Introducing Tenshi: Systematic Weather Observations
New technologies are redefining environmental monitoring, delivering unprecedented precision from expansive global models to critical hyper-local intelligence. It is time to close the gap with ground-level weather truth. Meet our AI Robotics Observational Nowcasting Platform: Tenshi.
Empowering Decisions. Safeguarding Our Future.
Why closing this Weather Gap matters?
The Challenge
Sparse ground-based static sensors and fragmented data have created critical forecasting gaps, particularly in the Global South, but also within North America. Traditional systems struggle to provide the precision needed for life-saving decisions, considering the erratic nature of nowadays severe and extreme weather events.
The Silver Lining
A 2025 Nature study on FuXi Weather demonstrated that AI-based systems outperform the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution forecasts beyond day one in observation-sparse regions such as central Africa (Nature Communications, 2025).
The Bottom Line
In 2024, weather disasters cost the U.S. $182.7B. The resolution revolution provides the intelligence needed to reduce these losses and protect vulnerable communities.
The Urban Epicenter
Where Risk Meets Reality
While global models provide crucial context, environmental impacts hit our cities hardest. Densely populated urban centers amplify risks such as extreme heat and devastating flooding, making accurate, high-resolution forecasts indispensable. These precise predictions are vital for safeguarding lives, protecting critical infrastructure, and sustaining local economies.
Preserving Lives. Protecting Infrastructure. Sustaining Economies.
In 2024 alone, the U.S. experienced 27 billion-dollar weather and climate disasters, costing approximately $182.7 billion and causing at least 568 fatalities. Flooding remains the most frequent and costly severe weather-related disaster (NOAA National Centers for Environmental Information, 2025).
Closing the Global Forecasting Gap with AI Robotics
AI Robotics Fleeet
TENSHI utilizes Deep Generative Models (DGM) trained on broad meteorological radar imagery. This AI platform fuses the macro-patterns from 1 km×1 km radar images with the hyperlocal ground-truth sensor data to produce accurate broad-scale prediction with precise neighborhood detail. This process overcomes the inherent uncertainties of low-cost sensors by leveraging high spatial density and redundancy through advanced data assimilation techniques.
Ultra-High-Resolution Intelligence @ The Woodlands, Tx.
A LUDUM AI Robotics Initiative: Transforming Reactive Response into Predictive Prevention
The Woodlands, Texas, faces heightened vulnerability due to intensifying severe weather events. Traditional forecasting methods operating at the kilometer scale (1 km×1 km) are unable to capture the hyperlocal atmospheric boundary layer heterogeneity and fine spatial structures of convective rainfall necessary for accurate urban nowcasting.
TENSHI solves this Prediction Gap by deploying a proven AI Robotics system to establish a 0.06 mile resolution(∼96 meter) ground-truth network, achieving the required 10 1 to 10 2 meter scale resolution demanded by specialized scientific flux estimation and urban hydrology studies.
Integrating Macro-Patterns with Ground-Truth Data
Accelerating Response Times. Protecting Communities. Reclassifying Property Risk.
TENSHI harnesses Deep Generative Models (DGMs) to deliver unparalleled predictive capabilities. These models analyze broad-scale weather system patterns from 1km x 1km meteorological radar imagery, seamlessly fusing this information with real-time, hyper-local measurements gathered directly by the robot fleet.
Ground Sensors
Capturing temperature and pressure measurements
Radar Imagery
Identifying large-scale weather movements and patterns
Hyper-Local Nowcasts
Delivering precise, neighborhood-level detail
This innovative approach enables near real-time inference with rapid 10-second update cycles. The result is nowcasting that is 75-90% faster than conventional methods, providing a critical 1-3 hour lead time for block-level flood risk assessment. This level of predictive capability has direct implications for property risk assessment, insurance underwriting, and urban planning—enabling data-driven reclassification of flood risk zones at unprecedented granularity.
An AI Robotics' Vision with Systematic Collaboration
The next frontier in environmental intelligence centers on precise, real-time data, gathered at the granular scale of a city block, a construction site, or a single field. That's TENSHI.
Illustrative image of TENSHI Robots.
Each robot carries an advanced 8-sensor suite, precisely measuring temperature, pressure, wind, precipitation, ground stability, air quality, UV radiation, and CO₂ concentration.
Mobile sensing networks represent a paradigm shift in environmental monitoring. Research demonstrates that autonomous robotic systems can dramatically increase spatial and temporal data resolution while reducing deployment and maintenance costs compared to traditional fixed sensor networks.
Key Figures
5.3B
Annual Data Points
This represents a 2,650-fold increase from the 2 million data points provided by traditional fixed networks.
96m
Spatial Resolution
Achieving neighborhood-level precision, critical for actionable weather intelligence.
10s
Update Intervals
Delivering near real-time measurements during active sensing operations.
The Foundation of Trust: Data Quality in the New Era
Building Confidence. Ensuring Accuracy. Enabling Informed Action.
The resolution revolution promises unprecedented insight. However, realizing this potential demands accurate, reliable, and representative underlying data. Scientific rigor, from precise sensor siting to validating complex models, forms the bedrock of trustworthy environmental intelligence.
How do we ensure a 2,650x increase in data points translates to a meaningful increase in understanding?
01
Sensor Siting
WMO Siting Classification ensures measurements accurately represent the larger region, a critical factor distinct from instrument precision.
02
Sensor Quality
While low-cost sensors expand networks, they introduce uncertainty. Studies demonstrate this can be effectively mitigated by assimilating other observational data.
03
Model Validation
Testing models in complex environments reveals their limitations. For example, the UVIOS2 UV Index model exhibited 40% variability under broken clouds due to spatial averaging.
December 1st, 2025: The resolution revolution transcends simply generating more data; it demands rigorously quantifying uncertainty at every scale. True environmental intelligence emerges from understanding both what we can clearly see and the inherent limits of our vision.
More information coming soon!
Our MIT-Powered Team
R&D Founder
Tech Partner
References & Data Sources
  1. Nature Communications (2025). "A data-to-forecast machine learning system for global weather." FuXi Weather study demonstrating AI outperformance in observation-sparse regions.
  1. World Meteorological Organization (2024). "Closing the gaps in the observing network." Report on global observation infrastructure gaps.
  1. NOAA National Centers for Environmental Information (2025). "U.S. Billion-Dollar Weather and Climate Disasters." 2024 analysis showing $182.7B in damages from 27 events.
  1. NOAA National Weather Service (2025). "Flood Inundation Mapping expansion to 60% of U.S. population." September 2025 announcement.
  1. Remote Sensing (2025). "A High Resolution Spatially Consistent Global Dataset for CO₂." Study on super-resolution techniques achieving 0.1° × 0.1° spatial resolution.
  1. Bulletin of the World Health Organization (2025). "A global assessment of urban extreme weather early warning systems." Analysis of 182 large cities and public health integration.
  1. Frontiers in Robotics and AI (2024). "A survey on autonomous environmental monitoring approaches." Review of mobile sensing systems and reinforcement learning applications.
Visit TenshiWeather.org for more information.