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license: apache-2.0
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---
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license: apache-2.0
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language:
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- en
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tags:
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- Pytorch
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- gravity wave
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- Weather & Climate
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- Foundation model
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datasets:
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- Prithvi-WxC/Gravity_wave_Parameterization
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base_model:
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- Prithvi-WxC/prithvi.wxc.2300m.v1
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---
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This repository contains pretrained model for Gravity Wave Flux Parametrization downstream task.
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6488f1d3e22a0081a561ec8f/lOFP_1dAVKCw90uLpj2vu.png" alt="Gravity Wave" width="1024"/>
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### Model
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The pretrained [Prithvi WxC](https://huggingface.co/Prithvi-WxC/prithvi.wxc.2300m.v1) parameter model is finetuned to predict momentum fluxes from
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the [Gravity Wave Parameterization dataset](https://huggingface.co/datasets/Prithvi-WxC/Gravity_wave_Parameterization).
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<b>Input:</b> 491 (3 + 4x122) channels.
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1. latitude (1)
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2. longitude (1)
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3. surface elevation (1)
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4. zonal winds \\(u\\) (122)
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5. meridional winds \\(v\\) (122) 6.
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6. temperature \\(T\\) (122)
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7. pressure \\(P\\) (122)
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<b>Output:</b> 366 (3x122) channels.
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1. potential temperature \\(\theta\\) (122)
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2. zonal flux of vertical momentum \\(u'\omega'\\) (122)
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3. meridional flux of vertical momentum \\(v'\omega'\\) (122)
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### Code
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Code for fine-tuning is available through [Github](https://github.com/NASA-IMPACT/gravity-wave-finetuning).
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### Results
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For the Andes (mountain waves) and the Southern Ocean (non-mountain waves),
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the fine-tuned model achieves correlation coefficients of 0.99 and 0.97, respectively, when compared to the observed fluxes.
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### Inference and demo
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The github repo includes an inference script that allows to run
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the [gravity_wave_model](https://huggingface.co/Prithvi-WxC/Gravity_wave_Parameterization/blob/main/magnet-flux-uvtp122-epoch-99-loss-0.1022.pt) model
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for inference on [sample dataset](https://huggingface.co/datasets/Prithvi-WxC/Gravity_wave_Parameterization/blob/main/wxc_input_u_v_t_p_output_theta_uw_vw_era5_training_data_hourly_2015_constant_mu_sigma_scaling05.nc).
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