Workflow Name: snowcast_wormhole

Description

The complete workflow for snowcast workflow creation, train, test, validation, deploy, predict.

Processes

data_sentinel2, model_creation_lstm, model_creation_ghostnet, data_integration, model_train_validate, model_predict, data_terrainFeatures, data_gee_modis_station_only, data_gee_sentinel1_station_only, data_associate_station_grid_cell, data_gee_modis_real_time, data_gee_sentinel1_real_time, base_hole, data_gee_gridmet_station_only, data_gee_gridmet_real_time, model_creation_xgboost, testing_data_integration, snowcast_utils, model_create_kehan, data_snotel_real_time, all_dependencies, data_WUS_UCLA_SR, data_nsidc_4km_swe, model_creation_et, data_snotel_station_only, model_creation_rf, model_creation_pycaret, model_creation_autokeras, model_creation_autopytorch, western_us_dem.py, download_srtm_1arcsec (caution!), gridmet_testing, create_output_tif_template, resample_dem, convert_results_to_images, deploy_images_to_website, training_feature_selection, amsr_testing_realtime, perform_download.sh, merge_custom_traning_range, training_data_range, amsr_features, amsr_swe_data_download, install_dependencies, model_evaluation, interpret_model_results, train_test_pattern_compare, correct_slope, convert_to_time_series, data_merge_hackweek, data_gee_smap_station_only, data_merge_hackweek_testing, training_sanity_check, fSCA_training, fsCA_testing, fsca_py, fSCA_training_extract_data, data_ghcnd_station_only, mod_water_mask, prepare_water_mask_template, download_modis_09, data_mod09_extract_csvs, duplicated_feature_selection, train_self_attention_xgb_slurm, train_xgb_slurm, train_novel_transformerxgb, predict_transformerxgb, bttf_swe_predict, bttf_train, xgb_train, xgb_predict, snodas_test, transformer_snodas_train, snodas_tabnet, snodas_testing_realtime, snodas_tabnet_slurm, add_snodas_mask_column, snodas_fttransformer, snodas_dnn_new, base_nn_hole, clip_basins_for_eval, snodas_resnet

Process Descriptions

data_sentinel2: null model_creation_lstm: python model_creation_ghostnet: python data_integration: null model_train_validate: null model_predict: null data_terrainFeatures: null data_gee_modis_station_only: null data_gee_sentinel1_station_only: null data_associate_station_grid_cell: null data_gee_modis_real_time: null data_gee_sentinel1_real_time: null base_hole: null data_gee_gridmet_station_only: null data_gee_gridmet_real_time: null model_creation_xgboost: null testing_data_integration: null snowcast_utils: null model_create_kehan: null data_snotel_real_time: null all_dependencies: null data_WUS_UCLA_SR: null data_nsidc_4km_swe: null model_creation_et: null data_snotel_station_only: null model_creation_rf: null model_creation_pycaret: null model_creation_autokeras: null model_creation_autopytorch: null western_us_dem.py: null download_srtm_1arcsec (caution!): null gridmet_testing: null create_output_tif_template: null resample_dem: null convert_results_to_images: null deploy_images_to_website: null training_feature_selection: null amsr_testing_realtime: null perform_download.sh: null merge_custom_traning_range: null training_data_range: null amsr_features: null amsr_swe_data_download: null install_dependencies: null model_evaluation: null interpret_model_results: null train_test_pattern_compare: null correct_slope: null convert_to_time_series: null data_merge_hackweek: null data_gee_smap_station_only: null data_merge_hackweek_testing: null training_sanity_check: null fSCA_training: null fsCA_testing: null fsca_py: null fSCA_training_extract_data: null data_ghcnd_station_only: null mod_water_mask: null prepare_water_mask_template: null download_modis_09: null data_mod09_extract_csvs: null duplicated_feature_selection: python train_self_attention_xgb_slurm: null train_xgb_slurm: null train_novel_transformerxgb: null predict_transformerxgb: null bttf_swe_predict: null bttf_train: null xgb_train: null xgb_predict: null snodas_test: null transformer_snodas_train: null snodas_tabnet: null snodas_testing_realtime: null snodas_tabnet_slurm: null add_snodas_mask_column: null snodas_fttransformer: null snodas_dnn_new: null base_nn_hole: null clip_basins_for_eval: python snodas_resnet: null

Steps to use the workflow

This section provides detailed instructions on how to use the workflow. Follow these steps to set up and execute the workflow using Geoweaver.

Step-by-Step Instructions

Step 1: Download the zip file

Step 2: Import the Workflow into Geoweaver

Open Geoweaver running on your local machine. video guidance

1. Click on "Weaver" in the top navigation bar.
2. A workspace to add a workflow opens up. Select the "Import" icon in the top navigation bar.
3. Choose the downloaded zip file4. Click on "Start" to upload the file. If the file is valid, a prompt will ask for your permission to upload. Click "OK".
4. Once the file is uploaded, Geoweaver will create a new workflow.

Step 3: Execute the Workflow

1. Click on the execute icon in the top navigation bar to start the workflow execution process.video guidance
2. A wizard will open where you need to select the video guidance and environment video guidance.
3. Click on "Execute" to initiate the workflow. Enter the required password when prompted and click "Confirm" to start executing the workflow.

Step 4: Monitor Execution and View Results

1. The workflow execution will begin.
2. Note: Green indicates the process is successful, Yellow indicates the process is running, and Red indicates the process has failed.
3. Once the execution is complete, the results will be available immediately.

By following these steps, you will be able to set up and execute the snow cover mapping workflow using Geoweaver.