From 04f36783fc18556164c24fc861332a0a5b2b0f7a Mon Sep 17 00:00:00 2001 From: Erlend Date: Tue, 13 Jan 2026 11:26:32 +0100 Subject: [PATCH 1/7] remove sandbox as data is transformed in another file --- .gitmodules | 3 --- OPENSENSE_sandbox | 1 - 2 files changed, 4 deletions(-) delete mode 160000 OPENSENSE_sandbox diff --git a/.gitmodules b/.gitmodules index d97078d..0d34059 100644 --- a/.gitmodules +++ b/.gitmodules @@ -7,9 +7,6 @@ [submodule "pycomlink"] path = pycomlink url = https://github.com/pycomlink/pycomlink -[submodule "OPENSENSE_sandbox"] - path = OPENSENSE_sandbox - url = https://github.com/OpenSenseAction/OPENSENSE_sandbox [submodule "pypwsqc"] path = pypwsqc url = https://github.com/OpenSenseAction/pypwsqc.git diff --git a/OPENSENSE_sandbox b/OPENSENSE_sandbox deleted file mode 160000 index c0935a4..0000000 --- a/OPENSENSE_sandbox +++ /dev/null @@ -1 +0,0 @@ -Subproject commit c0935a4e4814ef7fb20ae7bbb241b4020a1a6709 From 592638f0595661341064592f7acdb3d475edbbe0 Mon Sep 17 00:00:00 2001 From: Erlend Date: Wed, 14 Jan 2026 14:15:16 +0100 Subject: [PATCH 2/7] first draf data download notebook --- 1_data_preparation.ipynb | 3103 ++++++++++++++++++++++++++++++++++---- 1 file changed, 2845 insertions(+), 258 deletions(-) diff --git a/1_data_preparation.ipynb b/1_data_preparation.ipynb index a59b658..9c88ab0 100644 --- a/1_data_preparation.ipynb +++ b/1_data_preparation.ipynb @@ -5,18 +5,12 @@ "id": "9e8629e9-a534-43b1-b0a2-6d6f0a6df943", "metadata": {}, "source": [ - "# 1. Data preparation\n", - "This notebook \n", - "* downloads the used CML dataset [OpenMRG (Andersson et al. 2022)]()\n", - "* transform the into a common data format \n", - "* shows some statisitcs and comparisons of the CML and reference data\n", - "\n", - "As result, the cml datasets are ready for processing and the reference data from gauges and radar are already in their final resolution of 15 minutes for evaluation. Radar rainfall along the CML paths is computed." + "# 1. Data preparation" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "b4b2f5b7-7acf-4539-b4df-5177f81e4dba", "metadata": {}, "outputs": [], @@ -24,26 +18,16 @@ "import sys\n", "import os\n", "\n", - "# # Add submodules needed for data preparation\n", - "sys.path.insert(0, os.path.abspath(\"./poligrain/src\"))\n", - "sys.path.insert(0, os.path.abspath(\"./OPENSENSE_sandbox/notebooks/\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f583a92c-71da-43c8-8238-34d9ebe4ff31", - "metadata": {}, - "outputs": [], - "source": [ "import xarray as xr\n", "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm\n", "import numpy as np\n", - "import opensense_data_downloader_and_transformer as oddt\n", - "import pandas as pd\n", - "import poligrain as plg\n", - "import pyproj" + "\n", + "# Add submodules needed for data preparation\n", + "sys.path.insert(0, os.path.abspath(\"./poligrain/src\"))\n", + "sys.path.insert(0, os.path.abspath(\"./pycomlink\"))\n", + "\n", + "import pycomlink as pycml\n", + "import poligrain as plg" ] }, { @@ -51,42 +35,79 @@ "id": "3ad81ae9-0dee-452c-9270-2dd0a0f42f3a", "metadata": {}, "source": [ - "#### Download OpenMRG dataset with code from [OepnSense sandbox](link) and transform to the data format standards given in [Fencl et al. 2023](https://open-research-europe.ec.europa.eu/articles/3-169)." + "## Download OpenMRG dataset " ] }, { "cell_type": "code", "execution_count": 3, - "id": "67d6362e-0b53-4e1f-96bc-969599e4fa5e", + "id": "9a387abc-ea0f-4154-b556-98432216ceb2", + "metadata": {}, + "outputs": [], + "source": [ + "# Use gdown to iterate through folder, we will replace this later\n", + "!pip install --quiet gdown" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5fadac94-a328-4db2-a7d3-a0f1b14e2e33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Downloading https://zenodo.org/record/7107689/files/OpenMRG.zip\n", - "to data/andersson_2022_OpenMRG//OpenMRG.zip\n" + "skip ./data/cml/cml.nc\n", + "skip ./data/radar/radar.nc\n", + "Downloading ./data/gauges/city_gauge.nc ...\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1190fQu3ie_93e7iJpZEC-p92h9y8XtOW\n", + "To: /home/erlend/git/OpenMRG2/data/gauges/city_gauge.nc\n", + "100%|██████████████████████████████████████| 11.7M/11.7M [00:00<00:00, 28.8MB/s]\n", + "Downloading ./data/gauges/smhi_gauge.nc ...\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1j7MdStiY1xdWeJrrsEXgHCqMoT9Qy67z\n", + "To: /home/erlend/git/OpenMRG2/data/gauges/smhi_gauge.nc\n", + "100%|████████████████████████████████████████| 152k/152k [00:00<00:00, 4.58MB/s]\n", + "skip ./data/era5/era5.nc\n", + "skip ./data/gpm_imerg/gpm_imerg_early.nc\n", + "skip ./data/gpm_imerg/gpm_imerg_final.nc\n", + "skip ./data/seviri/seviri.nc\n", + "skip ./data/netatmo/netatmo_pressure.nc\n", + "skip ./data/netatmo/netatmo_temp.nc\n", + "skip ./data/netatmo/netatmo_humidity.nc\n", + "skip ./data/netatmo/netatmo_rain.nc\n", + "skip ./data/netatmo/netatmo_raw.nc\n" ] - }, - { - "data": { - "text/plain": [ - "('data/andersson_2022_OpenMRG/OpenMRG.zip',\n", - " )" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "local_path=\"data/andersson_2022_OpenMRG/\"\n", + "# Individual data files, {where to store: download_id}\n", + "file_ids = {\n", + " './data/cml/cml.nc': '1MqH4Dzb7Ff-inpWNWQyOFHBWDCO5uKfc',\n", + " './data/radar/radar.nc': '168zv5u9Qb4eZ5W62CuKmHrWyTSS3Z9U2',\n", + " './data/gauges/city_gauge.nc': '1190fQu3ie_93e7iJpZEC-p92h9y8XtOW', \n", + " './data/gauges/smhi_gauge.nc': '1j7MdStiY1xdWeJrrsEXgHCqMoT9Qy67z', \n", + " './data/era5/era5.nc': '1ryNLVox1me_5orrAaTHw8-nROkhpffw1', \n", + " './data/gpm_imerg/gpm_imerg_early.nc': '1rck6vKonptGsV9sPauRmzrAmtD1Xan_u',\n", + " './data/gpm_imerg/gpm_imerg_final.nc': '1zOZD5wPezuVIXl-PaUFWvUrM_cn_O2Nq', \n", + " './data/seviri/seviri.nc': '1aeSovV7K-H5i0sHmpdqFYHsX0zgHmRXC',\n", + " './data/netatmo/netatmo_pressure.nc': '1gq_wFdCOyF697teOEYGH8yg8mTgvvzOa',\n", + " './data/netatmo/netatmo_temp.nc': '1AhFxkOxqpyNBOhLFHm4f7lpSA745imwI',\n", + " './data/netatmo/netatmo_humidity.nc': '12fw2nO1f_BRDm3sLKJDfsIKRYl65RRLQ',\n", + " './data/netatmo/netatmo_rain.nc': '1hjJq5sdHBDjNvw5oeLMXVjkdH-P36cpC',\n", + " './data/netatmo/netatmo_raw.nc': '1Nc9pMi6zFJ7eN7kJGyJLoU_c7bWRrMWT',\n", + "}\n", "\n", - "# function from OpenSense sandbox \n", - "oddt.download_andersson_2022_OpenMRG(\n", - " local_path=local_path, print_output=True\n", - ")" + "for file_path, file_id in file_ids.items():\n", + " os.makedirs(os.path.dirname(file_path), exist_ok=True)\n", + " if not os.path.exists(file_path):\n", + " print(f\"Downloading {file_path} ...\")\n", + " !gdown {file_id} -O {file_path}\n", + " else:\n", + " print(f\"skip {file_path}\")" ] }, { @@ -94,362 +115,2928 @@ "id": "105901a0-00da-448b-ba84-7887c76be4b3", "metadata": {}, "source": [ - "#### Transform CML data" + "## Process data" + ] + }, + { + "cell_type": "markdown", + "id": "b4e56d57-f087-4332-af5d-470e21a239a3", + "metadata": {}, + "source": [ + "### Process radar data" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "a68471ed-5f42-400d-91fd-94d36d40fc3a", + "execution_count": 17, + "id": "40f5826d-8595-4faf-8c1f-64420c3f9d74", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/erlend/Documents/GitHub/OpenMRG2/OPENSENSE_sandbox/notebooks/opensense_data_downloader_and_transformer.py:302: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sublink' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_multindex = ds.assign_coords({'sublink':df_metadata.index})\n", - "/home/erlend/Documents/GitHub/OpenMRG2/OPENSENSE_sandbox/notebooks/opensense_data_downloader_and_transformer.py:302: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'sublink' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.\n", - " ds_multindex = ds.assign_coords({'sublink':df_metadata.index})\n" - ] - } - ], + "outputs": [], "source": [ - "# Transform data in two steps due to memory constraints when transforming the full dataset\n", - "# Transform first part of the data\n", - "ds1 = oddt.transform_andersson_2022_OpenMRG(\n", - " fn=local_path + \"OpenMRG.zip\", # navigate to your local sandbox clone\n", - " path_to_extract_to=local_path,\n", - " time_start_end=(\n", - " None,\n", - " \"2015-07-15T00:00\",\n", - " ), # default (None, None) -> no timeslicing. ie. ('2015-08-31T00', None),\n", - " restructure_data=True,\n", - ")\n", - "# Transform second part of the data\n", - "ds2 = oddt.transform_andersson_2022_OpenMRG(\n", - " fn=local_path + \"OpenMRG.zip\", # navigate to your local sandbox clone\n", - " path_to_extract_to=local_path,\n", - " time_start_end=(\n", - " \"2015-07-15T00:00\",\n", - " None,\n", - " ), # default (None, None) -> no timeslicing. ie. ('2015-08-31T00', None),\n", - " restructure_data=True,\n", + "# Load raw radar files\n", + "ds_rad = xr.open_dataset(\"./data/radar/radar.nc\")\n", + "\n", + "# Apply marsha- palmer to get rainfall rates\n", + "ds_rad[\"rainfall_radar\"] = (10 ** (ds_rad.data / 10) / 200) ** (5 / 8)\n", + "\n", + "# Flip along y axis to work in the grid intersection function\n", + "ds_rad[\"latitudes\"] = ((\"y\", \"x\"), np.flip(ds_rad.lat.data, axis=0))\n", + "ds_rad[\"rainfall_radar\"] = (\n", + " (\"time\", \"y\", \"x\"),\n", + " np.flip(ds_rad.rainfall_radar.data, axis=1),\n", ")\n", - "# Resample to 1 minute temporal resolution, save memory when merging the two periods loaded above\n", - "ds1 = ds1.resample(time=\"1min\").first(skipna=True)\n", - "ds2 = ds2.resample(time=\"1min\").first(skipna=True)\n", "\n", - "# concat and drop overlaying duplicate\n", - "ds_cml = xr.concat([ds1, ds2], dim=\"time\").drop_duplicates(dim=\"time\")" + "# Convert to 15min resolution\n", + "ds_rad_15min = ds_rad[['crs', 'lat', 'lon', 'rainfall_radar']].resample(time='15min', label='right', closed='right').mean()\n", + "ds_rad_15min.rainfall_radar.attrs[\"units\"] = \"15min rainfall rate [mm/h]\"\n", + "\n", + "# Save radar\n", + "ds_rad_15min.to_netcdf('data/radar/radar_15min.nc')" ] }, { "cell_type": "markdown", - "id": "4f3cbe9c-7e97-4ea9-948a-f2e02b43e296", + "id": "b094271a-773d-494a-8e11-f9ab27a4b6a2", "metadata": {}, "source": [ - "#### Transform rain gauge data" + "### Visualize radar data" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "dc13f35b-31e2-405c-bbc9-2724d6c293fd", + "execution_count": 18, + "id": "4c759f04-8ed8-4516-a07a-ff0df5b94008", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    crs             int32 4B 1\n",
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+       "    source:       Swedish Meteorological and Hydrological Institute (SMHI), H...\n",
+       "    contact:      hydro.fou@smhi.se, remco.vandebeek@smhi.se\n",
+       "    title:        OpenMRG-Radar\n",
+       "    license:      https://creativecommons.org/licenses/by-sa/4.0\n",
+       "    version:      1.1\n",
+       "    doi:          https://doi.org/10.5281/zenodo.6673750\n",
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+       "    comment:      Created by Remco van de Beek, Victor Näslund and Johan Thur...
" + ], + "text/plain": [ + " Size: 126MB\n", + "Dimensions: (time: 8832, y: 48, x: 37)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 71kB 2015-06-01T00:15:00 ... 2015-0...\n", + " * y (y) float64 384B -3.413e+06 -3.415e+06 ... -3.507e+06\n", + " * x (x) float64 296B -1.542e+05 -1.522e+05 ... -8.22e+04\n", + " crs int32 4B 1\n", + " lat (y, x) float32 7kB 58.04 58.04 58.04 ... 57.23 57.23 57.23\n", + " lon (y, x) float32 7kB 11.41 11.45 11.48 ... 12.59 12.62 12.66\n", + "Data variables:\n", + " rainfall_radar (time, y, x) float64 125MB nan nan nan ... 2.719 2.597 3.137\n", + "Attributes:\n", + " source: Swedish Meteorological and Hydrological Institute (SMHI), H...\n", + " contact: hydro.fou@smhi.se, remco.vandebeek@smhi.se\n", + " title: OpenMRG-Radar\n", + " license: https://creativecommons.org/licenses/by-sa/4.0\n", + " version: 1.1\n", + " doi: https://doi.org/10.5281/zenodo.6673750\n", + " proj_string: +proj=stere +lat_ts=60 +ellps=bessel +lon_0=14 +lat_0=90\n", + " comment: Created by Remco van de Beek, Victor Näslund and Johan Thur..." + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df_gauges_city = pd.read_csv(\n", - " \"data/andersson_2022_OpenMRG/gauges/city/CityGauges-2015JJA.csv\",\n", - " index_col=0,\n", - " parse_dates=True,\n", - ")\n", - "\n", - "df_gauges_city_metadata = pd.read_csv(\n", - " \"data/andersson_2022_OpenMRG/gauges/city/CityGauges-metadata.csv\",\n", - " index_col=0,\n", - ")" + "# Display dataset\n", + "ds_rad_15min" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "2338d4cc-ef4f-4ddf-97da-65d91667d2c4", + "execution_count": 26, + "id": "4d10b5fa-adc8-46c6-8bdf-3273092cf964", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "ds_gauges_city_org = xr.Dataset(\n", - " data_vars=dict(\n", - " rainfall_amount=([\"id\", \"time\"], df_gauges_city.T),\n", - " ),\n", - " coords=dict(\n", - " id=df_gauges_city_metadata.index.values,\n", - " time=df_gauges_city.index.values,\n", - " longitude=([\"id\"], df_gauges_city_metadata.Longitude_DecDeg),\n", - " latitude=([\"id\"], df_gauges_city_metadata.Latitude_DecDeg),\n", - " location=([\"id\"], df_gauges_city_metadata.Location),\n", - " type=([\"id\"], df_gauges_city_metadata.Type),\n", - " quantization=([\"id\"], df_gauges_city_metadata[\"Resolution (mm)\"]),\n", - " ),\n", - ")\n", - "ds_gauges_city = ds_gauges_city_org.resample(time=\"15min\", label=\"right\", closed=\"right\").sum()" + "# Plot radar field\n", + "plg.plot_map.plot_plg(\n", + " da_grid=ds_rad_15min.rainfall_radar.sel(time = '2015-07-25T13:00'),\n", + " use_lon_lat=True,\n", + " colorbar_label=\"mean rainfall intensity (mm/h)\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "2276b5a1-ee11-4f5a-aafe-ced512fe6598", + "metadata": {}, + "source": [ + "### Process CML data" ] }, { "cell_type": "code", "execution_count": 7, - "id": "38a3dbe2-e123-410a-a340-961d202cbfc9", + "id": "8648c36c-85a3-4383-b560-9d86793ce849", "metadata": {}, "outputs": [], "source": [ - "df_gauge_smhi = pd.read_csv(\n", - " \"data/andersson_2022_OpenMRG/gauges/smhi/GbgA-71420-2015JJA.csv\",\n", - " index_col=0,\n", - " parse_dates=True,\n", - ")\n", + "# Load CML data, select sublink 0 and resample to 1 min resolution\n", + "ds_cmls = xr.open_dataset(\"./data/cml/cml.nc\").isel(sublink_id = 0).load().resample(time=\"1min\").first(skipna=True)\n", "\n", + "# Calculate total loss\n", + "ds_cmls[\"tl\"] = ds_cmls.tsl - ds_cmls.rsl\n", "\n", - "ds_gauges_smhi = xr.Dataset(\n", - " data_vars=dict(\n", - " rainfall_amount=([\"id\", \"time\"], [df_gauge_smhi.Pvol_mm.values]),\n", - " ),\n", - " coords=dict(\n", - " id=[\"SMHI\"],\n", - " time=df_gauge_smhi.index.values,\n", - " longitude=([\"id\"], [11.9924]),\n", - " latitude=([\"id\"], [57.7156]),\n", - " location=([\"id\"], [\"Goeteburg A\"]),\n", - " type=([\"id\"], [\"15 min rainfall sum\"]),\n", - " quantization=([\"id\"], [0.1]),\n", - " ),\n", - ")" + "# Interpolate na\n", + "ds_cmls['tl'] = ds_cmls.tl.interpolate_na(dim='time', method='linear', max_gap='5min')\n", + "\n", + "# Flag cmls with strong diurnal fluctuations\n", + "qc_diurnalcycle = (ds_cmls.tl.rolling(time=60 * 5, center=True).std() > 2).mean(dim=\"time\") > 0.1\n", + "\n", + "# Flag cmls with very noisy periods\n", + "qc_noisyperiods = (ds_cmls.tl.rolling(time=60, center=True).std() > 0.8).mean(dim=\"time\") > 0.20\n", + "\n", + "# Drop flagged CMLs\n", + "ds_cmls.where(qc_diurnalcycle, drop=True);\n", + "ds_cmls.where(qc_noisyperiods, drop=True);" ] }, { "cell_type": "code", "execution_count": 8, - "id": "2364ab26-c472-4b13-9e2f-f3ef2ea17b3a", + "id": "73340b72-afbc-4101-bc7f-3e0c7eb6198e", "metadata": {}, "outputs": [], "source": [ - "ds_gauges = xr.concat([ds_gauges_city, ds_gauges_smhi], dim=\"id\")\n", - "ds_gauges = ds_gauges.sel(time=slice(ds_cml.time.min(),ds_cml.time.max()))" + "# CML wet/dry detection using radar\n", + "da_intersect_weights = plg.spatial.calc_sparse_intersect_weights_for_several_cmls(\n", + " x1_line=ds_cmls.site_0_lon.values,\n", + " y1_line=ds_cmls.site_0_lat.values,\n", + " x2_line=ds_cmls.site_1_lon.values,\n", + " y2_line=ds_cmls.site_1_lat.values,\n", + " cml_id=ds_cmls.cml_id.values,\n", + " x_grid=ds_rad.lon.values,\n", + " y_grid=ds_rad.lat.values,\n", + " grid_point_location='center',\n", + ")\n", + "\n", + "da_radar_along_cmls = plg.spatial.get_grid_time_series_at_intersections(\n", + " grid_data=ds_rad.rainfall_radar, # In mm/h\n", + " intersect_weights=da_intersect_weights,\n", + ").resample(time = '1min').bfill()\n", + "\n", + "# Set wet periods above threshold\n", + "ds_cmls['wet_radar'] = (da_radar_along_cmls > 0.01).rolling(time=5, center=True).max()" ] }, { "cell_type": "code", "execution_count": 9, - "id": "5e0c1800-cb40-4a3d-a1c2-5965cc60a085", + "id": "56932e25-8c05-46dc-bbf0-e7738d448ba3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/erlend/miniforge3/envs/openmrg/lib/python3.12/site-packages/numpy/lib/_nanfunctions_impl.py:1617: RuntimeWarning: All-NaN slice encountered\n", + " return fnb._ureduce(a,\n" + ] + } + ], "source": [ - "# ds_gauges=ds_gauges.reindex(time=pd.date_range(\n", - "# \"2015-06-01T00:00:00\",\n", - "# \"2015-08-31T23:45:00\",freq=\"15min\"), fill_value=np.nan)\n", - "\n", - "ds_gauges = ds_gauges.rename({\"rainfall_amount\": \"R\"})" + "# CML wet/dry detection using CML tl\n", + "roll_std_dev = ds_cmls.tl.rolling(time=60, center=True).std()\n", + "threshold = 1.12 * roll_std_dev.quantile(0.8, dim=\"time\")\n", + "ds_cmls[\"wet_cml\"] = (roll_std_dev > threshold)" ] }, { "cell_type": "code", "execution_count": 10, - "id": "34c122fc", + "id": "f6c06935-ac25-4b0f-8967-7efd1ccdbbbc", "metadata": {}, "outputs": [], "source": [ - "ds_gauges.to_netcdf('data/andersson_2022_OpenMRG/gauges/openmrg_gauges.nc')" - ] - }, - { - "cell_type": "markdown", - "id": "a16a0196-a834-408f-94e0-c62f753f1845", - "metadata": {}, - "source": [ - "#### Transform radar data" + "# Estimate baseline\n", + "ds_cmls[\"baseline\"] = pycml.processing.baseline.baseline_constant(\n", + " trsl=ds_cmls.tl,\n", + " wet=ds_cmls.wet_cml.astype(bool)| ds_cmls.wet_radar.astype(bool), # wet period if radar or CML is wet\n", + " n_average_last_dry=5,\n", + ")\n", + "\n", + "ds_cmls[\"A_obs\"] = ds_cmls.tl - ds_cmls.baseline\n", + "ds_cmls[\"A_obs\"] = ds_cmls.A_obs.where(ds_cmls.A_obs >= 0, 0)\n", + "\n", + "# WAA using Pastorek with parameters that looks good \n", + "ds_cmls[\"waa\"] = pycml.processing.wet_antenna.waa_pastorek_2021_from_A_obs(\n", + " A_obs=ds_cmls.A_obs,\n", + " f_Hz=ds_cmls.frequency * 1e6,\n", + " pol=ds_cmls.polarization.data,\n", + " L_km=ds_cmls.length / 1000,\n", + " A_max=6,\n", + " zeta=0.7, \n", + " d=0.15,\n", + ")\n", + "\n", + "# Calculate attenuation caused by rain and remove negative attenuation\n", + "ds_cmls[\"A\"] = ds_cmls.tl - ds_cmls.baseline - ds_cmls.waa\n", + "ds_cmls[\"A\"].data[ds_cmls.A < 0] = 0\n", + "\n", + "# Derive rain rate via the k-R relation\n", + "ds_cmls[\"R\"] = pycml.processing.k_R_relation.calc_R_from_A(\n", + " A=ds_cmls.A,\n", + " L_km=ds_cmls.length.astype(float) / 1000, # convert to km\n", + " f_GHz=ds_cmls.frequency / 1000, # convert to GHz\n", + " pol=ds_cmls.polarization,\n", + ")" ] }, { "cell_type": "code", "execution_count": 11, - "id": "ee7dd2ea-2111-41bc-9ea2-a325bd67f886", + "id": "242ee98b-2a15-43fa-9a43-be208a4d37e0", "metadata": {}, "outputs": [], "source": [ - "# read radar data and convert to Opensense naming conventions\n", - "ds_rad = (\n", - " xr.open_dataset(local_path + \"radar/radar.nc\")\n", - " .rename( \n", - " {\"lat\": \"latitudes\", \"lon\": \"longitudes\"}\n", - " )\n", - " .transpose(\"time\", \"y\", \"x\")\n", - ")\n", + "# Resample to 15 min resolution\n", + "ds_cmls_15min = ds_cmls.R.resample(time=\"15min\", label='right', closed='right').mean(skipna=True).to_dataset()\n", + "ds_cmls_15min.R.attrs[\"units\"] = \"15min rainfall rate [mm/h]\"\n", "\n", - "# Turn into coordinates and fix naming error\n", - "ds_rad.coords['latitudes'] = ds_rad.latitudes\n", - "ds_rad.coords['longitudes'] = ds_rad.longitudes\n", - "ds_rad = ds_rad.rename({\"longitudes\": \"lon\", \"latitudes\": \"lat\"})" + "# Save CML\n", + "ds_cmls_15min.to_netcdf('data/cml/cml_15min.nc')" ] }, { "cell_type": "code", "execution_count": 12, - "id": "d5ef8650-b677-4b04-a713-9d27c524e702", + "id": "24d50490-4bcf-4d8e-82ab-9268844dc94c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    title:                 NetAtmo data from Gothenburg for OpenMRG+\n",
+       "    file author:           Remco van de Beek\n",
+       "    institution:           Swedish Meteorological and Hydrologcial Institute\n",
+       "    date:                  2025-10-29 09:01:00\n",
+       "    source:                Netamo PWS\n",
+       "    history:               Data derived and reformated from original dataset ...\n",
+       "    naming convention:     OpenSense-0.1\n",
+       "    license restrictions:  CC-BY 4.0 https://creativecommons.org/licenses/by/...\n",
+       "    reference:             https://doi.org/10.1029/2019GL083731\n",
+       "    comment:
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CML processing \n", - "In this notebook cml are processed according to the following steps:\n", - "* calculate total loss\n", - "* simple quality control\n", - "* Wet&Dry Classification\n", - "* baseline computation \n", - "* rain-induced attenuation\n", - "* rain rate estimate" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2f5aeefc-6065-4428-bdf6-446743f3f610", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "\n", - "# # Add the poligrain and mergeplg src directories to Python's path\n", - "sys.path.insert(0, os.path.abspath(\"./poligrain/src\"))\n", - "sys.path.insert(0, os.path.abspath(\"./pycomlink\"))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f583a92c-71da-43c8-8238-34d9ebe4ff31", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/erlend/Documents/GitHub/OpenMRG2/pycomlink/pycomlink/io/examples.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " import pkg_resources\n" - ] - } - ], - "source": [ - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pycomlink as pycml\n", - "import poligrain as plg\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "5987e619", - "metadata": {}, - "outputs": [], - "source": [ - "# Load CML and radar data\n", - "ds_cmls = xr.open_dataset('data/andersson_2022_OpenMRG/openMRG_cml.nc')\n", - "ds_rad = xr.open_dataset('data/andersson_2022_OpenMRG/radar/openmrg_rad.nc') " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b6075cb5-8908-4ded-90cf-17ddf2d264be", - "metadata": {}, - "outputs": [], - "source": [ - "# Select sublink 0\n", - "ds_cmls = ds_cmls.isel(sublink_id = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "03e6b7ac-172a-404f-892a-e91c97a1381f", - "metadata": {}, - "outputs": [], - "source": [ - "# calculate total loss\n", - "ds_cmls[\"tl\"] = ds_cmls.tsl - ds_cmls.rsl\n", - "\n", - "# Interpolate na\n", - "ds_cmls['tl'] = ds_cmls.tl.interpolate_na(dim='time', method='linear', max_gap='5min')\n", - "\n", - "# flag cmls with strong diurnal fluctuations\n", - "qc_diurnalcicle = (ds_cmls.tl.rolling(time=60 * 5, center=True).std() > 2).mean(dim=\"time\") > 0.1\n", - "\n", - "# flag cmls with very noisy periods\n", - "qc_noisyperiods = (ds_cmls.tl.rolling(time=60, center=True).std() > 0.8).mean(dim=\"time\") > 0.20\n", - "\n", - "ds_cmls.where(qc_diurnalcicle, drop=True);\n", - "ds_cmls.where(qc_noisyperiods, drop=True);" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f0d93e38-115d-477c-9f35-8bc3570620cd", - "metadata": {}, - "outputs": [], - "source": [ - "da_intersect_weights = plg.spatial.calc_sparse_intersect_weights_for_several_cmls(\n", - " x1_line=ds_cmls.site_0_lon.values,\n", - " y1_line=ds_cmls.site_0_lat.values,\n", - " x2_line=ds_cmls.site_1_lon.values,\n", - " y2_line=ds_cmls.site_1_lat.values,\n", - " cml_id=ds_cmls.cml_id.values,\n", - " x_grid=ds_rad.lon.values,\n", - " y_grid=ds_rad.lat.values,\n", - " grid_point_location='center',\n", - ")\n", - "\n", - "da_radar_along_cmls = plg.spatial.get_grid_time_series_at_intersections(\n", - " grid_data=ds_rad.rainfall_amount,\n", - " intersect_weights=da_intersect_weights,\n", - ").resample(time = '1min').bfill()*4 # to mm/h\n", - "\n", - "# Set wet periods above threshold\n", - "ds_cmls['wet_radar'] = (da_radar_along_cmls > 0.01).rolling(time=5, center=True).max() # the radar is a bit low" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7bdade66-6cba-4ff3-b9a6-2a0a180826b1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/erlend/miniforge3/envs/openmrg/lib/python3.12/site-packages/numpy/lib/_nanfunctions_impl.py:1620: RuntimeWarning: All-NaN slice encountered\n", - " return fnb._ureduce(a,\n" - ] - } - ], - "source": [ - "roll_std_dev = ds_cmls.tl.rolling(time=60, center=True).std()\n", - "threshold = 1.12 * roll_std_dev.quantile(0.8, dim=\"time\")\n", - "ds_cmls[\"wet_cml\"] = (roll_std_dev > threshold)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "75a9dfa8-4e11-453a-92f7-a3554b84244c", - "metadata": {}, - "outputs": [], - "source": [ - "# Estimate baseline\n", - "ds_cmls[\"baseline\"] = pycml.processing.baseline.baseline_constant(\n", - " trsl=ds_cmls.tl,\n", - " wet=ds_cmls.wet_cml.astype(bool)| ds_cmls.wet_radar.astype(bool), # wet period if radar or CML is wet\n", - " n_average_last_dry=5,\n", - ")\n", - "\n", - "ds_cmls[\"A_obs\"] = ds_cmls.tl - ds_cmls.baseline\n", - "ds_cmls[\"A_obs\"] = ds_cmls.A_obs.where(ds_cmls.A_obs >= 0, 0)\n", - "\n", - "# Pastorek using parameters that looks good for the German,\n", - "# Swedish and Norwegian dataset\n", - "ds_cmls[\"waa\"] = pycml.processing.wet_antenna.waa_pastorek_2021_from_A_obs(\n", - " A_obs=ds_cmls.A_obs,\n", - " f_Hz=ds_cmls.frequency * 1e6,\n", - " pol=ds_cmls.polarization.data,\n", - " L_km=ds_cmls.length / 1000,\n", - " A_max=6,\n", - " zeta=0.7, # 0.55 is default\n", - " d=0.15,\n", - ")\n", - "\n", - "# calculate attenuation caused by rain and remove negative attenuation\n", - "ds_cmls[\"A\"] = ds_cmls.tl - ds_cmls.baseline - ds_cmls.waa\n", - "ds_cmls[\"A\"].data[ds_cmls.A < 0] = 0\n", - "\n", - "# derive rain rate via the k-R relation\n", - "ds_cmls[\"R\"] = pycml.processing.k_R_relation.calc_R_from_A(\n", - " A=ds_cmls.A,\n", - " L_km=ds_cmls.length.astype(float) / 1000, # convert to km\n", - " f_GHz=ds_cmls.frequency / 1000, # convert to GHz\n", - " pol=ds_cmls.polarization,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3474b820-8570-4b3d-8f3b-ecdabef5cc68", - "metadata": {}, - "outputs": [], - "source": [ - "ds_cml_res = (\n", - " ds_cmls[[\"R\"]]\n", - " .resample(time=\"15min\", label='right', closed='right')\n", - " .sum(skipna=True)\n", - " / 60\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "058f0d35-f2c8-4e89-b32b-dab97878b3e6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0.00026499, 0.0018549 , 0.00105994, 0.00211988, 0.00211988,\n", - " 0.00635965, 0.00635965, 0.00768458, 0.00768458, 0.00635965,\n", - " 0.00556469, 0.00688962, 0.00662463, 0.00503472, 0.00397478,\n", - " 0.00423977, 0.00238487, 0.0018549 , 0.0018549 , 0.00211988,\n", - " 0.00264985, 0.00132493, 0.00026499, 0.00079496, 0.00026499,\n", - " 0.00158991, 0.00105994, 0.00026499, 0. ]),\n", - " array([180. , 191.03448276, 202.06896552, 213.10344828,\n", - " 224.13793103, 235.17241379, 246.20689655, 257.24137931,\n", - " 268.27586207, 279.31034483, 290.34482759, 301.37931034,\n", - " 312.4137931 , 323.44827586, 334.48275862, 345.51724138,\n", - " 356.55172414, 367.5862069 , 378.62068966, 389.65517241,\n", - " 400.68965517, 411.72413793, 422.75862069, 433.79310345,\n", - " 444.82758621, 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(ds_cml_res.R.sum(dim='time')).plot.hist(bins=np.linspace(180,500,30), density=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a9698890", - "metadata": {}, - "outputs": [], - "source": [ - "# saving output\n", - "ds_cml_res.to_netcdf('data/processed_cml_OpenMRG.nc')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/3_adjust_radar.ipynb b/examples_merge.ipynb similarity index 100% rename from 3_adjust_radar.ipynb rename to examples_merge.ipynb From 1565af1bf997c46edae48da490af1548f4662230 Mon Sep 17 00:00:00 2001 From: Erlend Date: Wed, 14 Jan 2026 15:13:44 +0100 Subject: [PATCH 4/7] fix radar flip error --- 1_data_preparation.ipynb | 174 +++++++++++++++++++-------------------- 1 file changed, 87 insertions(+), 87 deletions(-) diff --git a/1_data_preparation.ipynb b/1_data_preparation.ipynb index 9c88ab0..5c6580f 100644 --- a/1_data_preparation.ipynb +++ b/1_data_preparation.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "id": "5fadac94-a328-4db2-a7d3-a0f1b14e2e33", "metadata": {}, "outputs": [ @@ -61,16 +61,8 @@ "text": [ "skip ./data/cml/cml.nc\n", "skip ./data/radar/radar.nc\n", - "Downloading ./data/gauges/city_gauge.nc ...\n", - "Downloading...\n", - "From: https://drive.google.com/uc?id=1190fQu3ie_93e7iJpZEC-p92h9y8XtOW\n", - "To: /home/erlend/git/OpenMRG2/data/gauges/city_gauge.nc\n", - "100%|██████████████████████████████████████| 11.7M/11.7M [00:00<00:00, 28.8MB/s]\n", - "Downloading ./data/gauges/smhi_gauge.nc ...\n", - "Downloading...\n", - "From: https://drive.google.com/uc?id=1j7MdStiY1xdWeJrrsEXgHCqMoT9Qy67z\n", - "To: /home/erlend/git/OpenMRG2/data/gauges/smhi_gauge.nc\n", - "100%|████████████████████████████████████████| 152k/152k [00:00<00:00, 4.58MB/s]\n", + "skip ./data/gauges/city_gauge.nc\n", + "skip ./data/gauges/smhi_gauge.nc\n", "skip ./data/era5/era5.nc\n", "skip ./data/gpm_imerg/gpm_imerg_early.nc\n", "skip ./data/gpm_imerg/gpm_imerg_final.nc\n", @@ -128,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "40f5826d-8595-4faf-8c1f-64420c3f9d74", "metadata": {}, "outputs": [], @@ -140,7 +132,7 @@ "ds_rad[\"rainfall_radar\"] = (10 ** (ds_rad.data / 10) / 200) ** (5 / 8)\n", "\n", "# Flip along y axis to work in the grid intersection function\n", - "ds_rad[\"latitudes\"] = ((\"y\", \"x\"), np.flip(ds_rad.lat.data, axis=0))\n", + "ds_rad[\"lat\"] = ((\"y\", \"x\"), np.flip(ds_rad.lat.data, axis=0))\n", "ds_rad[\"rainfall_radar\"] = (\n", " (\"time\", \"y\", \"x\"),\n", " np.flip(ds_rad.rainfall_radar.data, axis=1),\n", @@ -164,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "id": "4c759f04-8ed8-4516-a07a-ff0df5b94008", "metadata": {}, "outputs": [ @@ -656,7 +648,7 @@ " * y (y) float64 384B -3.413e+06 -3.415e+06 ... -3.507e+06\n", " * x (x) float64 296B -1.542e+05 -1.522e+05 ... -8.22e+04\n", " crs int32 4B 1\n", - " lat (y, x) float32 7kB 58.04 58.04 58.04 ... 57.23 57.23 57.23\n", + " lat (y, x) float32 7kB 57.21 57.21 57.21 ... 58.06 58.06 58.06\n", " lon (y, x) float32 7kB 11.41 11.45 11.48 ... 12.59 12.62 12.66\n", "Data variables:\n", " rainfall_radar (time, y, x) float64 125MB nan nan nan ... 2.719 2.597 3.137\n", @@ -668,10 +660,10 @@ " version: 1.1\n", " doi: https://doi.org/10.5281/zenodo.6673750\n", " proj_string: +proj=stere +lat_ts=60 +ellps=bessel +lon_0=14 +lat_0=90\n", - " comment: Created by Remco van de Beek, Victor Näslund and Johan Thur...
  • source :
    Swedish Meteorological and Hydrological Institute (SMHI), Hydrology Research, http://www.smhi.se/hydrology-research
    contact :
    hydro.fou@smhi.se, remco.vandebeek@smhi.se
    title :
    OpenMRG-Radar
    license :
    https://creativecommons.org/licenses/by-sa/4.0
    version :
    1.1
    doi :
    https://doi.org/10.5281/zenodo.6673750
    proj_string :
    +proj=stere +lat_ts=60 +ellps=bessel +lon_0=14 +lat_0=90
    comment :
    Created by Remco van de Beek, Victor Näslund and Johan Thuresson, SMHI. Time is in UTC. The data are in pseudo-dBZ (integer 0-255) and are linked to the scale_factor (0.4) and add_offset (-30) attributes. Therefore the data will be automatically converted to dBZ while reading.
    • " ], "text/plain": [ " Size: 126MB\n", @@ -766,7 +758,7 @@ " * y (y) float64 384B -3.413e+06 -3.415e+06 ... -3.507e+06\n", " * x (x) float64 296B -1.542e+05 -1.522e+05 ... -8.22e+04\n", " crs int32 4B 1\n", - " lat (y, x) float32 7kB 58.04 58.04 58.04 ... 57.23 57.23 57.23\n", + " lat (y, x) float32 7kB 57.21 57.21 57.21 ... 58.06 58.06 58.06\n", " lon (y, x) float32 7kB 11.41 11.45 11.48 ... 12.59 12.62 12.66\n", "Data variables:\n", " rainfall_radar (time, y, x) float64 125MB nan nan nan ... 2.719 2.597 3.137\n", @@ -781,7 +773,7 @@ " comment: Created by Remco van de Beek, Victor Näslund and Johan Thur..." ] }, - "execution_count": 18, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -793,13 +785,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "id": "4d10b5fa-adc8-46c6-8bdf-3273092cf964", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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    " ] @@ -827,7 +819,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "8648c36c-85a3-4383-b560-9d86793ce849", "metadata": {}, "outputs": [], @@ -854,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "73340b72-afbc-4101-bc7f-3e0c7eb6198e", "metadata": {}, "outputs": [], @@ -882,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "56932e25-8c05-46dc-bbf0-e7738d448ba3", "metadata": {}, "outputs": [ @@ -904,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "f6c06935-ac25-4b0f-8967-7efd1ccdbbbc", "metadata": {}, "outputs": [], @@ -945,7 +937,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "242ee98b-2a15-43fa-9a43-be208a4d37e0", "metadata": {}, "outputs": [], @@ -960,7 +952,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "24d50490-4bcf-4d8e-82ab-9268844dc94c", "metadata": {}, "outputs": [ @@ -1460,10 +1452,10 @@ " length (cml_id) float64 3kB 691.4 614.6 323.7 ... 4.806e+03 1.412e+03\n", " quantile float64 8B 0.8\n", "Data variables:\n", - " R (cml_id, time) float64 26MB 0.0 0.01244 ... 2.838 3.665