ua-snap · charparr · Sep 11, 2023 · Aug 16, 2023 · Aug 16, 2023 · Aug 16, 2023
diff --git a/AR_QC.ipynb b/AR_QC.ipynb
diff --git a/AR_avalanche_exploration.ipynb b/AR_avalanche_exploration.ipynb
diff --git a/AR_detection.ipynb b/AR_detection.ipynb
diff --git a/AR_full_pipeline.ipynb b/AR_full_pipeline.ipynb
diff --git a/README.md b/README.md
@@ -1,7 +1,7 @@
 # Atmospheric Rivers and Avalanches
 
 ## Background
-This codebase will download [ERA5 6 hourly pressure level data](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview) from 1993-2023 in the vicinity of SE Alaska and apply an atmospheric river (AR) detection algorithm. Outputs will include a datacube of AR objects and multiple attributed shapefiles for future work correlating avalanche events to ARs. 
+This codebase will download [ERA5 6 hourly pressure level data](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview) in the vicinity of Alaska and apply an atmospheric river (AR) detection algorithm. Outputs will include a datacube of AR objects and multiple attributed shapefiles for future work correlating avalanche events to ARs. 
 
 The AR detection algorithm used here is adapted from [Guan & Waliser (2015)](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015JD024257) and uses a combination of vertically integrated water vapor transport (IVT), geometric shape, and directional criteria to define ARs. See the annotated bibilography document for more detail and other references. Users of this codebase should know the following information about atmospheric river criteria:
 
@@ -24,7 +24,7 @@ The overall orientation of the object (i.e., the direction of the shape elongati
  - All download specifications and model parameters are defined in `config.py`.
  - The `download.py` script will download the necessary ERA5 input data.
  - The `compute_ivt.py` script will transform the downloaded ERA5 input data into a datacube with the additional variables of IVT magnitude, direction, and an IVT quantile value.
- - The `ar_detection.py` module contains a collection of AR detection functions that will filter AR candidates based on the criteria and create shapefile outputs of objects classified as ARs. These functions may be orchestrated from a notebook (see `AR_detection.ipynb` and `AR_full_pipeline.ipynb`) or ran as a script. 
+ - The `ar_detection.py` module contains a collection of AR detection functions that will filter AR candidates based on the criteria and create shapefile outputs of objects classified as ARs. These functions may be orchestrated from a notebook (see `AR_detection.ipynb`) or ran as a script. 
 
 ## Usage
 1. Register for a Climate Data Store (CDS) account and install the CDS API client according to the instructions [here].(https://cds.climate.copernicus.eu/api-how-to). Be sure to accept the user agreement. 
@@ -33,4 +33,6 @@ The overall orientation of the object (i.e., the direction of the shape elongati
 5. Review parameters in `config.py` and adjust if desired. Note that there is a download request limit of 120,000 items, so adjusting the timestep or date range may overload the request and break the script.
 6. Execute `download.py`
 7. Execute `compute_ivt.py`
-8. Execute `ar_detection.py`, or use either `AR_detection.ipynb` or `AR_full_pipeline.ipynb` to orchestrate the detection.
+8. Execute `ar_detection.py` or use `AR_detection.ipynb` to orchestrate the detection.
+9. Examine the results using the `AR_QC.ipynb` notebook.
+10. Explore spatiotemporal relationships between avalanches and AR events using the `AR_avalanche_exploration.ipynb` notebook.
diff --git a/annotated_bib.md b/annotated_bib.md
@@ -1,12 +1,40 @@
 # Bibliography
+
+
+### Atmospheric Rivers Literature:
 
-Guan, B., and Waliser, D. E. (2015), Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies, J. Geophys. Res. Atmos., 120, 12514– 12535, doi:10.1002/2015JD024257. 
+- Guan, B., and Waliser, D. E. (2015). Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies. doi:10.1002/2015JD024257. 
 
+- Ralph et al. (2004). Satellite and CALJET Aircraft Observations of Atmospheric Rivers over the Eastern North Pacific Ocean during the Winter of 1997/98. doi:10.1175/1520-0493(2004)132<1721:SACAOO>2.0.CO;2.
 
-[Ralph et al., 2004; Neiman et al., 2008]
+- Neiman et al. (2008). Meteorological Characteristics and Overland Precipitation Impacts of Atmospheric Rivers Affecting the West Coast of North America Based on Eight Years of SSM/I Satellite Observations doi:10.1175/2007JHM855.1.
 
-[Zhu and Newell, 1998],
+- Zhu and Newell (1998). A Proposed Algorithm for Moisture Fluxes from Atmospheric Rivers. doi:10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.
 
-[Wick et al., 2013; Jiang et al., 2014; Backes et al., 2015]. 
+- Wick et al. (2013). Evaluation of Forecasts of the Water Vapor Signature of Atmospheric Rivers in Operational Numerical Weather Prediction Models. doi: 10.1175/WAF-D-13-00025.1.
 
-Rutz et al. [2014]
+- Jiang et al. (2014). Intermediate frequency atmospheric disturbances: A dynamical bridge connecting western U.S. extreme precipitation with East Asian cold surges. doi: 10.1002/2013JD021209.
+
+- Backes et al. (2015). A Climatology of the Vertical Structure of Water Vapor Transport to the Sierra Nevada in Cool Season Atmospheric River Precipitation Events. doi: 10.1175/JHM-D-14-0077.1.
+
+- Rutz et al. (2014). Climatological Characteristics of Atmospheric Rivers and Their Inland Penetration over the Western United States. doi: 10.1175/MWR-D-13-00168.1.
+
+
+
+
+### Geospatial Data:
+
+- ERA5: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-complete?tab=overview
+
+- Alaska Polygon: https://github.com/ua-snap/geospatial-vector-veracity/tree/main/vector_data/polygon/boundaries/alaska_coast_simplified
+
+- Canada Polygon: https://www.sciencebase.gov/catalog/item/5ab555c6e4b081f61ab78093
+
+
+
+
+### Atmospheric Phenomena Data:
+
+- ENSO: https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php
+
+- PDO Index: https://ds.data.jma.go.jp/tcc/tcc/products/elnino/decadal/pdo_doc.html#PDO_INDEX ; https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/index/ersst.v5.pdo.dat