Methodology: Climate Data Processing, Resolution & Downscaling

What AlphaGeo turns raw data into

AlphaGeo transforms raw climate model outputs and raw geospatial datasets into globally benchmarkable climate risk scores and asset-type-adjusted financial impact metrics for any address on Earth. This page documents the data sources, resolutions, and processing methodology behind that transformation.

Climate inputs come from NASA's NEX-GDDP-CMIP6 v2.0 — the latest peer-reviewed, globally downscaled CMIP6 dataset. Hazard, adaptation, population, and asset-context layers are sourced from well-established geospatial datasets (Aqueduct 4.0, Copernicus, IPCC AR6, NOAA IBTrACS, EarthEnv, WUDAPT, FEMA, PANGAEA, OpenStreetMap, WorldPop). Every input is processed onto a unified global H3 hexagonal grid and combined through hazard-specific damage functions, adaptation overlays, and asset-type sensitivity coefficients to produce a single answer at the asset's location.

Resolution & methodology at a glance

Climate inputs

Source

NASA NEX-GDDP-CMIP6 v2.0 (peer-reviewed, publicly documented)

Native resolution

~25 km, daily values, 1950–2100

GCM ensemble

CanESM5, MRI-ESM1, and other CMIP6 models

Variables consumed

TAS, TASMAX, TASMIN, PR (and derived indices: CDD, HOTDAYS95/105, PR_DAYS_10MM, MAX_CONS_DRYDAYS, etc.)

Scenarios

SSP2-4.5, SSP3-7.0, SSP5-8.5 (CMIP6 / IPCC AR6)

Horizons

2025, 2035, 2050, 2100 (baseline: 2015–2025)

In-house downscaling

None. AlphaGeo relies on NASA's downscaling rather than duplicating it.

Processing onto a global H3 grid

Each input layer is mapped onto an H3 hexagonal cell at the resolution that most closely matches its native pixel size. Climate cells from NEX 2.0 are buffered and bilinearly interpolated onto the grid. All physical hazard scores are then computed on a uniform global grid of H3 resolution 6 (~4 km hexagons) for cross-hazard consistency.

H3 resolution

Approximate cell size

Typical use

Resolution 11

~25 m

Highest-granularity engineered adaptation features

Resolution 10

~70 m

Population rasters (e.g., WorldPop ~100 m native)

Resolution 9

~175 m

Mid-resolution land cover and adaptation context

Resolution 8

~460 m

Coarser adaptation and exposure layers

Resolution 7

~1.2 km

Intermediate hazard and climate layers

Resolution 6

~4 km — physical hazard scoring grid

Per-hazard input layers

All physical hazard scores are aggregated to H3 res 6 (~4 km). The native input layers feeding each hazard differ in resolution and are processed at the closest matching H3 level.

Hazard

Native input resolution

Key source data

Heat Stress

~25 km climate + WUDAPT urban thermal layers

NEX 2.0, WUDAPT LCZ

Drought

~25 km climate + Aqueduct water stress (~450 m)

NEX 2.0, WRI Aqueduct 4.0

Inland Flooding

~25 km precipitation + FEMA NFHL flood depth (~300 m) + Aqueduct return-period layers (~450 m)

NEX 2.0, WRI Aqueduct 4.0

Coastal Flooding

IPCC AR6 sea-level rise (~100 km) + Copernicus storm surge (~25 km)

IPCC AR6, Copernicus

Wildfire

~25 km hot/dry day counts + EarthEnv land cover (~900 m)

NEX 2.0, EarthEnv

Hurricane Wind

IBTrACS tracks (~10 km) + STORM simulated tracks

NOAA IBTrACS, STORM

Hail

PANGAEA hail hazard (~70 km)

PANGAEA

Earthquake

GEM global seismic hazard model (non-climate)

GEM (Global Earthquake Model Foundation)

Landslide

LHASA landslide hazard model (terrain + soil + precipitation)

NASA LHASA (Landslide Hazard Assessment for Situational Awareness)

Address-level differentiation

The physical hazard score at H3 res 6 is the foundation. Three additional layers refine the answer to the individual asset:

Adaptation layers — Processed at finer H3 levels (down to res 11, ~25 m) for engineered defenses sourced from OpenStreetMap and the Global Adaptation Layer. These produce the Resilience-Adjusted (RAJ) Risk Score.
Asset-type sensitivity coefficients — Adjust the Financial Impact Analytics (FIA) so that a data center, a warehouse, a residential tower, and a logistics yard receive different cashflow, OpEx, downtime, and CapEx impacts from the same climate signal.
The Remediation Checklist — Captures asset-specific mitigation measures (flood barriers, roof rating, fire-resistant construction, backup power) and refines the RAJ score at the individual building level.

Update cadence

Quarterly refreshes as NASA releases new NEX-GDDP-CMIP6 ensemble members, peer-reviewed datasets update, and methodological improvements are integrated.
Historical events (hurricanes, wildfires, floods) are used for accuracy benchmarking rather than as scoring inputs, to avoid biasing forward projections.

Limitations of the current framework

We document limitations explicitly so users can interpret CRRI scores with appropriate context.

Two addresses in the same ~4 km hex share the same physical hazard score. Address-level differentiation comes from the adaptation, asset-type, and remediation layers — not from the climate signal itself.
Climate model skill degrades sharply below kilometer scales. Finer scoring grids are technically possible but would not necessarily be more accurate. The ~4 km grid reflects what downscaled CMIP6 can actually resolve.
Adaptation coverage depends on source inventories. If a flood defense, drainage system, or fire response asset is not captured in OpenStreetMap or the Global Adaptation Layer, it will not be reflected in the resilience-adjusted score.
SSP scenarios are structured plausibilities, not forecasts. They bound a range of climate futures without assigning probabilities to any single outcome.
Climate refresh cadence is tied to NASA NEX-GDDP releases. When NASA publishes a new NEX-GDDP-CMIP6 version, it flows through to CRRI on our next refresh cycle.
Long-horizon projections compound uncertainty. 2050 horizons are robust; 2100 horizons should be read as directional signals rather than precise estimates.
The ~4 km scoring grid is a current balance, not a hard limit. As compute scales and NEX-GDDP releases finer-resolution products, the scoring grid can move to a finer H3 level without changing the rest of the pipeline.

For the full set of methodological assumptions, see Limitations, Assumptions, and Data Transparency.

What makes AlphaGeo different

For an investment, valuation, ESG disclosure, or underwriting decision, the question is not "which provider has the finest single pixel?" It is "which provider can tell me what this specific asset is actually exposed to, after accounting for what protects it and what it is built for?" That is what AlphaGeo answers.

Three things make AlphaGeo's product the strongest fit for that question:

1. The Global Adaptation Layer

The first and only commercially available global, hazard-specific database of adaptation capacity. It covers 20+ engineered and nature-based adaptation measures — flood defenses, drainage, fire response infrastructure, building stock, natural buffers — at resolutions down to ~25 m where source data supports it. No other provider offers this layer globally. It is what turns a hazard exposure number into a defensible view of likely real-world impact.

2. The Remediation Checklist

Captures mitigation measures at the building or asset level — roof rating, flood barriers, fire-resistant cladding, drainage, backup power, structural reinforcement. This lets a buyer differentiate adjacent buildings of the same type and quantify how specific resilience investments would reduce risk. Hazard-only providers cannot answer this question.

3. Financial Impact Analytics

Most climate analytics stop at expected losses (CVaR, AAL). AlphaGeo models climate as a driver of cashflow — revenue, OpEx, CapEx, downtime, productivity loss, insurance premium changes — adjusted for each asset type. These metrics plug directly into DCF, valuation, and capital-budgeting models. This is what ESG, real estate investment, and underwriting teams actually need to make decisions.

Built for the decision

An investment, valuation, ESG disclosure, or underwriting decision needs three things at once: a credible hazard signal, a view of what protects the asset, and a financial impact metric tuned to what the asset actually is. The pipeline above is designed to deliver all three in a single query — climate from NASA's peer-reviewed downscaling, adaptation from the Global Adaptation Layer, and financial impact calibrated to asset type and remediation. A hazard-only product, however high its resolution, leaves two of those three questions unanswered.

Key references

Climate models and scenarios

Thrasher, B., Wang, W., Michaelis, A. et al. (2022). NASA Global Daily Downscaled Projections, CMIP6. Scientific Data 9, 262. https://doi.org/10.1038/s41597-022-01393-4

NASA NEX-GDDP-CMIP6 v2.0 — current downscaled CMIP6 dataset underlying CRRI climate inputs. https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization. Geoscientific Model Development 9 (5): 1937–58. https://doi.org/10.5194/gmd-9-1937-2016

O'Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., Mathur, R., & van Vuuren, D. P. (2014). A New Scenario Framework for Climate Change Research: The Concept of Shared Socioeconomic Pathways. Climatic Change 122 (3): 387–400. https://doi.org/10.1007/s10584-013-0905-2

Hazard datasets

Heat Stress — Local Climate Zones (WUDAPT): Stewart, I. D., & Oke, T. R. (2012). Local Climate Zones for Urban Temperature Studies. Bulletin of the American Meteorological Society 93 (12): 1879–1900. https://doi.org/10.1175/bams-d-11-00019.1

Drought & Inland Flooding — Aqueduct 4.0 (WRI): Kuzma, S., Bierkens, M. F. P., Lakshman, S., Luo, T., Saccoccia, L., Sutanudjaja, E. H., & Van Beek, R. (2023). Aqueduct 4.0: Updated Decision-Relevant Global Water Risk Indicators. World Resources Institute. https://www.wri.org/research/aqueduct-40-updated-decision-relevant-global-water-risk-indicators

Coastal Flooding — IPCC AR6 sea-level rise: IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the IPCC. Cambridge University Press. https://www.ipcc.ch/report/ar6/wg1/

Coastal Flooding — Copernicus storm surge: Muis, S., Aerts, J. C. J. H., Antolínez, J. A. Á., Dullaart, J. C., Duong, T. M., Erikson, L., Haarsma, R. J., et al. (2023). Global Projections of Storm Surges Using High-Resolution CMIP6 Climate Models. Earth's Future 11 (9): e2023EF003479. https://doi.org/10.1029/2023EF003479

Wildfire — EarthEnv land cover: Tuanmu, M.-N., & Jetz, W. (2014). A Global 1-Km Consensus Land-Cover Product for Biodiversity and Ecosystem Modelling. Global Ecology and Biogeography 23 (9): 1031–45. https://doi.org/10.1111/geb.12182

Hurricane Wind — IBTrACS observed tracks: Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., & Neumann, C. J. (2010). The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data. Bulletin of the American Meteorological Society 91 (3): 363–76. https://doi.org/10.1175/2009BAMS2755.1

Hurricane Wind — STORM simulated tracks: Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., & Aerts, J. C. J. H. (2020). Generation of a global synthetic tropical cyclone hazard dataset using STORM. Scientific Data 7, 40. https://doi.org/10.1038/s41597-020-0381-2

Hail — PANGAEA global hail hazard: Prein, A. F., & Holland, G. (2018). Daily large hail probability on a global scale (1979 to 2015), Version 2 [dataset]. PANGAEA. https://doi.org/10.1594/PANGAEA.893160 — Supplement to: Prein, A. F., & Holland, G. (2018). Global estimates of damaging hail hazard. Weather and Climate Extremes 22: 10–23. https://doi.org/10.1016/j.wace.2018.10.004

Earthquake — Global Earthquake Model (GEM): Pagani, M., Garcia-Pelaez, J., Gee, R., Johnson, K., Poggi, V., Styron, R., Weatherill, G., et al. (2020). The 2018 version of the Global Earthquake Model: Hazard Component.Earthquake Spectra 36 (S1): 226–251. https://doi.org/10.1177/8755293020931866

Landslide — NASA LHASA: Stanley, T., & Kirschbaum, D. B. (2017). A heuristic approach to global landslide susceptibility mapping. Natural Hazards 87: 145–164. https://doi.org/10.1007/s11069-017-2757-y

Spatial indexing technology

Uber Engineering. H3: Hexagonal Hierarchical Spatial Index. https://h3geo.org/