From orbit, today’s space-based laser instruments measure Earth’s forests and terrain one narrow line at a time. Between those lines are enormous gaps that include millions of acres of land scientists never directly measure. NASA’s CASALS instrument will blend machine learning with an ultra-compact lidar instrument to adaptively ‘sweep’ large swaths of Earth’s surface. This new capability for space-based remote sensors allows CASALS to create truly three-dimensional (3D) topographical renderings. 

A team of researchers at NASA’s Goddard Space Flight Center (GSFC) has developed a new instrument designed to eliminate blind spots between lines of lidar data on the Earth’s surface. Built with support from NASA’s Earth Science Technology Office (ESTO), the Concurrent Artificially-intelligent Spectrometry Lidar System (CASALS) could help researchers map the features of Earth’s surface with unprecedented detail. Rather than mechanically steering a laser with moving mirrors, CASALS steers light using wavelength itself, generating a continuous, 3D swath of the ground below. 

John Yorks, a physical research scientist at GSFC and Principal Investigator for CASALS, explained that the instrument features a novel transmitter and optical grating system that manipulates laser pulses without heavy, failure-prone motorized mirrors. 

Instead, CASALS uses a fast-pulsing, tunable “seed” laser centered at 1040 nanometers, rapidly cycling through 256 unique wavelengths as it pulses through an optical component with a microscopic pattern of ridges called a transmission grating. 

At this point, the physics of diffraction takes over. Because light bends at slightly different angles depending on its exact wavelength, the grating naturally “steers” each of the 256 pulses into a distinct position across the flight path. 

This non-mechanical approach paints a continuous 3D swath of the ground. Not only does this eliminate the large unmapped gaps left between the isolated tracks of legacy spaceborne lidars, but stripping away moving parts drastically reduces the instrument’s size, weight, and power (SWaP) requirements. This makes it more reliable for the harsh environment of space. 

Another key innovation within CASALS is its detector array. This component features a uniquely high Quantum Efficiency (QE), a variable that describes how effectively a detector converts incoming light (photons) into an electrical signal. CASALS operates at 80% QE. This means CASALS wastes almost none of the light that makes it back to the sensor. 

With QE that high, CASALS can detect much weaker return signals, allowing it to collect useful measurements from darker surfaces or through denser vegetation. 

This technology isn’t limited to a laboratory environment. In November 2024, an airborne CASALS prototype flew aboard NASA’s B200 King Air aircraft. The instrument produced science-quality datasets on its first campaign, exceeding the team’s expectations. 

“I’m very impressed with Guang Yang, Jeffrey Chen, and all our engineers, and how well CASALS performed. This worked really well from the first time we flew it, which is pretty impressive,” said Yorks. 

Flying at an altitude of roughly five kilometers, CASALS can distinguish individual tree branches and subtle changes in ground elevation, thanks to its 22-centimeter horizontal resolution. 

“We’re able to see really fine details of vegetation structure and changes in the topography of the surface,” Yorks said. 

The team aims to fly CASALS again as part of the Airborne Surface, Cryosphere, Ecosystem, and Nearshore Topography (ASCENT) campaign, scheduled for summer 2027. 

But there’s more work to do to prepare the technology for space. The team plans to make several improvements to CASALS before creating a space-ready prototype. Specifically, they’re widening the instrument’s swath and adding onboard data processing software. 

Transitioning from a successful airborne prototype to a fully realized space mission is a challenge, but the CASALS team is up to the task. 

“The team’s really done a great job of sticking together and keeping on task and on schedule. We are being funded to optimize and improve the performance, and we’re also then looking at ways to take the technologies used in the airborne instrument and mature them and prepare them for a spaceborne version in the future,” said Yorks. 

If successful, CASALS could become the first spaceborne lidar capable of mapping Earth’s forests and terrain as continuous three-dimensional swaths rather than isolated profiles, giving scientists a dramatically more complete picture of our changing planet. 

For additional details, see the entry for this project on NASA TechPort. 

Project Lead(s): John Yorks, NASA’s Goddard Space Flight Center 

Sponsoring Organization(s): NASA’s Decadal Survey Incubation (DSI) program; NASA’s Instrument Incubation Program (IIP); NASA’s Goddard Space Flight Center; University of California, Santa Barbara; Virginia Polytechnic Institute and State University 

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The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently. This new software is the result of a collaboration between NASA’s Jet Propulsion Laboratory, the University of California, San Diego (UCSD), and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS).

Created with support from NASA’s Earth Science Technology Office (ESTO), TACLS leverages machine learning to automatically locate evidence (unusual increases in atmospheric moisture) of impending flash flooding that meteorologists may otherwise miss as they analyze large amounts of data. TACLS flags that evidence, indicates where flash flooding could likely occur, and displays that information via a user-friendly visualization for human analysts to interpret. Those analysts can then decide whether to issue a flash flood warning or weather advisory.

This novel framework for tracking extreme weather events and predicting imminent flash floods operates in near real-time, producing forecasts in as little as fifteen minutes.

“That’s really what we wanted to do, to give meteorologists a tool to help decision making for flash flood warnings,” said Yehuda Bock, Distinguished Researcher at the UCSD Scripps Institution of Oceanography and principal investigator for TACLS.

In simulations testing, TACLS used data from diverse severe weather events—including atmospheric rivers, monsoonal convection, and tropical cyclone remnants—between 2017 and 2023 and successfully captured 93% of the issued flash-flood warnings. Meteorologists from the National Weather Service are currently working to incorporate TACLS into their existing systems for forecasting flash floods in Southern California.

This learning system has two main components. First, an analytic back-end software suite uses machine learning algorithms to process satellite data and determine areas at risk for flooding. Second, user-friendly visualization software highlights those areas for further analysis by humans.

The ACLS back-end software analyzes data from satellites in the Global Navigation Satellite System (GNSS), a constellation of satellite networks that drive navigation services around the world. Water vapor in the troposphere delays signals from these satellites as they travel to Earth. This signal delay can be analyzed to calculate the amount of water vapor in the atmosphere over a particular location on Earth.

The TACLS analytic back-end software suite features a machine learning model trained using more than 30 years of past GNSS data. This model is an anomaly detector that tracks unusual increases in atmospheric moisture. The model then carefully examines that atmospheric moisture data and determines whether it’s either an artifact (a false feature or distortion in the data) or a transient (a time-sensitive physical event, like heavy precipitation) that requires interpretation by human analysts.

If TACLS determines the data represents a transient, such as an extreme weather event that warrants a flash flood warning, it will forward that data to the TACLS visualization software (MGViz) for further evaluation by humans. The analysts use their judgement and experience to interpret these events and determine whether the flagged data indicates a flash flood is likely, and, if necessary, issue a flash flood warning.

Several past innovations developed at JPL are leveraged by TACLS to process GNSS data and present the results. The analytic back-end software suite incorporates elements from JPL’s Domain-agnostic Outlier Ranking Algorithms program and the Time-series Forecasting, Evaluation, and Deployment program. The TACLS visualizer is based on the Multi-Mission Geographic Information System, originally developed at JPL for NASA’s Mars missions.

The TACLS software binds all these components within a novel system that enhances existing methods to reduce the amount of time it takes for a human analyst to determine whether to issue a flash flood warning.

Both the TACLS software and the data used to train it will be open-source, allowing scientists to either tailor this model in response to their unique research needs or create their own model from scratch.

For additional details, see the entry for this project on NASA TechPort. 

Project Lead: Dr. Yehuda Bock, University of California, San Diego. 

Sponsoring Organization(s): NASA’s Earth Science Technology Office Advanced Information Systems Technology Program; JPL; NOAA; National Weather Service. 

The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently. This new software is the result of a collaboration between NASA’s Jet Propulsion Laboratory, the University of California, San Diego (UCSD), and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS).

Created with support from NASA’s Earth Science Technology Office (ESTO), TACLS leverages machine learning to automatically locate evidence (unusual increases in atmospheric moisture) of impending flash flooding that meteorologists may otherwise miss as they analyze large amounts of data. TACLS flags that evidence, indicates where flash flooding could likely occur, and displays that information via a user-friendly visualization for human analysts to interpret. Those analysts can then decide whether to issue a flash flood warning or weather advisory.

This novel framework for tracking extreme weather events and predicting imminent flash floods operates in near real-time, producing forecasts in as little as fifteen minutes.

“That’s really what we wanted to do, to give meteorologists a tool to help decision making for flash flood warnings,” said Yehuda Bock, Distinguished Researcher at the UCSD Scripps Institution of Oceanography and principal investigator for TACLS.

In simulations testing, TACLS used data from diverse severe weather events—including atmospheric rivers, monsoonal convection, and tropical cyclone remnants—between 2017 and 2023 and successfully captured 93% of the issued flash-flood warnings. Meteorologists from the National Weather Service are currently working to incorporate TACLS into their existing systems for forecasting flash floods in Southern California.

This learning system has two main components. First, an analytic back-end software suite uses machine learning algorithms to process satellite data and determine areas at risk for flooding. Second, user-friendly visualization software highlights those areas for further analysis by humans.

The ACLS back-end software analyzes data from satellites in the Global Navigation Satellite System (GNSS), a constellation of satellite networks that drive navigation services around the world. Water vapor in the troposphere delays signals from these satellites as they travel to Earth. This signal delay can be analyzed to calculate the amount of water vapor in the atmosphere over a particular location on Earth.

The TACLS analytic back-end software suite features a machine learning model trained using more than 30 years of past GNSS data. This model is an anomaly detector that tracks unusual increases in atmospheric moisture. The model then carefully examines that atmospheric moisture data and determines whether it’s either an artifact (a false feature or distortion in the data) or a transient (a time-sensitive physical event, like heavy precipitation) that requires interpretation by human analysts.

If TACLS determines the data represents a transient, such as an extreme weather event that warrants a flash flood warning, it will forward that data to the TACLS visualization software (MGViz) for further evaluation by humans. The analysts use their judgement and experience to interpret these events and determine whether the flagged data indicates a flash flood is likely, and, if necessary, issue a flash flood warning.

Several past innovations developed at JPL are leveraged by TACLS to process GNSS data and present the results. The analytic back-end software suite incorporates elements from JPL’s Domain-agnostic Outlier Ranking Algorithms program and the Time-series Forecasting, Evaluation, and Deployment program. The TACLS visualizer is based on the Multi-Mission Geographic Information System, originally developed at JPL for NASA’s Mars missions.

The TACLS software binds all these components within a novel system that enhances existing methods to reduce the amount of time it takes for a human analyst to determine whether to issue a flash flood warning.

Both the TACLS software and the data used to train it will be open-source, allowing scientists to either tailor this model in response to their unique research needs or create their own model from scratch.

For additional details, see the entry for this project on NASA TechPort. 

Project Lead: Dr. Yehuda Bock, University of California, San Diego. 

Sponsoring Organization(s): NASA’s Earth Science Technology Office Advanced Information Systems Technology Program; JPL; NOAA; National Weather Service. 

The post NASA Uses Machine Learning to Enhance Flash Flood Warnings appeared first on NASA Science.

A compact, multifrequency radar built by a team at NASA’s Jet Propulsion Laboratory will make it easier to collect information about dynamic cloud systems. Called CloudCube, this new instrument simultaneously probes the atmosphere with three radar signals, spanning 36 to 240 GHz, for optimized sensitivity to a wide range of water droplet and ice particle sizes. 

Built with funding from NASA’s Earth Science Technology Office Instrument Incubator Program, CloudCube transmits and receives Ka-, W-, and G-band signals, making it the first compact radar system capable of simultaneously probing meteorological targets at wavelengths spanning approximately one to ten millimeters. Researchers will be able to combine information from the three signals to learn more about the initiation and evolution of precipitation, as well as cloud microphysics and radiative properties.

“We’re making a low-power, low-mass instrument to facilitate new cost-efficient missions for atmospheric observations. Building a multi-frequency radar, especially at G-band, is very novel,” said Raquel Rodriguez Monje, a systems engineer at JPL and principal investigator for CloudCube.

Each of CloudCube’s three signals observes a different element of cloud physics. Ka-band radar signals are ideal for collecting precipitation profiles; W-band radar signals are preferred for measuring cloud particles that give rise to precipitation; and G-band radar signals, which have never been collected from a space-based instrument, are ideal for measuring ice and liquid water content inside very light clouds (a paper describing this measurement can be found here).

Probing the atmosphere simultaneously with three signals allows researchers to collect data on all these cloud features at once, which is valuable for improving weather forecasts and especially climate modeling. CloudCube leverages innovations in millimeter-wave hardware to pack three radar modules–one for each signal–within a single compact system.

One CloudCube innovation concerns the specialized components required to transmit G-band power from a compact, low-power instrument. The detection of cloud signals requires high transmit power, which CloudCube achieves by combining the outputs of multiple high-efficiency frequency-multiplication devices that allow the instrument to generate hundreds of milliWatts at 240 GHz. Another innovation of CloudCube is that it was designed to use as few radio frequency components as possible to reduce its mass and power consumption, which could lower the cost of future Earth-observing orbital instruments.

Flying an instrument equipped with G-band radar in space will be a new capability and will allow researchers to achieve greater spatial resolution and sensitivity in the study of cloud microphysical processes.

“Basically, we’re weighing clouds using these combinations of frequencies in a way that we couldn’t do before we had the G-band,” said Matt Lebsock, a researcher at JPL and co-investigator for CloudCube.

The instrument has been tested in the field. A ground-based prototype of CloudCube’s G-band channel operated continuously for 11 months during the Department of Energy’s Cloud and Precipitation Experiment at Kennaook (CAPE-K) campaign. CloudCube also participated in the Eastern Pacific Cloud Aerosol Precipitation Experiment, a ground campaign sponsored by the Department of Energy. A paper describing the results of that experiment can be found here.

Most recently, CloudCube successfully operated all three frequency bands from NASA’s Gulfstream III aircraft and collected its first airborne observations of snowfall as part of the North American Upstream Feature-Resolving and Tropopause Uncertainty Reconnaissance Experiment campaign—a NASA-funded campaign designed to improve forecasts of high-impact winter weather. The CloudCube team is currently calibrating and processing the data for public release.

For additional details, see the entry for this project on NASA TechPort.

Project Lead: Dr. Raquel Rodriguez Monje, NASA’s Jet Propulsion Laboratory

Sponsoring Organization: NASA’s Earth Science Technology Office Instrument Incubation Program

A compact, multifrequency radar built by a team at NASA’s Jet Propulsion Laboratory will make it easier to collect information about dynamic cloud systems. Called CloudCube, this new instrument simultaneously probes the atmosphere with three radar signals, spanning 36 to 240 GHz, for optimized sensitivity to a wide range of water droplet and ice particle sizes. 

Built with funding from NASA’s Earth Science Technology Office Instrument Incubator Program, CloudCube transmits and receives Ka-, W-, and G-band signals, making it the first compact radar system capable of simultaneously probing meteorological targets at wavelengths spanning approximately one to ten millimeters. Researchers will be able to combine information from the three signals to learn more about the initiation and evolution of precipitation, as well as cloud microphysics and radiative properties.

“We’re making a low-power, low-mass instrument to facilitate new cost-efficient missions for atmospheric observations. Building a multi-frequency radar, especially at G-band, is very novel,” said Raquel Rodriguez Monje, a systems engineer at JPL and principal investigator for CloudCube.

Each of CloudCube’s three signals observes a different element of cloud physics. Ka-band radar signals are ideal for collecting precipitation profiles; W-band radar signals are preferred for measuring cloud particles that give rise to precipitation; and G-band radar signals, which have never been collected from a space-based instrument, are ideal for measuring ice and liquid water content inside very light clouds (a paper describing this measurement can be found here).

Probing the atmosphere simultaneously with three signals allows researchers to collect data on all these cloud features at once, which is valuable for improving weather forecasts and especially climate modeling. CloudCube leverages innovations in millimeter-wave hardware to pack three radar modules–one for each signal–within a single compact system.

One CloudCube innovation concerns the specialized components required to transmit G-band power from a compact, low-power instrument. The detection of cloud signals requires high transmit power, which CloudCube achieves by combining the outputs of multiple high-efficiency frequency-multiplication devices that allow the instrument to generate hundreds of milliWatts at 240 GHz. Another innovation of CloudCube is that it was designed to use as few radio frequency components as possible to reduce its mass and power consumption, which could lower the cost of future Earth-observing orbital instruments.

Flying an instrument equipped with G-band radar in space will be a new capability and will allow researchers to achieve greater spatial resolution and sensitivity in the study of cloud microphysical processes.

“Basically, we’re weighing clouds using these combinations of frequencies in a way that we couldn’t do before we had the G-band,” said Matt Lebsock, a researcher at JPL and co-investigator for CloudCube.

The instrument has been tested in the field. A ground-based prototype of CloudCube’s G-band channel operated continuously for 11 months during the Department of Energy’s Cloud and Precipitation Experiment at Kennaook (CAPE-K) campaign. CloudCube also participated in the Eastern Pacific Cloud Aerosol Precipitation Experiment, a ground campaign sponsored by the Department of Energy. A paper describing the results of that experiment can be found here.

Most recently, CloudCube successfully operated all three frequency bands from NASA’s Gulfstream III aircraft and collected its first airborne observations of snowfall as part of the North American Upstream Feature-Resolving and Tropopause Uncertainty Reconnaissance Experiment campaign—a NASA-funded campaign designed to improve forecasts of high-impact winter weather. The CloudCube team is currently calibrating and processing the data for public release.

For additional details, see the entry for this project on NASA TechPort.

Project Lead: Dr. Raquel Rodriguez Monje, NASA’s Jet Propulsion Laboratory

Sponsoring Organization: NASA’s Earth Science Technology Office Instrument Incubation Program

The post NASA’s CloudCube Pioneers Miniaturized Radar to Study Clouds, Precipitation appeared first on NASA Science.