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| | |
| | # Earth-2 Checkpoints: CorrDiff-CMIP6-ERA5 |
| |
|
| | ## Description |
| |
|
| | Corrector Diffusion (CorrDiff) CMIP6-ERA5 model performs a spatio-temporal downscaling |
| | of global climate data comprising several surface, atmospheric, land ice and |
| | sea ice variables from the Coupled Model Intercomparison Project Phase 6 |
| | (CMIP6) to the European Reanalysis v5 (ERA5). |
| | The CMIP6 source data consists of daily variables on multiple regular and |
| | curvilinear grids, that are interpolated onto a common 300-km resolution global |
| | climate grid. The model downscales the input CMIP6 data onto hourly 25-km |
| | resolution data. CorrDiff CMIP6-ERA5 allows the prediction of high-fidelity stochastic climate |
| | phenomena over the globe from low-fidelity input data that would otherwise require |
| | expensive global numerical simulations. |
| |
|
| | CorrDiff CMIP6-ERA5 is a generative spatio-temporal downscaling model trained over |
| | the globe. For details on the CMIP6 grids, see the |
| | [CMIP6](https://wcrp-cmip.org/cmip-phases/cmip6/). |
| |
|
| | This model is ready for commercial/non-commercial use. |
| |
|
| | ## License/Terms of Use: |
| |
|
| | Governing Terms: Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). |
| |
|
| | ## Deployment Geography: |
| |
|
| | Global. |
| |
|
| | ## Use Case: |
| |
|
| | Climate scientists accelerating climate prediction with AI, |
| | financial institutions and insurance companies for climate risk management, |
| | utilities companies for energy planning, and public policy makers for |
| | decision-making. |
| |
|
| | ## Reference(s) |
| |
|
| | * [CorrDiff Paper](https://arxiv.org/pdf/2309.15214) <br> |
| | * [Coupled Model Intercomparison Project Phase 6]( |
| | https://wcrp-cmip.org/cmip-phases/cmip6/) <br> |
| | * [European Reanalysis v5]( |
| | https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) <br> |
| | |
| | ## Codebase |
| |
|
| | * [Earth2Studio](https://github.com/NVIDIA/earth2studio) <br> |
| | * [PhysicsNeMo](https://github.com/NVIDIA/physicsnemo) <br> |
| |
|
| | ## Model Architecture |
| |
|
| | **Architecture Type:** U-Net<br> |
| | **Network Architecture:** Corrector Diffusion U-Net with 158M parameters<br> |
| |
|
| | ## Computational Load |
| |
|
| | **Cumulative Compute:** 2.3E15 FLOP |
| | **Estimated Energy and Emissions for Model Training:** 4.48266 tCO2e |
| |
|
| | ## Input |
| |
|
| | **Input Type(s):** |
| |
|
| | * Tensor (74 Surface, Atmospheric, and Oceanic Variables from the previous, |
| | current, and next day + land-sea mask + elevation + solar zenith angle + |
| | distance to the ocean coastline + sine and cosine of the latitude and |
| | longitude) <br> |
| | * Input data hour of the day in 24-hour format <br> |
| |
|
| | **Input Format(s):** PyTorch Tensor <br> |
| | **Input Parameters:** |
| |
|
| | * Four Dimensional (4D) (batch, variable, latitude, longitude) <br> |
| | * Integer (Hour of the day in 24-hour format) <br> |
| |
|
| | **Other Properties Related to Input:** |
| |
|
| | * 2.8 degree latitude-longitude grid over the globe |
| | * Input spatial resolution: [64, 128] |
| | * Input temporal resolution: 24 hours |
| | * Latitude Coordinates: [90, 87.2, 84.4, ..., -84.4, -87.2, -90] |
| | * Longitude Coordinates: [0, 2.8, 5.6, ..., 354.4, 357.2, 360] |
| | * Input weather variables: va10, vas, prc, ua10, ta850, rls, |
| | tasmin, wap850, hursmax, ua850, ua50, va850, q10, |
| | rlut, va1000, pr, zg1000, sfcWindmax, hurs, ta50, rsus, |
| | sfcWind, wap10, ta500, ua100, hus1000, zg500, |
| | hus250, ua500, ua1000, hursmin, ta700, va250, |
| | hus700, hus100, ua700, wap100, zg100, ta100, |
| | va500, tas, ua250, wap1000, zg700, va100, rlds, |
| | tasmax, va700, clt, rsds, zg100, ta1000, zg850, uas, |
| | wap700, snc, zg50, wap50, zg250, psl, hus50, |
| | hus850, hus500, siconc, ts<br> |
| | For variable name information, review the Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio/). |
| |
|
| | ## Output |
| |
|
| | **Output Type(s):** Tensor (75 Surface, Atmospheric, and Oceanic Variables) <br> |
| | **Output Format:** PyTorch Tensor <br> |
| | **Output Parameters:** 5D (batch, samples, variable, latitude, longitude)<br> |
| | **Other Properties Related to Output:** |
| |
|
| | * 2.8 degree latitude-longitude grid over the globe |
| | * Output spatial resolution: [721, 1440] |
| | * Output temporal resolution: 1 hour |
| | * Output weather variables: u10m, v10m, u100m, v100m, t2m, sp, msl, tcwv, u50, u100, |
| | u150, u200, u250, u300, u400, u500, u600, u700, u850, u925, u1000, v50, v100, v150, |
| | v200, v250, v300, v400, v500, v600, v700, v850, v925, v1000, z50, z100, z150, z200, |
| | z250, z300, z400, z500, z600, z700, z850, z925, z1000, t50, t100, t150, t200, t250, |
| | t300, t400, t500, t600, t700, t850, t925, t1000, q50, q100, q150, q200, q250, q300, |
| | q400, q500, q600, q700, q850, q925, q1000, sst, d2m<br> |
| |
|
| | ## Software Integration |
| |
|
| | Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated |
| | systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software |
| | frameworks (e.g., CUDA libraries), the model achieves faster training and |
| | inference times compared to CPU-only solutions. |
| |
|
| | **Runtime Engine(s):** |
| |
|
| | * PyTorch >= 2.4.0 <br> |
| | * PhysicsNeMo >= 1.2.0 <br> |
| |
|
| | **Supported Hardware Microarchitecture Compatibility:** <br> |
| |
|
| | * NVIDIA Ampere <br> |
| | * NVIDIA Blackwell <br> |
| | * NVIDIA Hopper <br> |
| | * NVIDIA Turing <br> |
| |
|
| | **Supported Operating System(s):** |
| |
|
| | * Linux <br> |
| |
|
| | ## Model Version(s) |
| |
|
| | **Model version:** v1 <br> |
| |
|
| | # Training, Testing, and Evaluation Datasets: |
| |
|
| | The integration of foundation and fine-tuned models into AI systems requires |
| | additional testing using use-case-specific data to ensure safe and effective |
| | deployment. Following the V-model methodology, iterative testing and validation |
| | at both unit and system levels are essential to mitigate risks, meet technical |
| | and functional requirements, and ensure compliance with safety and ethical |
| | standards before deployment. |
| |
|
| | ## Training Dataset |
| |
|
| | **Link:** [CMIP6](https://wcrp-cmip.org/cmip-phases/cmip6/) <br> |
| |
|
| | **Data Collection Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Labeling Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Properties (Quantity, Dataset Descriptions, Sensor(s)):** |
| |
|
| | CMIP6 data for the ranges of 1981-1989, 1991-1999, 2001-2009, 2011-2016. The CMIP6 is a |
| | climate dataset with global coverage of the Earth's atmosphere, ocean, and land. <br> |
| |
|
| | **Link:** [ERA5](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) <br> |
| |
|
| | **Data Collection Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Labeling Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Properties (Quantity, Dataset Descriptions, Sensor(s)):** |
| |
|
| | ERA5 data for the date range of 1981-1989, 1991-1999, 2001-2009, 2011-2016. The |
| | ERA5 is a global hourly reanalysis that blends historical observations with a |
| | consistent modern weather model to produce gridded estimates of past |
| | atmospheric conditions. <br> |
| |
|
| | ## Testing Dataset |
| |
|
| | **Link:** [CMIP6](https://wcrp-cmip.org/cmip-phases/cmip6/) <br> |
| |
|
| | **Data Collection Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Labeling Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Properties (Quantity, Dataset Descriptions, Sensor(s)):** |
| |
|
| | CMIP6 data for the years 1980, 1990, 2000. The CMIP6 is a |
| | climate dataset with global coverage of the Earth's atmosphere, ocean, and |
| | land. <br> |
| |
|
| | **Link:** [ERA5](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) <br> |
| |
|
| | **Data Collection Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Labeling Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Properties (Quantity, Dataset Descriptions, Sensor(s)):** |
| |
|
| | ERA5 data for the years 1980, 1990, 2000. The ERA5 is a global |
| | hourly reanalysis that blends historical observations with a consistent modern |
| | weather model to produce gridded estimates of past atmospheric conditions. <br> |
| |
|
| | ## Evaluation Dataset |
| |
|
| | **Link:** [CMIP6](https://wcrp-cmip.org/cmip-phases/cmip6/) <br> |
| |
|
| | **Data Collection Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Labeling Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Properties (Quantity, Dataset Descriptions, Sensor(s)):** |
| |
|
| | CMIP6 data for the year 2010. The CMIP6 is a |
| | climate dataset with global coverage of the Earth's atmosphere, ocean, and |
| | land. <br> |
| |
|
| | **Link:** [ERA5](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) <br> |
| |
|
| | **Data Collection Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Labeling Method by dataset** <br> |
| |
|
| | * Automatic/Sensors <br> |
| |
|
| | **Properties (Quantity, Dataset Descriptions, Sensor(s)):** |
| |
|
| | ERA5 data for the year 2010. The ERA5 is a global |
| | hourly reanalysis that blends historical observations with a consistent modern |
| | weather model to produce gridded estimates of past atmospheric conditions. <br> |
| |
|
| | # Inference: |
| |
|
| | **Engine:** [PyTorch](https://github.com/pytorch/pytorch) <br> |
| | **Test Hardware:** |
| |
|
| | * A100 <br> |
| | * H100 <br> |
| | * L40S <br> |
| | * RTX6000 <br> |
| |
|
| | ## Ethical Considerations: |
| |
|
| | NVIDIA believes Trustworthy AI is a shared responsibility and we have established |
| | policies and practices to enable development for a wide array of AI applications. |
| | When downloaded or used in accordance with our terms of service, developers should |
| | work with their internal model team to ensure this model meets requirements for the |
| | relevant industry and use case and addresses unforeseen product misuse. |
| | For more detailed information on ethical considerations for this model, please see the |
| | Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. |
| | Please report model quality, risk, security vulnerabilities or NVIDIA AI |
| | Concerns |
| | [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). |
