> For the complete documentation index, see [llms.txt](https://jonas.gitbook.io/seismo_rain/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jonas.gitbook.io/seismo_rain/rainfall-data/extracting-rainfall-data.md). # Extracting rainfall data Due to the remote location of the seismic network and the lack of nearby weather stations with rain gauges, as well as the highly dynamic nature of rainstorms on a local scale (less than a kilometre), we will use a satellite-derived precipitation reanalysis model to extract the rainfall data of interest. ## [**CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1** ](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00948) The bias-corrected CMORPH dataset provides high-resolution global satellite precipitation estimates on an 8 km x 8 km grid, updated at 30-minute intervals. These estimates are derived from integrating passive microwave measurements and corrected using rain gauges and other numerical models. To construct a robust result, this is the highest temporal and spatial resolution of rainfall data that we can achieve in this instance. ## Opening precipitation NetCDF files To obtain precipitation estimates from the CMORPH\_V1.0 dataset for the period from April 19, 2016, UTC 22:00 to April 20, 2016, UTC 02:00, four netCDF (.nc) files are needed. These files can be downloaded from the following link: [CMORPH\_V1.0\_ADJ\_8km-30min Dataset](https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/access/30min/8km/). Make sure to change the file path to your local directory when opening it in `xarray`. ```python import xarray as xr #Precipitation data 2016-04-19 UTC 22:00 - 23:00 prep22 = xr.open_dataset("/Users/laichungheijonas/Downloads/CMORPH_V1.0_ADJ_8km-30min_2016041922.nc") #Precipitation data 2016-04-19 UTC 23:00 - 00:00(+1) prep23 = xr.open_dataset("/Users/laichungheijonas/Downloads/CMORPH_V1.0_ADJ_8km-30min_2016041923.nc") #Precipitation data 2016-04-20 UTC 00:00 - 01:00 prep00 = xr.open_dataset("/Users/laichungheijonas/Downloads/CMORPH_V1.0_ADJ_8km-30min_2016042000.nc") #Precipitation data 2016-04-20 UTC 01:00 - 02:00 prep01 = xr.open_dataset("/Users/laichungheijonas/Downloads/CMORPH_V1.0_ADJ_8km-30min_2016042001.nc") ``` ## Concatenate all netCDF files Using `xr.concat`, we can concatenate all the netCDF files along with the time dimension. ```python prep = xr.concat([prep22,prep23,prep00,prep01],dim='time') print(prep) ``` Upon executing `print(prep)`, you should observe the following output. Given the 4-hour interval, there should be a total of 8 timesteps. ``` Size: 262MB Dimensions: (time: 8, nv: 2, lat: 1649, lon: 4948) Coordinates: * time (time) datetime64[ns] 64B 2016-04-19T22:00:00 ... 2016-04-20... * lat (lat) float64 13kB -59.96 -59.89 -59.82 ... 59.82 59.89 59.96 * lon (lon) float64 40kB 0.03638 0.1091 0.1819 ... 359.8 359.9 360.0 Dimensions without coordinates: nv Data variables: time_bounds (time, nv) datetime64[ns] 128B 2016-04-19T22:00:00 ... 2016-... lat_bounds (time, lat, nv) float64 211kB -60.0 -59.93 ... 59.93 60.0 lon_bounds (time, lon, nv) float64 633kB 0.0 0.07276 ... 359.9 360.0 cmorph (time, lat, lon) float32 261MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 Attributes: (12/57) ncei_template_version: NCEI_NetCDF_Grid_template_V2.0 title: NOAA Climate Data Record (CDR) of CPC Morphin... keywords: Precipitation, Satellite, High-Resolution, Gl... summary: The CMORPH CDR is a reprocessed and bias-corr... references: Xie, P., et al. (2017), Reprocessed, Bias-Cor... Conventions: CF-1.6, ACDD-1.3 ... ... geospatial_lat_resolution: 0.072771376 geospatial_lat_units: degrees_north geospatial_lon_min: 0.0 geospatial_lon_max: 360.0 geospatial_lon_resolution: 0.072756669 geospatial_lon_units: degrees_east ``` ## Extracting the rainfall data for each seismic station To obtain the coordinates of each seismic station, we can utilize the inventory `inv` previously defined. The inventory can be divided into different levels as follows: * `inv[0]`: Seismic Network Details * `inv[0][0]`: Seismic Station Details * `inv[0][0][0]`: Channel Details ```python for x in range(4): print(f'{inv[0][x].code}: latitude: {inv[0][x].latitude}, longitude: {inv[0][x].longitude}') ``` Output: ``` 1517: latitude: 36.796766, longitude: -98.105601 199: latitude: 36.796824, longitude: -98.020063 465: latitude: 36.7962, longitude: -97.92993 763: latitude: 36.796074, longitude: -97.839733 ``` Subsequently, we can use the `.sel` command to extract the rainfall data for the specific grid cell. It's important to note that we specify `method='nearest'` to identify the nearest rainfall data grid cell to the seismic station. `prep['cmorph']` is the variable for rainfall intensity in mm/hour/ ```python prep_1517 = prep['cmorph'].sel(lat=36.796766, lon=360-98.105601 , method='nearest') prep_199 = prep['cmorph'].sel(lat=36.796824, lon=360-98.020063, method='nearest') prep_465 = prep['cmorph'].sel(lat=36.7962, lon=360-97.92993, method='nearest') prep_763 = prep['cmorph'].sel(lat=36.796074, lon=360-97.839733, method='nearest') ``` ## Plotting the rainfall intensity at all seismic stations Since rainfall intensity is measured per half-hourly interval, the data point at 22:00 represents the aggregated rainfall intensity during the period from 22:00 to 22:30, and so forth for subsequent intervals. ```python plt.figure(figsize=(15,3)) plt.plot(prep_1517.time,prep_1517,linewidth=3,label='2A.1517') plt.plot(prep_199.time,prep_199,linewidth=3,label='2A.199') plt.plot(prep_465.time,prep_465,linewidth=3,label='2A.465') plt.plot(prep_763.time,prep_763,linewidth=3,label='2A.763') plt.legend() plt.ylabel("Precipitation (mm/hour)") plt.xlabel("Time") plt.show() ```

Rainfall Intensity at all four seismic station during 19 April 2016 UTC 22:00 - 20 April 2016 UTC 02:00

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