New satellite constellations to provide high-resolution atmospheric observations from microwave (MW) sounders operating in low-Earth orbit are now coming online and are providing operationally useful data. The first of these missions, the NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Earth Venture (EVI-3) mission, was successfully launched into orbit on May 7 and 25, 2023 (Eastern Daylight Time, two CubeSats in each of the two launches). TROPICS is now providing nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones (TCs). TROPICS is providing rapid-refresh MW measurements (median refresh rate of better than 60 min early in the mission with four functional CubeSats, and now approximately 70–90 min with three functional CubeSats) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Hundreds of high-resolution images of TCs have been captured thus far by the TROPICS mission, revealing the detailed structure of the eyewall and surrounding rain bands. The new 205-GHz channel in particular (together with a traditional channel near 92 GHz) is providing new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, is already informing TC analysis at operational centers. Here, we present an overview of the TROPICS mission after two years of successful science operations with a focus on the suite of geophysical (Level 2) products (atmospheric vertical temperature and moisture profiles, instantaneous surface rain rate, and TC intensity) and the science investigations that have been enabled by these new measurements.

The four NASA TROPICS Earth Venture (EVI-3) CubeSat constellation satellites were successfully launched into orbit on May 8 and May 26, 2023 (NZST) – two satellites were deployed in each launch. TROPICS is now providing nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at a median refresh rate of approximately 60 minutes to conduct high-value science investigations of tropical cyclones. TROPICS provides microwave measurements in twelve channels spanning 90-205 GHz over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Hundreds of high-resolution images of tropical cyclones have been captured thus far by the TROPICS mission, revealing detailed structure of the eyewall and surrounding rain bands. The new 205-GHz channel in particular (together with a traditional channel near 91.65 GHz) is providing new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, is informing tropical cyclone analysis at operational centers. In this paper, the radiance and geophysical performance of Pathfinder and the constellation satellites is presented, showing that the mission is on track to meet its baseline requirements.

We have developed and implemented a near-real-time retrieval algorithm to estimate temperature and water vapor vertical profiles from TROPICS [1] L1b brightness temperature observations. A neural network approach, with heritage in our past work [2] in the operational Atmospheric Infrared Sounder science products [3], was selected due to fast execution time, overall accuracy, and robustness to a wide variety of meteorological conditions. Here, we describe our methodology, present initial performance results on the TROPICS Pathfinder mission on test data sets, and describe our ongoing efforts to validate the algorithm.

There are a multitude of civilian and military applications for the detection of anomalous changes in hyperspectral images. Anomalous changes occur when the material within a pixel is replaced. Environmental factors that change over time, such as illumination, will affect the radiance of all the pixels in a scene, despite the materials within remaining constant. The goal of an anomalous change detection algorithm is to suppress changes caused by the environment, and detect pixels where the materials within have changed. Anomalous change detection is a two step process. Two co-registered images of a scene are first transformed to maximize the overall correlation between the images, then an anomalous change detector (ACD) is applied to the transformed images. The transforms maximize the correlation between the two images to attenuate the environmental differences that distract from the anomalous changes of importance. Several categories of transforms with different optimization parameters are discussed and compared. One of two types of ACDs are then applied to the transformed images. The first ACD uses the difference of the two transformed images. The second concatenates the spectra of two images and uses an aggregated ACD. A comparison of the two ACD methods and their effectiveness with the different transforms is done for the first time.

The remote detection and identification of gaseous chemical plumes is an important problem with many military and commercial applications. In this paper, we consider the performance of a chemical identification system that consists of a detector bank and a model averaging algorithm. Using standard detection metrics and an identification metric, we compare the two algorithms and show that cascading the two algorithms can lead to superior performance.

The adaptive cosine estimator is a popular and effective algorithm for detecting materials in hyperspectral images. To predict the performance of this algorithm in real hyperspectral scenes, a statistical model using a mixture of multivariate t-distributions for the background and a Gaussian distribution for the target is utilized. In this paper, two methods for finding the response of the adaptive cosine estimator (ACE) and Beta-detector when applied to a statistical model. To verify that the proposed techniques work as expected, t-distribution and F-distribution quantiles are computed and compared to standard values. Finally, a preliminary validation with Monte Carlo simulation based on real hyperspectral data is presented. We build on previous work for the matched filter and extends it to use two more detectors.

Detection of targets in background clutter using hyperspectral imaging sensors, is a problem of great practical interest [1]. This paper addresses some practical problems related to the adaptive estimation of clutter models and their effects on the performance of matched-signature detection algorithms. More specifically, we compare clutter estimation algorithms using spatially-local adaptation or spectral clustering to deal with the nonstationarity of hyperspectral backgrounds.

Neural networks are developed for estimating the rms accuracy profiles of individual infrared and microwave atmospheric temperature and humidity profile retrievals, thus potentially significantly improving their assimilation into numerical weather prediction models. Currently most assimilation processes compute retrieval variances or error-covariance matrices as ensemble averages over diverse profiles, or simply flag problematic retrievals. Although retrieval accuracies vary considerable from profile to profile because of clouds, even in cloud-free cases they can differ markedly. The ability to estimate accurately the variances of individual profiles is one of the benefits of hyperspectral infrared and microwave sounding. The variance-estimating neural network was trained to estimate the logarithm of variance, which was then mapped to standard deviation. Examples utilizing AIRS/AMSU/HSB soundings [1] on the NASA Aqua satellite and those from a proposed hyperspectral microwave sounder [2], [3] show that when the predicted rms errors for a single altitude are stratified, they agree with the actual rms errors within perhaps ten percent of the dynamic range of the stratifications thus significantly improving the potential for accurately weighting soundings against model parameters during assimilation. Simple quality indicators using the new variance estimates also favorably compare to AIRS Level 2 Version 5 quality flags.

We introduce a new hyperspectral microwave remote sensing modality for atmospheric sounding, driven by recent advances in microwave device technology that now permit receiver arrays that can multiplex multiple broad frequency bands into more than 100 spectral channels, thus improving both the vertical and horizontal resolutions of the retrieved atmospheric profile. Global simulation studies over ocean and land in clear and cloudy atmospheres using three different atmospheric profile databases are presented that assess the temperature, moisture, and precipitation sounding capability of several notional hyperspectral systems with channels sampled near the 50-60-, 118.75-, and 183.31-GHz absorption lines. These analyses demonstrate that hyperspectral microwave operation using frequency multiplexing techniques substantially improves temperature and moisture profiling accuracy, particularly in atmospheres that challenge conventional nonhyperspectral microwave sounding systems because of high water vapor and cloud liquid water content. Retrieval performance studies are also included that compare hyperspectral microwave sounding performance to conventional microwave and hyperspectral infrared approaches, both in a geostationary and a low-Earth-orbit context, and a path forward to a new generation of high-performance all-weather sounding is discussed.

We introduce a new hyperspectral microwave remote sensing modality for atmospheric sounding, driven by recent advances in microwave device technology that now permit receiver arrays that can multiplex multiple broad frequency bands into more than ~ 100 spectral channels, thus improving both the vertical and horizontal resolution of the retrieved atmospheric profile. Global simulation studies over ocean and land in clear and cloudy atmospheres using three different atmospheric profile databases are presented that assess the temperature, moisture, and precipitation sounding capability of several notional hyperspectral systems with channels sampled near the 50-60-GHz, 118.75-GHz, and 183.31-GHz absorption lines. These analyses demonstrate that hyperspectral microwave operation using frequency multiplexing techniques substantially improves temperature and moisture profiling accuracy, especially in atmospheres that challenge conventional non-hyperspectral microwave sounding systems because of high water vapor and cloud liquid water content. Retrieval performance studies are also included that compare hyperspectral microwave sounding performance to conventional microwave and hyperspectral infrared approaches, both in a geostationary and low-earth orbit context, and a path forward to a new generation of high-performance all-weather sounding is discussed.

A novel statistical method for the retrieval of atmospheric temperature and water vapor profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) on the NASA Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) and AMSU on the EUMETSAT MetOp-A satellite. The present work focuses on the cloud impact on the AIRS and IASI radiances and explores the use of the stochastic cloud clearing methodology together with neural network estimation. A stand-alone statistical algorithm will be presented that operates directly on cloud-impacted AIRS/AMSU and IASI/AMSU data, with no need for a physical cloud clearing process. The performance of this method was evaluated using global (ascending and descending) EOS-Aqua orbits collocated with ECMWF fields for a variety of days throughout 2003, 2004, 2005, and 2006. Over 1,000,000 fields of regard (3×3 arrays of footprints) over ocean and land were used in the study. The method requires significantly less computation than traditional variational retrieval methods, while achieving comparable performance. Retrieval accuracy will be evaluated using ECMWF atmospheric fields as ground truth. The accuracy of the neural network retrieval method will be compared to the accuracy of the AIRS Level 2 (Version 5) retrieval method.