Merge pull request #9 from nasa/dev

add IMERG download option

README.md

@@ -2,6 +2,9 @@
 
 LHASA was created at Goddard Space Flight Center to identify the potential for rainfall-triggered landslides in real time. 
 
+## What's new
+2022-6-23 Added an option to choose between OpenDAP and file download servers for obtaining IMERG. OpenDAP enables reduced data volumes, both for transfer and storage; while file downloads from the PPS site may offer reduced latency and improved reliability. This capacity is controlled with the '-od' or '--opendap' argument in [lhasa.py](https://github.com/nasa/LHASA/blob/master/lhasa.py).
+
 ## LHASA 2.0
 
 LHASA version 2 adopts machine learning to estimate the probability of landslide occurrence at a 30-arcsecond (~1 km) daily resolution. In addition, it estimates the potential exposure of human population and roads to landslide hazard and maps the basins likely to experience post-fire debris flows. 
@@ -28,6 +31,9 @@ After cloning this repository, some setup is required prior to running LHASA. Th
     touch ~/.netrc
     echo "machine urs.earthdata.nasa.gov login <uid> password <password>" >> .netrc
     touch ~/.dodsrc
+    # Manage PPS connection, which is only necessary for downloading IMERG HDF5
+    # See for more info: https://registration.pps.eosdis.nasa.gov/registration/
+    echo "machine jsimpsonhttps.pps.eosdis.nasa.gov login <email>  password <email>" >> .netrc
     echo "HTTP.NETRC=~/.netrc" >> .dodsrc
     echo "HTTP.COOKIEJAR=~/.urs_cookies" >> .dodsrc
 
@@ -48,7 +54,7 @@ After cloning this repository, some setup is required prior to running LHASA. Th
     rm exposure.zip
 
     wget https://gpm.nasa.gov/sites/default/files/data/landslides/ref_data.zip &&
-    unzip ref_data.zip pfdf/ &&
+    unzip ref_data.zip -d pfdf/ &&
     rm ref_data.zip
 
     # Configure post-fire debris flow model
@@ -69,17 +75,29 @@ Then run [lhasa.sh](https://github.com/nasa/LHASA/blob/master/lhasa.sh) at the d
 
 Khan, S., D. Kirschbaum, and T. Stanley. 2021. "Investigating the potential of a global precipitation forecast to inform landslide prediction." Weather and Climate Extremes, 33: 100364 [10.1016/j.wace.2021.100364](https://doi.org/10.1016/j.wace.2021.100364)
 
-Stanley, T. A., D. B. Kirschbaum, G. Benz, et al. 2021. "Data-Driven Landslide Nowcasting at the Global Scale." Frontiers in Earth Science, 9: [10.3389/feart.2021.640043](https://doi.org/10.3389/feart.2021.640043)
+Khan, S., D. B. Kirschbaum, T. A. Stanley, P. M. Amatya, and R. A. Emberson. 2022. "Global Landslide Forecasting System for Hazard Assessment and Situational Awareness." Frontiers in Earth Science, 10: [10.3389/feart.2022.878996](https://doi.org/10.3389/feart.2022.878)
 
 Emberson, R., D. Kirschbaum, and T. Stanley. 2020. "New global characterisation of landslide exposure." Natural Hazards and Earth System Sciences, 20 (12): 3413-3424 [10.5194/nhess-20-3413-2020](https://doi.org/10.5194/nhess-20-3413-2020)
 
+Stanley, T. A., D. B. Kirschbaum, G. Benz, et al. 2021. "Data-Driven Landslide Nowcasting at the Global Scale." Frontiers in Earth Science, 9: [10.3389/feart.2021.640043](https://doi.org/10.3389/feart.2021.640043)
+
 ### Model training
 
 The software released here enables the user to run the global landslide forecast, but it does not enable the user to retrain the model on new datasets or domains. However, a demonstration workflow similar to that used in global LHASA 2.0 can be viewed [here](https://git.mysmce.com/eis-freshwater/landslides/-/blob/master/brendan/Landslide-Case-Study.ipynb). This demo was created as part of the [EIS](https://eis.mysmce.com/) project funded by NASA. 
 
 ### Archive
 
-No long-term archive for predictions from LHASA 2.0 has been established.
+No long-term archive for predictions from LHASA 2.0 has been established. Users of Google Earth Engine can find an interim archive at *users/nbiswasuw/Hazard*. 
+
+### Contributing
+
+Users are encouraged to participate in this project in various ways. 
+
+New landslide reports can be made through [Landslide Reporter](https://landslides.nasa.gov/reporter), which will enable NASA to better validate the model in the future. 
+
+Bug reports can be made through GitHub issues, while bug fixes and feature updates are welcome through pull requests. However, it's best to contact NASA prior to embarking on a major feature, as some improvements may lie outside the scope of this project. 
+
+Various forms of documentation are also needed. For example, a guide to installation of LHASA on Windows has already been requested. 
 
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