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.
Figure 1: A prototype of CloudCube’s G-band channel was installed at Cape Grim, Tasmania, as a guest instrument for the Department of Energy’s Cloud and Precipitation Experiment at Kennaook (CAPE-K)
Credit: Raquel Rodriguez Monje / JPL
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.
Figure 2. A photo of the radar electronics for CloudCube’s compact G-band radar. Producing G-band radar signals requires a large amount of energy, and CloudCube is one of the first instruments to produce those signals effectively from a compact platform.
Credit: Raquel Rodriguez Monje / NASA JPL
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 foundhere.
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 onNASA TechPort.
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.
Figure 1: A prototype of CloudCube’s G-band channel was installed at Cape Grim, Tasmania, as a guest instrument for the Department of Energy’s Cloud and Precipitation Experiment at Kennaook (CAPE-K)
Credit: Raquel Rodriguez Monje / JPL
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.
Figure 2. A photo of the radar electronics for CloudCube’s compact G-band radar. Producing G-band radar signals requires a large amount of energy, and CloudCube is one of the first instruments to produce those signals effectively from a compact platform.
Credit: Raquel Rodriguez Monje / NASA JPL
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 foundhere.
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 onNASA TechPort.
A quantum metasurface breakthrough boosts terahertz detection by 20 times, bringing practical THz technology a step closer. Detecting light and radiation is essential across the electromagnetic spectrum, but some regions remain difficult to work with. One of the biggest challenges involves terahertz (THz) radiation, which sits between microwaves and infrared light. Existing detectors for these [...]
L’intelligence artificielle va-t-elle remplacer ou sublimer nos capacités ? Comment la « GreenTech » peut-elle nous aider à relever le défi climatique ? Pour toucher du doigt ces réponses, direction Viva Technology ! Le plus grand salon de la tech et des startups en Europe ouvre ses portes au...
A new propulsion system feeds the same "monopropellant" fuel into electrical and chemical thrusters, potentially saving on mass and complications for future missions to Mars.
A new Chinese quantum computing system pairs two independent neutral-atom arrays in one processor, aiming to boost stability, efficiency and scalability.
Last year, Microsoft announced its Majorana quantum computing chip, which supposedly used topological qubits to make calculations. The problem was that the paper published alongside the announcement didn’t show any evidence for those claims. Now they’ve announced a new quantum chip. Is it any better this time?. Let’s take a look.
Across the stories today, a common thread emerges: the growing tension between our rapidly expanding digital world and the natural systems struggling to sustain it. From AI data centers planned for drought-stricken regions to coal plants getting emergency extensions, we’re witnessing the collision between technological acceleration and environmental limits.
The numbers tell a stark story. Two-thirds of new AI data centers are slated for areas already grappling with water scarcity, while these facilities demand massive amounts of both electricity and cooling water. Meanwhile, Florida’s coal plant—scheduled for retirement as part of a clean energy transition—gets an emergency lifeline to keep the lights on. It’s a reminder that progress and pressure often arrive together, forcing difficult choices between immediate needs and long-term sustainability.
The day’s coverage points to growing momentum around accountability, particularly in how we fund and regulate resource-intensive industries. Australian lawmakers are pushing for fair taxation of AI infrastructure, determined not to repeat what they see as mistakes with gas exports. North Carolina legislators face their own paradox, crafting bills that simultaneously curb data center expansion while boosting fossil fuel development. These policy tensions reflect a broader reckoning: how do we harness technological progress without overwhelming the ecosystems that support us?
Yet signs of genuine environmental recovery punctuate today’s more challenging headlines. Water voles have returned to an English nature reserve after four decades, while marine protected areas now cover 10% of the world’s oceans—a significant milestone toward the 30% goal by 2030. In Sumatra’s hills, coffee farmers like Sri Atmiatun are proving that conservation and community prosperity can grow together, restoring forests while securing livelihoods through innovative social forestry programs.
Behind the numbers are real communities adapting in real time. Alaska’s fishing industry faces uncertainty as critical ocean monitoring systems face budget cuts, while Arizona’s San Carlos Lake sits closed indefinitely after drought and dam releases killed all fish life. These aren’t abstract environmental statistics—they’re disruptions to ways of life, forcing entire communities to reimagine their relationships with changing landscapes.
The financial flows reveal where power and priorities truly lie. The world’s largest banks poured a record $906 billion into fossil fuels despite climate commitments, an 8% increase that environmental researchers call “unfathomable.” Meanwhile, China launched the world’s first wind-powered underwater data center, signaling that sustainable technology infrastructure is possible when political will aligns with innovation.
Perhaps most troubling is what we’re losing while focused on these larger battles. Wildfire smoke has erased four years of air quality progress across the United States, while sea levels are now rising at twice the rate recorded just a decade ago. Scientists are racing to record forest sounds before wild spaces fall silent—a poignant reminder that some losses can’t be reversed.
The day’s stories also illuminate the gap between good intentions and effective action. Louisiana legislators unanimously approved expanding their wood pellet industry despite the bill’s sponsor admitting he knows little about the sector. FIFA appears unprepared for wildfire smoke threats at upcoming World Cup venues. These moments reveal how quickly environmental challenges can outpace institutional readiness.
As the week unfolds, all eyes will be on whether emerging solutions—from underwater data centers to marine protection expansions—can scale fast enough to match the magnitude of the challenges. The central question remains: can we innovate our way toward sustainability, or will the pace of environmental change force more fundamental reckonings with how we live and build in a resource-constrained world?
A new proposed Senate bill aims to establish clear guardrails for the U.S. military’s use of artificial intelligence (AI), drawing a definitive line on one of the most consequential questions in modern warfare: who has the final say when lethal force is used?
On June 8, Senator Adam Schiff (D-Calif.) introduced the proposed Human Authority in Lethal Operations Act of 2026, or “HALO Act,” which would impose sweeping requirements on the Department of War (DoW) to ensure that autonomous and semi-autonomous weapon systems remain under meaningful human control.
The legislation would establish new design standards, testing requirements, oversight procedures, and legal safeguards for military systems that use artificial intelligence to identify, recommend, prioritize, or engage targets.
The bill aims to ensure that responsibility for battlefield decisions remains with human commanders, rather than shifting to algorithms. The legislation would require the Pentagon to designate accountable commanders for any engagement involving AI-enabled autonomous or semi-autonomous weapons and mandates that those commanders retain “ultimate discretion, judgment, and control over the use of force.”
“The past few months have shown us that there is an urgent need for commonsense guardrails to ensure the Defense Department’s use of AI is in line with Americans’ national security and privacy priorities,” Sen. Schiff said in a press release. “There are good reasons to use AI technology to advance our national security,” although he emphasized that, like with any tool, AI should not be allowed solely to guide human enterprises, “particularly when the risks of harm can be fatal.”
The proposal arrives amid growing concern among policymakers, military planners, and technology experts about the increasing role of artificial intelligence in combat operations.
Significantly, these concerns are no longer theoretical. As The Debrief previously reported, Israel’s reported use of the AI targeting system “Lavender” in Gaza triggered renewed debate over how much trust militaries should place in algorithmic target selection, particularly when civilian lives may be at risk.
Other recent examples have added to the concern, including studies showing that AI models can escalate dangerously in simulated war games, as well as the Pentagon’s continued push toward autonomous drones, AI-controlled aircraft, and robotic battlefield systems.
Even Pope Leo XIV recently weighed in on the issue in Magnifica Humanitas, his first encyclical, or formal papal teaching letter, warning that autonomous weapons could make war more feasible and less subject to human control, and arguing that lethal decisions must not be delegated to opaque or automated processes.
While the Pentagon has repeatedly stressed its commitment to responsible AI development, the HALO Act would codify many of those principles into law and create new statutory requirements governing how military AI systems are designed, tested, approved, and monitored.
Among the bill’s most significant provisions are requirements that autonomous weapon systems operate within predefined geographic areas, target sets, and timeframes. If a system cannot complete an engagement within those authorized parameters, it would be required to terminate the engagement until additional human operator and commander review is completed.
The legislation also calls for extensive record-keeping, cybersecurity protections, and continuous oversight to guard against unintended engagements and hostile manipulation.
The bill would also require senior Pentagon officials to conduct formal reviews before AI-enabled weapons enter development and again before they are fielded. Those reviews would assess legal compliance, operational safety, reliability, cybersecurity, and risks to civilians and protected infrastructure under the Law of Armed Conflict.
Beyond battlefield autonomy, the legislation targets several controversial uses of artificial intelligence in national security. It would prohibit covered Defense Department AI capabilities from inferring a person’s religion, race, political identity, sexual orientation, or emotional state. It would also restrict the purchase or receipt of Americans’ personal data from data brokers or other third-party sources when that data was obtained in a manner that would violate the bill’s rules.
Perhaps most notably, the proposed HALO Act explicitly forbids removing humans from decision-making processes involving presidential actions related to nuclear weapons.
“No covered artificial intelligence capability may be used with the intent, purpose, or outcome of… removing a human from the chain of decisionmaking for actions critical to informing and executing decisions by the President, including initiating or terminating nuclear weapons employment,” the proposed bill reads.
The measure also includes whistleblower protections for personnel working on military AI systems, mandates regular reporting to Congress, and requires the Pentagon to identify and remediate any fielded systems that fail to meet the law’s requirements.
Existing autonomous systems would undergo a comprehensive review, and any system found to be noncompliant could face retirement, modification, or suspension pending corrective action.
While the bill includes exemptions for certain defensive systems, cyber capabilities, mines, and other categories of military technology, its wider message is that as artificial intelligence becomes more deeply integrated into national defense, Congress wants humans, not machines, to remain responsible for high-stakes decisions.
The HALO Act is not the only effort underway on Capitol Hill to establish guardrails around military AI.
In March, Senator Elissa Slotkin (D-MI) introduced the AI Guardrails Act. Last week, Senator Kirsten Gillibrand (D-N.Y.) introduced separate legislation that would similarly prohibit the Pentagon from using artificial intelligence to launch nuclear weapons, conduct warrantless surveillance of Americans, or deploy fully autonomous weapons.
“Right now, the Pentagon is moving toward deploying incredibly powerful AI technology without commonsense guardrails in place, which could have catastrophic consequences that make all of us less safe,” Sen. Gillibrand said in a statement. “We must act now – not to stifle technological progress, but to establish clear rules of the road that keep humans in charge and keep AI’s use in warfare smart and safe.”
The competing proposals could set the stage for a broader congressional debate over the role of artificial intelligence in future military operations, particularly if lawmakers seek to incorporate them into the Fiscal Year 2027 National Defense Authorization Act (NDAA).
Before that can happen, however, the HALO Act and similar proposals must navigate the legislative process. The bills will first be considered by the relevant Senate committee, where lawmakers may hold hearings, debate their provisions, amend the text, or decline to advance them. If approved, it would then need to pass both the Senate and the House before being sent to the president for signature, either as a standalone measure or as part of a larger NDAA.
“My legislation would protect Americans from unlawful domestic surveillance, ensure that humans in the chain of command exercise responsibility for the use of any lethal technology, and maintain strong ethical protections in the deployment of autonomous and semi-autonomous weapons,” Sen. Schiff said in a statement.
Tim McMillan is a retired law enforcement executive, investigative reporter and co-founder of The Debrief. His writing typically focuses on defense, national security, the Intelligence Community and topics related to psychology. You can follow Tim on Twitter:@LtTimMcMillan. Tim can be reached by email: tim@thedebrief.org or through encrypted email:LtTimMcMillan@protonmail.com
As the United States grapples with record-breaking drought conditions across much of the country, the booming artificial intelligence industry appears to be on a collision course with water scarcity. New analysis reveals that approximately two-thirds of planned AI data centers are slated for construction in areas that have experienced some of the most severe drought conditions over the past year.
This concerning trend highlights a fundamental conflict between technological advancement and environmental sustainability. AI data centers are notorious for their enormous water consumption, requiring vast quantities of water for cooling systems to prevent the massive server farms from overheating. These facilities can consume millions of gallons of water daily, putting additional strain on already stressed water resources in drought-affected regions.
The timing couldn’t be worse for many communities already struggling with water shortages. The current drought has shattered previous records, leaving reservoirs at critically low levels and forcing water restrictions in numerous states. Yet the AI industry’s expansion continues largely unabated, with companies apparently prioritizing factors such as energy costs, tax incentives, and connectivity over water availability when selecting locations for new facilities.
This development raises serious questions about the sustainability of AI expansion and whether current planning approaches adequately consider long-term environmental impacts. As public concern grows over the water demands of AI infrastructure, communities and policymakers are increasingly calling for more responsible site selection and water-efficient technologies in data center development.
A growing grassroots movement in Australia is demanding that technology companies pay their fair share of taxes if they plan to establish energy-intensive AI data centers across the country. The campaign draws parallels to Australia’s experience with gas exports, where critics argue the nation failed to capture adequate revenue from companies extracting and exporting natural resources.
Over recent months, tens of thousands of Australians have contacted their representatives advocating for a 25% tax on gas exports, with more than 2,200 citizens funding billboard campaigns to promote the initiative. This momentum reflects broader concerns about ensuring foreign corporations contribute appropriately to public coffers when utilizing Australia’s natural resources and infrastructure.
The debate now extends to artificial intelligence infrastructure, as tech giants eye Australia for data center expansion. These facilities require enormous amounts of electricity, water for cooling systems, and land – all drawing heavily on public resources and utilities. Environmental advocates worry that without proper taxation frameworks, Australia could repeat past mistakes where resource extraction generated substantial corporate profits while providing minimal returns to taxpayers.
Proponents argue that fair taxation of AI data centers would not only generate revenue for public services and infrastructure but also ensure that the environmental costs of these energy-intensive operations are properly accounted for. As the AI industry continues rapid expansion globally, Australia faces a critical decision about how to balance economic opportunities with fair compensation for the use of national resources.
Datacentre off Shanghai coast uses less power and water than land-based equivalent
The world’s first wind-powered underwater datacentre has started operations off the coast of Shanghai, as China presses forwards with solutions for energy challenges created by the country’s artificial intelligence boom.
The Shanghai Lingang undersea datacentre demonstration project, which launched in May, has a capacity of 24 megawatts. It is a joint effort between HiCloud Technology and China Communications Construction, a state-owned company.
Scientists have finally packed a laboratory-class ultrafast laser onto a tiny photonic chip. Ultrafast lasers generate bursts of light that last only a few hundred femtoseconds, each one just a quadrillionth of a second long. These extremely short pulses are used in a wide range of technologies, including precision manufacturing, eye surgery, and optical frequency [...]
Researchers may have unlocked the future of computing by turning flat silicon chips into densely stacked 3D architectures. For decades, the semiconductor industry has boosted computing power by making transistors smaller and fitting more of them onto a single chip. That strategy has fueled remarkable advances in electronics, but it is now approaching fundamental physical [...]
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