Southern California wildfires result in a significant loss for Los Angeles and San Diego County from January 7 to 31, 2025. Palisades Fire and Eaton fires, the two largest fires that caused most of that damage, were observed by the infrared sounder CrIS and microwave sounder ATMS aboard a polar-orbit satellite of the National Oceanic and Atmospheric Administration, that is, NOAA-20. The high spatial resolution Single Field-of-view Sounder Atmospheric Products (SiFSAP) retrieved from CrIS and ATMS measurement have been used to illustrate the emission and transport of the plume generated by the wildfires. The CO total column and UV aerosol index (UVAI) observed by TROPOMI, CO total column observed by AIRS, the aerosol optical thickness (AOT) provided by VIIRS, and wind provided by ERA5 were used to illustrate plume produced by wildfires. Furthermore, cloud optical depths and top pressures retrieved by SiFSAP and VIIRS were compared to assess the IR sounder's capability to retrieve clouds. Results indicated that the SiFSAP data products effectively capture not only large scale dry airmass transport but also the movement of wildfire-generated plumes containing high CO concentrations. The study shows that the SiFSAP CO retrieved from IR measurements is less affected by aerosols as compared to solar measurements like TROPOMI. This study demonstrates that the SiFSAP products provides a high-quality data set for wildfire observations and disaster observations.

The ratio of potential temperature (Tp) and dewpoint temperature (Td), which is derived from retrievals of infrared hyperspectral measurements, is adopted as a new parameter for better estimating planetary boundary layer height (PBLH). A case study, conducted with National Airborne Sounder Testbed-Interferometer (NAST-I) measurements obtained during the Fire Influence on Regional to Global Environments and Air Quality field campaign, is presented herein. We use NAST-I geophysical parameter retrievals from the Single Field-of-view Sounder Atmospheric Product algorithm, which ensures higher vertical resolution of temperature and moisture profiles as well as accurate surface temperature and emissivity, to estimate PBLH with a higher horizontal spatial resolution of 2.6 km. As a result of using the ratio of potential and dewpoint temperatures, instead of individual thermodynamic retrievals, a more robust parameter for estimating PBLH is obtained. A quality control process is developed to filter out abnormal outliers. Additionally, those outliers are modified using statistics from nominal distributions of the Tp/Td ratio and PBLH. A high consistency between NAST-I thermodynamically-retrieved PBLH and that from the European Centre for Medium-Range Weather Forecasts Reanalysis-5, which uses both dynamic and thermodynamic information, successfully supports the validity and significance of our approach.

Large wildfires and their induced thunderstorms, pyrocumulonimbus (pyroCb), are getting increasing interest and attention. Most previous studies are based on broadband satellite data, and the use of hyperspectral infrared (IR) data for pyroCb detection has not been explored. Australia's unprecedented fires at the end of 2019 to early 2020 induced an extreme pyroCb outbreak, and the largest event occurred on December 30, 2019 was successfully captured by the cross-track IR sounder (CrIS) onboard JPSS-1 and Suomi National Polar-orbiting Partnership (S-NPP). Analysis of these CrIS data demonstrated that the slopes and standard deviations (STDs) of the spectral brightness temperature (BT), derived from a linear fitting to the spectrum in 800-980 cm(-1), along with the split-window technique using selected CrIS channels, can aid in pyroCb detection. An inverted "V" feature in the spectra near 9.6 mu m is associated with high pyroCb, and its depth, H_index, can provide information on cloud-top height. These characteristics are verified through radiative transfer model (RTM) simulations and analysis of another case of pyroCb induced from California Creek Fire on September 5, 2020. It is found that a combination of H_index and BT difference between 1231 and 677 cm(-1) is promising to distinguish high pyroCb. A more accurate determination of pyroCb plume height can be achieved from the single field of view (SFOV) sounder atmospheric products (SiFSAPs), which indicate that several pyroCb pixels reach 3-6 km above the tropopause. These approaches could be applied to study more pyroCb using over 20 years of hyperspectral sounder data.

The Hunga Tonga-Hunga Ha'apai volcanic eruption, with the largest eruption occurring on 15 January 2022, ejected unprecedented amounts of water vapor (H2O) and SO2 into the stratosphere. Using the hyperspectral infrared sounder CrIS on S-NPP and JPSS-1 (also NOAA-20), we present some unique features of the spectral near 9.6.m and its great potential value for detecting plumes or clouds with tops near or above tropopause. We identified the existence of two umbrella clouds and observed the continued westward propagation of the upper plume even 8-9 hours after the eruption onset, as well as its dissipation from two CrIS observations onboard JPSS-1 and S-NPP taken in 50 minutes apart. Using a new Single Field-of-view Sounder Atmospheric Products (SiFSAP) from CrIS and ATMS on JPSS-1, which has a high spatial resolution of about 14 km, this study analyzes the impact of Hunga Tonga eruption on the distribution of temperature, H2O and ozone near the tropopause. These results demonstrate the value of hyperspectral infrared sounder and its single-field-view retrieval products for monitoring the volcanic eruptions and their impact on the lower stratosphere.

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the ν1 vibrational band near 1300 cm−1 (7.7 μm); therefore, they can be used to monitor CH4 concentrations in the atmosphere. However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH4 profiles that operational sounder products may not provide. The latitudinal variations in CH4 concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane’s role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability.

A new method utilizing hyperspectral infrared sounders is developed to estimate cloud top height (CTH) for deep convective clouds associated with hurricane, or tropical cyclone (TC). By analyzing measurements from the Cross-track Infrared Sounder (CrIS), and further validating with radiative transfer simulations, we found an inverted-V spectral feature in the ozone (O-3) band near 9.6 mu m for thick clouds, and its depth, designated as H_index, has a strong correlation with CTH. Using a linear regression, a formula is derived to calculate the CTH based on H_index and brightness temperature (BT) in other three channels. This method effectively captures the cloud structure of a TC's eyewall and surrounding rainbands, with an error of -0.05 +/- 0.19 km (or -0.41 +/- 1.96%). Further analysis reveals that the retrieved temperature profiles near hurricane's eye from a new single Field of View sounder products (SiFSAP) from CrIS agree reasonably well with the reanalysis data from MERRA-2 and ERA-5. This method can be easily applied to operationally monitor hurricane cloud and its development, and the estimated CTH can be also used as a-priori to improve the retrieval products.

The principal-component-based radiative transfer model retrieval algorithm (PCRTM-RA) for carbon monoxide (CO) retrieval has been improved for better use of National Airborne Sounder Testbed-Interferometer (NAST-I) measurements obtained during the Fire Influence on Regional to Global Environments and Air Quality field campaign. One of the explicit purposes of the campaign was to characterize wildfire-induced atmospheric changes. Coincidental measurements from various airborne instruments, including NAST-I infrared hyperspectral measurements from the NASA ER-2 aircraft, provided us an opportunity to test and improve the PCRTM-RA CO retrieval. By relaxing the vertical correlation of CO profiles in the a priori covariance constraint, a significant improvement in the vertical structure of the CO retrieval has been confirmed. The methodology is validated using a synthetic testing dataset that covers observations associated with various CO vertical profiles, including that of an extremely polluted atmosphere. The methodology is also applied to real NAST-I measurements, and the results have successfully demonstrated the capability of using PCRTM-RA retrieval results for CO plume evolution and transport monitoring.

The single field-of-view (SFOV) sounder atmospheric product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high-spatial-resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal-estimation inversion scheme that processes data from the satellite-based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM), which performs radiative transfer calculations monochromatically and includes accurate cloud-scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the top-of-atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to the common use of the “cloud-clearing” technique to compensate for the lack of cloud-scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, a rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With the advantages of the higher spatial resolution; the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information; and the establishment of “radiance closure” in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of the SiFSAP retrieval algorithm and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

The Single Field-of-view Sounder Atmospheric Products (SiFSAP) answer the need for a novel high spatial resolution atmospheric data product for major hyper-spectral infrared (IR) sounder missions. SiFSAP include a complete set of atmospheric vertical profiles, cloud, and surface properties, which are physically retrieved from top-of-atmosphere (TOA) spectral radiances under all-sky conditions via a rigorously defined radiative transfer relationship. By using a state-of-art fast radiative transfer model and a carefully designed optimal estimation based physical retrieval scheme, the SiFSAP algorithm ensures both an ultra-fast data processing speed needed for operational weather applications and the radiometric consistency desired by long-term climate studies. SiFSAP supplement existing operational products by providing data at the native spatial resolution of the sounder instruments, the direct and accurate retrieval of cloud scattering properties, and the establishment of 'radiance closure.'

Ultra-spectrally resolved infrared measurements from aircraft and space-based observations contain information about tropospheric carbon monoxide (CO) and ozone (O-3), as well as other trace species. A methodology for retrieving these tropospheric trace species from such remotely sensed spectral data has been developed and validated for the National Airborne Sounder Testbed-Interferometer (NAST-I). The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was conducted during August 2019 to investigate the impact of wildfire and biomass smoke on air quality and weather in the continental United States. NAST-I CO and O-3 measurements from the recent FIREX-AQ field campaign are presented and used to estimate wildfire plume age. Results show enhanced levels of CO in the evolving plume as it is transported away from the fire ground site, and its plume age is associated with the plume distance in both the vertical and horizontal directions from the wildfire location. These results are enabled by the moderate-vertical and high-horizontal resolution obtained from the NAST-I IR spectrometer onboard the NASA ER-2 aircraft. This study advances our knowledge of fire-induced plumes with their evolution and age characterized in three-dimensional space using information from NAST-I retrieved CO and O-3 and relative changes in their concentrations. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.

Global surface skin temperature has been retrieved from MetOp IASI ultraspectral infrared measurements over the past 13+ years. Monthly and spatially gridded surface skin temperature is produced to show some phenomena associated with its natural variability. This article has aimed to demonstrate that thermal infrared remote sensing data can be used for monitoring global surface environmental characteristics and associated change through the continuity observations of MetOp series. The time-series anomalies of surface skin temperature are used to estimate its associated trend. Error estimation and evaluation has been performed and discussed in order to understand the uncertainty and variability in the trends. The trends derived from IASI global surface skin temperatures are compared with those of the NASA GISS global surface air temperature. Despite the physical differences between surface skin and air temperatures, reasonable agreement is shown between these two datasets indicating consistency and global surface warming, achieving our core objective of investigating the surface skin temperature retrieved from MetOp satellites' measurements and associated trends. The inferred trend of IASI global surface skin temperature illustrates an approximate 0.037±0.002 K/yr. global average increase has occurred during the September 2007-present (November 2020) time period; this warming trend is more pronounced in the northern hemisphere.

We introduce a novel spectral fingerprinting scheme that can be used to derive long-term atmospheric temperature and water vapor anomalies from hyperspectral infrared sounders such as Cross-track Infrared Sounder (CrIS) and Atmospheric Infrared Sounder (AIRS). It is a challenging task to derive climate trends from real satellite observations due to the difficulty of carrying out accurate cloudy radiance simulations and constructing radiometrically consistent radiative kernels. To address these issues, we use a principal component based radiative transfer model (PCRTM) to perform multiple scattering calculations of clouds and a PCRTM-based physical retrieval algorithm to derive radiometrically consistent radiative kernels from real satellite observations. The capability of including the cloud scattering calculations in the retrieval process allows the establishment of a rigorous radiometric fitting to satellite-observed radiances under all-sky conditions. The fingerprinting solution is directly obtained via an inverse relationship between the atmospheric anomalies and the corresponding spatiotemporally averaged radiance anomalies. Since there is no need to perform Level 2 retrievals on each individual satellite footprint for the fingerprinting approach, it is much more computationally efficient than the traditional way of producing climate data records from spatiotemporally averaged Level 2 products. We have applied the spectral fingerprinting method to six years of CrIS and 16 years of AIRS data to derive long-term anomaly time series for atmospheric temperature and water vapor profiles. The CrIS and AIRS temperature and water vapor anomalies derived from our spectral fingerprinting method have been validated using results from the PCRTM-based physical retrieval algorithm and the AIRS operational retrieval algorithm, respectively.

A physical retrieval system is developed for all sky single field-of-view (FOV) hyperspectral sounding. This system can be used to retrieve atmospheric temperature, moisture, and trace gas profiles, along with cloud height and cloud microphysical properties simultaneously from single FOV radiances measured by hyperspectral sounders. Single FOV retrieval results allow the users to extract horizontal gradient information with a spatial resolution defined by the size of one FOV. Moreover, the system finds solutions by directly fitting observed spectral radiances and therefore can be used to obtain radiative kernels that fulfill the 'radiance closure' needed for climate applications. As a comparison, current operational retrieval algorithms find solutions by fitting the 'cloud cleared' radiances generated from several adjacent FOVs. Not only the spatial resolution of those results is several times coarser than that defined by a single FOV, but the results cannot be used to build radiometric consistent radiative kernels under cloudy sky conditions. In this paper, some applications of the system are demonstrated to highlight the benefit of the full spatial resolution retrieval and the capability of building radiometric consistent radiative kernels under all sky conditions.

Measurement system validation is critical for advanced satellite sensors to achieve their full potential of improving observations of the Earth's atmosphere, clouds, and surface for enabling enhancements in weather prediction, climate monitoring capability, and environmental change detection. Field campaigns focusing on satellite under-flights with validation sensors aboard high-altitude aircraft provide an essential component important for performing such satellite measurement system validation. The NASA Langley Research Center National Airborne Sounder Testbed - Interferometer (NAST-I) is a cross-track scanning Fourier Transform Spectrometer system that is frequently deployed aboard NASA aircraft as part of the key payload sensors in validation and airborne science field experiments. One recent experiment, the Suomi NPP (SNPP) Arctic airborne field campaign (SNPP-2), was conducted out of Keflavik, Iceland between 7-31 March 2015 to address SNPP validation and JPSS risk mitigation for very cold scene observations and satellite sensor cross-validation (i.e. between the advanced satellite infrared sounders CrIS, AIRS and IASI) in the Arctic region. This paper addresses benefits achieved from such airborne validation field experiments and focuses on cold scene radiances observed during the SNPP-2 campaign; emphasis is placed on inter-comparisons between the NAST-I airborne observations and those from the Cross-track Infrared Sounder (CrIS) aboard the SNPP satellite and, with a particular focus on, handling the presence of non-uniform scene conditions.

Modern hyper- and ultra- spectral remote sensors are capable of providing spectra with thousands of channels. These channels are not independent of each other. We will analyze the information content of the hyperspectral data using principal component analysis. We will show that the information content of the original spectrum is conserved by Empirical Orthogonal Function (EOF) transformations. A radiative transfer model and a physical inversion algorithm based on principal component analysis will be presented.

Time-series of global satellite measurements allow for monitoring the global and regional environmental characteristics and associated change. Global surface skin temperature has been retrieved from MetOp-A/IASI hyperspectral infrared measurements over the past decade. Monthly and spatially-gridded surface skin temperature is produced to show some phenomena associated with its natural variability. The anomalies of surface skin temperature are used to estimate its recent decadal trend. Error estimation and evaluation has been performed and discussed in order to understand the uncertainty in the estimated trends. The trends of IASI global surface skin temperature anomalies are compared with those of the NASA Goddard Institute for Space Studies global surface air temperature anomalies. Despite the physical differences between surface skin and air temperature, reasonable agreement is shown between these two datasets indicating consistency and global surface warming during the past decade. The trend of IASI global surface skin temperature anomaly illustrates that an approximate 0.027°K/yr. global average increase has evolved during a decade long period of June 2007 – April 2018. This decadal warming trend is more pronounced in the northern hemisphere. This work demonstrates the utility of using Earth’s surface skin temperature derived from satellite measurements for monitoring global and regional surface characteristics.

The relationship between surface infrared (IR) emissivity and soil moisture content has been investigated based on satellite measurements. Surface soil moisture content can be estimated by IR remote sensing, namely using the surface parameters of IR emissivity, temperature, vegetation coverage, and soil texture. It is possible to separate IR emissivity from other parameters affecting surface soil moisture estimation. The main objective of this paper is to examine the correlation between land surface IR emissivity and soil moisture. To this end, we have developed a simple yet effective scheme to estimate volumetric soil moisture (VSM) using IR land surface emissivity retrieved from satellite IR spectral radiance measurements, assuming those other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme is applied to a decade of global IR emissivity data retrieved from MetOp-A infrared atmospheric sounding interferometer measurements. The VSM estimated from these IR emissivity data (denoted as IR-VSM) is used to demonstrate its measurement-to-measurement variations. Representative 0.25-deg spatially-gridded monthly-mean IR-VSM global datasets are then assembled to compare with those routinely provided from satellite microwave (MW) multi-sensor measurements (denoted as MW-VSM), demonstrating VSM spatial variations as well as seasonal-cycles and interannual variability. Initial positive agreement is shown to exist between IR- and MW-VSM (i.e., R-2 = 0.85). IR land surface emissivity contains surface water content information. So, when IR measurements are used to estimate soil moisture, this correlation produces results that correspond with those customarily achievable from MW measurements. A decade-long monthly-gridded emissivity atlas is used to estimate IR-VSM, to demonstrate its seasonal-cycle and interannual variation, which is spatially coherent and consistent with that from MW measurements, and, moreover, to achieve our objective of investigating the relationship between land surface IR emissivity and soil moisture. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.