Add additional meteo data

Add additional meteo data#

Collect additional meteo data from various sources: SWC TS PREC

Imports#

DIRCONF = r'L:\Sync\luhk_work\20 - CODING\22 - POET\configs'
TIMEZONE_OFFSET_TO_UTC_HOURS = 1  # Timezone, e.g. "1" is translated to timezone "UTC+01:00" (CET, winter time)
REQUIRED_TIME_RESOLUTION = '30min'  # 30MIN time resolution

from pathlib import Path
import importlib.metadata
import pandas as pd
from dbc_influxdb import dbcInflux
from diive.core.io.files import load_parquet, save_parquet
from diive.core.times.times import TimestampSanitizer
from diive.pkgs.createvar.timesince import TimeSince
from influxdb_client.client.warnings import MissingPivotFunction
import warnings
warnings.filterwarnings(action='ignore', category=MissingPivotFunction)

version_diive = importlib.metadata.version("diive")
print(f"diive version: v{version_diive}")
version_dbc = importlib.metadata.version("dbc_influxdb")
print(f"dbc-influxdb version: v{version_dbc}")
dbc = dbcInflux(dirconf=DIRCONF)  # Connect to database

diive version: v0.85.0
dbc-influxdb version: v0.12.0
Reading configuration files was successful.
Connection to database works.

Load meteo data so far (M7)#

meteo7 = load_parquet(filepath="16.1_CH-CHA_meteo7_gapfilled_2005-2024.parquet")
meteo7

Loaded .parquet file 16.1_CH-CHA_meteo7_gapfilled_2005-2024.parquet (0.038 seconds).
    --> Detected time resolution of <30 * Minutes> / 30min 

	LW_IN_T1_2_1	PA_GF1_0.9_1	FLAG_PA_GF1_0.9_1_ISFILLED	PPFD_IN_T1_2_2	FLAG_PPFD_IN_T1_2_2_ISFILLED	VPD_T1_2_1	FLAG_VPD_T1_2_1_ISFILLED	SW_IN_T1_2_1	FLAG_SW_IN_T1_2_1_ISFILLED	TA_T1_2_1	FLAG_TA_T1_2_1_ISFILLED	RH_T1_2_1	FLAG_RH_T1_2_1_ISFILLED
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	NaN	978.100000	1.0	0.0	0	0.099893	0	0.0	3.0	1.566667	3.0	85.400000	1.0
2005-01-01 00:45:00	NaN	977.933333	1.0	0.0	0	0.097606	0	0.0	3.0	1.533333	3.0	85.700000	1.0
2005-01-01 01:15:00	NaN	977.900000	1.0	0.0	0	0.091683	0	0.0	3.0	1.566667	3.0	86.600000	1.0
2005-01-01 01:45:00	NaN	977.833333	1.0	0.0	0	0.071157	0	0.0	3.0	1.566667	3.0	89.600000	1.0
2005-01-01 02:15:00	NaN	977.833333	1.0	0.0	0	0.058333	0	0.0	3.0	1.500000	3.0	91.433333	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	304.613900	983.370890	NaN	0.0	0	0.000011	0	0.0	0.0	-1.919472	0.0	99.997990	NaN
2024-12-31 22:15:00	303.039890	983.052160	NaN	0.0	0	0.000011	0	0.0	0.0	-2.104678	0.0	99.997990	NaN
2024-12-31 22:45:00	302.093633	982.851140	NaN	0.0	0	0.000011	0	0.0	0.0	-2.089444	0.0	99.997990	NaN
2024-12-31 23:15:00	302.217307	982.896827	NaN	0.0	0	0.000010	0	0.0	0.0	-2.355761	0.0	99.997990	NaN
2024-12-31 23:45:00	298.392973	982.856613	NaN	0.0	0	0.000010	0	0.0	0.0	-2.578839	0.0	99.997990	NaN

350640 rows × 13 columns

Load additional meteo data from Feigenwinter et al. (2023a) (2005-2020)#

Variables: PREC SWC TS

# Load data from Feigenwinter et al. (2023a)
df_feigenw_2005_2020 = load_parquet(filepath="13.1_CH-CHA_FEIGENW_meteo_2005-2020.parquet")
keepcols = [
    'PREC_RAIN_TOT_GF1_0.5_1',
    'FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED',
    'SWC_GF1_0.05_1',
    'SWC_GF1_0.15_1',
    'SWC_GF1_0.75_1',
    'TS_GF1_0.04_1',
    'TS_GF1_0.15_1',
    'TS_GF1_0.4_1'
]
df_feigenw_2005_2020 = df_feigenw_2005_2020[keepcols].copy()
df_feigenw_2005_2020

Loaded .parquet file 13.1_CH-CHA_FEIGENW_meteo_2005-2020.parquet (0.020 seconds).
    --> Detected time resolution of <30 * Minutes> / 30min 

	PREC_RAIN_TOT_GF1_0.5_1	FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED	SWC_GF1_0.05_1	SWC_GF1_0.15_1	SWC_GF1_0.75_1	TS_GF1_0.04_1	TS_GF1_0.15_1	TS_GF1_0.4_1
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	0.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN
2005-01-01 00:45:00	0.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN
2005-01-01 01:15:00	0.1	1.0	NaN	NaN	NaN	NaN	NaN	NaN
2005-01-01 01:45:00	0.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN
2005-01-01 02:15:00	0.1	1.0	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...
2020-12-31 21:45:00	0.0	0.0	43.15512	37.83649	42.34799	7.059905	4.751553	5.665280
2020-12-31 22:15:00	0.0	0.0	43.14826	37.83116	42.34475	7.037867	4.753286	5.666638
2020-12-31 22:45:00	0.0	0.0	43.14190	37.82694	42.34330	7.021161	4.754451	5.668180
2020-12-31 23:15:00	0.0	0.0	43.13696	37.82402	42.34280	6.998506	4.753085	5.669790
2020-12-31 23:45:00	0.0	0.0	43.13130	37.82018	42.30388	6.990282	4.749334	5.671485

280512 rows × 8 columns

Load meteo data from FLUXNET v2024 (2021-2023)#

Variables: PREC

%%time
df_fluxnet_prec_2021_2023, data_detailed_fluxnet, assigned_measurements_fluxnet = dbc.download(
    bucket=f'ch-cha_processed',
    measurements=['PREC'],
    fields=['PREC_F', 'PREC_F_QC'],
    start='2021-01-01 00:00:01',  # Download data starting with this date (the start date itself IS included),
    stop='2024-01-01 00:00:01',  # Download data before this date (the stop date itself IS NOT included),
    timezone_offset_to_utc_hours=TIMEZONE_OFFSET_TO_UTC_HOURS,
    data_version='fluxnet_v2024'
)

DOWNLOADING
    from bucket ch-cha_processed
    variables ['PREC_F', 'PREC_F_QC']
    from measurements ['PREC']
    from data version fluxnet_v2024
    between 2021-01-01 00:00:01 and 2024-01-01 00:00:01
    with timezone offset to UTC of 1
Used querystring: from(bucket: "ch-cha_processed") |> range(start: 2021-01-01T00:00:01+01:00, stop: 2024-01-01T00:00:01+01:00) |> filter(fn: (r) => r["_measurement"] == "PREC") |> filter(fn: (r) => r["data_version"] == "fluxnet_v2024") |> filter(fn: (r) => r["_field"] == "PREC_F" or r["_field"] == "PREC_F_QC") |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
querystring was constructed from:
    bucketstring: from(bucket: "ch-cha_processed")
    rangestring: |> range(start: 2021-01-01T00:00:01+01:00, stop: 2024-01-01T00:00:01+01:00)
    measurementstring: |> filter(fn: (r) => r["_measurement"] == "PREC")
    dataversionstring: |> filter(fn: (r) => r["data_version"] == "fluxnet_v2024")
    fieldstring: |> filter(fn: (r) => r["_field"] == "PREC_F" or r["_field"] == "PREC_F_QC")
    pivotstring: |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
Download finished.
Downloaded data for 2 variables:
<-- PREC_F  (52560 records)  first date: 2021-01-01 00:30:00  last date: 2024-01-01 00:00:00
<-- PREC_F_QC  (52560 records)  first date: 2021-01-01 00:30:00  last date: 2024-01-01 00:00:00
========================================
Fields in measurement PREC of bucket ch-cha_processed:
#1  ch-cha_processed  PREC  PREC
#2  ch-cha_processed  PREC  PREC_ERA
#3  ch-cha_processed  PREC  PREC_F
#4  ch-cha_processed  PREC  PREC_F_QC
#5  ch-cha_processed  PREC  PREC_RAIN
#6  ch-cha_processed  PREC  PREC_RAIN_SOURCE
#7  ch-cha_processed  PREC  PREC_RAIN_TOT_GF1_0.5_1
#8  ch-cha_processed  PREC  PREC_TOT_M1_1_1
#9  ch-cha_processed  PREC  P_RAIN_TOT_GF1_0.5_1
#10  ch-cha_processed  PREC  P_RAIN_TOT_M1_1_1
Found 10 fields in measurement PREC of bucket ch-cha_processed.
========================================
CPU times: total: 1.56 s
Wall time: 2.45 s

df_fluxnet_prec_2021_2023

	PREC_F	PREC_F_QC
TIMESTAMP_END
2021-01-01 00:30:00	0.0	0.0
2021-01-01 01:00:00	0.0	0.0
2021-01-01 01:30:00	0.0	0.0
2021-01-01 02:00:00	0.0	0.0
2021-01-01 02:30:00	0.0	0.0
...	...	...
2023-12-31 22:00:00	0.0	0.0
2023-12-31 22:30:00	0.0	0.0
2023-12-31 23:00:00	0.0	0.0
2023-12-31 23:30:00	0.0	0.0
2024-01-01 00:00:00	0.0	0.0

52560 rows × 2 columns

Sanitize timestamp#

df_fluxnet_prec_2021_2023 = TimestampSanitizer(data=df_fluxnet_prec_2021_2023, output_middle_timestamp=True).get()
df_fluxnet_prec_2021_2023

	PREC_F	PREC_F_QC
TIMESTAMP_MIDDLE
2021-01-01 00:15:00	0.0	0.0
2021-01-01 00:45:00	0.0	0.0
2021-01-01 01:15:00	0.0	0.0
2021-01-01 01:45:00	0.0	0.0
2021-01-01 02:15:00	0.0	0.0
...	...	...
2023-12-31 21:45:00	0.0	0.0
2023-12-31 22:15:00	0.0	0.0
2023-12-31 22:45:00	0.0	0.0
2023-12-31 23:15:00	0.0	0.0
2023-12-31 23:45:00	0.0	0.0

52560 rows × 2 columns

Load meteo data from database, screened with `diive` (2021-2024)#

Variables: PREC (2024) SWC TS

%%time
df_diive_2021_2024, data_detailed, assigned_measurements = dbc.download(
    bucket=f'ch-cha_processed',
    measurements=['SWC', 'TS', 'PREC'],
    fields=[
        'SWC_GF1_0.05_1',
        'SWC_GF1_0.15_1',
        'SWC_GF1_0.2_1',
        'SWC_LOWRES_GF1_0.75_3',
        'TS_LOWRES_GF1_0.05_3',
        'TS_LOWRES_GF1_0.2_3',
        'TS_LOWRES_GF1_0.4_3',
        'PREC_RAIN_TOT_GF1_0.5_1'
    ],
    start='2021-01-01 00:00:01',  # Download data starting with this date (the start date itself IS included),
    stop='2025-01-01 00:00:01',  # Download data before this date (the stop date itself IS NOT included),
    timezone_offset_to_utc_hours=TIMEZONE_OFFSET_TO_UTC_HOURS,
    data_version='meteoscreening_diive'
)

DOWNLOADING
    from bucket ch-cha_processed
    variables ['SWC_GF1_0.05_1', 'SWC_GF1_0.15_1', 'SWC_GF1_0.2_1', 'SWC_LOWRES_GF1_0.75_3', 'TS_LOWRES_GF1_0.05_3', 'TS_LOWRES_GF1_0.2_3', 'TS_LOWRES_GF1_0.4_3', 'PREC_RAIN_TOT_GF1_0.5_1']
    from measurements ['SWC', 'TS', 'PREC']
    from data version meteoscreening_diive
    between 2021-01-01 00:00:01 and 2025-01-01 00:00:01
    with timezone offset to UTC of 1
Used querystring: from(bucket: "ch-cha_processed") |> range(start: 2021-01-01T00:00:01+01:00, stop: 2025-01-01T00:00:01+01:00) |> filter(fn: (r) => r["_measurement"] == "SWC" or r["_measurement"] == "TS" or r["_measurement"] == "PREC") |> filter(fn: (r) => r["data_version"] == "meteoscreening_diive") |> filter(fn: (r) => r["_field"] == "SWC_GF1_0.05_1" or r["_field"] == "SWC_GF1_0.15_1" or r["_field"] == "SWC_GF1_0.2_1" or r["_field"] == "SWC_LOWRES_GF1_0.75_3" or r["_field"] == "TS_LOWRES_GF1_0.05_3" or r["_field"] == "TS_LOWRES_GF1_0.2_3" or r["_field"] == "TS_LOWRES_GF1_0.4_3" or r["_field"] == "PREC_RAIN_TOT_GF1_0.5_1") |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
querystring was constructed from:
    bucketstring: from(bucket: "ch-cha_processed")
    rangestring: |> range(start: 2021-01-01T00:00:01+01:00, stop: 2025-01-01T00:00:01+01:00)
    measurementstring: |> filter(fn: (r) => r["_measurement"] == "SWC" or r["_measurement"] == "TS" or r["_measurement"] == "PREC")
    dataversionstring: |> filter(fn: (r) => r["data_version"] == "meteoscreening_diive")
    fieldstring: |> filter(fn: (r) => r["_field"] == "SWC_GF1_0.05_1" or r["_field"] == "SWC_GF1_0.15_1" or r["_field"] == "SWC_GF1_0.2_1" or r["_field"] == "SWC_LOWRES_GF1_0.75_3" or r["_field"] == "TS_LOWRES_GF1_0.05_3" or r["_field"] == "TS_LOWRES_GF1_0.2_3" or r["_field"] == "TS_LOWRES_GF1_0.4_3" or r["_field"] == "PREC_RAIN_TOT_GF1_0.5_1")
    pivotstring: |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
Download finished.
Downloaded data for 8 variables:
<-- PREC_RAIN_TOT_GF1_0.5_1  (17555 records)  first date: 2024-01-01 00:30:00  last date: 2025-01-01 00:00:00
<-- SWC_GF1_0.2_1  (54711 records)  first date: 2021-11-17 13:30:00  last date: 2025-01-01 00:00:00
<-- SWC_GF1_0.15_1  (12363 records)  first date: 2021-01-01 00:30:00  last date: 2021-09-15 13:30:00
<-- SWC_GF1_0.05_1  (67073 records)  first date: 2021-01-01 00:30:00  last date: 2025-01-01 00:00:00
<-- SWC_LOWRES_GF1_0.75_3  (70030 records)  first date: 2021-01-01 00:30:00  last date: 2025-01-01 00:00:00
<-- TS_LOWRES_GF1_0.05_3  (70037 records)  first date: 2021-01-01 00:30:00  last date: 2025-01-01 00:00:00
<-- TS_LOWRES_GF1_0.2_3  (70036 records)  first date: 2021-01-01 00:30:00  last date: 2025-01-01 00:00:00
<-- TS_LOWRES_GF1_0.4_3  (70031 records)  first date: 2021-01-01 00:30:00  last date: 2025-01-01 00:00:00
========================================
Fields in measurement SWC of bucket ch-cha_processed:
#1  ch-cha_processed  SWC  SWC_0.05
#2  ch-cha_processed  SWC  SWC_0.15
#3  ch-cha_processed  SWC  SWC_0.75
#4  ch-cha_processed  SWC  SWC_F_MDS_1
#5  ch-cha_processed  SWC  SWC_F_MDS_1_QC
#6  ch-cha_processed  SWC  SWC_F_MDS_2
#7  ch-cha_processed  SWC  SWC_F_MDS_2_QC
#8  ch-cha_processed  SWC  SWC_F_MDS_3
#9  ch-cha_processed  SWC  SWC_F_MDS_3_QC
#10  ch-cha_processed  SWC  SWC_F_MDS_4
#11  ch-cha_processed  SWC  SWC_F_MDS_4_QC
#12  ch-cha_processed  SWC  SWC_F_MDS_5
#13  ch-cha_processed  SWC  SWC_F_MDS_5_QC
#14  ch-cha_processed  SWC  SWC_F_MDS_6
#15  ch-cha_processed  SWC  SWC_F_MDS_6_QC
#16  ch-cha_processed  SWC  SWC_F_MDS_7
#17  ch-cha_processed  SWC  SWC_F_MDS_7_QC
#18  ch-cha_processed  SWC  SWC_F_MDS_8
#19  ch-cha_processed  SWC  SWC_F_MDS_8_QC
#20  ch-cha_processed  SWC  SWC_F_MDS_9
#21  ch-cha_processed  SWC  SWC_F_MDS_9_QC
#22  ch-cha_processed  SWC  SWC_GF1_0.05_1
#23  ch-cha_processed  SWC  SWC_GF1_0.05_2
#24  ch-cha_processed  SWC  SWC_GF1_0.05_3
#25  ch-cha_processed  SWC  SWC_GF1_0.15_1
#26  ch-cha_processed  SWC  SWC_GF1_0.1_1
#27  ch-cha_processed  SWC  SWC_GF1_0.1_2
#28  ch-cha_processed  SWC  SWC_GF1_0.1_3
#29  ch-cha_processed  SWC  SWC_GF1_0.25_1
#30  ch-cha_processed  SWC  SWC_GF1_0.2_1
#31  ch-cha_processed  SWC  SWC_GF1_0.2_2
#32  ch-cha_processed  SWC  SWC_GF1_0.2_3
#33  ch-cha_processed  SWC  SWC_GF1_0.3_1
#34  ch-cha_processed  SWC  SWC_GF1_0.3_2
#35  ch-cha_processed  SWC  SWC_GF1_0.3_3
#36  ch-cha_processed  SWC  SWC_GF1_0.4_1
#37  ch-cha_processed  SWC  SWC_GF1_0.4_3
#38  ch-cha_processed  SWC  SWC_GF1_0.5_1
#39  ch-cha_processed  SWC  SWC_GF1_0.5_2
#40  ch-cha_processed  SWC  SWC_GF1_0.5_3
#41  ch-cha_processed  SWC  SWC_GF1_0.6_3
#42  ch-cha_processed  SWC  SWC_GF1_0.75_1
#43  ch-cha_processed  SWC  SWC_GF1_0.75_3
#44  ch-cha_processed  SWC  SWC_GF1_1_3
#45  ch-cha_processed  SWC  SWC_GF4_0.05_1
#46  ch-cha_processed  SWC  SWC_GF4_0.05_1.1
#47  ch-cha_processed  SWC  SWC_GF4_0.05_2
#48  ch-cha_processed  SWC  SWC_GF4_0.05_2.1
#49  ch-cha_processed  SWC  SWC_GF4_0.05_3
#50  ch-cha_processed  SWC  SWC_GF4_0.05_4
#51  ch-cha_processed  SWC  SWC_GF4_0.05_5
#52  ch-cha_processed  SWC  SWC_GF4_0.1_1
#53  ch-cha_processed  SWC  SWC_GF4_0.1_2
#54  ch-cha_processed  SWC  SWC_GF4_0.1_2.1
#55  ch-cha_processed  SWC  SWC_GF4_0.1_3
#56  ch-cha_processed  SWC  SWC_GF4_0.1_4
#57  ch-cha_processed  SWC  SWC_GF4_0.1_5
#58  ch-cha_processed  SWC  SWC_GF4_0.2_1
#59  ch-cha_processed  SWC  SWC_GF4_0.2_2
#60  ch-cha_processed  SWC  SWC_GF4_0.3_4
#61  ch-cha_processed  SWC  SWC_GF4_0.3_5
#62  ch-cha_processed  SWC  SWC_GF4_0.5_4
#63  ch-cha_processed  SWC  SWC_GF4_0.5_5
#64  ch-cha_processed  SWC  SWC_GF4_1_1
#65  ch-cha_processed  SWC  SWC_GF5_0.05_1
#66  ch-cha_processed  SWC  SWC_GF5_0.05_1.1
#67  ch-cha_processed  SWC  SWC_GF5_0.05_2
#68  ch-cha_processed  SWC  SWC_GF5_0.05_2.1
#69  ch-cha_processed  SWC  SWC_GF5_0.05_3
#70  ch-cha_processed  SWC  SWC_GF5_0.05_4
#71  ch-cha_processed  SWC  SWC_GF5_0.1_1
#72  ch-cha_processed  SWC  SWC_GF5_0.1_1.1
#73  ch-cha_processed  SWC  SWC_GF5_0.1_2
#74  ch-cha_processed  SWC  SWC_GF5_0.1_2.1
#75  ch-cha_processed  SWC  SWC_GF5_0.1_3
#76  ch-cha_processed  SWC  SWC_GF5_0.1_4
#77  ch-cha_processed  SWC  SWC_GF5_0.2_2
#78  ch-cha_processed  SWC  SWC_LOWRES_GF1_0.75_3
Found 78 fields in measurement SWC of bucket ch-cha_processed.
========================================
========================================
Fields in measurement TS of bucket ch-cha_processed:
#1  ch-cha_processed  TS  TS_0.04
#2  ch-cha_processed  TS  TS_0.15
#3  ch-cha_processed  TS  TS_0.4
#4  ch-cha_processed  TS  TS_AVG_GF1_0.025_2
#5  ch-cha_processed  TS  TS_AVG_GF1_1_3
#6  ch-cha_processed  TS  TS_F_MDS_1
#7  ch-cha_processed  TS  TS_F_MDS_10
#8  ch-cha_processed  TS  TS_F_MDS_10_QC
#9  ch-cha_processed  TS  TS_F_MDS_11
#10  ch-cha_processed  TS  TS_F_MDS_11_QC
#11  ch-cha_processed  TS  TS_F_MDS_12
#12  ch-cha_processed  TS  TS_F_MDS_12_QC
#13  ch-cha_processed  TS  TS_F_MDS_13
#14  ch-cha_processed  TS  TS_F_MDS_13_QC
#15  ch-cha_processed  TS  TS_F_MDS_14
#16  ch-cha_processed  TS  TS_F_MDS_14_QC
#17  ch-cha_processed  TS  TS_F_MDS_1_QC
#18  ch-cha_processed  TS  TS_F_MDS_2
#19  ch-cha_processed  TS  TS_F_MDS_2_QC
#20  ch-cha_processed  TS  TS_F_MDS_3
#21  ch-cha_processed  TS  TS_F_MDS_3_QC
#22  ch-cha_processed  TS  TS_F_MDS_4
#23  ch-cha_processed  TS  TS_F_MDS_4_QC
#24  ch-cha_processed  TS  TS_F_MDS_5
#25  ch-cha_processed  TS  TS_F_MDS_5_QC
#26  ch-cha_processed  TS  TS_F_MDS_6
#27  ch-cha_processed  TS  TS_F_MDS_6_QC
#28  ch-cha_processed  TS  TS_F_MDS_7
#29  ch-cha_processed  TS  TS_F_MDS_7_QC
#30  ch-cha_processed  TS  TS_F_MDS_8
#31  ch-cha_processed  TS  TS_F_MDS_8_QC
#32  ch-cha_processed  TS  TS_F_MDS_9
#33  ch-cha_processed  TS  TS_F_MDS_9_QC
#34  ch-cha_processed  TS  TS_GF1_0.01_1
#35  ch-cha_processed  TS  TS_GF1_0.01_2
#36  ch-cha_processed  TS  TS_GF1_0.025_2
#37  ch-cha_processed  TS  TS_GF1_0.02_1
#38  ch-cha_processed  TS  TS_GF1_0.04_1
#39  ch-cha_processed  TS  TS_GF1_0.05_1
#40  ch-cha_processed  TS  TS_GF1_0.05_2
#41  ch-cha_processed  TS  TS_GF1_0.05_3
#42  ch-cha_processed  TS  TS_GF1_0.07_1
#43  ch-cha_processed  TS  TS_GF1_0.15_1
#44  ch-cha_processed  TS  TS_GF1_0.1_1
#45  ch-cha_processed  TS  TS_GF1_0.1_2
#46  ch-cha_processed  TS  TS_GF1_0.1_3
#47  ch-cha_processed  TS  TS_GF1_0.25_1
#48  ch-cha_processed  TS  TS_GF1_0.2_1
#49  ch-cha_processed  TS  TS_GF1_0.2_2
#50  ch-cha_processed  TS  TS_GF1_0.2_3
#51  ch-cha_processed  TS  TS_GF1_0.3_1
#52  ch-cha_processed  TS  TS_GF1_0.3_2
#53  ch-cha_processed  TS  TS_GF1_0.3_3
#54  ch-cha_processed  TS  TS_GF1_0.4_1
#55  ch-cha_processed  TS  TS_GF1_0.4_3
#56  ch-cha_processed  TS  TS_GF1_0.5_1
#57  ch-cha_processed  TS  TS_GF1_0.5_2
#58  ch-cha_processed  TS  TS_GF1_0.5_3
#59  ch-cha_processed  TS  TS_GF1_0.6_3
#60  ch-cha_processed  TS  TS_GF1_0.75_3
#61  ch-cha_processed  TS  TS_GF1_0.95_1
#62  ch-cha_processed  TS  TS_GF1_1_3
#63  ch-cha_processed  TS  TS_GF4_0.05_1
#64  ch-cha_processed  TS  TS_GF4_0.05_1.1
#65  ch-cha_processed  TS  TS_GF4_0.05_2
#66  ch-cha_processed  TS  TS_GF4_0.05_2.1
#67  ch-cha_processed  TS  TS_GF4_0.05_3
#68  ch-cha_processed  TS  TS_GF4_0.05_4
#69  ch-cha_processed  TS  TS_GF4_0.05_5
#70  ch-cha_processed  TS  TS_GF4_0.1_1
#71  ch-cha_processed  TS  TS_GF4_0.1_1.1
#72  ch-cha_processed  TS  TS_GF4_0.1_2
#73  ch-cha_processed  TS  TS_GF4_0.1_2.1
#74  ch-cha_processed  TS  TS_GF4_0.1_3
#75  ch-cha_processed  TS  TS_GF4_0.1_4
#76  ch-cha_processed  TS  TS_GF4_0.1_5
#77  ch-cha_processed  TS  TS_GF4_0.2_1
#78  ch-cha_processed  TS  TS_GF4_0.2_2
#79  ch-cha_processed  TS  TS_GF4_0.3_4
#80  ch-cha_processed  TS  TS_GF4_0.3_5
#81  ch-cha_processed  TS  TS_GF4_0.5_1
#82  ch-cha_processed  TS  TS_GF4_0.5_4
#83  ch-cha_processed  TS  TS_GF4_0.5_5
#84  ch-cha_processed  TS  TS_GF4_1_1
#85  ch-cha_processed  TS  TS_GF5_0.05_1
#86  ch-cha_processed  TS  TS_GF5_0.05_1.1
#87  ch-cha_processed  TS  TS_GF5_0.05_2
#88  ch-cha_processed  TS  TS_GF5_0.05_2.1
#89  ch-cha_processed  TS  TS_GF5_0.05_3
#90  ch-cha_processed  TS  TS_GF5_0.05_4
#91  ch-cha_processed  TS  TS_GF5_0.1_1
#92  ch-cha_processed  TS  TS_GF5_0.1_1.1
#93  ch-cha_processed  TS  TS_GF5_0.1_2
#94  ch-cha_processed  TS  TS_GF5_0.1_2.1
#95  ch-cha_processed  TS  TS_GF5_0.1_3
#96  ch-cha_processed  TS  TS_GF5_0.1_4
#97  ch-cha_processed  TS  TS_GF5_0.2_1
#98  ch-cha_processed  TS  TS_GF5_0.2_2
#99  ch-cha_processed  TS  TS_GF5_0.5_1
#100  ch-cha_processed  TS  TS_GF5_1_1
#101  ch-cha_processed  TS  TS_LOWRES_GF1_0.05_3
#102  ch-cha_processed  TS  TS_LOWRES_GF1_0.1_3
#103  ch-cha_processed  TS  TS_LOWRES_GF1_0.2_3
#104  ch-cha_processed  TS  TS_LOWRES_GF1_0.3_3
#105  ch-cha_processed  TS  TS_LOWRES_GF1_0.4_3
#106  ch-cha_processed  TS  TS_LOWRES_GF1_0.5_3
#107  ch-cha_processed  TS  TS_LOWRES_GF1_0.6_3
#108  ch-cha_processed  TS  TS_LOWRES_GF1_0.75_3
#109  ch-cha_processed  TS  TS_LOWRES_GF1_1_3
Found 109 fields in measurement TS of bucket ch-cha_processed.
========================================
========================================
Fields in measurement PREC of bucket ch-cha_processed:
#1  ch-cha_processed  PREC  PREC
#2  ch-cha_processed  PREC  PREC_ERA
#3  ch-cha_processed  PREC  PREC_F
#4  ch-cha_processed  PREC  PREC_F_QC
#5  ch-cha_processed  PREC  PREC_RAIN
#6  ch-cha_processed  PREC  PREC_RAIN_SOURCE
#7  ch-cha_processed  PREC  PREC_RAIN_TOT_GF1_0.5_1
#8  ch-cha_processed  PREC  PREC_TOT_M1_1_1
#9  ch-cha_processed  PREC  P_RAIN_TOT_GF1_0.5_1
#10  ch-cha_processed  PREC  P_RAIN_TOT_M1_1_1
Found 10 fields in measurement PREC of bucket ch-cha_processed.
========================================
CPU times: total: 7.98 s
Wall time: 11.1 s

df_diive_2021_2024

	PREC_RAIN_TOT_GF1_0.5_1	SWC_GF1_0.05_1	SWC_GF1_0.15_1	SWC_GF1_0.2_1	SWC_LOWRES_GF1_0.75_3	TS_LOWRES_GF1_0.05_3	TS_LOWRES_GF1_0.2_3	TS_LOWRES_GF1_0.4_3
TIMESTAMP_END
2021-01-01 00:30:00	NaN	43.126695	37.815501	NaN	42.622617	3.180832	4.691608	5.775482
2021-01-01 01:00:00	NaN	43.122169	37.812458	NaN	42.833337	3.182800	4.680240	5.780918
2021-01-01 01:30:00	NaN	43.115942	37.807812	NaN	42.705397	3.153244	4.672886	5.782277
2021-01-01 02:00:00	NaN	43.111309	37.804945	NaN	42.746237	3.153244	4.662855	5.790431
2021-01-01 02:30:00	NaN	43.106277	37.800922	NaN	42.731170	3.166388	4.653491	5.793149
...	...	...	...	...	...	...	...	...
2024-12-31 22:00:00	0.0	58.725733	NaN	52.459871	45.120877	3.474346	4.437078	5.528727
2024-12-31 22:30:00	0.0	58.725118	NaN	52.633365	45.144937	3.428224	4.440415	5.521962
2024-12-31 23:00:00	0.0	58.728398	NaN	52.381308	45.152280	3.384733	4.443751	5.523991
2024-12-31 23:30:00	0.0	58.731899	NaN	52.309913	45.095043	3.349179	4.439747	5.528050
2025-01-01 00:00:00	0.0	58.738572	NaN	52.309997	45.278093	3.316919	4.442417	5.523991

70077 rows × 8 columns

Sanitize timestamp#

df_diive_2021_2024 = TimestampSanitizer(data=df_diive_2021_2024, output_middle_timestamp=True).get()
df_diive_2021_2024

	PREC_RAIN_TOT_GF1_0.5_1	SWC_GF1_0.05_1	SWC_GF1_0.15_1	SWC_GF1_0.2_1	SWC_LOWRES_GF1_0.75_3	TS_LOWRES_GF1_0.05_3	TS_LOWRES_GF1_0.2_3	TS_LOWRES_GF1_0.4_3
TIMESTAMP_MIDDLE
2021-01-01 00:15:00	NaN	43.126695	37.815501	NaN	42.622617	3.180832	4.691608	5.775482
2021-01-01 00:45:00	NaN	43.122169	37.812458	NaN	42.833337	3.182800	4.680240	5.780918
2021-01-01 01:15:00	NaN	43.115942	37.807812	NaN	42.705397	3.153244	4.672886	5.782277
2021-01-01 01:45:00	NaN	43.111309	37.804945	NaN	42.746237	3.153244	4.662855	5.790431
2021-01-01 02:15:00	NaN	43.106277	37.800922	NaN	42.731170	3.166388	4.653491	5.793149
...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	0.0	58.725733	NaN	52.459871	45.120877	3.474346	4.437078	5.528727
2024-12-31 22:15:00	0.0	58.725118	NaN	52.633365	45.144937	3.428224	4.440415	5.521962
2024-12-31 22:45:00	0.0	58.728398	NaN	52.381308	45.152280	3.384733	4.443751	5.523991
2024-12-31 23:15:00	0.0	58.731899	NaN	52.309913	45.095043	3.349179	4.439747	5.528050
2024-12-31 23:45:00	0.0	58.738572	NaN	52.309997	45.278093	3.316919	4.442417	5.523991

70128 rows × 8 columns

MERGE DATA#

Start with meteo7, then add data

Meteo7 (2005-2024)#

df_merged = meteo7.copy()
df_merged.plot(x_compat=True, subplots=True, figsize=(20, 9));
print(f"Index duplicates: {df_merged.index.duplicated().sum()}")
print(f"Column duplicates: {df_merged.columns.duplicated().sum()}")

Index duplicates: 0
Column duplicates: 0

../../_images/4848720ad2271d9ea79e409f0ade3d238c2694e95fcbe9df5faa83ed87ce6494.png

`PREC` `SWC` `TS` from Feigenwinter (2005-2020) [new columns]#

df_merged = pd.concat([df_merged, df_feigenw_2005_2020], axis=1)
df_merged.plot(x_compat=True, subplots=True, figsize=(20, 9));
print(f"Index duplicates: {df_merged.index.duplicated().sum()}")
print(f"Column duplicates: {df_merged.columns.duplicated().sum()}")

Index duplicates: 0
Column duplicates: 0

../../_images/d195797f14a0fae92cb974b406a23dd880e0c03dc0e8bc2a6c8cb970ec06d1f9.png

`PREC` from FLUXNET (2021-2023) [add to existing column and 1 new column]#

rename_dict = {
    'PREC_F': 'PREC_RAIN_TOT_GF1_0.5_1',
    'PREC_F_QC': 'FLAG_PREC_RAIN_TOT_GF1_0.5_1_FLUXNET_ISFILLED'
}
df_fluxnet_prec_2021_2023 = df_fluxnet_prec_2021_2023.rename(columns=rename_dict, inplace=False)

df_merged['PREC_RAIN_TOT_GF1_0.5_1'] = df_merged['PREC_RAIN_TOT_GF1_0.5_1'].fillna(df_fluxnet_prec_2021_2023['PREC_RAIN_TOT_GF1_0.5_1'])
df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_FLUXNET_ISFILLED'] = df_fluxnet_prec_2021_2023['FLAG_PREC_RAIN_TOT_GF1_0.5_1_FLUXNET_ISFILLED']

df_merged.plot(x_compat=True, subplots=True, figsize=(20, 12));
print(f"Index duplicates: {df_merged.index.duplicated().sum()}")
print(f"Column duplicates: {df_merged.columns.duplicated().sum()}")

Index duplicates: 0
Column duplicates: 0

../../_images/234c3e4f794d8e7227a6e53a00582eb8c2b50f54d62f3f49876a13a8f1e0c019.png

df_diive_2021_2024

	PREC_RAIN_TOT_GF1_0.5_1	SWC_GF1_0.05_1	SWC_GF1_0.15_1	SWC_GF1_0.2_1	SWC_LOWRES_GF1_0.75_3	TS_LOWRES_GF1_0.05_3	TS_LOWRES_GF1_0.2_3	TS_LOWRES_GF1_0.4_3
TIMESTAMP_MIDDLE
2021-01-01 00:15:00	NaN	43.126695	37.815501	NaN	42.622617	3.180832	4.691608	5.775482
2021-01-01 00:45:00	NaN	43.122169	37.812458	NaN	42.833337	3.182800	4.680240	5.780918
2021-01-01 01:15:00	NaN	43.115942	37.807812	NaN	42.705397	3.153244	4.672886	5.782277
2021-01-01 01:45:00	NaN	43.111309	37.804945	NaN	42.746237	3.153244	4.662855	5.790431
2021-01-01 02:15:00	NaN	43.106277	37.800922	NaN	42.731170	3.166388	4.653491	5.793149
...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	0.0	58.725733	NaN	52.459871	45.120877	3.474346	4.437078	5.528727
2024-12-31 22:15:00	0.0	58.725118	NaN	52.633365	45.144937	3.428224	4.440415	5.521962
2024-12-31 22:45:00	0.0	58.728398	NaN	52.381308	45.152280	3.384733	4.443751	5.523991
2024-12-31 23:15:00	0.0	58.731899	NaN	52.309913	45.095043	3.349179	4.439747	5.528050
2024-12-31 23:45:00	0.0	58.738572	NaN	52.309997	45.278093	3.316919	4.442417	5.523991

70128 rows × 8 columns

`PREC` from diive (2024) [add to existing column]#

df_merged['PREC_RAIN_TOT_GF1_0.5_1'] = df_merged['PREC_RAIN_TOT_GF1_0.5_1'].fillna(df_diive_2021_2024['PREC_RAIN_TOT_GF1_0.5_1'])
df_merged.plot(x_compat=True, subplots=True, figsize=(20, 12));

../../_images/daab3bd9948abe06f4a947ad7f5947e021cb2af58eae45c55664d818aacd0f76.png

`SWC` from diive (2021-2024) [add to existing columns]#

df_merged['SWC_GF1_0.05_1'] = df_merged['SWC_GF1_0.05_1'].fillna(df_diive_2021_2024['SWC_GF1_0.05_1'])
df_merged['SWC_GF1_0.15_1'] = df_merged['SWC_GF1_0.15_1'].fillna(df_diive_2021_2024['SWC_GF1_0.15_1'])
df_merged['SWC_GF1_0.15_1'] = df_merged['SWC_GF1_0.15_1'].fillna(df_diive_2021_2024['SWC_GF1_0.2_1'])
df_merged['SWC_GF1_0.75_1'] = df_merged['SWC_GF1_0.75_1'].fillna(df_diive_2021_2024['SWC_LOWRES_GF1_0.75_3'])
df_merged.plot(x_compat=True, subplots=True, figsize=(20, 9));
print(f"Index duplicates: {df_merged.index.duplicated().sum()}")
print(f"Column duplicates: {df_merged.columns.duplicated().sum()}")

Index duplicates: 0
Column duplicates: 0

../../_images/4146c4d1beef164e3b01742bc67b75024c0cb260343ff38acf19b3671a2671a6.png

`TS` from diive (2021-2024) [add to existing columns]#

df_merged['TS_GF1_0.04_1'] = df_merged['TS_GF1_0.04_1'].fillna(df_diive_2021_2024['TS_LOWRES_GF1_0.05_3'])
df_merged['TS_GF1_0.15_1'] = df_merged['TS_GF1_0.15_1'].fillna(df_diive_2021_2024['TS_LOWRES_GF1_0.2_3'])
df_merged['TS_GF1_0.4_1'] = df_merged['TS_GF1_0.4_1'].fillna(df_diive_2021_2024['TS_LOWRES_GF1_0.4_3'])
df_merged.plot(x_compat=True, subplots=True, figsize=(20, 9));
print(f"Index duplicates: {df_merged.index.duplicated().sum()}")
print(f"Column duplicates: {df_merged.columns.duplicated().sum()}")

Index duplicates: 0
Column duplicates: 0

../../_images/5d31a35d7e9f482e72d541a8e6c4f301c66d826d76d687ed912a783032963878.png

Sanitize timestamp#

df_merged = TimestampSanitizer(data=df_merged, output_middle_timestamp=False).get()
df_merged

	LW_IN_T1_2_1	PA_GF1_0.9_1	FLAG_PA_GF1_0.9_1_ISFILLED	PPFD_IN_T1_2_2	FLAG_PPFD_IN_T1_2_2_ISFILLED	VPD_T1_2_1	FLAG_VPD_T1_2_1_ISFILLED	SW_IN_T1_2_1	FLAG_SW_IN_T1_2_1_ISFILLED	TA_T1_2_1	FLAG_TA_T1_2_1_ISFILLED	RH_T1_2_1	FLAG_RH_T1_2_1_ISFILLED	PREC_RAIN_TOT_GF1_0.5_1	FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED	SWC_GF1_0.05_1	SWC_GF1_0.15_1	SWC_GF1_0.75_1	TS_GF1_0.04_1	TS_GF1_0.15_1	TS_GF1_0.4_1	FLAG_PREC_RAIN_TOT_GF1_0.5_1_FLUXNET_ISFILLED
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	NaN	978.100000	1.0	0.0	0	0.099893	0	0.0	3.0	1.566667	3.0	85.400000	1.0	0.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2005-01-01 00:45:00	NaN	977.933333	1.0	0.0	0	0.097606	0	0.0	3.0	1.533333	3.0	85.700000	1.0	0.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2005-01-01 01:15:00	NaN	977.900000	1.0	0.0	0	0.091683	0	0.0	3.0	1.566667	3.0	86.600000	1.0	0.1	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2005-01-01 01:45:00	NaN	977.833333	1.0	0.0	0	0.071157	0	0.0	3.0	1.566667	3.0	89.600000	1.0	0.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2005-01-01 02:15:00	NaN	977.833333	1.0	0.0	0	0.058333	0	0.0	3.0	1.500000	3.0	91.433333	1.0	0.1	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	304.613900	983.370890	NaN	0.0	0	0.000011	0	0.0	0.0	-1.919472	0.0	99.997990	NaN	0.0	NaN	58.725733	52.459871	45.120877	3.474346	4.437078	5.528727	NaN
2024-12-31 22:15:00	303.039890	983.052160	NaN	0.0	0	0.000011	0	0.0	0.0	-2.104678	0.0	99.997990	NaN	0.0	NaN	58.725118	52.633365	45.144937	3.428224	4.440415	5.521962	NaN
2024-12-31 22:45:00	302.093633	982.851140	NaN	0.0	0	0.000011	0	0.0	0.0	-2.089444	0.0	99.997990	NaN	0.0	NaN	58.728398	52.381308	45.152280	3.384733	4.443751	5.523991	NaN
2024-12-31 23:15:00	302.217307	982.896827	NaN	0.0	0	0.000010	0	0.0	0.0	-2.355761	0.0	99.997990	NaN	0.0	NaN	58.731899	52.309913	45.095043	3.349179	4.439747	5.528050	NaN
2024-12-31 23:45:00	298.392973	982.856613	NaN	0.0	0	0.000010	0	0.0	0.0	-2.578839	0.0	99.997990	NaN	0.0	NaN	58.738572	52.309997	45.278093	3.316919	4.442417	5.523991	NaN

350640 rows × 22 columns

Fix missing `PREC` flags#

df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED'] = df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED'].fillna(df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_FLUXNET_ISFILLED'])
df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED'].plot()

<Axes: xlabel='TIMESTAMP_MIDDLE'>

../../_images/60bdfe288b42179a3154ccddba43832d4eb3eb6dea48719b499f786548f6d606.png

still_missing = df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED'].isnull()
df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED'][still_missing]

TIMESTAMP_MIDDLE
2024-01-01 00:15:00   NaN
2024-01-01 00:45:00   NaN
2024-01-01 01:15:00   NaN
2024-01-01 01:45:00   NaN
2024-01-01 02:15:00   NaN
                       ..
2024-12-31 21:45:00   NaN
2024-12-31 22:15:00   NaN
2024-12-31 22:45:00   NaN
2024-12-31 23:15:00   NaN
2024-12-31 23:45:00   NaN
Freq: 30min, Name: FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED, Length: 17568, dtype: float64

df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED'] = df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED'].fillna(0)
df_merged['FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED'][still_missing]

TIMESTAMP_MIDDLE
2024-01-01 00:15:00    0.0
2024-01-01 00:45:00    0.0
2024-01-01 01:15:00    0.0
2024-01-01 01:45:00    0.0
2024-01-01 02:15:00    0.0
                      ... 
2024-12-31 21:45:00    0.0
2024-12-31 22:15:00    0.0
2024-12-31 22:45:00    0.0
2024-12-31 23:15:00    0.0
2024-12-31 23:45:00    0.0
Freq: 30min, Name: FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED, Length: 17568, dtype: float64

Calc `TIMESINCE` variable for `PREC`#

series_prec = df_merged['PREC_RAIN_TOT_GF1_0.5_1'].copy()
ts_prec = TimeSince(series_prec, lower_lim=0, include_lim=False)
ts_prec.calc()
# ts_full_results = ts.get_full_results()
timesince = ts_prec.get_timesince()
df_merged[timesince.name] = timesince
# locs = (timesince.index.year == 2012)
# timesince[locs].plot(x_compat=True);
# timesince.index

Plot#

df_merged.plot(x_compat=True, subplots=True, figsize=(20, 14));

../../_images/e45b815037442195cea218b882632a8b0745af20eac765adae582f727e218f81.png

Save to file#

OUTNAME = "17.1_CH-CHA_meteo10_2005-2024"
OUTPATH = r""
filepath = save_parquet(filename=OUTNAME, data=df_merged, outpath=OUTPATH)
# df_merged.to_csv(Path(OUTPATH) / f"{OUTNAME}.csv")

Saved file 17.1_CH-CHA_meteo10_2005-2024.parquet (0.313 seconds).

Add additional meteo data

Contents

Add additional meteo data#

Imports#

Load meteo data so far (M7)#

Load additional meteo data from Feigenwinter et al. (2023a) (2005-2020)#

Load meteo data from FLUXNET v2024 (2021-2023)#

Sanitize timestamp#

Load meteo data from database, screened with diive (2021-2024)#

Sanitize timestamp#

MERGE DATA#

Meteo7 (2005-2024)#

PREC SWC TS from Feigenwinter (2005-2020) [new columns]#

PREC from FLUXNET (2021-2023) [add to existing column and 1 new column]#

PREC from diive (2024) [add to existing column]#

SWC from diive (2021-2024) [add to existing columns]#

TS from diive (2021-2024) [add to existing columns]#

Sanitize timestamp#

Fix missing PREC flags#

Calc TIMESINCE variable for PREC#

Plot#

Save to file#

Load meteo data from database, screened with `diive` (2021-2024)#

`PREC` `SWC` `TS` from Feigenwinter (2005-2020) [new columns]#

`PREC` from FLUXNET (2021-2023) [add to existing column and 1 new column]#

`PREC` from diive (2024) [add to existing column]#

`SWC` from diive (2021-2024) [add to existing columns]#

`TS` from diive (2021-2024) [add to existing columns]#

Fix missing `PREC` flags#

Calc `TIMESINCE` variable for `PREC`#