Remote Sensing of Clouds and the Atmosphere XXIX

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.

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 3-100 μm. 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.

Efficient radiative Transfer Models (forward models) and Retrieval algorithms (inverse model)s for hyperspectral satellite remote sensors are needed due to large number of spectral channels and millions of observations each day. In this presentation, we describe how to use Principal Component Analysis (PCA) to speed up radiative transfer forward model calculations and to numerically stabilize the inversion algorithms. We have developed a Principal Component-based radiative transfer model (PCRTM) which can simulate top of atmosphere (TOA) radiance or reflectance spectra from 50 to 40,000 wavenumber (200 to 0.25 micrometer) quickly and accurately. It has demonstrated very good accuracy relative to reference line-by-line radiative transfer models and saves orders of magnitude in computational time. We will show examples of the PCRTM model and retrieval alglrithms developed for and applied to hyperspectral sensors such as AIRS, CrIS, IASI,CPF, TEMPO, EMIS, SBG, OMI, and SCIAMACHY.

The Mediterranean basin faces severe consequences of climate change, with regions experiencing droughts and water deficits due to intense summer heatwaves. Monitoring these phenomena is crucial for understanding their evolution and mitigation strategies. Parameters like emissivity, surface and dew point temperature help identify water presence and loss. This study, conducted in Southern Italy (2014-2023), utilized satellite data to estimate Water Deficit and Emissivity Contrast Indices, providing monthly averages. Validation with in situ measurements was conducted to better understand heatwave-induced drought impacts on various land covers.

Climate change has increased the frequency of droughts, impacting 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. A new Water Deficit Index (wdi) was developed based on the Infrared Atmospheric Sounder Interferometer (IASI), demonstrating effectiveness in detecting droughts. However, infrared sensors like IASI cannot penetrate thick cloud cover, leading to sparse and uneven data distribution across regions. To address this, we tested two machine learning algorithms (gradient boosting and random forest) to convert scattered IASI L2 data into 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 together with vegetational products, soil indexes, and territorial and geographic information as covariates. We applied the methodology over the Po Valley region, which experienced intense droughts in the last three years. Overall, we found that these methods can yield good results and allow simultaneous 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 195 K, it condenses from the gas phase to NAT or nitric acid thryidate (HNO3·3H2O) in the form of ice crystals. Recently, we have developed a new forward/inverse model capable of computing the top-of-atmosphere 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 the years 2021 and 2023, and HNO3 total column retrievals have been compared to those observed with the MLS (Microwave Limb Sounder) instrument to check the capability of IASI to estimate HNO3 and its yearly cycle. The paper also addresses the relationship between HNO3 and O3, especially at the onset of the ozone hole in the early

Canadian Space Agency (CSA) is studying the concept of its Arctic Observing Mission that would use two satellites in a Highly Elliptical Orbit 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) will be used for the GHGs observation. CSA funded Canadian industry and academia to build an iFTS instrument suitable for sub-orbit flight and develop the instrument calibration technology and software suites for processing the acquired iFTS image. The CSA has flown the iFTS instrument in a stratospheric balloon campaign from its CSA-CNES Stratos Balloon Facility in Canada to demonstrate the iFTS instrument for measurement of GHGs over the Canadian boreal forest from an altitude of 37 km. This stratospheric balloon demonstration not only elevated the technology readiness level of the iFTS technology, but also provided lessons that greatly benefit the development of the spaceborne 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.

This presentation showcases case studies of biomass burning aerosol and volcanic ash plume height estimation from the measurements by the Second-generation global imager (SGLI) instrument. The stereoscopic method is well suited to the height estimation of explosive aerosol emission events as it does not depend on the prior knowledge of atmospheric vertical structure. To adapt the conventional approach with a particular focus on the plume height estimation from the SGLI data, we applied the plume screening and explicit handling of 3-D coordinates.

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 Weather Remote Sensing Systems Office (WRSSO) at 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. Orion Space Solutions’ Rapid Revisit Optical Cloud Imager (RROCI) is a next generation CubeSat approach to weather monitoring using 8 spectral bands corresponding to equivalent MODIS bands. We present simulations of RROCI sensor performance based on MODIS data, including 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 0-0.5 km (and at all levels), can estimate rainfall accurately. So, the authors suggest designing and installing an instrument at say, 0.5 km 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 to establish functional relations between the surface parameters, say, surface pressure (SP), surface temperature (ST), relative humidity (RH), and the upper-air parameters, viz. CLW, PW, and LH. Such relations will help in estimating rainfall at any location, in two steps-first estimating the upper-air parameters from the surface parameters and then surface rainfall from the latter.

A cloud detection scheme based on the similarity analysis of the measured and the simulated radiant images was proposed in this paper. A set of data measured by the FY-4B satellite was used as the example to demonstrate the usefulness of the scheme. The geometry of the observational scene and the performance of the FY-4B sensor were analyzed to characterize the necessary parameters for the physically-based simulation model. The simulated radiant images were generated using the typical atmospheric parameters for the spectral bands NO. 5 and NO. 7 of the FY-4B. The SSIM images of the measured and simulated images are calculated to separate the cloudy and the clear sky pixels.

Solar radiation will be scattered by atmospheric molecules and aerosol particles when it transfers through the earth atmosphere. Based on the BHU-ATM presented in our previous work, an atmospheric radiative transfer model considering the polarization effects is developed, 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. The scattering phase matrix in the developed model is calculated via the interpolation of a two-dimensional discrete table achieved with a set of preset 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.

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. 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. This study examined how this process can be handled using a chemical transport model for more reproducible simulations. 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 are important for numerical weather and climate models. Retrieval product uncertainties propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical 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 PCRTM-Solar. Here we present progress towards a reference retrieval employing an accurate and verified yet computationally slower radiative transfer modelling technique. The reference retrieval will allow us to study retrieval product behavior and verify the in-development fast retrieval. An uncertainty analysis considering the following sources are presented: reflectance uncertainty due to water vapor, uncertainty due to ice cloud scattering assumptions, and measurement uncertainty. The habit selection uncertainty is largest, while water vapor uncertainty is at most 0.6% relative to channel reflectance.

Geostationary Interferometric Infrared Sounder (GIIRS) onboard China's geostationary meteorological satellite, Fengyun-4A, 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. A bias corrected optimal cloud-cleared (BCOCC) method is introduced to generate the GIIRS cloud-cleared radiances (CCRs) with the help of the collocated imager’s clear radiances. The bias correction (BC) scheme ensures the radiometric consistency between sounder and imager for clear sky. BCOCC significantly increases the data yields of successful CCRs. The CCRs can be potentially assimilated as clear radiances in numerical weather prediction models without worry of cloud impact.

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. 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.