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 availability of high-throughput sequencing data creates opportunities to comprehensively understand human diseases as well as challenges to train machine learning models using such high dimensions of data. Here, we propose a denoised multi-omics integration framework, which contains a distribution-based feature denoising algorithm, Feature Selection with Distribution (FSD), for dimension reduction and a multi-omics integration framework, Attention Multi-Omics Integration (AttentionMOI) to predict cancer prognosis and identify cancer subtypes. We demonstrated that FSD improved model performance either using single omic data or multi-omics data in 15 The Cancer Genome Atlas Program (TCGA) cancers for survival prediction and kidney cancer subtype identification. And our integration framework AttentionMOI outperformed machine learning models and current multi-omics integration algorithms with high dimensions of features. Furthermore, FSD identified features that were associated to cancer prognosis and could be considered as biomarkers.

The TRMM Microwave Imager has provided a nearly two-decade satellite remote sensing data set for studying climate change, and the legacy data processing is in progress and will be released in late 2017. During this reprocessing, it was discovered that the hot-load suffers an occasional transient solar intrusion, which resulted in a small systematic brightness temperature error. This paper describes an approach to remove this error.

The Tropical Rainfall Measurements Mission (TRMM) Microwave Imager (TMI), has served as the radiometric transfer standard for the TRMM constellation radiometers for over 17 years. The final legacy processing for the TMI is in progress for the TMI 1B11 Tb Version-8 (V8), which will be released in late 2017. This paper presents a radiometric evaluation of the Tb differences between the current TMI Version-7 (V7) and the newly derived V8 dataset. For this evaluation, we perform an inter-satellite radiometric calibration (XCAL) between the Global Precipitation Mission Microwave Imager (GMI) Tb product and TMI Tb products for both V7 and V8. The results of XCAL using different datasets are compared to assess the TMI V8 calibration biases relative to V7.

In this paper the inter-calibration of Microwave Humidity Sounders and Advanced Microwave Sounding Unit-B on board of NOAA and METOP satellites which are cross track polar orbiters is been investigated, and regional and seasonal dependence of the calibration bias will be discussed.

A robust XCAL double difference (DD) approach for radiometric calibration has been successfully applied between the TRMM Microwave Imager, TMI, (previous calibration transfer standard for NASA's Precipitation Measuring Mission) and a number of precipitation measuring radiometers in polar sun-synchronous orbits. Now that the TRMM Mission has ended (April 2015), the radiometric transfer standard was changed from TMI to the current GPM Microwave Imager (GMI). The use of an ocean radiative transfer model (RTM) is an integral part of our XCAL DD approach. Because the RTM physics is imperfect and because the environmental parameters are likewise only estimates of their true values, it is important to assess the sensitivity of the derived brightness temperature biases to these limitations. Therefore, in this paper, we conduct the inter-calibration between GMI and other constellation satellite microwave radiometers using the XCAL DD approach with two different ocean surface emissivity models. Results are presented that demonstrate the robustness of the XCAL DD approach for multiple instruments over a wide range of channel frequencies.

Satellite precipitation measurements began a new era with the commissioning of the new Global Precipitation Measurements Microwave Imager (GMI) in March 2014. For the previous 17 years, the Tropical Rainfall Measurements Mission (TRMM) Microwave Imager (TMI), operating in a non-sun-synchronous orbit, served as the radiometric transfer standard for the constellation radiometers. Previously, the Central Florida Remote Sensing Lab has conducted independent inter-comparisons over oceans between TRMM Microwave Imager (TMI) and the Naval Research Lab's WindSat polarimetric radiometer, and found that the radiometric calibration of TMI relative to WindSat exhibited exceptional long-term radiometric stability over > 5 years. This paper presents results of the three-way inter-calibration of GMI, TMI and WindSat brightness temperatures during the GPM/TRMM/WindSat overlap period March 2014 – March 2015.

In this paper the calibration of sensors in polar orbiter satellites in the GPM constellation has been addressed. Since the lunch of TRMM at 1998, AMSU-B sensors flown on NOAA-15, NOAA-16, NOAA-17, and the MHS sensors flown on NOAA-18, NOAA-19, MetOp-A and MetOp-B have provided an invaluable datasets of full coverage of earth which have been extensively used in weather prediction. Here, the calibration between these radiometers and regional and scan position dependence of the calibration biases has been investigated.