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This file is an attempt to collect all the data from ever avy center I’ve worked on so far. 2. Not ever avy center published the same data and not ever forecast has the same data. Scanning though there is a mix between no-data and null. I’m not sure if its entirely consistent but null should mean that datacenter doesn’t publish that data while no-data means it wasn’t part of that forecast. @@ -13,9 +12,3 @@ e. “Day1DangerNearTreeline” is the forecast at the mid elevation provided fo f. “Day1DangerBelowTreeline” is the forecast at the lower elevation provided for the region g. “ForecastUrl” the archived url where I pulled the forecast in case the data needs to be checked against the source. 4. There are many other columns meant to encode avy-rose values, avy-rose avalanche problems and other elements of the forecast. Most of the column names should be self explanatory. - - -Older datasets for prior updates are archived below. -https://oapstorageprod.blob.core.windows.net/oap-training-data/Data/V1.1FeaturesWithLabels2013-2020.zip - -A small training set which has been processed for input in to an ML model (samples, features, timestep) can be downloaded here: https://oapstorageprod.blob.core.windows.net/oap-training-data/Data/MLDataWashington.zip The feature names are indexed according to the file https://oapstorageprod.blob.core.windows.net/oap-training-data/Data/FeatureLabels.csv diff --git a/README.md b/README.md index 46f32ff..0180026 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ Directories are organized as follows: This aspect of the tutorial will cover how you can obtain new weather input data for a new date range or region. This part assumes you have avalanche forecast labels for the dates and region (OAP currently has historical forecast labels for three avalanche centers in the US from the 15-16 season through the 20-21 season and is working on expanding that). -Due to the large size of the input GFS data and the fact that its already hosted by NCAR, OAP isn't currently providing copies of this data. If you want to start a data processing pipeline from the original data you can start with this process here. If you aren't interested in the data processing steps and only in the ML steps you can download the labels here: https://oapstorageprod.blob.core.windows.net/oap-training-data/Data/CleanedForecastsNWAC_CAIC_UAC_CAC.V1.2013-2021.zip and a subset of training data here: [TODO: replace with current link] and skip to the fourth notebook 4.TimeseriesAi +Due to the large size of the input GFS data and the fact that its already hosted by NCAR, OAP isn't currently providing copies of this data. If you want to start a data processing pipeline from the original data you can start with this process here. If you aren't interested in the data processing steps and only in the ML steps you can download the labels here: https://github.com/scottcha/OpenAvalancheProject/blob/master/Data/CleanedForecastsNWAC_CAIC_UAC_CAC.V1.2013-2021.zip and a subset of training data here: [TODO: replace with current link] and skip to the fourth notebook 4.TimeseriesAi The input data is derived from the .25 degree GFS model hosted by NCAR hosted at this site: https://rda.ucar.edu/datasets/ds084.1/ @@ -164,7 +164,7 @@ And then this is what it looks like when filtered to only the Olympics avalanche # Files on disk structure Training labels can be downloaded here: -https://oapstorageprod.blob.core.windows.net/oap-training-data/Data/CleanedForecastsNWAC_CAIC_UAC_CAC.V1.2013-2021.zip +https://github.com/scottcha/OpenAvalancheProject/blob/master/Data/CleanedForecastsNWAC_CAIC_UAC_CAC.V1.2013-2021.zip 1.RawWeatherData/ gfs/

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