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The symbiotic relationship between NWP and DNNs improves the forecast horizon, spatial, and temporal resolution. At Xaana, we believe that the world of physics and AI have a clear symbiotic relationship and we deliver it as a single source of truth. Turium’s deep learning post-processing systems not only have the capability to correct biases and phase shifts in numerical forecasts, but also have the potential to quantify forecast uncertainty—both epistemic (due to lack of knowledge) and aleatoric (natural randomness of a process)—more comprehensively, and in doing so provide better information for decision support. In this sense, Turium acts as a bridge between the physical representation of the atmosphere by NWP and provides deep-learning decision-making support to the end-users.
As ever more weather models come online, each with increasingly high resolution and with more numerous ensemble members, we understand that meteorologists are increasingly stretched to reliably and accurately summarize the available information into meaningful forecasts for end-users. The use of Turium’s solutions debias and ‘infills’ this probability distribution based on all available information. FABRIQ optimally integrates big data to extract meaningful information from the range of models it oversees in ALGOREUS. This has the potential to not only improve on probabilistic forecasting when it matters most but also facilitate individual model development by providing overarching consistency in output, so that drastically changing an individual model will not break the system.
Turium ingests data from both traditional and new sources to fill in the spatial and temporal gaps, the result is a more accurate forecast at a finer scale than coarser models, tuned for specific applications.
Unlike global models that cannot fully address the nuanced climates of specific regions, Turium is customizable by location, resolution, coverage, parameters.
From purpose-based access controls to tracing the lineage of data from ingress to application to egress.
Gives a physical representation of the kinetic and non-kinetic elements that affect weather prediction and information shareholders
Predictive climate models vs backward-looking catastrophic risk modeling
Validation and verification integrated throughout the modeling chain by human domain experts
Measure and provide known sources of modeling uncertainty for all metrics
Gives a physical representation of the kinetic and non-kinetic elements that affect weather prediction and information shareholders
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