MFRSR. The first, the MFRSR-7nch, includes a seventh narrowband channel at a 1625 nm wavelength, while the second, the Shortwave Array Spectroradiometer-Hemispheric (SAS-He), features increased spectral coverage (350-1700 nm) and hyperspectral capabilities. The performance of these successors is thoroughly evaluated under a wide range of atmospheric conditions, including different aerosol and cloud types and significant variability in aerosol loading. Our presentation will highlight the design, evaluation, and anticipated applications of these advanced radiometers.

In this paper, the authors attempt, in particular, to find functional relations between surface rainfall and Cloud Liquid Water (CLW), Precipitation Water (PW), and Latent Heat (LH) at the TRMM vertical profiling levels. The investigation shows strong correlations between rainfall and each of these parameters and a combination of all these. The study indicates that the functional relation at the closest TRMM profiling level to the Earth at 0-0.5km (and at all levels), can estimate rainfall accurately. So, the authors suggest designing and installing an instrument at say, 0.5km to measure the PW/CLW/ LH or all of the three parameters. This will estimate rainfall with excellent accuracy. Alternatively, the authors feel the need for establishing functional relations between the surface parameters say, surface pressure (SP), surface temperature (ST), and relative humidity (RH), and the upper-air parameters, viz. CLW, PW, and LH. Such relations will help to estimate rainfall at any location, in two steps- first estimating the upper-air parameters from the surface parameters and then surface rainfall from the latter.

In this work, a cloud detection scheme is proposed to process the multispectral images of space-based Earth observational sensors. With the assumption that the spectral and the spatial characteristics of the ground covers are invariant through a relatively long period, physically-based imagery simulation model is adopted to generate clear sky images for the specified sensor under the similar observational geometry with the same scene parameters. As the spectral bands of the selected sensor locate in atmospheric windows, the atmospheric condition are arbitrarily set as typical values to simulate the clear sky images. The structure similarity (SSIM) of the measured and the simulated images are calculated in the pixel by pixel manner to generate the SSIM image, in which the pixels with smaller SSIM values indicate the higher possibility of cloudy region. The cloud mask image can be obtained via selecting a suitable SSIM threshold for binary detection. A set of data measured by the Fengyun(FY)-4B geostationary satellite is used to demonstrate the usefulness of the proposed scheme. The images of the spectral bands NO.5 (1.58~1.64μm) and NO.7 (3.50~4.00μm) are selected as examples to implement cloud detection using monochromatic image alone as well as color ratio data. The results of the cloud detection validate the usefulness and the interpretability of the proposed scheme.

Solar radiation will be scattered by atmospheric molecules and aerosol particles when it transfers through the Earth atmosphere. The scattered radiance with different polarization state can be used to characterize atmospheric components. Based on the BHU-ATM presented in our previous work, an atmospheric radiative transfer model considering the polarization effects is developed in this paper, in which the parameter discretization method is used. To this end, the radiative transfer equation is adapted into the Stokes vector form, while the impacts of atmospheric molecules and aerosols on the polarization state of the scattered radiance are represented by means of the scattering phase matrix. The Curtis-Godson approximation and the two-stream approximation are used to obtain the analytical solution of the adapted radiative transfer equation. As the precise calculation of the scattering phase matrix varying with the scattering angle and the radiant wavelength is inefficient for the calculation of spectral path radiance, a novel aspect of this work is the efficient computation of the scattering phase matrix through a two-dimensional interpolation method, significantly reducing computational complexity while maintaining accuracy across a broad range of angles and wavelengths. The simulation results of the atmospheric transmittance, the spectral radiance and the degree of polarization (DOP) for an arbitrarily selected transfer path are given. As it can be seen, in the spectrum from the visible through the near infrared (VNIR), the polarization modeling showed a maximum transmittance difference of 0.0007 and a spectral radiance difference of 0.3W/m²/μm/sr. The DOP varied significantly, with a difference of up to 0.12 between urban and ocean aerosols. The developed polarization model can improve aerosol component identification in satellite-based remote sensing applications, aiding in more accurate air quality monitoring and enhancing climate models that account for aerosol scattering effects.

Several reports have indicated that fire plumes reach the free troposphere owing to large forest fires. Aerosols injected at high altitudes have a significant impact on atmospheric chemistry and climate. However, the effect of explosive convection on atmospheric aerosol transport has not been adequately quantified. Chemical transport models are effective tools for reproducing the behavior of atmospheric aerosol transportation. Aerosol emissions originating from wildfires were estimated based on the amount burned per unit area, burned area, combustion efficiency, and emission factors. Forest fires were detected based on abnormally high temperature data from satellite observations. Numerical model simulations were performed using emission inventory data. Aerosols are typically generated near the ground surface; however, aerosols from wildfires may be emitted at high altitudes because of the lift force from combustion. The results suggest that incorporating accurate injection altitudes in chemical transport models can improve the precision of air quality forecasts and contribute to more reliable climate models, particularly in regions affected by frequent wildfires. In this study, the chemical transport model used meteorological fields simulated by Scalable Computing for Advanced Library and the Environment Regional Model for offline calculations. The simulated results were validated using ground measurements and biomass-burning aerosol distributions derived from a second-generation global imager The results of this study show that the injection process in the model simulation has a significant impact on aerosol distribution.

Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral shortwave instruments produce high-resolution and information-dense spectra, thus offering the opportunity to reduce uncertainties in retrieval products. We are in the process of developing a retrieval that uses the very fast Principal Component Radiative Transfer Model in the solar spectral region (PCRTM-Solar) in the forward model calculations. This retrieval will use measured reflectances from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder) instruments. In this manuscript we present progress towards a reference retrieval employing a widely used, verified, accurate, yet computationally slower radiative transfer modeling technique. The reference retrieval, while too slow for using the complete hyperspectral measurement, will allow us to study the behavior of retrieval products and help us verify results from our in-development fast retrieval. Both retrievals will use the optimal estimation retrieval framework. In this manuscript we present results from an uncertainty analysis considering three uncertainty sources for a cirrus cloud retrieval in the form of error covariance matrices: reflectance uncertainty due to water vapor, the reflectance uncertainty due to ice crystal scattering assumptions, and the instrument measurement uncertainty. Results show that the uncertainty due to habit selection is the largest, while that due to water vapor is at most 0.6% relative to channel reflectance. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water.

Geostationary Interferometric Infrared Sounder (GIIRS) onboard China's geostationary meteorological satellite of the Fengyun-4A satellite provides high-spectral-resolution infrared observations with high temporal resolution. Due to high uncertainties in radiative transfer modeling of cloudy radiances, it is challenging to take full advantages of thermodynamic information from GIIRS in all sky. Advanced Geostationary Radiation Imager (AGRI) onboard the same platform provides a variety of cloud products with high spatial resolution. A bias corrected optimal cloud-cleared (BCOCC) method is introduced to generate the GIIRS cloud-cleared radiances (CCRs) with the help of the collocated clear radiances of AGRI. A bias correction (BC) scheme is applied by adjusting radiance differences between AGRI and GIIRS based on field-of-view and scene temperature. This correction ensures radiometric consistency across multiple bands and minimizes errors in CCR generation. Comparing to the optimal cloud-cleared method without BC, BCOCC increases the data yields of successful GIIRS CCRs by three times and improves the accuracy of retrieved CCRs. Evaluations show the mean biases of CCRs are 0.09K, -0.06K, and 0.06K, when compared to AGRI IR bands B12, B13 and B14, respectively. The GIIRS CCRs can be assimilated as clear radiances in numerical weather prediction (NWP) models, leading to enhanced forecast accuracy, particularly in regions of cloud edges and broken clouds, such as during typhoon or hurricane formation. Future work will involve integrating these CCRs into operational NWP systems and quantifying the improvement in forecast skill, particularly for severe weather events.

Health officials, policymakers and the public are increasingly aware of the dangers of long-term exposure to air pollution. Nitrogen dioxide (NO₂) is an air pollutant that is directly linked with human respiratory diseases and indirectly linked via the role of NO₂ in the production of surface ozone and particulate matter. Current NO₂ monitoring networks depend largely on stationary systems sampling air locally, but these lack adequate spatial coverage to fully characterise how pollution varies throughout a city on a street-by-street level. Because NO₂ is typically co-emitted with CO₂ during high-temperature combustion, detecting NO₂ plumes can also be used effectively to improve estimates of CO₂ emissions from human activities. There is therefore an urgent need to develop NO₂ monitoring systems that will deliver data with high spatial and temporal coverage to improve actionable information about air pollution and carbon emissions.

Here, we introduce SNEEZI (Sensing NO₂ Emissions to Evaluate net-Zero Initiatives) – a project to develop a lightweight NO₂ spectral imaging instrument designed for deployment on a constellation of small satellites to allow for NO₂ monitoring with high spatial and temporal resolution. We present the initial optical design for the system, as well as modelled instrument performance.

Methane is a potent greenhouse gas with a comparatively short atmospheric lifetime, and identifying and addressing methane emissions now is an effective strategy to mitigate the effects of climate change in the near-term. The Near Infrared Multispectral Camera for Atmospheric Methane, NIMCAM, is a new satellite instrument under development at the University of Edinburgh, designed to deliver high spatial resolution mapping of atmospheric methane. With a spatial resolution of 50m, NIMCAM will be the highest resolution instrument capable of continuous global monitoring among those currently available. The multispectral imaging system operates in the short-wave infrared and will be deployed on a constellation of small satellites, detecting methane emissions continuously without the need to pre-select target sites. We will present results from ground-based field trials, showing NIMCAM's ability to detect atmospheric methane. We will also describe the design of an aircraft demonstrator instrument, which will be used for future air-borne trials, and present concepts for the satellite instrument and mission.