+14.2% max 1-day rainfall
Full-ensemble projection with 32 of 34 CMIP6 models passing skill screening and 78% trusted-model agreement on increase.
Arasense helps cities, infrastructure owners, and climate-risk consultants produce defensible evidence for flood-driving rainfall, heat, and drought. For each location, the platform shows which models earn trust, which models are rejected, and what the trusted models imply for mid-century risk.
Current examples show how Arasense evaluates climate-model trust, reports mid-century hazard signals, and ranks locations by worsening climate-risk indicators.
Full-ensemble projection with 32 of 34 CMIP6 models passing skill screening and 78% trusted-model agreement on increase.
Portfolio view ranks cities by worsening flood-driving rainfall signal, with Rome at +19% and Venice at +3% in the sample.
The same workflow can support local adaptation, infrastructure, and advisory conversations in Puglia.
Arasense is built around interpretable model evaluation and transparent evidence generation. The platform connects the Aras Diagram framework with operational geospatial data sources and validation-stage screening workflows.
Structured decomposition of model behavior across bias, variability, and phase alignment, supporting clearer judgement than a single aggregate score.
Climate diagnostics and projections are framed around established reanalysis and global climate model datasets, with model skill made explicit.
Flood-screening pilots can be compared against satellite-derived evidence windows to document where the workflow performs and where it remains uncertain.
Arasense combines climate model evaluation, bias-aware interpretation, projection reporting, portfolio screening, and validation-stage flood analytics into one technical stack. The objective is not another generic dashboard. The objective is a more defensible basis for planning, screening, and risk communication.
Compare climate model behavior through Aras Diagram signal decomposition, model-trust tiers, and transparent ranking logic.
Estimate mid-century changes for rainfall, heat, drought, and scenario differences using trust-weighted models.
Rank locations by worsening hazard signal so teams can see which exposures deserve attention first.
The Arasense console is available through guided demos, pilot studies, and selected collaborations. Teams select climate points and flood-screening regions directly on a map, then generate structured outputs for model trust, projections, portfolio ranking, and validation review.
Set a climate region of interest with one map click, draw flood-screening boxes, and keep the spatial inputs synchronized across forms and reports.
Score models by bias, variability, and alignment, then surface which models are credible enough to drive projections at the selected location.
Generate mid-century reports for rainfall extremes, heavy-rain frequency, heat, and drought, including SSP2-4.5 vs SSP5-8.5 comparisons.
Combine hydrological graph structure, climate-driven precipitation features, GNN screening probabilities, and Sentinel-1 validation windows for regional pilots.
Arasense supports public authorities, infrastructure owners, and climate/adaptation consultants that need transparent climate evidence for planning, prioritization, advisory work, or investment-facing communication.
Support resilience planning, regional screening, asset prioritization, and early-stage adaptation decisions with more structured evidence.
Strengthen client deliverables, due diligence workflows, and comparative climate-risk assessments with clearer interpretability.
Arasense is aimed at shortening the path from complex data to a first defensible interpretation, especially where teams need to compare scenarios, rank locations, or communicate uncertainty clearly.
Early collaborations can begin with a geography, asset class, or decision question, then expand into repeatable workflows as the value is demonstrated.
Flood outputs are validation-stage screening evidence. They are not a replacement for hydraulic modelling, field validation, local calibration, or engineering-grade flood forecasting.
Arasense can support single-location studies, regional screening, and multi-asset portfolio ranking through the same model-trust workflow.
Example applications include coastal rainfall, flood-driving rainfall, heat-risk screening for infrastructure, and portfolio ranking for advisory teams. View the Bari / Puglia regional application.
Focused pilots and decision-support engagements can expand into live product access and API workflows as needs mature.
Arasense is available for guided demos, pilot studies, and selected institutional collaborations.