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1_data_processing_old.py
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1_data_processing_old.py
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# -*- coding: utf-8 -*-
"""
@author: [email protected]
tip list:
%matplotlib inline
%matplotlib qt
import pdb; pdb.set_trace()
"""
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from scipy.interpolate import interp1d
import progressbar
import matplotlib.pyplot as plt
import firn_temp_lib as ftl
import time
import os
import xarray as xr
import time
import matplotlib
matplotlib.use('Agg')
def interp_pandas(s, kind="quadratic"):
# A mask indicating where `s` is not null
m = s.notna().values
s_save = s.copy()
# Construct an interpolator from the non-null values
# NB 'kind' instead of 'method'!
kw = dict(kind=kind, fill_value="extrapolate")
f = interp1d(s[m].index, s.loc[m].values.reshape(1, -1)[0], **kw)
# Apply this to the indices of the nulls; reconstruct a series
s[~m] = f(s[~m].index)[0]
plt.figure()
s.plot(ax=plt.gca(), marker="o", linestyle="none")
s_save.plot(ax=plt.gca(), marker="o", linestyle="none")
# plt.xlim(0, 60)
return s
needed_cols = ["date", "site", "latitude", "longitude", "elevation", "depthOfTemperatureObservation", "temperatureObserved", "reference", "reference_short", "note", "error", "durationOpen", "durationMeasured", "method"]
# %% Mock and Weeks
print("Loading Mock and Weeks")
df_all = pd.DataFrame(
columns=[ "date", "site", "latitude", "longitude", "elevation", "depthOfTemperatureObservation", "temperatureObserved", "reference", "reference_short", "note", "error", "durationOpen", "durationMeasured", "method"]
)
df_MW = pd.read_excel("Data/MockandWeeks/CRREL RR- 170 digitized.xlsx")
df_MW.loc[df_MW.Month.isnull(), "Day"] = 1
df_MW.loc[df_MW.Month.isnull(), "Month"] = 1
df_MW["date"] = pd.to_datetime(df_MW[["Year", "Month", "Day"]])
df_MW["note"] = "as reported in Mock and Weeks 1965"
df_MW["depthOfTemperatureObservation"] = 10
df_MW = df_MW.rename(
columns={
"Refstationnumber": "site",
"Lat(dec_deg)": "latitude",
"Lon(dec_deg)": "longitude",
"Elevation": "elevation",
"10msnowtemperature(degC)": "temperatureObserved",
"Reference": "reference",
}
)
df_MW = df_MW.loc[df_MW["temperatureObserved"].notnull()]
df_MW["durationOpen"] = "NA"
df_MW["durationMeasured"] = "NA"
df_MW["error"] = 0.5
df_MW[
"method"
] = "thermohms and a Wheats tone bridge, standard mercury or alcohol thermometers"
df_all = pd.concat((df_all,
df_MW[needed_cols],
), ignore_index=True,
)
# %% Benson (not reported in Mock and Weeks)
print("Loading Benson 1962")
df_benson = pd.read_excel("Data/Benson 1962/Benson_1962.xlsx")
df_benson.loc[df_benson.Month.isnull(), "Day"] = 1
df_benson.loc[df_benson.Month.isnull(), "Month"] = 1
df_benson["date"] = pd.to_datetime(df_MW[["Year", "Month", "Day"]])
df_benson["note"] = ""
df_benson["depthOfTemperatureObservation"] = 10
df_benson = df_benson.rename(
columns={
"Refstationnumber": "site",
"Lat(dec_deg)": "latitude",
"Lon(dec_deg)": "longitude",
"Elevation": "elevation",
"10msnowtemperature(degC)": "temperatureObserved",
"Reference": "reference",
}
)
# only keeping measurements not in Mock and Weeks
msk = (df_benson.site == "0-35") | (df_benson.site == "French Camp VI")
df_benson = df_benson.loc[msk, :]
df_benson["durationOpen"] = "measured in pit wall or borehole bottom after excavation"
df_benson["durationMeasured"] = "few minutes"
df_benson["error"] = "NA"
df_benson["method"] = "Weston bimetallic thermometers"
df_all = pd.concat((df_all,
df_benson[needed_cols],
), ignore_index=True,
)
# %% Polashenski
print("Loading Polashenski")
df_Pol = pd.read_csv("Data/Polashenski/2013_10m_Temperatures.csv")
df_Pol.columns = df_Pol.columns.str.replace(" ", "")
df_Pol.date = pd.to_datetime(df_Pol.date, format="%m/%d/%y")
df_Pol[
"reference"
] = "Polashenski, C., Z. Courville, C. Benson, A. Wagner, J. Chen, G. Wong, R. Hawley, and D. Hall (2014), Observations of pronounced Greenland ice sheet firn warming and implications for runoff production, Geophys. Res. Lett., 41, 4238–4246, doi:10.1002/2014GL059806."
df_Pol["reference_short"] = "Polashenski et al. (2014)"
df_Pol["note"] = ""
df_Pol["longitude"] = -df_Pol["longitude"]
df_Pol["depthOfTemperatureObservation"] = (
df_Pol["depthOfTemperatureObservation"].str.replace("m", "").astype(float)
)
df_Pol[
"durationOpen"
] = "string lowered in borehole and left 30min for equilibrating with surrounding firn prior measurement start"
df_Pol["durationMeasured"] = "overnight ~10 hours"
df_Pol["error"] = 0.1
df_Pol["method"] = "thermistor string"
df_all = pd.concat((df_all,
df_Pol[needed_cols],
), ignore_index=True,
)
# %% Stauffer and Oeschger 1979
print('Loading Stauffer and Oeschger measurements')
df_s_o = pd.read_excel("Data/Stauffer and Oeschger 1979/Stauffer&Oeschger1979.xlsx")
df_s_o[
"reference"
] = "Stauffer B. and H. Oeschger. 1979. Temperaturprofile in bohrloechern am rande des Groenlaendischen Inlandeises. Hydrologie und Glaziologie an der ETH Zurich. Mitteilung Nr. 41."
df_s_o["reference_short"] = "Stauffer and Oeschger (1979)"
df_s_o["note"] = "site location estimated by M. Luethi"
df_s_o["method"] = "Fenwal Thermistor UUB 31-J1"
df_s_o["durationOpen"] = 0
df_s_o["durationMeasured"] = 0
df_s_o["error"] = 0.1
df_all = pd.concat((df_all, df_s_o[needed_cols]), ignore_index=True)
# %% Ken's dataset
print("Loading Kens dataset")
df_Ken = pd.read_excel(
"Data/greenland_ice_borehole_temperature_profiles-main/data_filtered.xlsx"
)
df_Ken['reference_short'] = df_Ken.reference_short+' as in Mankoff et al. (2022)'
df_all = pd.concat((df_all, df_Ken[needed_cols]), ignore_index=True)
# %% Sumup
df_sumup = pd.read_csv("Data/Sumup/SUMup_temperature_2022.csv")
df_sumup = df_sumup.loc[df_sumup.Latitude > 0]
df_sumup = df_sumup.loc[
df_sumup.Citation != 30
] # Redundant with Miege and containing positive temperatures
df_sumup["date"] = pd.to_datetime(df_sumup.Timestamp)
df_sumup["note"] = "as reported in Sumup"
df_sumup["reference"] = ""
df_sumup["site"] = ""
df_sumup.loc[
df_sumup.Citation == 32, "reference"
] = "Graeter, K., Osterberg, E. C., Ferris, D., Hawley, R. L., Marshall, H. P. and Lewis, G.: Ice Core Records of West Greenland Surface Melt and Climate Forcing, Geophys. Res. Lett., doi:10.1002/2017GL076641, 2018."
df_sumup.loc[
df_sumup.Citation == 32, "reference_short"
] = "Graeter et al. (2018) as in SUMup"
df_sumup.loc[
df_sumup.Citation == 33, "reference"
] = "Lewis, G., Osterberg, E., Hawley, R., Marshall, H. P., Meehan, T., Graeter, K., McCarthy, F., Overly, T., Thundercloud, Z. and Ferris, D.: Recent precipitation decrease across the western Greenland ice sheet percolation zone, Cryosph., 13(11), 2797–2815, doi:10.5194/tc-13-2797- 2019, 2019."
df_sumup.loc[
df_sumup.Citation == 33, "reference_short"
] = "Lewis et al. (2019) as in SUMup"
site_list = pd.DataFrame(
np.array(
[
[44, "GTC01"],
[45, "GTC02"],
[46, "GTC04"],
[47, "GTC05"],
[48, "GTC06"],
[49, "GTC07"],
[50, "GTC08"],
[51, "GTC09"],
[52, "GTC11"],
[53, "GTC12"],
[54, "GTC13"],
[55, "GTC14"],
[56, "GTC15"],
[57, "GTC16"],
]
),
columns=["id", "site"],
).set_index("id")
for ind in site_list.index:
df_sumup.loc[df_sumup.Name == int(ind), "site"] = site_list.loc[ind, "site"]
df_sumup = df_sumup.drop(["Name", "Citation", "Timestamp"], axis=1)
df_sumup = df_sumup.rename(
columns={
"Latitude": "latitude",
"Longitude": "longitude",
"Elevation": "elevation",
"Depth": "depthOfTemperatureObservation",
"Temperature": "temperatureObserved",
"Duration": "durationMeasured",
"Error": "error",
"Open_Time": "durationOpen",
"Method": "method",
}
)
df_all = pd.concat((df_all,
df_sumup[needed_cols],
), ignore_index=True,
)
# ====> only temperature at 18m depth
# %% McGrath
print("Loading McGrath")
df_mcgrath = pd.read_excel(
"Data/McGrath/McGrath et al. 2013 GL055369_Supp_Material.xlsx"
)
df_mcgrath = df_mcgrath.loc[df_mcgrath["Data Type"] != "Met Station"]
df_mcgrath["depthOfTemperatureObservation"] = np.array(
df_mcgrath["Data Type"].str.split("m").to_list()
)[:, 0].astype(int)
df_mcgrath = df_mcgrath.rename(
columns={
"Observed\nTemperature (°C)": "temperatureObserved",
"Latitude\n(°N)": "latitude",
"Longitude (°E)": "longitude",
"Elevation\n(m)": "elevation",
"Reference": "reference",
"Location": "site",
}
)
df_mcgrath["note"] = "as reported in McGrath et al. (2013)"
df_mcgrath["date"] = pd.to_datetime(
(df_mcgrath.Year * 10000 + 101).apply(str), format="%Y%m%d"
)
df_mcgrath["site"] = df_mcgrath["site"].str.replace("B4", "4")
df_mcgrath["site"] = df_mcgrath["site"].str.replace("B5", "5")
df_mcgrath["site"] = df_mcgrath["site"].str.replace("4-425", "5-0")
df_mcgrath["method"] = "digital Thermarray system from RST©"
df_mcgrath["durationOpen"] = 0
df_mcgrath["durationMeasured"] = 30
df_mcgrath["error"] = 0.07
df_mcgrath
df_all = pd.concat((df_all,
df_mcgrath[needed_cols],
), ignore_index=True,
)
# adding real date to Benson's measurement
df_fausto = pd.read_excel(
"Data/misc/Data_Sheet_1_ASnowDensityDatasetforImprovingSurfaceBoundaryConditionsinGreenlandIceSheetFirnModeling.XLSX",
skiprows=[0],
)
df_fausto.Name = df_fausto.Name.str.replace("station ", "")
for site in df_fausto.Name:
if any(df_all.site == site):
if np.sum(df_all.site == site) > 1:
print(
df_all.loc[
df_all.site == site,
["temperatureObserved", "depthOfTemperatureObservation"],
]
)
print("duplicate, removing from McGrath")
df_all = df_all.loc[
~np.logical_and(
df_all.site == site,
df_all["note"] == "as reported in McGrath et al. (2013)",
)
]
print(
site,
df_all.loc[df_all.site == site].date.values,
df_fausto.loc[df_fausto.Name == site, ["year", "Month", "Day"]].values,
)
if (
df_all.loc[df_all.site == site].date.iloc[0].year
== df_fausto.loc[df_fausto.Name == site, ["year"]].iloc[0].values
):
df_all.loc[df_all.site == site, "date"] = (
pd.to_datetime(
df_fausto.loc[df_fausto.Name == site, ["year", "Month", "Day"]]
)
.astype("datetime64[ns]")
.iloc[0]
)
print("Updating date")
else:
print("Different years")
# %% Hawley GrIT
print("Loading Hawley GrIT")
import warnings
warnings.filterwarnings('ignore', category=UserWarning, module='openpyxl')
df_hawley = pd.read_excel("Data/Hawley GrIT/GrIT2011_9m-borehole_calc-temps.xlsx")
df_hawley = df_hawley.rename(
columns={
"Pit name (tabs)": "site",
"Date": "date",
"Lat (dec.degr)": "latitude",
"Long (dec.degr)": "longitude",
"Elevation": "elevation",
"9-m temp": "temperatureObserved",
}
)
df_hawley["depthOfTemperatureObservation"] = 9
df_hawley["note"] = ""
df_hawley[
"reference"
] = "Bob Hawley. 2014. Traverse physical, chemical, and weather observations. arcitcdata.io, doi:10.18739/A2W232. "
df_hawley["reference_short"] = "Hawley (2014) GrIT"
df_hawley = df_hawley.loc[[isinstance(x, float) for x in df_hawley.temperatureObserved]]
df_hawley = df_hawley.loc[df_hawley.temperatureObserved.notnull()]
df_hawley["method"] = "thermistor"
df_hawley["durationOpen"] = 2
df_hawley["durationMeasured"] = 0
df_hawley["error"] = "not reported"
df_all = pd.concat((df_all, df_hawley[needed_cols]), ignore_index=True)
# %% PROMICE
print("Loading PROMICE")
df_promice = pd.read_csv("Data/PROMICE/PROMICE_10m_firn_temperature.csv", sep=";")
df_promice = df_promice.loc[df_promice.temperatureObserved.notnull()]
df_promice = df_promice.loc[df_promice.site != "QAS_A", :]
df_promice.loc[(df_promice.site == "CEN") & (df_promice.temperatureObserved > -18),
"temperatureObserved"] = np.nan
df_promice["method"] = "RS Components thermistors 151-243"
df_promice["durationOpen"] = 0
df_promice["durationMeasured"] = 30 * 24
df_promice["error"] = 0.2
df_promice["note"] = ""
df_all = pd.concat((df_all, df_promice[needed_cols]), ignore_index=True)
# %% GC-Net
print("Loading GC-Net")
df_GCN = pd.read_csv("Data/GC-Net/10m_firn_temperature.csv")
df_GCN = df_GCN.loc[df_GCN.temperatureObserved.notnull()]
df_GCN["method"] = "thermocouple"
df_GCN["durationOpen"] = 0
df_GCN["durationMeasured"] = 30 * 24
df_GCN["error"] = 0.5
df_all = pd.concat((df_all, df_GCN[needed_cols]), ignore_index=True)
# %% Steffen 2001 table (that could not be found in the GC-Net AWS data)
print('Loading Steffen 2001 table')
df = pd.read_excel("Data/GC-Net/steffen2001.xlsx")
df["depthOfTemperatureObservation"] = 10
df["temperatureObserved"] = df["temperature"]
df["note"] = "annual average"
df["date"] = [pd.to_datetime(str(yr) + "-12-01") for yr in df.year]
df[
"reference"
] = "Steffen, K., Box, J.E. and Abdalati, W., 1996. Greenland climate network: GC-Net. US Army Cold Regions Reattach and Engineering (CRREL), CRREL Special Report, pp.98-103. and Steffen, K. and J. Box: Surface climatology of the Greenland ice sheet: Greenland Climate Network 1995-1999, J. Geophys. Res., 106, 33,951-33,972, 2001 and Steffen, K., Vandecrux, B., Houtz, D., Abdalati, W., Bayou, N., Box, J., Colgan, L., Espona Pernas, L., Griessinger, N., Haas-Artho, D., Heilig, A., Hubert, A., Iosifescu Enescu, I., Johnson-Amin, N., Karlsson, N. B., Kurup, R., McGrath, D., Cullen, N. J., Naderpour, R., Pederson, A. Ø., Perren, B., Philipps, T., Plattner, G.K., Proksch, M., Revheim, M. K., Særrelse, M., Schneebli, M., Sampson, K., Starkweather, S., Steffen, S., Stroeve, J., Watler, B., Winton, Ø. A., Zwally, J., Ahlstrøm, A.: GC-Net Level 1 automated weather station data, https://doi.org/10.22008/FK2/VVXGUT, GEUS Dataverse, V2, 2023. and Vandecrux, B., Box, J.E., Ahlstrøm, A.P., Andersen, S.B., Bayou, N., Colgan, W.T., Cullen, N.J., Fausto, R.S., Haas-Artho, D., Heilig, A., Houtz, D.A., How, P., Iosifescu Enescu , I., Karlsson, N.B., Kurup Buchholz, R., Mankoff, K.D., McGrath, D., Molotch, N.P., Perren, B., Revheim, M.K., Rutishauser, A., Sampson, K., Schneebeli, M., Starkweather, S., Steffen, S., Weber, J., Wright, P.J., Zwally, J., Steffen, K.: The historical Greenland Climate Network (GC-Net) curated and augmented Level 1 dataset, Submitted to ESSD, 2023"
df["reference_short"] = "Historical GC-Net: Steffen et al. (1996, 2001, 2023); Vandecrux et al. (2023)"
df["error"] = 0.5
df_all = pd.concat((df_all, df[needed_cols]), ignore_index=True)
# %% Historical swc
df_swc = pd.DataFrame()
df_swc["depthOfTemperatureObservation"] = [10]*3
df_swc["site"] = ["Swiss Camp"]*3
df_swc["latitude"] = [69.57346306]*3
df_swc["longitude"] = [-49.2955275]*3
df_swc["elevation"] = [1155]*3
df_swc["note"] = [""]*3
df_swc["temperatureObserved"] = [-9.1, -9.3, -9.3]
df_swc["date"] = pd.to_datetime(["1990-07-01","1990-08-01","1990-08-24" ])
df_swc["reference_short"] = ["Ohmura et al. (1992)"]*3
df_swc[
"reference"
] = ["Ohmura, A., Steffen, K., Blatter, H., Greuell, W., Rotach, M., Stober, M., Konzelmann, T., Forrer, J., Abe-Ouchi, A., Steiger, D. and Niederbaumer, G.: Energy and mass balance during the melt season at the equilibrium line altitude. Paakitsoq, Greenland ice sheet: Progress report, 2, 1992."]*3
df_swc["method"] = ["NA"]*3
df_swc["durationOpen"] = ["NA"]*3
df_swc["durationMeasured"] =[ "NA"]*3
df_swc["error"] = ["NA"]*3
df_all = pd.concat((df_all, df_swc[needed_cols]), ignore_index=True)
# %% Miege aquifer
print("Loading firn aquifer data")
metadata = np.array([ ["FA-13", 66.181, 39.0435, 1563],
["FA-15-1", 66.3622, 39.3119, 1664],
["FA-15-2", 66.3548, 39.1788, 1543]])
# mean_accumulation = 1 # m w.e. from Miege et al. 2014
# thickness_accum = 2.7 # thickness of the top 1 m w.e. in the FA13 core
thickness_accum = 1.4 # Burial of the sensor between their installation in Aug 2015 and revisit in Aug 2016
df_miege = pd.DataFrame()
for k, site in enumerate(["FA_13", "FA_15_1", "FA_15_2"]):
depth = pd.read_csv(
"Data/Miege firn aquifer/" + site + "_Firn_Temperatures_Depths.csv"
).transpose()
if k == 0:
depth = depth.iloc[5:].transpose()
else:
depth = depth.iloc[5:, 0]
temp = pd.read_csv("Data/Miege firn aquifer/" + site + "_Firn_Temperatures.csv")
dates = pd.to_datetime((temp.Year * 1000000 + temp.Month * 10000
+ temp.Day * 100 + temp["Hours (UTC)"]).apply(str),
format="%Y%m%d%H")
temp = temp.iloc[:, 4:]
ellapsed_hours = (dates - dates[0]).dt.total_seconds()/60/60
accum_depth = ellapsed_hours.values * thickness_accum / 365 / 24
depth_cor = pd.DataFrame()
depth_cor = depth.values.reshape((1, -1)).repeat(
len(dates), axis=0
) + accum_depth.reshape((-1, 1)).repeat(len(depth.values), axis=1)
df_10 = ftl.interpolate_temperature(
dates, depth_cor, temp.values, title=site + " Miller et al. (2020)"
)
df_10.loc[np.greater(df_10["temperatureObserved"], 0), "temperatureObserved"] = 0
df_10 = df_10.set_index("date", drop=False).resample("M").mean()
df_10["site"] = site
df_10["latitude"] = float(metadata[k, 1])
df_10["longitude"] = -float(metadata[k, 2])
df_10["elevation"] = float(metadata[k, 3])
df_10["depthOfTemperatureObservation"] = 10
df_10[
"reference"
] = "Miller, O., Solomon, D.K., Miège, C., Koenig, L., Forster, R., Schmerr, N., Ligtenberg, S.R., Legchenko, A., Voss, C.I., Montgomery, L. and McConnell, J.R., 2020. Hydrology of a perennial firn aquifer in Southeast Greenland: an overview driven by field data. Water Resources Research, 56(8), p.e2019WR026348. Dataset doi:10.18739/A2R785P5W"
df_10["reference_short"] = "Miller et al. (2020)"
df_10["note"] = "interpolated to 10 m, monthly snapshot"
# plt.figure()
# df_10.temperatureObserved.plot()
df_miege = pd.concat((df_miege, df_10.drop(columns=['date']).reset_index()))
df_miege["method"] = "digital thermarray system from RST©"
df_miege["durationOpen"] = 0
df_miege["durationMeasured"] = 30
df_miege["error"] = 0.07
df_all = pd.concat((df_all, df_miege[needed_cols]), ignore_index=True)
# %% Harper ice temperature
print("Loading Harper ice temperature")
df_harper = pd.read_csv(
"Data/Harper ice temperature/harper_iceTemperature_2015-2016.csv"
)
num_row = df_harper.shape[0]
df_harper["date"] = np.nan
df_harper["temperatureObserved"] = np.nan
df_harper["note"] = ""
df_harper.iloc[:num_row, df_harper.columns.get_loc("borehole")] = df_harper["borehole"].iloc[:num_row] + "_2015"
df_harper.iloc[num_row:, df_harper.columns.get_loc("borehole")] = df_harper["borehole"].iloc[num_row:] + "_2016"
df_harper.iloc[:num_row, df_harper.columns.get_loc("date")] = pd.to_datetime("2015-01-01")
df_harper.iloc[num_row:, df_harper.columns.get_loc("date")] = pd.to_datetime("2016-01-01")
df_harper.iloc[:num_row, df_harper.columns.get_loc("temperatureObserved")] = df_harper["temperature_2015_celsius"].iloc[:num_row]
df_harper.iloc[num_row:, df_harper.columns.get_loc("temperatureObserved")] = df_harper["temperature_2016_celsius"].iloc[num_row:]
df_harper["depth"] = df_harper.depth_m - df_harper.height_m
df_harper = df_harper.loc[df_harper.depth < 100]
df_harper = df_harper.drop(
columns=["height_m", "temperature_2015_celsius", "temperature_2016_celsius", "yearDrilled", "dateDrilled", "depth_m"]
)
df_harper = df_harper.loc[df_harper.temperatureObserved.notnull()]
for borehole in df_harper["borehole"].unique():
# print(borehole, df_harper.loc[df_harper["borehole"] == borehole, "depth"].min())
if df_harper.loc[df_harper["borehole"] == borehole, "depth"].min() > 20:
df_harper = df_harper.loc[df_harper["borehole"] != borehole]
continue
new_row = df_harper.loc[df_harper["borehole"] == borehole].iloc[0, :].copy()
new_row["depth"] = 10
new_row["temperatureObserved"] = np.nan
df_harper = pd.concat((df_harper, new_row))
df_harper = df_harper.set_index(["depth"]).sort_index()
# not interpolating anymore
# plt.figure()
# for borehole in df_harper["borehole"].unique():
# s = df_harper.loc[df_harper["borehole"] == borehole, "temperatureObserved"]
# s.iloc[0] = interp1d(
# s.iloc[1:].index, s.iloc[1:], kind="linear", fill_value="extrapolate"
# )(10)
# s.plot(marker='o',label='_no_legend_')
# df_harper.loc[df_harper['borehole']==borehole,'temperatureObserved'].plot(marker='o',label=borehole)
# plt.legend()
# df_harper.loc[df_harper["borehole"] == borehole, "temperatureObserved"] = s.values
# df_harper.loc[df_harper["borehole"] == borehole, "note"] = (
# "interpolated from " + str(s.iloc[1:].index.values) + " m depth"
# )
df_harper = df_harper.reset_index()
# df_harper = df_harper.loc[df_harper.depth == 10]
df_harper[
"reference"
] = "Hills, B. H., Harper, J. T., Humphrey, N. F., & Meierbachtol, T. W. (2017). Measured horizontal temperature gradients constrain heat transfer mechanisms in Greenland ice. Geophysical Research Letters, 44. https://doi.org/10.1002/2017GL074917; https://doi.org/10.18739/A24746S04"
df_harper["reference_short"] = "Hills et al. (2017)"
df_harper = df_harper.rename(
columns={
"borehole": "site",
"latitude_WGS84": "latitude",
"longitude_WGS84": "longitude",
"Elevation_m": "elevation",
"depth": "depthOfTemperatureObservation",
}
)
df_harper["method"] = "TMP102 digital temperature sensor"
df_harper["durationOpen"] = 0
df_harper["durationMeasured"] = 30 * 24
df_harper["error"] = 0.1
df_all = pd.concat((df_all,
df_harper[needed_cols],
), ignore_index=True,
)
# %% FirnCover
print("Loading FirnCover")
time.sleep(0.2)
filepath = os.path.join("Data/FirnCover/FirnCoverData_2.0_2021_07_30.h5")
sites = ["Summit", "KAN-U", "NASA-SE", "Crawford", "EKT", "Saddle", "EastGrip", "DYE-2"]
statmeta_df, sonic_df, rtd_df, rtd_dep, metdata_df = ftl.load_metadata(filepath, sites)
statmeta_df["elevation"] = [1840, 2119, 2361, 2370, 2456, 1942, 3208, 2666]
rtd_df = rtd_df.reset_index()
rtd_df = rtd_df.set_index(["sitename", "date"])
df_firncover = pd.DataFrame()
for site in sites:
df_d = rtd_df.xs(site, level="sitename").reset_index()
# df_d.to_csv('FirnCover_'+site+'.csv')
df_10 = ftl.interpolate_temperature(
df_d["date"],
df_d[["depth_" + str(i) for i in range(24)]].values,
df_d[["rtd" + str(i) for i in range(24)]].values,
title=site + " FirnCover",
)
df_10 = df_10.set_index("date").resample("M").mean().reset_index()
df_10["site"] = site
if site == "Crawford":
df_10["site"] = "CP1"
df_10["latitude"] = statmeta_df.loc[site, "latitude"]
df_10["longitude"] = statmeta_df.loc[site, "longitude"]
df_10["elevation"] = statmeta_df.loc[site, "elevation"]
df_firncover = pd.concat((df_firncover, df_10))
df_firncover[
"reference"
] = "MacFerrin, M. J., Stevens, C. M., Vandecrux, B., Waddington, E. D., and Abdalati, W. (2022) The Greenland Firn Compaction Verification and Reconnaissance (FirnCover) dataset, 2013–2019, Earth Syst. Sci. Data, 14, 955–971, https://doi.org/10.5194/essd-14-955-2022,"
df_firncover["reference_short"] = "MacFerrin et al. (2021, 2022)"
df_firncover["note"] = ""
df_firncover["depthOfTemperatureObservation"] = 10
# Correction of FirnCover bias
p = np.poly1d([1.03093649, -0.49950273])
df_firncover["temperatureObserved"] = p(df_firncover["temperatureObserved"].values)
df_firncover["method"] = "Resistance Temperature Detectors + correction"
df_firncover["durationOpen"] = 0
df_firncover["durationMeasured"] = 30 * 24
df_firncover["error"] = 0.5
df_all = pd.concat((df_all,
df_firncover[needed_cols],
), ignore_index=True,
)
# %% SPLAZ KAN_U
print("Loading SPLAZ at KAN-U")
site = "KAN_U"
num_therm = [32, 12, 12]
df_splaz = pd.DataFrame()
for k, note in enumerate(["SPLAZ_main", "SPLAZ_2", "SPLAZ_3"]):
ds = xr.open_dataset("Data/SPLAZ/T_firn_KANU_" + note + ".nc")
df = ds.to_dataframe()
df.reset_index(inplace=True)
df2 = pd.DataFrame()
df2["date"] = df.loc[df["level"] == 1, "time"]
for i in range(1, num_therm[k] + 1):
df2["rtd" + str(i - 1)] = df.loc[df["level"] == i, "Firn temperature"].values
df2["depth_" + str(i - 1)] = df.loc[df["level"] == i, "depth"].values
df2[df2 == -999] = np.nan
df2 = df2.set_index(["date"]).resample("D").mean()
df_10 = ftl.interpolate_temperature(
df2.index,
df2[["depth_" + str(i) for i in range(num_therm[k])]].values,
df2[["rtd" + str(i) for i in range(num_therm[k])]].values,
min_diff_to_depth=1.5,
kind="linear",
title="KAN_U " + note,
)
# for i in range(10):
# plt.figure()
# plt.plot(df2.iloc[i*20,0:12].values,-df2.iloc[i*20,12:].values)
# plt.plot(df_10.iloc[i*20,1],-10,marker='o')
# plt.title(df2.index[i*20])
# plt.xlim(-15,0)
df_10 = df_10.set_index("date").resample("M").mean().reset_index()
df_10["note"] = note
df_10["latitude"] = 67.000252
df_10["longitude"] = -47.022999
df_10["elevation"] = 1840
df_splaz = pd.concat((df_splaz, df_10))
df_splaz[
"reference"
] = "Charalampidis, C., Van As, D., Colgan, W.T., Fausto, R.S., Macferrin, M. and Machguth, H., 2016. Thermal tracing of retained meltwater in the lower accumulation area of the Southwestern Greenland ice sheet. Annals of Glaciology, 57(72), pp.1-10."
df_splaz["reference_short"] = "Charalampidis et al. (2016); Charalampidis et al. (2022) "
df_splaz["site"] = site
df_splaz["depthOfTemperatureObservation"] = 10
df_splaz["method"] = "RS 100 kΩ negative-temperature coefficient thermistors"
df_splaz["durationOpen"] = 0
df_splaz["durationMeasured"] = 30 * 24
df_splaz["error"] = 0.2
df_all = pd.concat((df_all, df_splaz[needed_cols]), ignore_index=True)
# %% Load Humphrey data
print("loading Humphrey")
df = pd.read_csv("Data/Humphrey string/location.txt", delim_whitespace=True)
df_humphrey = pd.DataFrame(
columns=["site", "latitude", "longitude", "elevation", "date", "T10m"]
)
for site in df.site:
try:
df_site = pd.read_csv(
"Data/Humphrey string/" + site + ".txt",
header=None,
delim_whitespace=True,
names=["doy"] + ["IceTemperature" + str(i) + "(C)" for i in range(1, 33)],
)
except: # Exception as e:
# print(e)
continue
print(site)
temp_label = df_site.columns[1:]
# the first column is a time stamp and is the decimal days after the first second of January 1, 2007.
df_site["time"] =[ pd.to_datetime('2007-01-01') + pd.Timedelta(days=d) for d in df_site.iloc[:, 0]]
if site == "T1old":
df_site["time"] = [
datetime(2006, 1, 1) + timedelta(days=d) for d in df_site.iloc[:, 0]
]
df_site = df_site.loc[df_site["time"] <= df_site["time"].values[-1], :]
df_site = df_site.set_index("time")
df_site = df_site.resample("H").mean()
depth = [0.25, 0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 2.00, 2.25, 2.50, 2.75, 3.00, 3.25, 3.50, 3.75, 4.00, 4.25, 4.50, 4.75, 5.00, 5.25, 5.00, 5.50, 6.00, 6.50, 7.00, 7.50, 8.00, 8.50, 9.00, 9.50, 10.0]
if site != "H5": df_site = df_site.iloc[24 * 30 :, :]
if site == "T4": df_site = df_site.loc[:"2007-12-05"]
if site == "H2": depth = np.array(depth) - 1
if site == "H4": depth = np.array(depth) - 0.75
if site in ["H3", "G165", "T1new"]: depth = np.array(depth) - 0.50
df_hs = pd.read_csv("Data/Humphrey string/" + site + "_surface_height.csv")
df_hs.time = pd.to_datetime(df_hs.time)
df_hs = df_hs.set_index("time")
df_hs = df_hs.resample("H").mean()
df_site["surface_height"] = np.nan
df_site["surface_height"] = df_hs.iloc[df_hs.index.get_indexer(df_site.index, method="nearest")].values
depth_label = ["depth_" + str(i) for i in range(1, len(temp_label) + 1)]
for i in range(len(temp_label)):
df_site[depth_label[i]] = (
depth[i] + df_site["surface_height"].values - df_site["surface_height"].iloc[0]
)
if site != "H5":
df_site[temp_label[i]] = (
df_site[temp_label[i]].rolling(24 * 3, center=True).mean().values
)
df_10 = ftl.interpolate_temperature(
df_site.index,
df_site[depth_label].values,
df_site[temp_label].values,
title=site + " Humphrey et al. (2012)",
)
df_10 = df_10.set_index("date").resample("M").mean().reset_index()
df_10["site"] = site
df_10["latitude"] = df.loc[df.site == site, "latitude"].values[0]
df_10["longitude"] = df.loc[df.site == site, "longitude"].values[0]
df_10["elevation"] = df.loc[df.site == site, "elevation"].values[0]
df_humphrey = pd.concat((df_humphrey, df_10))
df_humphrey = df_humphrey.reset_index(drop=True)
df_humphrey = df_humphrey.loc[df_humphrey.temperatureObserved.notnull()]
df_humphrey["depthOfTemperatureObservation"] = 10
df_humphrey["reference"] = "Humphrey, N. F., Harper, J. T., and Pfeffer, W. T. (2012), Thermal tracking of meltwater retention in Greenlands accumulation area, J. Geophys. Res., 117, F01010, doi:10.1029/2011JF002083. Data available at: https://instaar.colorado.edu/research/publications/occasional-papers/firn-stratigraphy-and-temperature-to-10-m-depth-in-the-percolation-zone-of/"
df_humphrey["reference_short"] = "Humphrey et al. (2012)"
df_humphrey["note"] = "no surface height measurements, using interpolating surface height using CP1 and SwissCamp stations"
df_humphrey["method"] = "sealed 50K ohm thermistors"
df_humphrey["durationOpen"] = 0
df_humphrey["durationMeasured"] = 30 * 24
df_humphrey["error"] = 0.5
df_all = pd.concat((df_all, df_humphrey[needed_cols]), ignore_index=True)
# %% loading Hills
print("Loading Hills")
df_meta = pd.read_csv("Data/Hills/metadata.txt", sep=" ")
df_meta.date_start = pd.to_datetime(df_meta.date_start, format="%m/%d/%y")
df_meta.date_end = pd.to_datetime(df_meta.date_end, format="%m/%d/%y")
df_meteo = pd.read_csv("Data/Hills/Hills_33km_meteorological.txt", sep="\t")
df_meteo["date"] = pd.to_datetime("2014-07-18") + pd.to_timedelta((df_meteo.Time.values - 197) *24*60, unit='minute').round('T')
df_meteo = df_meteo.set_index("date").resample("D").mean()
df_meteo["surface_height"] = df_meteo.DistanceToTarget.iloc[0] - df_meteo.DistanceToTarget
df_hills = pd.DataFrame()
for site in df_meta.site[:-1]:
print(site)
df = pd.read_csv("Data/Hills/Hills_" + site + "_IceTemp.txt", sep="\t")
df["date"] = [
df_meta.loc[df_meta.site == site, "date_start"].values[0]
+ pd.Timedelta(int(f * 24 * 60 * 60), "seconds")
for f in (df.Time.values - int(df.Time.values[0]))
]
df = df.set_index("date")
df["surface_height"] = np.nan
df.loc[[i in df_meteo.index for i in df.index], "surface_height"] = df_meteo.loc[
[i in df.index for i in df_meteo.index], "surface_height"
]
df["surface_height"] = df.surface_height.interpolate(
method="linear", limit_direction="both"
)
if all(np.isnan(df.surface_height)):
df["surface_height"] = 0
plt.figure()
df.surface_height.plot()
plt.title(site)
depth = df.columns[1:-1].str.replace("Depth_", "").values.astype(float)
print(len(depth))
temp_label = ["temp_" + str(len(depth) - i) for i in range(len(depth))]
depth_label = ["depth_" + str(len(depth) - i) for i in range(len(depth))]
for i in range(len(temp_label)):
df = df.rename(columns={df.columns[i + 1]: temp_label[i]})
df.iloc[:14, i + 1] = np.nan
if site in ["T-14", "T-11b"]:
df.iloc[:30, i + 1] = np.nan
df[depth_label[i]] = (
depth[i] + df["surface_height"].values - df["surface_height"].iloc[0]
)
df = df.resample("D").mean()
df_10 = ftl.interpolate_temperature(
df.index,
df[depth_label].values,
df[temp_label].values,
title=site,
)
df_10 = df_10.set_index("date").resample("M").mean().reset_index()
df_10["latitude"] = df_meta.latitude[df_meta.site == site].iloc[0]
df_10["longitude"] = df_meta.longitude[df_meta.site == site].iloc[0]
df_10["elevation"] = df_meta.elevation[df_meta.site == site].iloc[0]
df_10["depthOfTemperatureObservation"] = 10
df_10["site"] = site
df_hills = pd.concat((df_hills, df_10))
df_hills["note"] = "monthly mean, interpolated at 10 m"
df_hills[
"reference"
] = "Hills, B. H., Harper, J. T., Meierbachtol, T. W., Johnson, J. V., Humphrey, N. F., and Wright, P. J.: Processes influencing heat transfer in the near-surface ice of Greenlands ablation zone, The Cryosphere, 12, 3215–3227, https://doi.org/10.5194/tc-12-3215-2018, 2018. data: https://doi.org/10.18739/A2QV3C418"
df_hills["reference_short"] = "Hills et al. (2018)"
df_hills[
"method"
] = "digital temperature sensor model DS18B20 from Maxim Integrated Products, Inc."
df_hills["durationOpen"] = 0
df_hills["durationMeasured"] = 30 * 24
df_hills["error"] = 0.0625
df_all = pd.concat((df_all, df_hills[needed_cols]), ignore_index=True)
# %% Achim Dye-2
from datetime import datetime
print("Loading Achim Dye-2")
# loading temperature data
df = pd.read_csv("Data/Achim/CR1000_PT100.txt", header=None)
df.columns = [ "time_matlab", "temp_1", "temp_2", "temp_3", "temp_4", "temp_5", "temp_6", "temp_7", "temp_8"]
df["time"] = pd.to_datetime(
[
datetime.fromordinal(int(matlab_datenum))
+ timedelta(days=matlab_datenum % 1)
- timedelta(days=366)
for matlab_datenum in df.time_matlab
]
)
df = df.set_index("time")
df = df.resample("D").mean().drop(columns="time_matlab")
# loading surface height data
df_surf = pd.read_csv("Data/Achim/CR1000_SR50.txt", header=None)
df_surf.columns = ["time_matlab", "sonic_m", "height_above_upgpr"]
df_surf["time"] = pd.to_datetime(
[
datetime.fromordinal(int(matlab_datenum))
+ timedelta(days=matlab_datenum % 1)
- timedelta(days=366)
for matlab_datenum in df_surf.time_matlab
]
)
df_surf = df_surf.set_index("time")
df_surf = (
df_surf.resample("D").mean().drop(columns=["time_matlab", "height_above_upgpr"])
)
# loading surface height data from firncover
filepath = os.path.join("Data/FirnCover/FirnCoverData_2.0_2021_07_30.h5")
sites = ["Summit", "KAN-U", "NASA-SE", "Crawford", "EKT", "Saddle", "EastGrip", "DYE-2"]
_, sonic_df, _, _, _ = ftl.load_metadata(filepath, sites)
sonic_df = sonic_df.xs("DYE-2", level="sitename").reset_index()
sonic_df = sonic_df.set_index("date").drop(columns="delta").resample("D").mean()
sonic_df = pd.concat((sonic_df, df_surf.loc[sonic_df.index[-1] :] - 1.83))
plt.figure()
sonic_df.sonic_m.plot()
df_surf.sonic_m.plot()
sonic_df = sonic_df.resample("D").mean()
df["surface_height"] = -(
sonic_df.loc[df.index[0] : df.index[-1]] - sonic_df.loc[df.index[0]]
).values
depth = 3.4 - np.array([3, 2, 1, 0, -1, -2, -4, -6])
temp_label = ["temp_" + str(i + 1) for i in range(len(depth))]
depth_label = ["depth_" + str(i + 1) for i in range(len(depth))]
for i in range(len(depth)):
df[depth_label[i]] = (
depth[i] + df["surface_height"].values - df["surface_height"].iloc[0]
)
df.loc["2018-05-18":, "depth_1"] = df.loc["2018-05-18":, "depth_1"].values - 1.5
df.loc["2018-05-18":, "depth_2"] = df.loc["2018-05-18":, "depth_2"].values - 1.84
# ds = xr.open_dataset('Data/Achim/T_firn_DYE-2_16.nc')
# df = ds.to_dataframe()
# df = df.reset_index(0).groupby('level').resample('D').mean()
# df.reset_index(0,inplace=True, drop=True)
# df.reset_index(inplace=True)
# df_d = pd.DataFrame()
# df_d['date'] = df.loc[df['level']==1,'time']
# for i in range(1,9):
# df_d['rtd'+str(i)] = df.loc[df['level']==i,'Firn temperature'].values
# for i in range(1,9):
# df_d['depth_'+str(i)] = df.loc[df['level']==i,'depth'].values
# df.to_csv('Heilig_Dye-2_thermistor.csv')
df_achim = ftl.interpolate_temperature(
df.index, df[depth_label].values, df[temp_label].values, title="Dye-2 Achim"
)
df_achim = df_achim.set_index("date").resample("M").mean().reset_index()
df_achim["site"] = "DYE-2"
df_achim["latitude"] = 66.4800
df_achim["longitude"] = -46.2789
df_achim["elevation"] = 2165.0
df_achim["depthOfTemperatureObservation"] = 10
df_achim[
"note"
] = "interpolated at 10 m, monthly mean, using surface height from FirnCover station"
df_achim[
"reference"
] = "Heilig, A., Eisen, O., MacFerrin, M., Tedesco, M., and Fettweis, X.: Seasonal monitoring of melt and accumulation within the deep percolation zone of the Greenland Ice Sheet and comparison with simulations of regional climate modeling, The Cryosphere, 12, 1851–1866, https://doi.org/10.5194/tc-12-1851-2018, 2018. "
df_achim["reference_short"] = "Heilig et al. (2018)"
df_achim["method"] = "thermistors"
df_achim["durationOpen"] = 0
df_achim["durationMeasured"] = 30 * 24
df_achim["error"] = 0.25
df_all = pd.concat((df_all, df_achim[needed_cols]), ignore_index=True)
# %% Camp Century Climate
print("Loading Camp Century data")
df = pd.read_csv("Data/Camp Century Climate/data_long.txt", sep=",", header=None)
df = df.rename(columns={0: "date"})
df["date"] = pd.to_datetime(df.date)
df[df == -999] = np.nan
df = df.set_index("date").resample("D").first()
df = df.iloc[:, :-2]
df_promice = pd.read_csv(
"C:/Users/bav/OneDrive - Geological survey of Denmark and Greenland/Code/PROMICE/PROMICE-AWS-toolbox/out/v03_L3/CEN_hour_v03_L3.txt",
sep="\t",
)
df_promice[df_promice == -999] = np.nan
df_promice = df_promice.rename(columns={"time": "date"})
df_promice["date"] = pd.to_datetime(df_promice.date)
df_promice = df_promice.set_index("date").resample("D").first()
df_promice = df_promice["SurfaceHeight_summary(m)"]
df_cen2 = pd.read_csv('Data/Camp Century Climate/CEN2_day.csv')
df_cen2['time'] = pd.to_datetime(df_cen2.time, utc=True)
df_cen2 = df_cen2.set_index('time')[['z_boom_l']].resample('D').first()
df_cen2["SurfaceHeight_summary(m)"] = 2.1130 - df_cen2.z_boom_l + 1.9
df_cen2.loc[:'2021-08-24',"SurfaceHeight_summary(m)"] = np.nan
df_cen2.loc['2022-06-17':,"SurfaceHeight_summary(m)"] =df_cen2.loc['2022-06-17':,"SurfaceHeight_summary(m)"] + 0.85
df_promice = pd.concat((df_promice, df_cen2["SurfaceHeight_summary(m)"] ))
df_promice = df_promice.resample('D').mean().interpolate()
temp_label = ["T_" + str(i + 1) for i in range(len(df.columns))]
depth_label = ["depth_" + str(i + 1) for i in range(len(df.columns))]
df["surface_height"] = df_promice.loc[df.index[0].strftime('%Y-%m-%d'):df.index[-1].strftime('%Y-%m-%d')].values
depth = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 53, 58, 63, 68, 73]
for i in range(len(temp_label)):
df = df.rename(columns={i + 1: temp_label[i]})
df[depth_label[i]] = (
depth[i] + df["surface_height"].values - df["surface_height"].iloc[0]
)
df_10 = ftl.interpolate_temperature(
df.index,
df[depth_label].values,
df[temp_label].values,
title="Camp Century Climate (long)",
)
df_10.loc[df_10.temperatureObserved>-15, "temperatureObserved"] = np.nan
df_10 = df_10.set_index("date").resample("M").mean().reset_index()
df_10["site"] = "CEN_THM_long"
df_10["latitude"] = 77.1333
df_10["longitude"] = -61.0333
df_10["elevation"] = 1880
df_10["depthOfTemperatureObservation"] = 10
df_10["note"] = "THM_long, interpolated at 10 m"
df_10[
"reference"
] = "Vandecrux, B., Colgan, W., Solgaard, A.M., Steffensen, J.P., and Karlsson, N.B.(2021). Firn evolution at Camp Century, Greenland: 1966-2100, Frontiers in Earth Science, https://doi.org/10.3389/feart.2021.578978, 2021 dataset: https://doi.org/10.22008/FK2/SR3O4F"
df_10["reference_short"] = "Vandecrux et al. (2021); Colgan and Vandecrux (2021)"
df_10["method"] = "thermistors"
df_10["durationOpen"] = 0
df_10["durationMeasured"] = 30 * 24
df_10["error"] = 0.2
df_all = pd.concat((df_all,
df_10[needed_cols],
), ignore_index=True,