Decades of climate records. Petabytes of data. The archive grows every day and so does the cost to keep it.
RNDA encodes weather and environmental data and discards the originals. Century-scale retention becomes affordable. The raw data stops accumulating.
Request an Environmental POC →The Problem
Climate modeling, insurance underwriting, and environmental monitoring all require comparison against decades of historical weather records. Storing and querying petabytes of raw station data is the infrastructure bottleneck.
How RNDA Solves It
1.06 discrimination gap on real global climate data
Proven on 76 real weather stations across Arctic, desert, tropical, and temperate zones. Arctic finds Arctic. Desert finds Desert. Discrimination gap 1.06 — the highest of any structured data type tested.
Historical climate analog search
Submit current conditions and find the most similar historical periods from decades of records in milliseconds. Climate analog search — the core methodology for seasonal forecasting — without raw data retention.
Insurance risk modeling without raw data exposure
Encode weather events as signatures. Query for similar historical loss events. Underwrite risk from patterns, not raw data. Raw station data permanently discarded after encoding.
How RNDA Applies
Storage Elimination
Atmospheric model output, reanalysis datasets, and satellite imagery compressed 23x — allowing agencies to retain full historical climate records at a fraction of current infrastructure cost. A 2,000 TB/year national weather agency saves ~$528K/year.
Privacy Protection
Proprietary forecast models and commercially licensed satellite data are protected in compressed form, preventing unauthorized redistribution of high-value meteorological IP. The encoding carries predictive meaning without carrying the underlying models.
Compliance Management
WMO standards and national archive requirements for long-term climate data preservation become economically sustainable — retaining 100-year records without exponential storage budget growth. Compliance cost scales with signature count, not with the number of decades preserved.
Intelligent Retrieval
Submit current conditions and find the most similar historical periods from decades of records in ~10ms. Proven on 76 real weather stations across Arctic, desert, tropical, and temperate zones — discrimination gap 1.07, the highest of any structured data type tested.
Collaborative Intelligence
International climate science consortia — CMIP, Copernicus — share compressed model outputs across institutions, accelerating research without copying petabyte-scale raw datasets. Climate intelligence crosses borders; petabyte archives do not.
Storage Impact
Industry stat: ECMWF adds ~400 TB of meteorological data per day to its MARS archive; a national weather agency or large climate research center ingests 500–5,000 TB/year (ECMWF Key Facts & Figures)
2,000 TB × 20% × $276/TB ÷ 23x compression (national weather agency)
2,000 TB/year national weather agency saves ~$528K/year — 23x compression on real data from 76 global weather stations
Proof of Concept Results
Real data. Measured numbers. No synthetic results.
Source: Real weather station data — 76 global locations, full year
What Becomes Possible
"An insurer encodes 40 years of weather station data. Raw records are discarded. When a new policy is submitted, RNDA queries the signature store for similar historical climate patterns and their associated loss events — underwriting risk from behavioral similarity, not raw data."
Ready to see it on your data?
Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.
Request an Environmental POC →