GCRI · Climate and Hazards

Computing for climate and hazard science

High-performance, cloud and edge computing run the models and handle the data volumes that hazard science needs. In this field computing determines which ensembles, resolutions and analyses are feasible, and how reproducible they are.

01Register

Computing for climate and hazard science on the record.

Method recordSTEM method
  1. MethodHigh-performance, cloud and edge computing
  2. Applied inClimate and Hazards
  3. Canonical pageSTEM Commons
02Application

What high-performance, cloud and edge computing does here.

High-performance, cloud and edge computing provide the processing and storage capacity for simulation, analytics and digital services. They are both an enabler of risk analysis and a critical dependency for society. In climate and hazards and disaster and crisis, computing capacity determines how fast and at what resolution risks can be modelled. Its concentration is itself a systemic dependency for finance, health and government services. The canonical description is on the STEM Commons.

Running and storing ensembles

Large ensembles of climate and hazard models are run on shared high-performance systems, and outputs are stored in standard formats with full provenance.

Analysis near the data

Analyses run on cloud copies of reanalyses and model archives, avoiding large downloads and keeping analysis code beside the data version it used.

Edge processing for observations

Automatic stations and sensors filter and compress data before transmission where bandwidth is limited, with quality flags kept.

03Geography

Climate signals worldwide.

Rain, daytime surface heat and population density across the world, from NASA GIBS. Each layer carries its own date.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Climate signals, for this page

About this layer
Evidence
Estimated
Source
GPM IMERG (NASA GES DISC)
Provider
NASA GES DISC
Licence
NASA open data
Vintage
daily; Satellite estimate, near real time
Newest at build
2026-10-12
Resolution
about 2 km grid
Uncertainty
Satellite estimate, near real time. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed (persistent cloud still leaves gaps)
Source
VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
8-day; Satellite 8-day composite; persistent cloud still leaves gaps
Newest at build
2026-09-14
Resolution
about 1 km grid
Uncertainty
Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Estimated
Source
Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
Provider
NASA SEDAC (CIESIN, Columbia University)
Licence
CC BY 4.0
Vintage
null; Census-based estimate, static (2020)
Resolution
about 1 km grid
Uncertainty
Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed
Source
VIIRS NOAA-20 corrected reflectance (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite image
Newest at build
2026-10-14
Resolution
about 250 m grid
Uncertainty
Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed (clouds leave gaps)
Source
VIIRS Global Flood Product (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite detection; clouds leave gaps
Newest at build
2026-10-14
Resolution
about 250 m grid
Uncertainty
Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed
Source
VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
Provider
NASA LAADS DAAC
Licence
NASA open data
Vintage
daily; Satellite radiance, corrected for moonlight and atmosphere
Newest at build
2026-10-12
Resolution
about 500 m grid
Uncertainty
Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Climate signals, framed on the world.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERT · NASA AIRSSources and licences

  • We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • Live: NASA EONET Events are indicative, not authority warnings.
  • Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • Observed (EO): NASA AIRS. We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
  • Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
  • Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
  • Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
  • Optional layers (22 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026

04Data

What the method consumes.

Datasets and records used by high-performance, cloud and edge computing on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

DataRoleLabel
CMIP6 and CORDEX archives in cloud-optimised formatsAnalysis-ready model outputMODELLED
ERA5 in cloud object storesReanalysis accessMODELLED
Workflow provenance logsReproducibilityOBSERVED
05Simulator

Stocks, flows, delays and a tipping threshold.

Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction.

Controls

Growth of pressure
6.0% a year
Efficacy of capacity in reducing pressure
4.0% a year
Investment response to perceived pressure
20%
Perception delay
4.0 years
Tipping threshold of pressure
60 index
Amplification past the threshold
2.0 times
Years simulated
50 years

Readouts

Peak pressure
113 index
Capacity at the end
186 index
Years over the threshold
15.8 years
Numbers, methods and sources
YearPressureCapacityPerceived
020.050.020.0
423.344.921.2
827.242.224.0
1231.742.327.7
1636.845.532.2
2042.351.537.2
2447.960.342.6
2853.271.348.0
3257.884.353.1
3666.498.458.4
4082.7115.969.7
4497.8139.783.8
48109.1169.497.3

Methods

  1. System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
  2. Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
  3. Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
  4. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  5. One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Data

No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.

Stocks, flows, delays and a tipping threshold. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: System dynamics (Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics); Critical transitions and tipping points (Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461); Causal loop diagram (Sterman (2000), Business Dynamics, McGraw-Hill).
06Validation

How results are checked.

Systems are tested through benchmarks, load testing and failover exercises. Numerical reproducibility is checked across hardware and software configurations.

CheckHow it is done
Bit-for-bit reproducibilityKey analyses are re-run from recorded code and data versions to confirm identical outputs
Format and metadata complianceOutputs follow CF conventions so that other tools read them correctly
Resource accountingCompute and storage used by each analysis are recorded

Concentration in few providers or regions creates shared points of failure. Energy, water and supply chain constraints limit capacity growth.

07Twin and cascade simulator

Model computing for climate and hazard science as a system. Run the cascade.

Assembled from the Nexus Standards area profile Climate risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.

Hazards

  • Sea-level rise
  • Coastal flood (set off downstream)
  • Storm surge (set off downstream)
  • High-tide flooding (set off downstream)
  • Coastal erosion (set off downstream)
  • Saltwater intrusion (set off downstream)
  • Estuary flood (set off downstream)
  • Coastal habitat squeeze (set off downstream)
  • Wetland loss (set off downstream)
  • Mangrove loss (set off downstream)
  • Groundwater flood (set off downstream)
  • Climate-driven migration and relocation (set off downstream)
  • Mass displacement (set off downstream)

Controls

  • NS-0 Constitution and principles
  • NS-1 The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)
  • NS-3 Risk record UNIs (grammar, legacy crosswalk, allocation authority)
  • NS-3b Dataset and product metadata profile (STAC, DCAT, PROV-O)
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NS-14 Conformance and crosswalk to external standards
  • NRS-H65 Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)
  • NRS-H62 Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)
  • NRS-H71 Hazard family: Deep-time and cosmic catastrophes (NHS-1 planet.deep-time)
  • NRS-H79 Hazard family: Mental health crises (NHS-1 health.mental)
  • NRS-H75 Hazard family: Climate-sensitive illness (NHS-1 health.climate-sensitive)
  • NRS-H7 Hazard family: Large-scale circulation anomalies (NHS-1 atmo.climate-mode)

Observation

  • ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
  • CMIP6 climate projections (Copernicus Climate Data Store) (listed for reference)
  • NGFS Scenario Explorer (listed for reference)
  • INFORM Risk Index (listed for reference)
  • Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
  • Total greenhouse gas emissions per year
  • Direct economic loss attributed to disasters in relation to global GDP

Example geography

  • Pacific Island states: 150 hexagonal cells
  • Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
  • Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
  • Rain rate, last half hour (IMERG), NASA GIBS
  • Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
08Connections

Topics that rely on this method.

09Methods and evidence

How computing for climate and hazard science is modelled, measured and tested.

The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.

Uncertainty bands

How far the outlook for computing for climate and hazard science could spread, shown as ranges rather than a single line.

Now+5+10+15+20+25+30 yhighlowvery likely rangelikely range
Central estimate
median of the ensemble
Ranges
likely and very likely, in IPCC calibrated language
Ensemble
members, scenarios and initial conditions stated
Read with
the sensitivity of the result to each input
Illustrative ensemble spread on an index scale; the form follows IPCC uncertainty guidance. Not a projection.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
10Stewardship

Who maintains the method.

Data and Compute stewards high-performance, cloud and edge computing for the Consortium and reviews how it is applied across platforms.

12Questions

Questions and answers.

What are the limits of high-performance, cloud and edge computing in this field?
Concentration in few providers or regions creates shared points of failure. Energy, water and supply chain constraints limit capacity growth.
How are results checked?
Systems are tested through benchmarks, load testing and failover exercises. Numerical reproducibility is checked across hardware and software configurations.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
ReportsContribute

Contribute evidence from this page.

Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

GCRIClimate and HazardsGlobal Systems Risk and ResilienceObservatory SignalScenario and Stress-Test
Climate and Hazards

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Information and enquiries are open today. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.

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GCRI is a not-for-profit institution. To ask who funds a piece of work, use the funding questions form.

Independence

Editors and the expert reviewers named on each article decide what we publish. Funders and partners do not review or approve articles before publication.

Licence

Articles: CC BY 4.0. Figures, maps, twins and data: CC BY 4.0. Other uses: ask through the permissions form.