scale precipitation value, add data description in readme

README.md

@@ -3,9 +3,14 @@
 Near real-time rainfall monitoring. Part of the project RIPOSTE for Cameroon Red Cross Society.
 
 ## Description
-The pipeline roughly consists of three steps:
+Data consumed:
+- Level 3 IMERG Early Run: PPS Near Real-time, see https://gpm.nasa.gov/data/directory
+- Value: 1-day precipitation accumulation (mm)
+- Link to download: https://jsimpsonhttps.pps.eosdis.nasa.gov/imerg/gis/early/
+- Temporal range: most recent past days (e.g. last 14 days)
 
-- Extract daily data on rainfall measurement of the past days (e.g. last 14 days) from [NOAA's GPM](https://gpm.nasa.gov/data/directory)
+The pipeline roughly consists of three steps:
+- Extract the 1-day data on rainfall measurement as above.
 - Transform the data into pre-defined areas (health districts) and calculate average rainfall of the past days. Then determine which area has its average rainfall higher than pre-defined thresholds.
 - Send this data as alert to the EspoCRM for the NS.
 

nrt_rainfall_pipeline/transform.py

@@ -55,6 +55,7 @@ def compute_rainfall(self, country: str, dateend):
 
     def __calculate_average_raster(self):
         """
+        Scale precipitation x0.1
         Sum precipiatation per cell of all raster files and take average per cell
         """
         all_files = glob.glob(f'{self.inputGPM}/{self.country}_*.tif')
@@ -68,7 +69,7 @@ def __calculate_average_raster(self):
             with rasterio.open(f) as src:
                 result_profile = src.profile
                 result_array = result_array + src.read()
-        result_array = result_array/n
+        result_array = result_array*0.1/n
 
         file_name = f"{self.country}_{self.datestart.strftime('%Y-%m-%d')}_{self.dateend.strftime('%Y-%m-%d')}"
         with rasterio.open(f"{self.inputGPM}/{file_name}.tif", 'w', **result_profile) as dst: