Introduction

A bootcamp was developed for Earth Scientists all about helping them get comfortable with the wide range of artificial intelligence and machine learning (AI/ML) methods that can power their research. Each lesson had two parts. First, a walk through of the method, followed by a hands-on activity using Earth science examples in Jupyter notebooks packed with helpful comments.

In addition to the basics of ML and popular deep learning techniques, two sessions also covered how to use the keras and pytorch, the go to libraries for running these models and other useful resources. The full story on the bootcamp and a follow-on hackathon can be found in our EOS article1.

Here is the exciting news for you. The bootcamp materials developed by PNNL scientists have been updated and are now publically available on PNNL’s github [IPID 33445, IR#373274]. Each lesson folder contains a clearly ordered set of notebooks, a dedicated environment yaml file, datasets, and a PowerPoint to start with.

A pre-lesson to the bootcamp not included in the github, introduction to python, was taught by Rob Hetland based on Texas A&M’s python4geosciences course covered syntax, data containers, basic logical control, reading and plotting data with pandas and xarray, georeferenced data and projections, and basic analysis techniques.

Lesson 1️⃣: Machine Learning 101

by TC Chakraborty & Sally Wang

This lesson covered the basics of machine learning such as the ML workflow and gave an overview of different machine learning methods. It dived deeper into clustering and tree-based algorithms. It gave examples of simple use-cases of supervised and unsupervised learning using sea surface temperature (SST) data for predicting El Nino Southern Oscillation (ENSO).

Surface maps of SST Anomaly

Figure generated during bootcamp module ML101: Unsupervised Learning, applying K-Means composites for El Nino and La Nina cases. Based on tutorial created by Ben A. Toms for AMS 2020 Short Course.

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Lesson 2️⃣: Artificial Neural Networks

by Maruti Mudunuru & TC Chakraborty

Lesson 2 describes the concept of deep learning and covers shallow versus deep neural networks. It discusses the components of the neural network and the deep learning workflow. There were two activities for this lesson. Part one focused on the workflow aspect using a simple end-to-end data pipeline to pre-process, train, and predict hydrological model parameters from time-series data. The synthetic data and process model is based on the Soil & Water Assessment Tool (SWAT) hydrologic model. Part two returns to the ENSO dataset from lesson one. This time, the tutorial uses neural networks for unsupervised learning to try to detect ENSO phases from sea surface temperature data. It dives into clustering and autoencoder methods that rely on neural networks using monthly sea-surface temperature data to explore problems related to the ENSO.

Two Plots related to model training, Epoch vs Loss and Ground Truth vs Prediction

Figure generated during bootcamp module Lesson 2 Part 1: DNN Inverse Models (dnn_im_notebook.ipynb), input is river discharge and output is the SWAT model parameters. Once the loss function evens out for the validation dataset, cease running epochs to reduce risk of overfitting wave parameters.

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Lesson 3️⃣: TensorFlow and Keras Library Packages

by Erol Cromwell

Lesson 3 provided an overview of the TensorFlow and keras library packages. TensorFlow is an end-to-end, open-source machine learning platform. For this lesson we focused on it’s python APIs (application programming interface). keras is a user interface for deep learning, dealing with layers, models, optimizers, loss functions, metrics, and more. keras makes TensorFlow simple and productive.

The example in lesson three sets up and trains a deep neural network model2 using keras to estimate five subsurface (soil and geologic) permeability parameters from simulated watershed discharge time series data. This is an example of inverse modeling: using observed data (stream discharge) to infer causal factors (subsurface permeability). Subsurface permeability is one of the key parameters that determine the subsurface flow and transport processes in watershed models. However, this parameter is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed, physics-based watershed model. The linkages between permeability and stream flow provide a new opportunity to estimate subsurface permeability from stream flow monitoring data that are made available through monitoring networks. The data was generated using ensemble forward simulations of the Rock Creek watershed in Colorado using the Advanced Terrestrial Simulator (ATS).

Schematic of a DNN

Schematic of DNN trained in lesson 3 using keras. The input is the normalized discharge time-series data. The outputs from the DNN are five normalized permeability parameters.

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Lesson 4️⃣: Convolutional Neural Networks

by Maruti Mudunuru, Sam Dixon, Robin Cosby, & Andrew Geiss

In lesson 4, the keras pipeline is extended to include CNN along with hyperparameter tuning using KerasTuner for CNNs. Participants learned about the convolutional neural networks and its components, the difference between CNNs and DNNs, and also hyperparameter tuning to find optimal DNN and CNN architectures. The hands on exercise teaches how to develop a workflow to load, visualize, pre-process, and develop a CNN models revisiting the same synthetic data and SWAT model used in lesson 2.

Timeseries of streamflow

Observed vs Modeled streamflow discharge using CNN.

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Lesson 5️⃣: Generative Adversarial Networks

by Andrew Geiss and Melissa Swift

Lesson five defines generative models, their implementation, identifies practical concerns, describes combined loss functions, and discusses ethical concerns for generative ML. GANs are ML models that generate realistic data samples. The first example is the classic number generator, MNIST. The second demonstration is atmospheric science oriented and uses GAN to generate realistic samples of true color MODIS imagery. The training image samples are taken from a dataset of labeled marine cloud regimes and contain primarily low-level clouds over the subtropical oceans. GANS provide highly realistic but potentially false outputs, this leads to obvious ethical concerns in environmental science. This presentation deliberates ethical ML stewardship.

Nine tiles each with an image of a cloud from satellite generated by a GAN

After 100,000 training updates the GAN can produce extremely convincing MODIS imagery (photos of earth and earth’s features, such as clouds, taken from the terra and aqua satellites). It generates a variety of cloud morphologies and even includes features like sunlight and shadow.

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Lesson 6️⃣: Pytorch Library Package

by Sally Wang

This lesson covers the basics and key elements of pytorch in comparison with keras. In the hands on tutorial, annual mean meterology from MERRA2, a NASA global reanalysis dataset based on space-based observations (Modern-Era Retrospective analysis for Research and Applications), is used to predict the annual surface PM2.5 over China during 2000-2017. The goal of this project is to see whether we can use annual meteorology (surface temperature, RH, precipitation, surface pressure, 10-m U wind and 10-m V wind) to predict/explain annual surface PM2.5 concentration.

To challenge bootcamp participants, this exercise asks participants to try and add a 1-D convolutional layer (with kernel_size of 2, no padding, stride=1) before the fully connected layer to try to see whether the model performance improves after including a convolutional layer. If not, why?

Surface map of PM2.5 over China

The surface PM2.5 and MERRA2 meteorology are regridded to a spatial resolution of 0.25º x 0.3125º.

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Lesson 7️⃣: Recurrent Neural Networks

by Peishi Jiang

Lesson 7 discusses sequence modeling, identifies the purpose of RNN, when to use long short-term memory models (LSTM), and the advantage of bidirectional LSTM. By learning information propagating back, biLSTMs effectively increase the amount of information available to the network. This lesson uses two examples to demonstrate the strength of sequence modeling: rainfall-runoff modeling with unidirectional LSTM and soil respiration modeling with bidirectional LSTM. Both of these examples use time series datasets and the pytorch library package covered in the previous lesson.

Plot of Lag Time versus Mutual Information

Modeling soil respiration using ecosystem observations at AmeriFlux US-Hn1 site.

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What are the Next Steps

The success of the two-step approach speaks for itself. As of January 2026, three of the hackathon teams (made up of both participants and instructors from the bootcamp) published their projects. Li et al., (2024)3 describes a data-driven framework for predicting aerosol-cloud interactions. Another team used a CNN model to identify open- versus closed-cell atmospheric convection in radar data, which helps explain distributions of clouds and rainfall, published in Journal of Geophysical Research (JGR) Machine Learning4 and received a highlight in EOS5. A third project also compared different classification models on classifying thermodynamic cloud phase in Atmospheric Measurement Technology (AMT) 6. PNNL reported an uptick in proposals from its Earth scientists for various internal funding opportunities focused on leveraging AI/ML methods, signifying steps towards independent AI driven research.

Whether organizing workforce training or learning AI/ML independently, success with AI like any skill takes time, but the effort is worthwhile, and a collaborative, cross-disciplinary environment accelerates such learning. Our two-step approach can rather be described as a pyramid, building a strong foundation leading to independence in stages:

  1. Community (Foundation): A strong community is essential. This includes having peers, mentors, and collaborators you can turn to for questions, feedback, and shared learning. This is finding your machine learning experts, data scientists, and domain scientists interested in the next frontier of AI/ML.
  2. AI/ML Literacy: The next level is developing a baseline understanding of AI/ML concepts—what methods exist, what they do well, and which approaches are most relevant to your research. This is the bootcamp.
  3. Framing Research Questions: With foundational knowledge in place, researchers can begin applying AI/ML by framing appropriate scientific questions, choosing appropriate methodologies, and curating data suited to those methods. This is the hackathon.
  4. Independence (Apex): At the top of the pyramid is independence: the ability to analyze results critically, apply methods autonomously, and identify new scientific directions enabled by AI/ML. Independence will be identified through journal articles and proposals.
Pyramid describing steps for AI success

Schematic of AI learning success. Generated by OpenAI with input from Lexie Goldberger

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Summary

  • Learning domain specific AI/ML methods to apply to your research can be daunting. Bootcamp learning modules can help.
  • Community resources are a valuable asset when learning a new skill, including AI/ML.
  • The bootcamp modules PNNL released cover 7 AI/ML methodologies applicable to Earth science research, each with a python tutorial available on Github.
  • When applying these methods for the first time, be mindful of the question to solve, the appropriate methodology, and data curation, as these are known challenge points.
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References

  1. Goldberger, L., Jiang, P., Chakraborty, T., Geiss, A., & Chen, X. (2025). A Two-Step Approach to Training Earth Scientists in AI. Eos, 106(PNNL-SA–203739). ↩︎

  2. Cromwell, E., Shuai, P., Jiang, P., Coon, E.T., Painter, S.L., Moulton, J.D., Lin, Y. and Chen, X., 2021. Estimating watershed subsurface permeability from stream discharge data using deep neural networks. Frontiers in Earth Science, 9, p.613011. ↩︎

  3. Li, X.-Y., Wang, H., Chakraborty, TC., Sorooshian, A., Ziemba, L. D., Voigt, C., et al. (2024). On the prediction of aerosol-cloud interactions within a data-driven framework. Geophysical Research Letters, 51, e2024GL110757. ↩︎

  4. Tian, J., Comstock, J., Geiss, A., Wu, P., Silber, I., Zhang, D., et al. (2025). Mesoscale cellular convection detection and classification using convolutional neural networks: Insights from long-term observations at ARM Eastern North Atlantic site. Journal of Geophysical Research: Machine Learning and Computation, 2, e2024JH000486. ↩︎

  5. Folini, D. (2025). Machine Learning Provides a New Perspective of Low-level Clouds. Eos, 106(PNNL-SA–203739). ↩︎

  6. Goldberger, L., Levin, M., Harris, C., Geiss, A., Shupe, M. D., and Zhang, D.: Classifying thermodynamic cloud phase using machine learning models, Atmos. Meas. Tech., 18, 5393–5414, 2025. ↩︎