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.

Passive microwave (PMW) radiometric observations of tropical cyclones (TCs) from the special sensor microwave imager/sounder (SSMIS) continue the legacy monitoring capabilities initiated with the special sensor microwave/imager (SSM/I) that began in 1987. The SSMIS has the following several important differences that should be factored into applications when compared to SSM/I data: 1) channel changes from 85 to 91 GHz result in a 2-8-K brightness temperature depression for many TC inner core scenes; 2) the inclusion of bore-sighted 150-GHz data can help in detecting rapidly growing convective cells that are frequently obscured by upper level clouds in visible and infrared data and are often associated with rapid intensification; and 3) the sensor swath increases by 300 km and permits enhanced spatial and temporal coverage of global TCs. All three attributes can be incorporated to maintain and/or enhance the satellite analyst's ability to monitor critical TC structure via these PMW observations.

Passive microwave (PMW) radiometric observations of clouds from multichannel imaging sensors onboard low Earth-orbiting environmental satellites are now a vital operational dataset. The first operational passive microwave sensor was the Special Sensor Microwave/Imager onboard the Defense Meteorological Satellite Program satellites, which has been gathering hydrological data records since 1987, and continued with the Tropical Rainfall Measuring Mission (TRMM) and the Advanced Microwave Scanning Radiometer onboard Aqua. These sensors view the underlying scene with an Earth incidence angle near 53/spl deg/ and with a variable azimuthal angle, depending upon the orbit direction and scan position. The WindSat sensor onboard the Coriolis satellite, launched in January 2003, is a five-channel polarimetric PMW radiometer designed to optimize ocean surface wind vector retrievals. While it does not have 85-GHz channels, an added feature is its unique fore-aft viewing capability across a portion of its fore scan swath. This provides a view of the underlying scene from two separate azimuthal directions, which provides added information on the three-dimensional (3-D) structure of clouds and their evolution. In this paper, we compare WindSat and TRMM Precipiation Radar observations of tropical cyclones (TCs) with Monte Carlo radiative transfer simulations performed on idealized 3-D convective cloud structures. The TC 3-D structure and possible tilt in the convective cloud structure are inferred from the difference between the 37-GHz equivalent blackbody brightness temperatures (T/sub B/) from the corresponding fore and aft view observations. The information gained from this analysis is important since asymmetries in the cloud vertical and horizontal structure may be an indication of upper level wind shear, which plays a major role in influencing changes of the TC intensity.

This paper examines WindSat wind retrievals from two perspectives. The first is a statistical analysis, comparing both WindSat and QuikSCAT to model output. The second is an analysis geared toward weather forecasters based on individual case studies.

The Naval Research Laboratory Marine Meteorology Division has designed a dynamically scalable Web-based interface to serve as portal for rapid transition of its cutting-edge satellite applications. Multispectral value-added products, leveraging high spectral/spatial datasets, populate the "Satellite Focus" Web page in near real-time. Satellite Focus has played an integral and ongoing role in Naval operations during the War on Terror, including Operation Enduring Freedom and most recently the Operation Iraqi Freedom campaigns. A structural overview precedes a case study detailing the role of Satellite Focus in supporting Coalition assets during a heavy dust outbreak.

Satellite data from various sensors and platforms are being used to develop automated algorithms to assist in U.S. Navy operational weather assessment and forecasting. Supervised machine learning techniques are used to discover patterns in the data and develop associated classification and parameter estimation algorithms. These methods are applied to cloud classification in GOES imagery, tropical cyclone intensity estimation using SSM/I data, and cloud ceiling height estimation at remote locations using appropriate geostationary and polar orbiting satellite data in conjunction with numerical weather prediction output and climatology. All developed algorithms rely on training data sets that consist of records of attributes (computed from the appropriate data source) and the associated ground truth.