In EURAC-Ecohydro/SnowSeasonAnalysis: Analysis of snow and precipitation during winter season

Title: "Filtering_precipitation_readings.R"

Introduction

The script Filtering_precipitation_readings.R in folder inst classify every precipitation measurement of tipping rain gauge in different class using additional parameters measured on the station. The script would like improve precipitation data quality based on paper ESOLIP, Mair et al.[1].

Description of script

• Section 1: in this section you can select inputs, for example the name of the station to process, the elevation, and the file of Climareport. See the example: /data/Climareport/Climareport.csv. Climareport is a csv who we can find information about weather in Vinschgau valley, derived from textual Climareport published by Province of South Tyrol. The file Climareport.csv distinguishes 4 possible events during a day (put the flag 1 to the events):

  • Clear: Sunny day with clear sky and without precipitation.
  • Cloudy: Day with some clouds but without precipitation.
  • Variable: Day with variable weather conditions. In Vinschgau precipitation is possible but not sure.
  • Precipitation: Days with rain. Precipitation in Vinschgau is highly possible.
  • other optional paramater are Snow (snow limit) and Notes for additional informations such extreme events etc...

  In this section the script require the information of variable names. Assign to precipitation, air_temperature , relative_humidity and radiatio string of names of corresponding variable measured in input file. This variables are required for an evaluation of precipitation

• Section 2: here the script run the function ESOLIP_QC (in the file esqc_ESOLIP_quality_check.R in folder R) that classify the data measured by Tipping Rain Gauges using additional informations: wheather station parameters (e.g. as Air Temperature, Solar Radiation, ...), Climareport, and other experimental knowledge (e.g. improbable snow melting during summer, ...).

• Section 3: in the previous section the algorithm generate an output that in this section are saved. There are two possibilities:

  • .RData: required to examine result with shiny app in script Visualize_Filtering_precipitation_readings.R
  • .csv: to have the output in the same format as the input. Output is a single column called Precip_T_Int15_Metadata. In this report you can find the description of metadata

Datasets

INPUT:

1. git_folder: source of package SnowSeasonAnalysis
2. path: path of Input data (don't edit this path)
3. file: name of .csv file to process available in folder /data/Input data
4. elevation: elevation of station
5. climareport: path of Climareport file (don't edit this path)
6. precipitation: the column name of file corresponding with precipitation parameter. Default (LTER stations) is "Precip_T_Int15"
7. air_temperature: the column name of file corresponding with air_temperature parameter. Default (LTER stations) is "T_Air"
8. relative_humidity: the column name of file corresponding with relative_humidity parameter. Default (LTER stations) is "RH"
9. radiation: the column name of file corresponding with radiation parameter. Default (LTER stations) is "SR_Sw"

INPUT DATA FORMAT:

The files in folder /data/Input_data/ should be hourly aggregatedor hourly sampled and have:

• the datetime (in LTER called TIMESTAMP) at the first column and should have this format: YYYY-MM-DD HH:MM. Example: "2017-07-15 09:00"
• a precipitation variable. Example: "Precip_T_Int15"
• an air temperature variable. Example: "T_Air"
• a relative humidity variable. Example: "RH"
• a solar radiation variable. Example: "SR_Sw"

OUTPUT:

• ESQC_file.RData: in folder data/Output/Precipitation_metadata_RData/ an .RData file which contains:

  • esolip_data -> Time seried of Precip_T_Int15_Metadata
  • esolip_events -> Steps results. Every single step of ESOLIP_QC function give in output some informations used for final result More details about algorithm

• Prec_Metadata_file.csv: in folder data/Output/Precipitation_metadata/Prec_Metadata/ a.csv file which contains a time series of ESOLIP metadata (See above esolip_data)

• ESQC_Steps_file.csv: in folder data/Output/Precipitation_metadata/ESOLIP_QC_Steps/ a.csv file which contains a time series of steps results of ESOLIP alghorithm (See above esolip_events) More details about algorithm

Note: in the output names above the algorithm substitute automatically "file" with the name of station, setting in file <- "..." (INPUT 3)

Description of Precip_T_Int15_Metadata:

This new column is the result of ESOLIP_QC function. It could contain 7 value :

• No precipitation: no precipitation is recorded and no precipitation is possible
• Dew/Fog: precipitation measured but probabily incorrect. Possible dew, fog condensed on tipping bucket
• Precipitation: precipitation measured is plausible
• Uncertain: precipitation is not measured but is to not exclude the possibility of precipitation
• SnowMelting: precipitation is recorded but improbable. Possible snow melting (during winter)
• Irrigation/Dirt :precipitation is measured but probabily incorrect. Possible Irrigation or dirt in the tipping bucket
• NaN: evaluation impossible due missing data of additional parameters (T_Air, RH, etc...)

How to use

Open script Filtering_precipitation_readings.R and:

1. Set git folder, the path where the package is download or used.
2. Run Section 1 to explore data available in folder data/Input data
3. Select file, the station you want process.
4. Select elevation, the elevation of the station
5. Select climareport, the path and the file of Climareport (Suggest: update existing Climareport.csv in folder data/Climareport)
6. Run Section 2 to produce output
7. Run Section 3 to save output (Decide which type of output you want)

References:

[1] Mair, E., Bertoldi, G., Leitinger, G., Della Chiesa, S., Niedrist, G., and Tappeiner, U.: ESOLIP - estimate of solid and liquid precipitation at sub-daily time resolution by combining snow height and rain gauge measurements, Hydrol. Earth Syst. Sci. Discuss., 10, 8683-8714, doi:10.5194/hessd-10-8683-2013, 2013.

EURAC-Ecohydro/SnowSeasonAnalysis documentation built on Dec. 6, 2020, 2:05 a.m.