Sample met data for testing ModVege#

Data used: Met Eireann daily data for Valentia Observatory, Co. Kerry (https://data.gov.ie/dataset/valentia-observatory-daily-data)

import os
from datetime import datetime, timezone
import pandas as pd
import pooch

DATA_DRIVE = "data"
URL = "https://cli.fusio.net/cli/climate_data/webdata/dly2275.csv"
SUB_DIR = os.path.join(DATA_DRIVE, "met", "MetEireann")
KNOWN_HASH = None
os.makedirs(SUB_DIR, exist_ok=True)
FILE_NAME = "dly2275.csv"

# download data if it doesn't exist
if not os.path.isfile(os.path.join(SUB_DIR, FILE_NAME)):
    pooch.retrieve(
        url=URL, known_hash=KNOWN_HASH, fname=FILE_NAME, path=SUB_DIR
    )

    with open(
        os.path.join(SUB_DIR, f"{FILE_NAME[:-4]}.txt"), "w", encoding="utf-8"
    ) as outfile:
        outfile.write(
            f"Data downloaded on: {datetime.now(tz=timezone.utc)}\n"
            f"Download URL: {URL}"
        )

val = pd.read_csv(
    os.path.join(SUB_DIR, FILE_NAME), skiprows=24, parse_dates=["date"]
)

val.head()
date ind maxtp ind.1 mintp igmin gmin ind.2 rain cbl ... ind.5 hg sun glorad soil pe evap smd_wd smd_md smd_pd
0 1942-01-01 0 11.1 0 9.8 0.0 8.0 0 1.7 1026.2 ... 0 32.0 NaN NaN NaN 0.9 1.1 NaN NaN NaN
1 1942-01-02 0 11.6 0 10.0 0.0 8.6 0 4.6 1023.8 ... 0 33.0 NaN NaN NaN 0.6 0.8 NaN NaN NaN
2 1942-01-03 0 12.1 0 9.8 0.0 9.4 0 16.7 1014.5 ... 0 39.0 NaN NaN NaN 0.3 0.5 NaN NaN NaN
3 1942-01-04 0 9.9 0 4.0 0.0 6.7 0 3.5 1012.0 ... 0 37.0 NaN NaN NaN 0.5 0.7 NaN NaN NaN
4 1942-01-05 0 4.9 0 1.4 0.0 0.4 0 0.0 1022.5 ... 0 45.0 NaN NaN NaN 0.9 1.1 NaN NaN NaN

5 rows × 25 columns

# filter data for 2019 - 2021
val = val[(val["date"].dt.year >= 2019) & (val["date"].dt.year <= 2021)]

val.sort_values(by=["date"], inplace=True)
val.reset_index(inplace=True)

# keep only min and max air temps, rainfall, global radiation, PET
val = val[["date", "maxtp", "mintp", "rain", "glorad", "pe"]]

val.head()
date maxtp mintp rain glorad pe
0 2019-01-01 11.4 8.3 0.0 272.0 0.3
1 2019-01-02 10.8 7.9 0.1 299.0 0.5
2 2019-01-03 10.0 7.9 0.9 171.0 0.8
3 2019-01-04 9.2 7.5 0.0 207.0 1.0
4 2019-01-05 10.8 8.3 0.0 122.0 0.9 
# calculate mean air temperature
val["T"] = val[["maxtp", "mintp"]].mean(axis=1)

# convert global radiation units to MJ m-2 from J cm-2
# see Allen et al. (1998)
val["RG"] = val["glorad"].astype(float) / 100.0

# convert global radiation to PAR
# see Papaioannou et al. (1993)
val["PAR"] = val["RG"] * 0.473

# rainfall
val["PP"] = val["rain"].astype(float)

# potential evapotranspiration
val["PET"] = val["pe"].astype(float)

val["time"] = val["date"]

# keep only relevant columns
val = val[["time", "T", "PAR", "PET", "PP", "RG"]]

val.head()
time T PAR PET PP RG
0 2019-01-01 9.85 1.28656 0.3 0.0 2.72
1 2019-01-02 9.35 1.41427 0.5 0.1 2.99
2 2019-01-03 8.95 0.80883 0.8 0.9 1.71
3 2019-01-04 8.35 0.97911 1.0 0.0 2.07
4 2019-01-05 9.55 0.57706 0.9 0.0 1.22 
val.tail()
time T PAR PET PP RG
1091 2021-12-27 9.60 1.71699 0.3 0.9 3.63
1092 2021-12-28 9.65 0.70004 0.6 7.7 1.48
1093 2021-12-29 12.60 1.19669 0.5 9.9 2.53
1094 2021-12-30 12.70 0.30272 0.3 22.2 0.64
1095 2021-12-31 13.20 0.44462 0.7 2.0 0.94 
# save as a CSV file
val.to_csv(os.path.join(SUB_DIR, "valentia.csv"), index=False)

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