stormcast-v1-era5-hrrr

Earth-2 Checkpoints: StormCast-V1-ERA5-HRRR

Description:

StormCast V1 is a mesoscale machine learning AI model that autoregressively predicts 99 state variables at km scale using a 1-hour time step, with dense vertical resolution in the atmosphere boundary layer.

For training recipes see NVIDIA PhysicsNeMo, for inference see NVIDIA Earth2Studio

This model is ready for commercial/non-commercial use.

License/Terms of Use:

This model is distributed under the Apache 2.0 license. <br>

Reference(s):

Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling <br>

Model Architecture:

Architecture Type: StormCast V1 uses a UNet architecture in a regression-diffusion generative model framework. <br> Network Architecture: UNet <br>

Input:

Input Type(s):

• Tensor (125 Surface and Model level variables - 99 state variables and 26 conditioning variables.)
• DateTime (NumPy Array) <br>

Input Format(s): PyTorch Tensor / NumPy array <br> Input Parameters:

• Four Dimensional (4D) (batch, variable, latitude, longitude) <br>
• Input DateTime (1D) <br>

Other Properties Related to Input:

• Input latitude/longitude grid:
• Input state weather variables: u10m, v10m, t2m, mslp, u_hl1, u_hl2, u_hl3, u_hl4, u_hl5, u_hl6, u_hl7, u_hl8, u_hl9, u_hl10, u_hl11, u_hl13, u_hl15, u_hl20, u_hl25, u_hl30, v_hl1, v_hl2, v_hl3, v_hl4, v_hl5, v_hl6, v_hl7, v_hl8, v_hl9, v_hl10, v_hl11, v_hl13, v_hl15, v_hl20, v_hl25, v_hl30, t_hl1, t_hl2, t_hl3, t_hl4, t_hl5, t_hl6, t_hl7, t_hl8, t_hl9, t_hl10, t_hl11, t_hl13, t_hl15, t_hl20, t_hl25, t_hl30, q_hl1, q_hl2, q_hl3, q_hl4, q_hl5, q_hl6, q_hl7, q_hl8, q_hl9, q_hl10, q_hl11, q_hl13, q_hl15, q_hl20, q_hl25, q_hl30, z_hl1, z_hl2, z_hl3, z_hl4, z_hl5, z_hl6, z_hl7, z_hl8, z_hl9, z_hl10, z_hl11, z_hl13, z_hl15, z_hl20, z_hl25, z_hl30, p_hl1, p_hl2, p_hl3, p_hl4, p_hl5, p_hl6, p_hl7, p_hl8, p_hl9, p_hl10, p_hl11, p_hl13, p_hl15, p_hl20, refc <br>
• Conditioning weather variables: u10m, v10m, t2m, tcwv, mslp, sp, u1000, u850, u500, u250, v1000, v850, v500, v250, z1000, z850, z500, z250, t1000, t850, t500, t250, q1000, q850, q500, q250

For lexicon information, review the HRRR Lexicon at Earth2Studio, but u, v, t, z and p refer to winds, temperature, geopotential, and pressure (respectively). Variables marked with _hl refer to natural/hybrid model levels.

Output:

Output Type(s): Tensor (99 Surface and Model level variables) <br> Output Format: Pytorch Tensors <br> Output Parameters: Four Dimensional (4D) (batch, variable, latitude, longitude) <br> Other Properties Related to Output:

• Output latitude/longitude grid:
• Output state weather variables: u10m, v10m, t2m, mslp, u_hl1, u_hl2, u_hl3, u_hl4, u_hl5, u_hl6, u_hl7, u_hl8, u_hl9, u_hl10, u_hl11, u_hl13, u_hl15, u_hl20, u_hl25, u_hl30, v_hl1, v_hl2, v_hl3, v_hl4, v_hl5, v_hl6, v_hl7, v_hl8, v_hl9, v_hl10, v_hl11, v_hl13, v_hl15, v_hl20, v_hl25, v_hl30, t_hl1, t_hl2, t_hl3, t_hl4, t_hl5, t_hl6, t_hl7, t_hl8, t_hl9, t_hl10, t_hl11, t_hl13, t_hl15, t_hl20, t_hl25, t_hl30, q_hl1, q_hl2, q_hl3, q_hl4, q_hl5, q_hl6, q_hl7, q_hl8, q_hl9, q_hl10, q_hl11, q_hl13, q_hl15, q_hl20, q_hl25, q_hl30, z_hl1, z_hl2, z_hl3, z_hl4, z_hl5, z_hl6, z_hl7, z_hl8, z_hl9, z_hl10, z_hl11, z_hl13, z_hl15, z_hl20, z_hl25, z_hl30, p_hl1, p_hl2, p_hl3, p_hl4, p_hl5, p_hl6, p_hl7, p_hl8, p_hl9, p_hl10, p_hl11, p_hl13, p_hl15, p_hl20, refc <br>

Software Integration

Runtime Engine(s): Not Applicable <br> Supported Hardware Microarchitecture Compatibility: <br>

• NVIDIA Ampere <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:

Training Dataset:

Link: ERA5 <br>

** Data Collection Method by dataset <br>

• Automatic/Sensors <br>

** Labeling Method by dataset <br>

• Automatic/Sensors <br>

Properties: ERA5 data for the period July 2018 - December 2021. ERA5 provides hourly estimates of various atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km grid and resolves the atmosphere at 137 levels. <br>

Link: HRRR <br>

Data Collection Method by dataset <br>

• Automatic/Sensors <br>

Labeling Method by dataset <br>

• Automatic/Sensors <br>

Properties: HRRR data for the date range of 2018/07/01 to 2021/12/31. The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. <br>

Testing Dataset:

Link: ERA5 <br>

** Data Collection Method by dataset <br>

• Automatic/Sensors <br>

** Labeling Method by dataset <br>

• Automatic/Sensors <br>

Properties: ERA5 data for the date range of 2022/01/01 - 2022/12/31. ERA5 provides hourly estimates of various atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km grid and resolves the atmosphere at 137 levels. <br>

Link: HRRR <br>

Data Collection Method by dataset <br>

• Automatic/Sensors <br>

Labeling Method by dataset <br>

• Automatic/Sensors <br>

Properties: HRRR data for the date range of 2022/01/01 - 2022/12/31. The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. <br>

Evaluation Dataset:

Link: ERA5 <br>

** Data Collection Method by dataset <br>

• Automatic/Sensors <br>

** Labeling Method by dataset <br>

• Automatic/Sensors <br>

Properties: ERA5 data for the date range of 2024/05/08 - 2024/06/15. ERA5 provides hourly estimates of various atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km grid and resolves the atmosphere at 137 levels. <br>

Link: HRRR <br>

Data Collection Method by dataset <br>

• Automatic/Sensors <br>

Labeling Method by dataset <br>

• Automatic/Sensors <br>

Properties: HRRR data for the date range of 2024/05/08 - 2024/06/15. The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. <br>

Inference:

Engine: PhysicsNeMo, PyTorch <br> Test Hardware:

• A100 <br>
• H100 <br>
• L40S <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.

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