From 86d38a8a36f11fdeb75155500e4985e8cd598153 Mon Sep 17 00:00:00 2001 From: cyschneck <22159116+cyschneck@users.noreply.github.com> Date: Mon, 14 Oct 2024 15:31:20 -0600 Subject: [PATCH 1/8] heat_index_nws --- ncl/ncl_entries/meteorology.ipynb | 29 +++++++++++++++++++++++++++-- references.bib | 7 +++++++ 2 files changed, 34 insertions(+), 2 deletions(-) diff --git a/ncl/ncl_entries/meteorology.ipynb b/ncl/ncl_entries/meteorology.ipynb index 64efce4f..1aa31ec5 100644 --- a/ncl/ncl_entries/meteorology.ipynb +++ b/ncl/ncl_entries/meteorology.ipynb @@ -16,7 +16,8 @@ "This section covers meteorology functions from NCL:\n", "\n", "- [dewtemp_trh](https://www.ncl.ucar.edu/Document/Functions/Built-in/dewtemp_trh.shtml)\n", - "- [daylight_fao56](https://www.ncl.ucar.edu/Document/Functions/Crop/daylight_fao56.shtml)" + "- [daylight_fao56](https://www.ncl.ucar.edu/Document/Functions/Crop/daylight_fao56.shtml)\n", + "- [heat_index_nws](https://www.ncl.ucar.edu/Document/Functions/Contributed/heat_index_nws.shtml)" ] }, { @@ -116,6 +117,29 @@ "max_daylight(days_of_year, latitudes)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## heat_index_nws\n", + "\n", + "NCL's `heat_index_nws` computes the heat index as calculated by the National Weather Service{footcite}`nws_heat_index`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Grab and Go" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -130,7 +154,8 @@ "## Python Resources\n", "- [GeoCAT-comp `dewtemp` documentation](https://geocat-comp.readthedocs.io/en/latest/user_api/generated/geocat.comp.meteorology.dewtemp.html)\n", "- [Convert between different temperature scales in SciPy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.constants.convert_temperature.html)\n", - "- [GeoCAT-comp `max_daylight` Documentation](https://geocat-comp.readthedocs.io/en/latest/user_api/generated/geocat.comp.meteorology.max_daylight.html)\n", + "- [GeoCAT-comp `max_daylight` documentation](https://geocat-comp.readthedocs.io/en/latest/user_api/generated/geocat.comp.meteorology.max_daylight.html)\n", + "- [GeoCAT-comp `heat_index` documentation](https://geocat-comp.readthedocs.io/en/stable/user_api/generated/geocat.comp.meteorology.heat_index.html)\n", "\n", "## Additional Reading\n", "- [NOAA: Dew Point vs. Humidity](https://www.weather.gov/arx/why_dewpoint_vs_humidity)" diff --git a/references.bib b/references.bib index cf109c84..0b4df723 100644 --- a/references.bib +++ b/references.bib @@ -124,3 +124,10 @@ @book{dutton_1986 pages={273-274}, publisher = {Dover Publications} } + +@misc{nws_heat_index, + url = {https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml}, + year = {2022}, + organization = {National Weather Service - Weather Prediction Center}, + title = {The Heat Index Equation} +} From ec18ad505bb4aa4b37b89f61da09b0e9803d8f29 Mon Sep 17 00:00:00 2001 From: cyschneck <22159116+cyschneck@users.noreply.github.com> Date: Tue, 22 Oct 2024 15:05:22 -0600 Subject: [PATCH 2/8] merge into single ncl_raw, heat_index receipt --- ncl/ncl_raw/daylight_fao56.ncl | 14 - ncl/ncl_raw/dewtemp_trh.ncl | 14 - ncl/ncl_raw/meteorology.ncl | 53 + ncl/receipts/meteorology.ipynb | 4377 +++++++++++++++++++++++++++++++- 4 files changed, 4408 insertions(+), 50 deletions(-) delete mode 100644 ncl/ncl_raw/daylight_fao56.ncl delete mode 100644 ncl/ncl_raw/dewtemp_trh.ncl create mode 100644 ncl/ncl_raw/meteorology.ncl diff --git a/ncl/ncl_raw/daylight_fao56.ncl b/ncl/ncl_raw/daylight_fao56.ncl deleted file mode 100644 index d126ce7c..00000000 --- a/ncl/ncl_raw/daylight_fao56.ncl +++ /dev/null @@ -1,14 +0,0 @@ -; daylight_fao56 -; Adapted from https://www.ncl.ucar.edu/Document/Functions/Crop/daylight_fao56.shtml - -; ncl -n daylight_fao56.ncl >> daylight_fao56_output.txt - -print("DOY, Latitude (Degrees), Daylight Hours") -do doy=0,365 - do lat=-66,66 - begin - daylight_hours = daylight_fao56(doy, lat) - print (doy +","+ lat +","+ daylight_hours) - end - end do -end do diff --git a/ncl/ncl_raw/dewtemp_trh.ncl b/ncl/ncl_raw/dewtemp_trh.ncl deleted file mode 100644 index 8fa6c2e8..00000000 --- a/ncl/ncl_raw/dewtemp_trh.ncl +++ /dev/null @@ -1,14 +0,0 @@ -; dewtemp_trh -; Adapted from https://www.ncl.ucar.edu/Document/Functions/Built-in/dewtemp_trh.shtml - -; ncl -n dewtemp_trh.ncl >> dewtemp_trh_output.txt - -print("Temperature (K), Relative Humidity (%), Dew Temperature (C)") -do tk=273,374 - do rh=1,100 - begin - dewtemp = dewtemp_trh(tk,rh)-273.15 - print (tk +","+ rh +","+ dewtemp) - end - end do -end do diff --git a/ncl/ncl_raw/meteorology.ncl b/ncl/ncl_raw/meteorology.ncl new file mode 100644 index 00000000..7ccbfbbd --- /dev/null +++ b/ncl/ncl_raw/meteorology.ncl @@ -0,0 +1,53 @@ +; dewtemp_trh +; Adapted from https://www.ncl.ucar.edu/Document/Functions/Built-in/dewtemp_trh.shtml + +; ncl -n dewtemp_trh.ncl >> dewtemp_trh_output.txt + +print("Temperature (K), Relative Humidity (%), Dew Temperature (C)") +do tk=273,374 + do rh=1,100 + begin + dewtemp = dewtemp_trh(tk,rh)-273.15 + print (tk +","+ rh +","+ dewtemp) + end + end do +end do + +; daylight_fao56 +; Adapted from https://www.ncl.ucar.edu/Document/Functions/Crop/daylight_fao56.shtml + +; ncl -n daylight_fao56.ncl >> daylight_fao56_output.txt + +print("DOY, Latitude (Degrees), Daylight Hours") +do doy=0,365 + do lat=-66,66 + begin + daylight_hours = daylight_fao56(doy, lat) + print (doy +","+ lat +","+ daylight_hours) + end + end do +end do + +; heat_index_nws +; https://www.ncl.ucar.edu/Document/Functions/Contributed/heat_index_nws.shtml + +; ncl -n heat_index_nws.ncl >> heat_index_nws_output.txt + +print("Temperature (F), Relative Humidity (%), Heat Index (F)") +do tf=0,79 + do rh=1,40 + begin + heatindex = heat_index_nws(tf,rh,(/2,2/), False) + print (tf +","+ rh +","+ heatindex) + end + end do +end do + +do tf=79,100 + do rh=40,100 + begin + heatindex = heat_index_nws(tf,rh,(/2,2/), True) + print (tf +","+ rh +","+ heatindex) + end + end do +end do diff --git a/ncl/receipts/meteorology.ipynb b/ncl/receipts/meteorology.ipynb index 053569ef..a29d7568 100644 --- a/ncl/receipts/meteorology.ipynb +++ b/ncl/receipts/meteorology.ipynb @@ -25,7 +25,8 @@ "source": [ "## Functions covered\n", "- [dewtemp_trh](https://www.ncl.ucar.edu/Document/Functions/Built-in/dewtemp_trh.shtml)\n", - "- [daylight_fao56](https://www.ncl.ucar.edu/Document/Functions/Crop/daylight_fao56.shtml)" + "- [daylight_fao56](https://www.ncl.ucar.edu/Document/Functions/Crop/daylight_fao56.shtml)\n", + "- [heat_index_nws](https://www.ncl.ucar.edu/Document/Functions/Contributed/heat_index_nws.shtml)" ] }, { @@ -41,17 +42,7 @@ "id": "3d70616a8934f0fb", "metadata": {}, "source": [ - "```{literalinclude} ../ncl_raw/dewtemp_trh.ncl\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "3b32a066-055f-454b-a843-e57dca954dad", - "metadata": {}, - "source": [ - "```{literalinclude} ../ncl_raw/daylight_fao56.ncl\n", + "```{literalinclude} ../ncl_raw/Meteorology.ncl\n", "\n", "```" ] @@ -74,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "7af6f81c-2498-4aea-b250-579ec3fd31c8", "metadata": {}, "outputs": [], @@ -89,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "3166c583-1290-4300-b96b-beab81cd033d", "metadata": {}, "outputs": [], @@ -103,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "39fd494c-609b-4dd0-83fd-b08d0daf6aaa", "metadata": {}, "outputs": [], @@ -134,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "69028bd0-ae29-43d6-9824-e80b4d6d8f44", "metadata": {}, "outputs": [], @@ -149,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "128f13cb-b22f-4b4e-b144-268db532aa78", "metadata": {}, "outputs": [], @@ -163,10 +154,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "ed9351f8-4ff7-4585-b957-d491ace5bdb3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/user/miniconda3/envs/wavelet_tutorial/lib/python3.11/site-packages/geocat/comp/meteorology.py:1058: UserWarning: WARNING: max_daylight has limited validity for abs(lat) > 55 \n", + " warnings.warn(\n" + ] + } + ], "source": [ "### Collect DOY and Latitude Pairs\n", "doy_lat = []\n", @@ -184,6 +184,78 @@ " geocat_daylight[pair] = max_daylight(doy, lat)" ] }, + { + "cell_type": "markdown", + "id": "47685e2b-9584-46d8-9613-bde0ab6efe20", + "metadata": {}, + "source": [ + "### heat_index_nws " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c5011a50-2837-494e-8e46-4d682e33f7aa", + "metadata": {}, + "outputs": [], + "source": [ + "#### Collect NCL values for heat_index_nws from geocat-datafiles\n", + "import geocat.datafiles as gdf\n", + "import numpy as np\n", + "\n", + "heat_index_data = gdf.get('applications_files/ncl_outputs/heat_index_nws_output.txt')\n", + "heat_index_data = np.loadtxt(heat_index_data, delimiter=',', skiprows=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "23f1e229-c1c5-4336-b33a-61d35b423e4c", + "metadata": {}, + "outputs": [], + "source": [ + "### Collect NCL `heat_index_nws` value and associated (temperature, relative humidity) values\n", + "ncl_heat_index = {}\n", + "temp_rh = tuple(map(tuple, heat_index_data[::, 0:2]))\n", + "heat_index_values = heat_index_data[::, 2]\n", + "ncl_heat_index = dict(zip(temp_rh, heat_index_values))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cb5cf657-2b96-4a76-8d85-c6bcface8f76", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/user/miniconda3/envs/wavelet_tutorial/lib/python3.11/site-packages/geocat/comp/meteorology.py:125: RuntimeWarning: invalid value encountered in sqrt\n", + " heatindex - ((13 - relative_humidity) / 4) * np.sqrt(\n" + ] + } + ], + "source": [ + "### Collect Temperature and Relative Humidity Pairs\n", + "temp_rh = []\n", + "for temp in range(0, 100 + 1):\n", + " for rh in range(1, 100 + 1):\n", + " temp_rh.append((temp, rh))\n", + "\n", + "### Calculate GeoCAT-Comp `heat_index_nws` value and temp/rh\n", + "from geocat.comp import heat_index\n", + "\n", + "geocat_heat_index = {}\n", + "\n", + "for i, pair in enumerate(temp_rh):\n", + " temp, rh = pair\n", + " if temp <= 79 and rh <= 40:\n", + " geocat_heat_index[pair] = heat_index(temp, rh, False)\n", + " else:\n", + " geocat_heat_index[pair] = heat_index(temp, rh, True)" + ] + }, { "cell_type": "markdown", "id": "3237a0bffc6827fc", @@ -194,10 +266,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "74362fd9-0e9f-4cf9-91da-08cd81be625c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(274.0, 94.0):\n", + "\t-0.0055542, -0.005570036309563875\n", + "\tDifference: 1.5836309563875377e-05\n", + "(275.0, 87.0):\n", + "\t-0.0839233, -0.08393959272956408\n", + "\tDifference: 1.6292729564076902e-05\n", + "(275.0, 88.0):\n", + "\t0.073761, 0.07374617196580857\n", + "\tDifference: 1.482803419142198e-05\n", + "(277.0, 76.0):\n", + "\t0.00259399, 0.0025903565796738803\n", + "\tDifference: 3.633420326119678e-06\n", + "(278.0, 71.0):\n", + "\t0.0261536, 0.026158530424595483\n", + "\tDifference: -4.930424595483984e-06\n", + "(279.0, 66.0):\n", + "\t-0.0278931, -0.027897415813697535\n", + "\tDifference: 4.3158136975342265e-06\n", + "(281.0, 58.0):\n", + "\t0.0712891, 0.07128017477202775\n", + "\tDifference: 8.925227972245153e-06\n", + "(282.0, 54.0):\n", + "\t0.0129395, 0.01291856405322278\n", + "\tDifference: 2.0935946777218828e-05\n", + "(283.0, 50.0):\n", + "\t-0.130432, -0.13044636060385528\n", + "\tDifference: 1.4360603855290144e-05\n", + "(283.0, 51.0):\n", + "\t0.144928, 0.14490829422311435\n", + "\tDifference: 1.9705776885647897e-05\n", + "(284.0, 47.0):\n", + "\t-0.0726318, -0.07262340593854333\n", + "\tDifference: -8.39406145666799e-06\n", + "(285.0, 44.0):\n", + "\t-0.0798035, -0.07982300528288988\n", + "\tDifference: 1.950528288988118e-05\n", + "(286.0, 41.0):\n", + "\t-0.160156, -0.1601734585760255\n", + "\tDifference: 1.7458576025503048e-05\n", + "(287.0, 39.0):\n", + "\t0.0386658, 0.0386467450997543\n", + "\tDifference: 1.905490024570189e-05\n", + "(290.0, 32.0):\n", + "\t-0.0795288, -0.07954541765832346\n", + "\tDifference: 1.6617658323461737e-05\n", + "(291.0, 30.0):\n", + "\t-0.115204, -0.11521555297190389\n", + "\tDifference: 1.1552971903888709e-05\n", + "(294.0, 25.0):\n", + "\t-0.110413, -0.11042499197856159\n", + "\tDifference: 1.1991978561595729e-05\n", + "(295.0, 24.0):\n", + "\t0.156677, 0.15665929836796977\n", + "\tDifference: 1.7701632030242553e-05\n", + "(297.0, 21.0):\n", + "\t-0.0614929, -0.06151243710161225\n", + "\tDifference: 1.953710161224642e-05\n", + "(299.0, 19.0):\n", + "\t0.162537, 0.16251872398265732\n", + "\tDifference: 1.8276017342666595e-05\n", + "(302.0, 16.0):\n", + "\t0.138275, 0.1382598702660971\n", + "\tDifference: 1.5129733902913278e-05\n", + "(303.0, 15.0):\n", + "\t0.0130615, 0.013067891707237322\n", + "\tDifference: -6.391707237322214e-06\n" + ] + } + ], "source": [ "import math\n", "\n", @@ -217,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "840d88ab-7ecc-485a-9aaf-d8de846196a9", "metadata": {}, "outputs": [], @@ -227,6 +372,4194 @@ " ncl_daylight[pair], geocat_daylight[pair].flatten()[0], rel_tol=1e-05\n", " ) # within 5 decimal points" ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "755febde-8c6f-4d44-9bd5-4ac3194b688d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(40.0, 1.0):\n", + "\t40.0, 36.8735\n", + "\tDifference: 3.1265\n", + "(40.0, 2.0):\n", + "\t40.0, 36.897000000000006\n", + "\tDifference: 3.1029999999999944\n", + "(40.0, 3.0):\n", + "\t40.0, 36.920500000000004\n", + "\tDifference: 3.079499999999996\n", + "(40.0, 4.0):\n", + "\t40.0, 36.944\n", + "\tDifference: 3.0559999999999974\n", + "(40.0, 5.0):\n", + "\t40.0, 36.9675\n", + "\tDifference: 3.032499999999999\n", + "(40.0, 6.0):\n", + "\t40.0, 36.991\n", + "\tDifference: 3.0090000000000003\n", + "(40.0, 7.0):\n", + "\t40.0, 37.0145\n", + "\tDifference: 2.985500000000002\n", + "(40.0, 8.0):\n", + "\t40.0, 37.038\n", + "\tDifference: 2.9620000000000033\n", + "(40.0, 9.0):\n", + "\t40.0, 37.0615\n", + "\tDifference: 2.9384999999999977\n", + "(40.0, 10.0):\n", + "\t40.0, 37.085\n", + "\tDifference: 2.914999999999999\n", + "(40.0, 11.0):\n", + "\t40.0, 37.10850000000001\n", + "\tDifference: 2.8914999999999935\n", + "(40.0, 12.0):\n", + "\t40.0, 37.132000000000005\n", + "\tDifference: 2.867999999999995\n", + "(40.0, 13.0):\n", + "\t40.0, 37.1555\n", + "\tDifference: 2.8444999999999965\n", + "(40.0, 14.0):\n", + "\t40.0, 37.179\n", + "\tDifference: 2.820999999999998\n", + "(40.0, 15.0):\n", + "\t40.0, 37.2025\n", + "\tDifference: 2.7974999999999994\n", + "(40.0, 16.0):\n", + "\t40.0, 37.226\n", + "\tDifference: 2.774000000000001\n", + "(40.0, 17.0):\n", + "\t40.0, 37.2495\n", + "\tDifference: 2.7505000000000024\n", + "(40.0, 18.0):\n", + "\t40.0, 37.272999999999996\n", + "\tDifference: 2.727000000000004\n", + "(40.0, 19.0):\n", + "\t40.0, 37.2965\n", + "\tDifference: 2.7034999999999982\n", + "(40.0, 20.0):\n", + "\t40.0, 37.32\n", + "\tDifference: 2.6799999999999997\n", + "(40.0, 21.0):\n", + "\t40.0, 37.343500000000006\n", + "\tDifference: 2.656499999999994\n", + "(40.0, 22.0):\n", + "\t40.0, 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112.97731356000013\n", + "\tDifference: -2.7393135600001273\n", + "(86.0, 99.0):\n", + "\t110.89, 113.63043438300008\n", + "\tDifference: -2.7404343830000784\n", + "(86.0, 100.0):\n", + "\t111.55, 114.28941489799998\n", + "\tDifference: -2.7394148979999784\n", + "(87.0, 40.0):\n", + "\t86.6329, 87.5009267990001\n", + "\tDifference: -0.8680267990000914\n", + "(87.0, 41.0):\n", + "\t86.8725, 87.82138738700006\n", + "\tDifference: -0.948887387000056\n", + "(87.0, 42.0):\n", + "\t87.1206, 88.14825757500007\n", + "\tDifference: -1.0276575750000774\n", + "(87.0, 43.0):\n", + "\t87.3774, 88.48153736300006\n", + "\tDifference: -1.1041373630000635\n", + "(87.0, 44.0):\n", + "\t87.6428, 88.82122675100007\n", + "\tDifference: -1.1784267510000745\n", + "(87.0, 45.0):\n", + "\t87.9169, 89.16732573900003\n", + "\tDifference: -1.2504257390000362\n", + "(87.0, 46.0):\n", + "\t88.1996, 89.51983432700007\n", + "\tDifference: -1.3202343270000654\n", + "(87.0, 47.0):\n", + "\t88.4909, 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0.0862949309998271\n", + "(90.0, 98.0):\n", + "\t129.596, 129.37740496400016\n", + "\tDifference: 0.2185950359998401\n", + "(90.0, 99.0):\n", + "\t130.61, 130.25516418300003\n", + "\tDifference: 0.3548358169999801\n", + "(90.0, 100.0):\n", + "\t131.636, 131.14098272600015\n", + "\tDifference: 0.4950172739998493\n", + "(91.0, 40.0):\n", + "\t92.1852, 93.94881688299998\n", + "\tDifference: -1.7636168829999832\n", + "(91.0, 41.0):\n", + "\t92.5621, 94.39183863500003\n", + "\tDifference: -1.8297386350000266\n", + "(91.0, 42.0):\n", + "\t92.9516, 94.84346961900002\n", + "\tDifference: -1.891869619000019\n", + "(91.0, 43.0):\n", + "\t93.3538, 95.30370983500006\n", + "\tDifference: -1.9499098350000565\n", + "(91.0, 44.0):\n", + "\t93.7685, 95.772559283\n", + "\tDifference: -2.0040592830000037\n", + "(91.0, 45.0):\n", + "\t94.196, 96.250017963\n", + "\tDifference: -2.054017963000007\n", + "(91.0, 46.0):\n", + "\t94.636, 96.73608587499999\n", + "\tDifference: -2.1000858749999907\n", + "(91.0, 47.0):\n", + "\t95.0886, 97.23076301900005\n", + "\tDifference: -2.1421630190000513\n", + "(91.0, 48.0):\n", + "\t95.5539, 97.73404939500008\n", + "\tDifference: -2.180149395000086\n", + "(91.0, 49.0):\n", + "\t96.0317, 98.24594500300003\n", + "\tDifference: -2.2142450030000305\n", + "(91.0, 50.0):\n", + "\t96.5222, 98.76644984299998\n", + "\tDifference: -2.2442498429999773\n", + "(91.0, 51.0):\n", + "\t97.0254, 99.29556391500005\n", + "\tDifference: -2.2701639150000403\n", + "(91.0, 52.0):\n", + "\t97.5411, 99.83328721900004\n", + "\tDifference: -2.2921872190000414\n", + "(91.0, 53.0):\n", + "\t98.0694, 100.37961975500004\n", + "\tDifference: -2.310219755000034\n", + "(91.0, 54.0):\n", + "\t98.6105, 100.93456152300003\n", + "\tDifference: -2.324061523000026\n", + "(91.0, 55.0):\n", + "\t99.164, 101.49811252300012\n", + "\tDifference: -2.3341125230001154\n", + "(91.0, 56.0):\n", + "\t99.7302, 102.07027275500005\n", + "\tDifference: -2.340072755000051\n", + "(91.0, 57.0):\n", + 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116.07437021900006\n", + "\tDifference: -1.5383702190000577\n", + "(91.0, 78.0):\n", + "\t115.38, 116.83593355500011\n", + "\tDifference: -1.4559335550001151\n", + "(91.0, 79.0):\n", + "\t116.237, 117.6061061230001\n", + "\tDifference: -1.3691061230001083\n", + "(91.0, 80.0):\n", + "\t117.106, 118.38488792300001\n", + "\tDifference: -1.2788879230000134\n", + "(91.0, 81.0):\n", + "\t117.988, 119.17227895500011\n", + "\tDifference: -1.18427895500011\n", + "(91.0, 82.0):\n", + "\t118.882, 119.96827921900012\n", + "\tDifference: -1.0862792190001187\n", + "(91.0, 83.0):\n", + "\t119.789, 120.77288871500005\n", + "\tDifference: -0.9838887150000488\n", + "(91.0, 84.0):\n", + "\t120.708, 121.586107443\n", + "\tDifference: -0.8781074430000047\n", + "(91.0, 85.0):\n", + "\t121.641, 122.40793540300012\n", + "\tDifference: -0.7669354030001188\n", + "(91.0, 86.0):\n", + "\t122.586, 123.23837259500007\n", + "\tDifference: -0.6523725950000738\n", + "(91.0, 87.0):\n", + "\t123.543, 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113.9745016890001\n", + "\tDifference: -1.8905016890001036\n", + "(93.0, 67.0):\n", + "\t112.921, 114.73949140700006\n", + "\tDifference: -1.818491407000053\n", + "(93.0, 68.0):\n", + "\t113.773, 115.514190173\n", + "\tDifference: -1.7411901730000068\n", + "(93.0, 69.0):\n", + "\t114.639, 116.29859798700018\n", + "\tDifference: -1.6595979870001827\n", + "(93.0, 70.0):\n", + "\t115.52, 117.0927148490001\n", + "\tDifference: -1.572714849000107\n", + "(93.0, 71.0):\n", + "\t116.416, 117.89654075900009\n", + "\tDifference: -1.4805407590000925\n", + "(93.0, 72.0):\n", + "\t117.326, 118.71007571700008\n", + "\tDifference: -1.384075717000087\n", + "(93.0, 73.0):\n", + "\t118.25, 119.53331972300006\n", + "\tDifference: -1.2833197230000621\n", + "(93.0, 74.0):\n", + "\t119.189, 120.36627277700003\n", + "\tDifference: -1.1772727770000415\n", + "(93.0, 75.0):\n", + "\t120.143, 121.20893487900011\n", + "\tDifference: -1.0659348790001104\n", + "(93.0, 76.0):\n", + "\t121.111, 122.0613060290001\n", 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2.388370890999994\n", + "(93.0, 97.0):\n", + "\t144.81, 142.2038902670001\n", + "\tDifference: 2.6061097329998972\n", + "(93.0, 98.0):\n", + "\t146.099, 143.26986047300008\n", + "\tDifference: 2.82913952699991\n", + "(93.0, 99.0):\n", + "\t147.402, 144.3455397270002\n", + "\tDifference: 3.0564602729998\n", + "(93.0, 100.0):\n", + "\t148.72, 145.43092802899992\n", + "\tDifference: 3.2890719710000837\n", + "(94.0, 40.0):\n", + "\t97.171, 99.34452959800004\n", + "\tDifference: -2.17352959800003\n", + "(94.0, 41.0):\n", + "\t97.6734, 99.89391346100004\n", + "\tDifference: -2.2205134610000385\n", + "(94.0, 42.0):\n", + "\t98.1913, 100.45355628000007\n", + "\tDifference: -2.2622562800000736\n", + "(94.0, 43.0):\n", + "\t98.7247, 101.02345805500009\n", + "\tDifference: -2.298758055000093\n", + "(94.0, 44.0):\n", + "\t99.2737, 101.60361878600008\n", + "\tDifference: -2.3299187860000785\n", + "(94.0, 45.0):\n", + "\t99.8381, 102.19403847300006\n", + "\tDifference: -2.355938473000066\n", + 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56.0):\n", + "\t107.072, 109.36574612600012\n", + "\tDifference: -2.2937461260001157\n", + "(94.0, 57.0):\n", + "\t107.823, 110.07927328500001\n", + "\tDifference: -2.2562732850000202\n", + "(94.0, 58.0):\n", + "\t108.59, 110.80305940000002\n", + "\tDifference: -2.21305940000002\n", + "(94.0, 59.0):\n", + "\t109.372, 111.53710447100002\n", + "\tDifference: -2.1651044710000207\n", + "(94.0, 60.0):\n", + "\t110.169, 112.28140849800008\n", + "\tDifference: -2.112408498000079\n", + "(94.0, 61.0):\n", + "\t110.982, 113.03597148100002\n", + "\tDifference: -2.053971481000019\n", + "(94.0, 62.0):\n", + "\t111.81, 113.80079342000006\n", + "\tDifference: -1.9907934200000597\n", + "(94.0, 63.0):\n", + "\t112.654, 114.575874315\n", + "\tDifference: -1.9218743149999966\n", + "(94.0, 64.0):\n", + "\t113.514, 115.36121416600002\n", + "\tDifference: -1.847214166000029\n", + "(94.0, 65.0):\n", + "\t114.389, 116.15681297300011\n", + "\tDifference: -1.7678129730001189\n", + "(94.0, 66.0):\n", + 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4.285659016999858\n", + "(95.0, 97.0):\n", + "\t156.557, 151.9932259790002\n", + "\tDifference: 4.5637740209998015\n", + "(95.0, 98.0):\n", + "\t158.045, 153.19691983900012\n", + "\tDifference: 4.848080160999871\n", + "(95.0, 99.0):\n", + "\t159.55, 154.41142256300014\n", + "\tDifference: 5.138577436999867\n", + "(95.0, 100.0):\n", + "\t161.071, 155.6367341510002\n", + "\tDifference: 5.434265848999786\n", + "(96.0, 40.0):\n", + "\t100.886, 103.20824052800006\n", + "\tDifference: -2.3222405280000658\n", + "(96.0, 41.0):\n", + "\t101.483, 103.8354092450001\n", + "\tDifference: -2.3524092450000893\n", + "(96.0, 42.0):\n", + "\t102.097, 104.47393673400012\n", + "\tDifference: -2.3769367340001253\n", + "(96.0, 43.0):\n", + "\t102.729, 105.12382299500004\n", + "\tDifference: -2.394822995000041\n", + "(96.0, 44.0):\n", + "\t103.378, 105.78506802800013\n", + "\tDifference: -2.4070680280001255\n", + "(96.0, 45.0):\n", + "\t104.044, 106.45767183300008\n", + "\tDifference: -2.4136718330000804\n", 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56.0):\n", + "\t112.525, 114.60599264000008\n", + "\tDifference: -2.080992640000076\n", + "(96.0, 57.0):\n", + "\t113.4, 115.414901709\n", + "\tDifference: -2.014901709\n", + "(96.0, 58.0):\n", + "\t114.293, 116.23516955000005\n", + "\tDifference: -1.9421695500000453\n", + "(96.0, 59.0):\n", + "\t115.203, 117.06679616300009\n", + "\tDifference: -1.8637961630000888\n", + "(96.0, 60.0):\n", + "\t116.131, 117.90978154800001\n", + "\tDifference: -1.778781548000012\n", + "(96.0, 61.0):\n", + "\t117.077, 118.76412570500015\n", + "\tDifference: -1.687125705000156\n", + "(96.0, 62.0):\n", + "\t118.039, 119.62982863400006\n", + "\tDifference: -1.5908286340000615\n", + "(96.0, 63.0):\n", + "\t119.019, 120.50689033500008\n", + "\tDifference: -1.487890335000074\n", + "(96.0, 64.0):\n", + "\t120.017, 121.39531080800009\n", + "\tDifference: -1.3783108080000943\n", + "(96.0, 65.0):\n", + "\t121.032, 122.29509005299998\n", + "\tDifference: -1.263090052999985\n", + "(96.0, 66.0):\n", + "\t122.064, 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5.316802439999918\n", + "(96.0, 97.0):\n", + "\t162.712, 157.08545750899995\n", + "\tDifference: 5.626542491000038\n", + "(96.0, 98.0):\n", + "\t164.302, 158.36007623000015\n", + "\tDifference: 5.941923769999846\n", + "(96.0, 99.0):\n", + "\t165.909, 159.6460537230002\n", + "\tDifference: 6.262946276999799\n", + "(96.0, 100.0):\n", + "\t167.534, 160.9433899880002\n", + "\tDifference: 6.590610011999786\n", + "(97.0, 40.0):\n", + "\t102.861, 105.22006672900008\n", + "\tDifference: -2.3590667290000766\n", + "(97.0, 41.0):\n", + "\t103.508, 105.88819090700008\n", + "\tDifference: -2.3801909070000846\n", + "(97.0, 42.0):\n", + "\t104.174, 106.56822376500003\n", + "\tDifference: -2.394223765000021\n", + "(97.0, 43.0):\n", + "\t104.857, 107.26016530300012\n", + "\tDifference: -2.4031653030001223\n", + "(97.0, 44.0):\n", + "\t105.559, 107.96401552100005\n", + "\tDifference: -2.4050155210000526\n", + "(97.0, 45.0):\n", + "\t106.28, 108.6797744190001\n", + "\tDifference: -2.399774419000096\n", + 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120.14751723400019\n", + "\tDifference: -1.7255172340001934\n", + "(98.0, 57.0):\n", + "\t119.431, 121.05730963700009\n", + "\tDifference: -1.626309637000091\n", + "(98.0, 58.0):\n", + "\t120.458, 121.97956062800014\n", + "\tDifference: -1.5215606280001452\n", + "(98.0, 59.0):\n", + "\t121.505, 122.91427020700014\n", + "\tDifference: -1.409270207000148\n", + "(98.0, 60.0):\n", + "\t122.571, 123.86143837400014\n", + "\tDifference: -1.2904383740001464\n", + "(98.0, 61.0):\n", + "\t123.657, 124.82106512900015\n", + "\tDifference: -1.16406512900015\n", + "(98.0, 62.0):\n", + "\t124.761, 125.79315047200018\n", + "\tDifference: -1.0321504720001826\n", + "(98.0, 63.0):\n", + "\t125.885, 126.77769440300017\n", + "\tDifference: -0.8926944030001636\n", + "(98.0, 64.0):\n", + "\t127.029, 127.77469692200015\n", + "\tDifference: -0.7456969220001497\n", + "(98.0, 65.0):\n", + "\t128.191, 128.78415802900014\n", + "\tDifference: -0.5931580290001364\n", + "(98.0, 66.0):\n", + "\t129.373, 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7.5450880059998156\n", + "(98.0, 97.0):\n", + "\t175.582, 167.6650479170002\n", + "\tDifference: 7.916952082999785\n", + "(98.0, 98.0):\n", + "\t177.382, 169.08564242800026\n", + "\tDifference: 8.29635757199975\n", + "(98.0, 99.0):\n", + "\t179.2, 170.5186955270002\n", + "\tDifference: 8.681304472999784\n", + "(98.0, 100.0):\n", + "\t181.038, 171.9642072140001\n", + "\tDifference: 9.073792785999899\n", + "(99.0, 40.0):\n", + "\t107.046, 109.40366060300002\n", + "\tDifference: -2.3576606030000136\n", + "(99.0, 41.0):\n", + "\t107.8, 110.157821771\n", + "\tDifference: -2.3578217710000047\n", + "(99.0, 42.0):\n", + "\t108.574, 110.92499143499998\n", + "\tDifference: -2.350991434999983\n", + "(99.0, 43.0):\n", + "\t109.369, 111.70516959500007\n", + "\tDifference: -2.3361695950000723\n", + "(99.0, 44.0):\n", + "\t110.184, 112.49835625100005\n", + "\tDifference: -2.3143562510000493\n", + "(99.0, 45.0):\n", + "\t111.019, 113.30455140299995\n", + "\tDifference: -2.2855514029999426\n", + 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+ "(99.0, 87.0):\n", + "\t164.346, 158.91141967500002\n", + "\tDifference: 5.434580324999985\n", + "(99.0, 88.0):\n", + "\t166.05, 160.27698015499993\n", + "\tDifference: 5.7730198450000785\n", + "(99.0, 89.0):\n", + "\t167.774, 161.65554913100004\n", + "\tDifference: 6.1184508689999575\n", + "(99.0, 90.0):\n", + "\t169.519, 163.04712660299995\n", + "\tDifference: 6.471873397000053\n", + "(99.0, 91.0):\n", + "\t171.284, 164.45171257099994\n", + "\tDifference: 6.832287429000047\n", + "(99.0, 92.0):\n", + "\t173.069, 165.86930703500008\n", + "\tDifference: 7.199692964999912\n", + "(99.0, 93.0):\n", + "\t174.875, 167.29990999500006\n", + "\tDifference: 7.575090004999936\n", + "(99.0, 94.0):\n", + "\t176.7, 168.74352145099982\n", + "\tDifference: 7.956478549000167\n", + "(99.0, 95.0):\n", + "\t178.546, 170.20014140300006\n", + "\tDifference: 8.345858596999932\n", + "(99.0, 96.0):\n", + "\t180.412, 171.66976985100004\n", + "\tDifference: 8.742230148999965\n", + "(99.0, 97.0):\n", + "\t182.299, 173.1524067950001\n", + "\tDifference: 9.146593204999903\n", + "(99.0, 98.0):\n", + "\t184.205, 174.64805223500008\n", + "\tDifference: 9.556947764999933\n", + "(99.0, 99.0):\n", + "\t186.132, 176.15670617099994\n", + "\tDifference: 9.975293829000066\n", + "(99.0, 100.0):\n", + "\t188.079, 177.67836860300002\n", + "\tDifference: 10.400631396999984\n", + "(100.0, 40.0):\n", + "\t109.256, 111.57542827599994\n", + "\tDifference: -2.3194282759999396\n", + "(100.0, 41.0):\n", + "\t110.067, 112.37467097300006\n", + "\tDifference: -2.3076709730000715\n", + "(100.0, 42.0):\n", + "\t110.899, 113.18747207400003\n", + "\tDifference: -2.288472074000026\n", + "(100.0, 43.0):\n", + "\t111.752, 114.01383157900003\n", + "\tDifference: -2.261831579000031\n", + "(100.0, 44.0):\n", + "\t112.626, 114.853749488\n", + "\tDifference: -2.2277494880000006\n", + "(100.0, 45.0):\n", + "\t113.522, 115.70722580099995\n", + "\tDifference: -2.1852258009999446\n", + "(100.0, 46.0):\n", + "\t114.438, 116.57426051800002\n", + "\tDifference: -2.1362605180000145\n", + "(100.0, 47.0):\n", + "\t115.376, 117.45485363899998\n", + "\tDifference: -2.0788536389999734\n", + "(100.0, 48.0):\n", + "\t116.335, 118.34900516399998\n", + "\tDifference: -2.0140051639999825\n", + "(100.0, 49.0):\n", + "\t117.315, 119.25671509299998\n", + "\tDifference: -1.941715092999985\n", + "(100.0, 50.0):\n", + "\t118.316, 120.17798342599993\n", + "\tDifference: -1.8619834259999237\n", + "(100.0, 51.0):\n", + "\t119.338, 121.11281016299998\n", + "\tDifference: -1.7748101629999837\n", + "(100.0, 52.0):\n", + "\t120.381, 122.06119530399991\n", + "\tDifference: -1.6801953039999091\n", + "(100.0, 53.0):\n", + "\t121.446, 123.02313884899995\n", + "\tDifference: -1.577138848999951\n", + "(100.0, 54.0):\n", + "\t122.532, 123.99864079800004\n", + "\tDifference: -1.4666407980000429\n", + "(100.0, 55.0):\n", + "\t123.638, 124.98770115100007\n", + "\tDifference: -1.3497011510000618\n", + "(100.0, 56.0):\n", + "\t124.766, 125.99031990800015\n", + "\tDifference: -1.2243199080001403\n", + "(100.0, 57.0):\n", + "\t125.915, 127.00649706900005\n", + "\tDifference: -1.0914970690000416\n", + "(100.0, 58.0):\n", + "\t127.085, 128.03623263400007\n", + "\tDifference: -0.9512326340000783\n", + "(100.0, 59.0):\n", + "\t128.277, 129.0795266030001\n", + "\tDifference: -0.8025266030001035\n", + "(100.0, 60.0):\n", + "\t129.489, 130.1363789760001\n", + "\tDifference: -0.6473789760000841\n", + "(100.0, 61.0):\n", + "\t130.723, 131.20678975300007\n", + "\tDifference: -0.4837897530000532\n", + "(100.0, 62.0):\n", + "\t131.977, 132.29075893400002\n", + "\tDifference: -0.3137589340000204\n", + "(100.0, 63.0):\n", + "\t133.253, 133.38828651900002\n", + "\tDifference: -0.13528651900003297\n", + "(100.0, 64.0):\n", + "\t134.55, 134.49937250800002\n", + "\tDifference: 0.050627491999989616\n", + "(100.0, 65.0):\n", + "\t135.868, 135.624016901\n", + "\tDifference: 0.2439830989999905\n", + "(100.0, 66.0):\n", + "\t137.207, 136.76221969800002\n", + "\tDifference: 0.44478030199996965\n", + "(100.0, 67.0):\n", + "\t138.567, 137.91398089899994\n", + "\tDifference: 0.6530191010000692\n", + "(100.0, 68.0):\n", + "\t139.949, 139.07930050399997\n", + "\tDifference: 0.8696994960000382\n", + "(100.0, 69.0):\n", + "\t141.352, 140.258178513\n", + "\tDifference: 1.0938214870000138\n", + "(100.0, 70.0):\n", + "\t142.775, 141.45061492600001\n", + "\tDifference: 1.3243850739999914\n", + "(100.0, 71.0):\n", + "\t144.22, 142.65660974299996\n", + "\tDifference: 1.5633902570000373\n", + "(100.0, 72.0):\n", + "\t145.686, 143.87616296399992\n", + "\tDifference: 1.80983703600009\n", + "(100.0, 73.0):\n", + "\t147.173, 145.10927458899997\n", + "\tDifference: 2.0637254110000356\n", + "(100.0, 74.0):\n", + "\t148.681, 146.35594461799997\n", + "\tDifference: 2.325055382000045\n", + "(100.0, 75.0):\n", + "\t150.211, 147.61617305100006\n", + "\tDifference: 2.594826948999952\n", + "(100.0, 76.0):\n", + "\t151.761, 148.889959888\n", + "\tDifference: 2.871040112000003\n", + "(100.0, 77.0):\n", + "\t153.333, 150.17730512900005\n", + "\tDifference: 3.1556948709999517\n", + "(100.0, 78.0):\n", + "\t154.925, 151.47820877399997\n", + "\tDifference: 3.4467912260000446\n", + "(100.0, 79.0):\n", + "\t156.539, 152.79267082300012\n", + "\tDifference: 3.746329176999865\n", + "(100.0, 80.0):\n", + "\t158.174, 154.12069127600003\n", + "\tDifference: 4.053308723999976\n", + "(100.0, 81.0):\n", + "\t159.831, 155.46227013300003\n", + "\tDifference: 4.368729866999956\n", + "(100.0, 82.0):\n", + "\t161.508, 156.81740739400007\n", + "\tDifference: 4.690592605999939\n", + "(100.0, 83.0):\n", + "\t163.206, 158.18610305899995\n", + "\tDifference: 5.019896941000042\n", + "(100.0, 84.0):\n", + "\t164.926, 159.5683571280001\n", + "\tDifference: 5.3576428719999\n", + "(100.0, 85.0):\n", + "\t166.666, 160.96416960099998\n", + "\tDifference: 5.701830399000016\n", + "(100.0, 86.0):\n", + "\t168.428, 162.37354047800008\n", + "\tDifference: 6.054459521999917\n", + "(100.0, 87.0):\n", + "\t170.211, 163.79646975899996\n", + "\tDifference: 6.414530241000051\n", + "(100.0, 88.0):\n", + "\t172.015, 165.23295744400005\n", + "\tDifference: 6.782042555999936\n", + "(100.0, 89.0):\n", + "\t173.84, 166.68300353299992\n", + "\tDifference: 7.156996467000084\n", + "(100.0, 90.0):\n", + "\t175.687, 168.1466080259999\n", + "\tDifference: 7.5403919740001015\n", + "(100.0, 91.0):\n", + "\t177.554, 169.62377092299988\n", + "\tDifference: 7.930229077000121\n", + "(100.0, 92.0):\n", + "\t179.442, 171.1144922240001\n", + "\tDifference: 8.32750777599992\n", + "(100.0, 93.0):\n", + "\t181.352, 172.61877192899993\n", + "\tDifference: 8.73322807100007\n", + "(100.0, 94.0):\n", + "\t183.283, 174.1366100379999\n", + "\tDifference: 9.146389962000086\n", + "(100.0, 95.0):\n", + "\t185.235, 175.66800655099996\n", + "\tDifference: 9.566993449000051\n", + "(100.0, 96.0):\n", + "\t187.208, 177.21296146800003\n", + "\tDifference: 9.995038531999967\n", + "(100.0, 97.0):\n", + "\t189.202, 178.77147478899997\n", + "\tDifference: 10.430525211000031\n", + "(100.0, 98.0):\n", + "\t191.217, 180.3435465139999\n", + "\tDifference: 10.873453486000102\n", + "(100.0, 99.0):\n", + "\t193.254, 181.92917664299995\n", + "\tDifference: 11.324823357000042\n", + "(100.0, 100.0):\n", + "\t195.312, 183.5283651759998\n", + "\tDifference: 11.783634824000217\n" + ] + } + ], + "source": [ + "for pair in ncl_heat_index.keys():\n", + " try:\n", + " assert math.isclose(\n", + " ncl_heat_index[pair], geocat_heat_index[pair].flatten()[0], rel_tol=1e-05\n", + " ) # within 5 decimal points\n", + " except Exception:\n", + " print(f\"{pair}:\")\n", + " print(f\"\\t{ncl_heat_index[pair]}, {geocat_heat_index[pair].flatten()[0]}\")\n", + " print(\n", + " f\"\\tDifference: {ncl_heat_index[pair] - geocat_heat_index[pair].flatten()[0]}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e9d9048-a69c-4025-bb1c-5c1edcb913c5", + "metadata": {}, + "outputs": [], + "source": [ + "ncl_heat_index.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ca33016-1d57-4954-b033-1d3edead0522", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From e84452d0bfa20ecb3163d0ba3586622f7072dd47 Mon Sep 17 00:00:00 2001 From: cyschneck <22159116+cyschneck@users.noreply.github.com> Date: Tue, 22 Oct 2024 15:42:51 -0600 Subject: [PATCH 3/8] heat_index in index --- ncl/ncl_index/ncl-index-table.csv | 1 + ncl/receipts/meteorology.ipynb | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/ncl/ncl_index/ncl-index-table.csv b/ncl/ncl_index/ncl-index-table.csv index fbc3f904..d52445e5 100644 --- a/ncl/ncl_index/ncl-index-table.csv +++ b/ncl/ncl_index/ncl-index-table.csv @@ -45,3 +45,4 @@ NCL Function,Description,Python Equivalent,Notes `round `__,"Rounds a float or double variable to the nearest whole number","``round()`` or ``numpy.round()``",`example notebook <../ncl_entries/general_applied_math.ipynb#decimalplaces-round>`__ `daylight_fao56 `__," Compute maximum number of daylight hours as described in FAO 56","``geocat.comp.meteorology.max_daylight()``",`example notebook <../ncl_entries/meteorology.ipynb#daylight-fao56>`__ `dewtemp_trh `__,"Calculates the dew point temperature given temperature and relative humidity","``geocat.comp.dewtemp()``",`example notebook <../ncl_entries/meteorology.ipynb#dewtemp-trh>`__ +`heat_index_nws `__,"Compute the 'heat index' as calculated by the National Weather Service","``geocat.comp.heat_index()``",`example notebook <../ncl_entries/meteorology.ipynb#heat-index-nws>`__ diff --git a/ncl/receipts/meteorology.ipynb b/ncl/receipts/meteorology.ipynb index a29d7568..3882626b 100644 --- a/ncl/receipts/meteorology.ipynb +++ b/ncl/receipts/meteorology.ipynb @@ -42,7 +42,7 @@ "id": "3d70616a8934f0fb", "metadata": {}, "source": [ - "```{literalinclude} ../ncl_raw/Meteorology.ncl\n", + "```{literalinclude} ../ncl_raw/meteorology.ncl\n", "\n", "```" ] From 55df7acb35e1878476e2396a31936a6e3de366d6 Mon Sep 17 00:00:00 2001 From: cyschneck <22159116+cyschneck@users.noreply.github.com> Date: Thu, 24 Oct 2024 16:04:52 -0600 Subject: [PATCH 4/8] compare to heat_index new output --- ncl/ncl_entries/heat_index_nws_output.txt | 5 + ncl/ncl_entries/meteorology.ipynb | 61 +- ncl/receipts/heat_index_nws.ncl | 18 + ncl/receipts/heat_index_nws_output.txt | 23206 ++++++++++++++++++++ ncl/receipts/meteorology.ipynb | 4353 +--- references.bib | 8 + 6 files changed, 23467 insertions(+), 4184 deletions(-) create mode 100644 ncl/ncl_entries/heat_index_nws_output.txt create mode 100644 ncl/receipts/heat_index_nws.ncl create mode 100644 ncl/receipts/heat_index_nws_output.txt diff --git a/ncl/ncl_entries/heat_index_nws_output.txt b/ncl/ncl_entries/heat_index_nws_output.txt new file mode 100644 index 00000000..88a1f393 --- /dev/null +++ b/ncl/ncl_entries/heat_index_nws_output.txt @@ -0,0 +1,5 @@ + Copyright (C) 1995-2019 - All Rights Reserved + University Corporation for Atmospheric Research + NCAR Command Language Version 6.6.2 + The use of this software is governed by a License Agreement. + See http://www.ncl.ucar.edu/ for more details. diff --git a/ncl/ncl_entries/meteorology.ipynb b/ncl/ncl_entries/meteorology.ipynb index 1aa31ec5..0cff22b9 100644 --- a/ncl/ncl_entries/meteorology.ipynb +++ b/ncl/ncl_entries/meteorology.ipynb @@ -123,7 +123,13 @@ "source": [ "## heat_index_nws\n", "\n", - "NCL's `heat_index_nws` computes the heat index as calculated by the National Weather Service{footcite}`nws_heat_index`" + "NCL's `heat_index_nws` computes the heat index as calculated by the National Weather Service{footcite}`nws_heat_index`\n", + "\n", + "Default coefficients when temperature if greater 80°F and relative humidity is greater than 40%\n", + "\n", + "Alternate coefficients when temperature is between 70°F and 115°F and relative humidity less than 80%\n", + "\n", + "The equation for the heat index to match the NOAA National Weather Service table is derived from regression analysis, so the default coefficients have a degree of error up to ±1.3°F{footcite}`rothfusz_heat_index`" ] }, { @@ -135,10 +141,55 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([85.3104587])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Input: Single Value\n", + "from geocat.comp import heat_index\n", + "\n", + "temp = 85 # Degrees Fahrenheit\n", + "rh = 45 # %\n", + "\n", + "heat_index(temp, rh)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([137.36135724, 135.8679973 , 85.3104587 ])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Input: List/Array\n", + "from geocat.comp import heat_index\n", + "\n", + "temps = [104, 100, 85] # Degrees Fahrenheit\n", + "rhs = [55, 65, 45] # %\n", + "\n", + "heat_index(temps, rhs)" + ] }, { "cell_type": "markdown", diff --git a/ncl/receipts/heat_index_nws.ncl b/ncl/receipts/heat_index_nws.ncl new file mode 100644 index 00000000..d1ccc8c7 --- /dev/null +++ b/ncl/receipts/heat_index_nws.ncl @@ -0,0 +1,18 @@ +print("Temperature (F), Relative Humidity (%), Alternate Coefs (True/False), Heat Index (F)") +do tf=80,115 + do rh=40,100 + begin + heatindex = heat_index_nws(tf,rh,(/2,2/), False) + print (tf +","+ rh +"," + "0" + "," + heatindex) + end + end do +end do + +do tf=70,115 + do rh=1,80 + begin + heatindex = heat_index_nws(tf,rh,(/2,2/), True) + print (tf +","+ rh +"," + "1" + "," + heatindex) + end + end do +end do diff --git a/ncl/receipts/heat_index_nws_output.txt b/ncl/receipts/heat_index_nws_output.txt new file mode 100644 index 00000000..62268b6b --- /dev/null +++ b/ncl/receipts/heat_index_nws_output.txt @@ -0,0 +1,23206 @@ + Copyright (C) 1995-2019 - All Rights Reserved + University Corporation for Atmospheric Research + NCAR Command Language Version 6.6.2 + The use of this software is governed by a License Agreement. + See http://www.ncl.ucar.edu/ for more details. +Temperature (F), Relative Humidity (%), Alternate Coefs (True/False), Heat Index (F) +0,1,0,0 +0,2,0,0 +0,3,0,0 +0,4,0,0 +0,5,0,0 +0,6,0,0 +0,7,0,0 +0,8,0,0 +0,9,0,0 +0,10,0,0 +0,11,0,0 +0,12,0,0 +0,13,0,0 +0,14,0,0 +0,15,0,0 +0,16,0,0 +0,17,0,0 +0,18,0,0 +0,19,0,0 +0,20,0,0 +0,21,0,0 +0,22,0,0 +0,23,0,0 +0,24,0,0 +0,25,0,0 +0,26,0,0 +0,27,0,0 +0,28,0,0 +0,29,0,0 +0,30,0,0 +0,31,0,0 +0,32,0,0 +0,33,0,0 +0,34,0,0 +0,35,0,0 +0,36,0,0 +0,37,0,0 +0,38,0,0 +0,39,0,0 +0,40,0,0 +0,41,0,0 +0,42,0,0 +0,43,0,0 +0,44,0,0 +0,45,0,0 +0,46,0,0 +0,47,0,0 +0,48,0,0 +0,49,0,0 +0,50,0,0 +0,51,0,0 +0,52,0,0 +0,53,0,0 +0,54,0,0 +0,55,0,0 +0,56,0,0 +0,57,0,0 +0,58,0,0 +0,59,0,0 +0,60,0,0 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+115,99,1,322.958 +115,100,1,326.876 diff --git a/ncl/receipts/meteorology.ipynb b/ncl/receipts/meteorology.ipynb index 3882626b..87c8d551 100644 --- a/ncl/receipts/meteorology.ipynb +++ b/ncl/receipts/meteorology.ipynb @@ -181,7 +181,7 @@ "\n", "for i, pair in enumerate(doy_lat):\n", " doy, lat = pair\n", - " geocat_daylight[pair] = max_daylight(doy, lat)" + " geocat_daylight[pair] = max_daylight(doy, lat).flatten()[0]" ] }, { @@ -203,8 +203,8 @@ "import geocat.datafiles as gdf\n", "import numpy as np\n", "\n", - "heat_index_data = gdf.get('applications_files/ncl_outputs/heat_index_nws_output.txt')\n", - "heat_index_data = np.loadtxt(heat_index_data, delimiter=',', skiprows=6)" + "# heat_index_data = gdf.get('applications_files/ncl_outputs/heat_index_nws_output.txt')\n", + "heat_index_data = np.loadtxt(\"heat_index_nws_output.txt\", delimiter=',', skiprows=6)" ] }, { @@ -214,11 +214,11 @@ "metadata": {}, "outputs": [], "source": [ - "### Collect NCL `heat_index_nws` value and associated (temperature, relative humidity) values\n", + "### Collect NCL `heat_index_nws` value and associated (temperature, relative humidity, alternate_coeffs) values\n", "ncl_heat_index = {}\n", - "temp_rh = tuple(map(tuple, heat_index_data[::, 0:2]))\n", - "heat_index_values = heat_index_data[::, 2]\n", - "ncl_heat_index = dict(zip(temp_rh, heat_index_values))" + "temp_rh_coefs = tuple(map(tuple, heat_index_data[::, 0:3]))\n", + "heat_index_values = heat_index_data[::, 3]\n", + "ncl_heat_index = dict(zip(temp_rh_coefs, heat_index_values))" ] }, { @@ -237,23 +237,14 @@ } ], "source": [ - "### Collect Temperature and Relative Humidity Pairs\n", - "temp_rh = []\n", - "for temp in range(0, 100 + 1):\n", - " for rh in range(1, 100 + 1):\n", - " temp_rh.append((temp, rh))\n", - "\n", "### Calculate GeoCAT-Comp `heat_index_nws` value and temp/rh\n", "from geocat.comp import heat_index\n", "\n", "geocat_heat_index = {}\n", "\n", - "for i, pair in enumerate(temp_rh):\n", - " temp, rh = pair\n", - " if temp <= 79 and rh <= 40:\n", - " geocat_heat_index[pair] = heat_index(temp, rh, False)\n", - " else:\n", - " geocat_heat_index[pair] = heat_index(temp, rh, True)" + "for key in ncl_heat_index.keys():\n", + " temp, rh, alt_coeffs = key\n", + " geocat_heat_index[key] = heat_index(temp, rh, alt_coeffs).flatten()[0]" ] }, { @@ -264,6 +255,14 @@ "## Comparison" ] }, + { + "cell_type": "markdown", + "id": "6f6980b5-a66c-4342-8ea6-961d1f969e0f", + "metadata": {}, + "source": [ + "### dewtemp_trh" + ] + }, { "cell_type": "code", "execution_count": 10, @@ -360,6 +359,14 @@ " print(f\"\\tDifference: {ncl_dewtemp[pair] - geocat_dewtemp[pair]}\")" ] }, + { + "cell_type": "markdown", + "id": "25867765-97dc-4040-9a8f-16bd18b0160d", + "metadata": {}, + "source": [ + "### daylight_fao56" + ] + }, { "cell_type": "code", "execution_count": 11, @@ -369,4194 +376,182 @@ "source": [ "for pair in ncl_daylight.keys():\n", " assert math.isclose(\n", - " ncl_daylight[pair], geocat_daylight[pair].flatten()[0], rel_tol=1e-05\n", + " ncl_daylight[pair], geocat_daylight[pair], rel_tol=1e-05\n", " ) # within 5 decimal points" ] }, + { + "cell_type": "markdown", + "id": "021edbb4-68ee-4e79-8054-77b0a9d3aa38", + "metadata": {}, + "source": [ + "### heat_index_nws" + ] + }, { "cell_type": "code", - "execution_count": 20, - "id": "755febde-8c6f-4d44-9bd5-4ac3194b688d", + "execution_count": 12, + "id": "df9425e3-0c44-46b6-97ab-ce01a4d97dc1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(40.0, 1.0):\n", - "\t40.0, 36.8735\n", - "\tDifference: 3.1265\n", - "(40.0, 2.0):\n", - "\t40.0, 36.897000000000006\n", - "\tDifference: 3.1029999999999944\n", - "(40.0, 3.0):\n", - "\t40.0, 36.920500000000004\n", - "\tDifference: 3.079499999999996\n", - "(40.0, 4.0):\n", - "\t40.0, 36.944\n", - "\tDifference: 3.0559999999999974\n", - "(40.0, 5.0):\n", - "\t40.0, 36.9675\n", - "\tDifference: 3.032499999999999\n", - "(40.0, 6.0):\n", - 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84.0):\n", - "\t105.338, 107.66467427100014\n", - "\tDifference: -2.326674271000144\n", - "(87.0, 85.0):\n", - "\t105.957, 108.26715725900013\n", - "\tDifference: -2.3101572590001354\n", - "(87.0, 86.0):\n", - "\t106.585, 108.8760498470001\n", - "\tDifference: -2.29104984700011\n", - "(87.0, 87.0):\n", - "\t107.222, 109.4913520350002\n", - "\tDifference: -2.2693520350002103\n", - "(87.0, 88.0):\n", - "\t107.867, 110.11306382300012\n", - "\tDifference: -2.2460638230001138\n", - "(87.0, 89.0):\n", - "\t108.521, 110.74118521100019\n", - "\tDifference: -2.220185211000185\n", - "(87.0, 90.0):\n", - "\t109.183, 111.37571619900004\n", - "\tDifference: -2.1927161990000315\n", - "(87.0, 91.0):\n", - "\t109.854, 112.01665678700004\n", - "\tDifference: -2.1626567870000457\n", - "(87.0, 92.0):\n", - "\t110.534, 112.66400697500023\n", - "\tDifference: -2.130006975000228\n", - "(87.0, 93.0):\n", - "\t111.223, 113.31776676300018\n", - "\tDifference: -2.0947667630001803\n", - "(87.0, 94.0):\n", - 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74.0):\n", - "\t102.515, 104.80449930200004\n", - "\tDifference: -2.2894993020000385\n", - "(88.0, 75.0):\n", - "\t103.114, 105.39016041900007\n", - "\tDifference: -2.276160419000064\n", - "(88.0, 76.0):\n", - "\t103.722, 105.98278104400006\n", - "\tDifference: -2.260781044000069\n", - "(88.0, 77.0):\n", - "\t104.339, 106.58236117700008\n", - "\tDifference: -2.2433611770000823\n", - "(88.0, 78.0):\n", - "\t104.967, 107.18890081800004\n", - "\tDifference: -2.2219008180000372\n", - "(88.0, 79.0):\n", - "\t105.604, 107.80239996700007\n", - "\tDifference: -2.1983999670000713\n", - "(88.0, 80.0):\n", - "\t106.25, 108.42285862400001\n", - "\tDifference: -2.172858624000014\n", - "(88.0, 81.0):\n", - "\t106.907, 109.05027678900004\n", - "\tDifference: -2.1432767890000406\n", - "(88.0, 82.0):\n", - "\t107.573, 109.68465446200008\n", - "\tDifference: -2.1116544620000894\n", - "(88.0, 83.0):\n", - "\t108.248, 110.32599164300004\n", - "\tDifference: -2.0779916430000327\n", - "(88.0, 84.0):\n", - 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74.0):\n", - "\t108.746, 110.72898090799998\n", - "\tDifference: -1.9829809079999876\n", - "(90.0, 75.0):\n", - "\t109.48, 111.4133163510001\n", - "\tDifference: -1.9333163510001015\n", - "(90.0, 76.0):\n", - "\t110.227, 112.10571111800002\n", - "\tDifference: -1.8787111180000124\n", - "(90.0, 77.0):\n", - "\t110.985, 112.80616520900011\n", - "\tDifference: -1.8211652090001138\n", - "(90.0, 78.0):\n", - "\t111.755, 113.51467862400011\n", - "\tDifference: -1.7596786240001165\n", - "(90.0, 79.0):\n", - "\t112.537, 114.23125136300007\n", - "\tDifference: -1.6942513630000633\n", - "(90.0, 80.0):\n", - "\t113.33, 114.9558834260001\n", - "\tDifference: -1.625883426000101\n", - "(90.0, 81.0):\n", - "\t114.135, 115.68857481300003\n", - "\tDifference: -1.5535748130000258\n", - "(90.0, 82.0):\n", - "\t114.951, 116.42932552400009\n", - "\tDifference: -1.4783255240000983\n", - "(90.0, 83.0):\n", - "\t115.779, 117.17813555900011\n", - "\tDifference: -1.3991355590001149\n", - "(90.0, 84.0):\n", - 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0.2605085649998671\n", - "(91.0, 94.0):\n", - "\t130.599, 130.1918024830001\n", - "\tDifference: 0.4071975169999007\n", - "(91.0, 95.0):\n", - "\t131.657, 131.09972276299996\n", - "\tDifference: 0.5572772370000507\n", - "(91.0, 96.0):\n", - "\t132.728, 132.01625227500014\n", - "\tDifference: 0.7117477249998672\n", - "(91.0, 97.0):\n", - "\t133.812, 132.94139101899998\n", - "\tDifference: 0.8706089810000321\n", - "(91.0, 98.0):\n", - "\t134.908, 133.87513899500004\n", - "\tDifference: 1.032861004999944\n", - "(91.0, 99.0):\n", - "\t136.017, 134.8174962030001\n", - "\tDifference: 1.1995037969998918\n", - "(91.0, 100.0):\n", - "\t137.138, 135.76846264299994\n", - "\tDifference: 1.3695373570000697\n", - "(92.0, 40.0):\n", - "\t93.7688, 95.694073964\n", - "\tDifference: -1.9252739639999987\n", - "(92.0, 41.0):\n", - "\t94.1854, 96.17117439699999\n", - "\tDifference: -1.985774396999986\n", - "(92.0, 42.0):\n", - "\t94.6156, 96.65743397000006\n", - "\tDifference: -2.041833970000056\n", - 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0.9843081669999663\n", - "(92.0, 94.0):\n", - "\t135.724, 134.564226686\n", - "\tDifference: 1.1597733139999775\n", - "(92.0, 95.0):\n", - "\t136.875, 135.53592067899996\n", - "\tDifference: 1.3390793210000425\n", - "(92.0, 96.0):\n", - "\t138.039, 136.5167738119999\n", - "\tDifference: 1.5222261880000758\n", - "(92.0, 97.0):\n", - "\t139.217, 137.5067860850001\n", - "\tDifference: 1.7102139149999118\n", - "(92.0, 98.0):\n", - "\t140.409, 138.50595749800016\n", - "\tDifference: 1.9030425019998347\n", - "(92.0, 99.0):\n", - "\t141.614, 139.51428805099997\n", - "\tDifference: 2.0997119490000387\n", - "(92.0, 100.0):\n", - "\t142.833, 140.53177774399992\n", - "\tDifference: 2.3012222560000737\n", - "(93.0, 40.0):\n", - "\t95.4309, 97.49264486900003\n", - "\tDifference: -2.0617448690000373\n", - "(93.0, 41.0):\n", - "\t95.8893, 98.00519933900007\n", - "\tDifference: -2.115899339000066\n", - "(93.0, 42.0):\n", - "\t96.3622, 98.52746285700002\n", - "\tDifference: -2.165262857000016\n", - 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1.7639000769998177\n", - "(93.0, 94.0):\n", - "\t141.031, 139.06423393700004\n", - "\tDifference: 1.966766062999966\n", - "(93.0, 95.0):\n", - "\t142.276, 140.10107699900016\n", - "\tDifference: 2.174923000999854\n", - "(93.0, 96.0):\n", - "\t143.536, 141.147629109\n", - "\tDifference: 2.388370890999994\n", - "(93.0, 97.0):\n", - "\t144.81, 142.2038902670001\n", - "\tDifference: 2.6061097329998972\n", - "(93.0, 98.0):\n", - "\t146.099, 143.26986047300008\n", - "\tDifference: 2.82913952699991\n", - "(93.0, 99.0):\n", - "\t147.402, 144.3455397270002\n", - "\tDifference: 3.0564602729998\n", - "(93.0, 100.0):\n", - "\t148.72, 145.43092802899992\n", - "\tDifference: 3.2890719710000837\n", - "(94.0, 40.0):\n", - "\t97.171, 99.34452959800004\n", - "\tDifference: -2.17352959800003\n", - "(94.0, 41.0):\n", - "\t97.6734, 99.89391346100004\n", - "\tDifference: -2.2205134610000385\n", - "(94.0, 42.0):\n", - "\t98.1913, 100.45355628000007\n", - "\tDifference: -2.2622562800000736\n", - "(94.0, 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2.5962842949998617\n", - "(94.0, 94.0):\n", - "\t146.52, 143.69182423599995\n", - "\tDifference: 2.828175764000065\n", - "(94.0, 95.0):\n", - "\t147.862, 144.79519172300002\n", - "\tDifference: 3.0668082769999785\n", - "(94.0, 96.0):\n", - "\t149.218, 145.90881816600017\n", - "\tDifference: 3.3091818339998156\n", - "(94.0, 97.0):\n", - "\t150.59, 147.03270356500002\n", - "\tDifference: 3.5572964349999836\n", - "(94.0, 98.0):\n", - "\t151.977, 148.16684791999995\n", - "\tDifference: 3.8101520800000515\n", - "(94.0, 99.0):\n", - "\t153.38, 149.3112512310001\n", - "\tDifference: 4.068748768999882\n", - "(94.0, 100.0):\n", - "\t154.799, 150.46591349800013\n", - "\tDifference: 4.3330865019998726\n", - "(95.0, 40.0):\n", - "\t98.9895, 101.24972815100001\n", - "\tDifference: -2.260228151000007\n", - "(95.0, 41.0):\n", - "\t99.5379, 101.83731676300005\n", - "\tDifference: -2.2994167630000533\n", - "(95.0, 42.0):\n", - "\t100.103, 102.435714239\n", - "\tDifference: -2.332714239000012\n", - 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3.4834608209998237\n", - "(95.0, 94.0):\n", - "\t152.192, 148.44699758300004\n", - "\tDifference: 3.7450024169999665\n", - "(95.0, 95.0):\n", - "\t153.631, 149.6182648510001\n", - "\tDifference: 4.012735148999894\n", - "(95.0, 96.0):\n", - "\t155.086, 150.80034098300015\n", - "\tDifference: 4.285659016999858\n", - "(95.0, 97.0):\n", - "\t156.557, 151.9932259790002\n", - "\tDifference: 4.5637740209998015\n", - "(95.0, 98.0):\n", - "\t158.045, 153.19691983900012\n", - "\tDifference: 4.848080160999871\n", - "(95.0, 99.0):\n", - "\t159.55, 154.41142256300014\n", - "\tDifference: 5.138577436999867\n", - "(95.0, 100.0):\n", - "\t161.071, 155.6367341510002\n", - "\tDifference: 5.434265848999786\n", - "(96.0, 40.0):\n", - "\t100.886, 103.20824052800006\n", - "\tDifference: -2.3222405280000658\n", - "(96.0, 41.0):\n", - "\t101.483, 103.8354092450001\n", - "\tDifference: -2.3524092450000893\n", - "(96.0, 42.0):\n", - "\t102.097, 104.47393673400012\n", - "\tDifference: -2.3769367340001253\n", - 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4.4254296549997605\n", - "(96.0, 94.0):\n", - "\t158.046, 153.32975397800024\n", - "\tDifference: 4.716246021999751\n", - "(96.0, 95.0):\n", - "\t159.584, 154.57029638300023\n", - "\tDifference: 5.013703616999777\n", - "(96.0, 96.0):\n", - "\t161.139, 155.8221975600001\n", - "\tDifference: 5.316802439999918\n", - "(96.0, 97.0):\n", - "\t162.712, 157.08545750899995\n", - "\tDifference: 5.626542491000038\n", - "(96.0, 98.0):\n", - "\t164.302, 158.36007623000015\n", - "\tDifference: 5.941923769999846\n", - "(96.0, 99.0):\n", - "\t165.909, 159.6460537230002\n", - "\tDifference: 6.262946276999799\n", - "(96.0, 100.0):\n", - "\t167.534, 160.9433899880002\n", - "\tDifference: 6.590610011999786\n", - "(97.0, 40.0):\n", - "\t102.861, 105.22006672900008\n", - "\tDifference: -2.3590667290000766\n", - "(97.0, 41.0):\n", - "\t103.508, 105.88819090700008\n", - "\tDifference: -2.3801909070000846\n", - "(97.0, 42.0):\n", - "\t104.174, 106.56822376500003\n", - "\tDifference: -2.394223765000021\n", - 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"\tDifference: 3.3331234969999173\n", - "(98.0, 84.0):\n", - "\t153.947, 150.3310507820002\n", - "\tDifference: 3.6159492179997983\n", - "(98.0, 85.0):\n", - "\t155.495, 151.5896836490002\n", - "\tDifference: 3.9053163509998114\n", - "(98.0, 86.0):\n", - "\t157.063, 152.8607751040003\n", - "\tDifference: 4.202224895999677\n", - "(98.0, 87.0):\n", - "\t158.65, 154.1443251470002\n", - "\tDifference: 4.505674852999817\n", - "(98.0, 88.0):\n", - "\t160.257, 155.44033377800028\n", - "\tDifference: 4.816666221999725\n", - "(98.0, 89.0):\n", - "\t161.882, 156.74880099700013\n", - "\tDifference: 5.1331990029998735\n", - "(98.0, 90.0):\n", - "\t163.527, 158.0697268040001\n", - "\tDifference: 5.457273195999875\n", - "(98.0, 91.0):\n", - "\t165.192, 159.40311119900028\n", - "\tDifference: 5.788888800999729\n", - "(98.0, 92.0):\n", - "\t166.875, 160.74895418200015\n", - "\tDifference: 6.126045817999852\n", - "(98.0, 93.0):\n", - "\t168.578, 162.10725575300032\n", - "\tDifference: 6.470744246999686\n", - "(98.0, 94.0):\n", - "\t170.3, 163.47801591200005\n", - "\tDifference: 6.821984087999965\n", - "(98.0, 95.0):\n", - "\t172.042, 164.86123465900022\n", - "\tDifference: 7.180765340999784\n", - "(98.0, 96.0):\n", - "\t173.802, 166.25691199400018\n", - "\tDifference: 7.5450880059998156\n", - "(98.0, 97.0):\n", - "\t175.582, 167.6650479170002\n", - "\tDifference: 7.916952082999785\n", - "(98.0, 98.0):\n", - "\t177.382, 169.08564242800026\n", - "\tDifference: 8.29635757199975\n", - "(98.0, 99.0):\n", - "\t179.2, 170.5186955270002\n", - "\tDifference: 8.681304472999784\n", - "(98.0, 100.0):\n", - "\t181.038, 171.9642072140001\n", - "\tDifference: 9.073792785999899\n", - "(99.0, 40.0):\n", - "\t107.046, 109.40366060300002\n", - "\tDifference: -2.3576606030000136\n", - "(99.0, 41.0):\n", - "\t107.8, 110.157821771\n", - "\tDifference: -2.3578217710000047\n", - "(99.0, 42.0):\n", - "\t108.574, 110.92499143499998\n", - "\tDifference: -2.350991434999983\n", - "(99.0, 43.0):\n", - "\t109.369, 111.70516959500007\n", - "\tDifference: -2.3361695950000723\n", - "(99.0, 44.0):\n", - "\t110.184, 112.49835625100005\n", - "\tDifference: -2.3143562510000493\n", - "(99.0, 45.0):\n", - "\t111.019, 113.30455140299995\n", - "\tDifference: -2.2855514029999426\n", - "(99.0, 46.0):\n", - "\t111.874, 114.12375505100006\n", - "\tDifference: -2.249755051000065\n", - "(99.0, 47.0):\n", - "\t112.75, 114.95596719499999\n", - "\tDifference: -2.2059671949999853\n", - "(99.0, 48.0):\n", - "\t113.645, 115.80118783500004\n", - "\tDifference: -2.15618783500004\n", - "(99.0, 49.0):\n", - "\t114.561, 116.65941697100011\n", - "\tDifference: -2.0984169710001055\n", - "(99.0, 50.0):\n", - "\t115.497, 117.53065460299997\n", - "\tDifference: -2.033654602999974\n", - "(99.0, 51.0):\n", - "\t116.454, 118.41490073100006\n", - "\tDifference: -1.9609007310000663\n", - "(99.0, 52.0):\n", - "\t117.43, 119.31215535499996\n", - "\tDifference: -1.8821553549999521\n", - "(99.0, 53.0):\n", - "\t118.427, 120.22241847500004\n", - "\tDifference: -1.7954184750000337\n", - "(99.0, 54.0):\n", - "\t119.444, 121.14569009100005\n", - "\tDifference: -1.7016900910000459\n", - "(99.0, 55.0):\n", - "\t120.481, 122.081970203\n", - "\tDifference: -1.6009702030000028\n", - "(99.0, 56.0):\n", - "\t121.539, 123.03125881100004\n", - "\tDifference: -1.4922588110000419\n", - "(99.0, 57.0):\n", - "\t122.616, 123.99355591500004\n", - "\tDifference: -1.3775559150000447\n", - "(99.0, 58.0):\n", - "\t123.714, 124.96886151499999\n", - "\tDifference: -1.2548615149999875\n", - "(99.0, 59.0):\n", - "\t124.832, 125.95717561100003\n", - "\tDifference: -1.1251756110000315\n", - "(99.0, 60.0):\n", - "\t125.97, 126.95849820300012\n", - "\tDifference: -0.9884982030001197\n", - "(99.0, 61.0):\n", - "\t127.129, 127.97282929100004\n", - "\tDifference: -0.8438292910000342\n", - "(99.0, 62.0):\n", - "\t128.308, 129.000168875\n", - "\tDifference: -0.6921688750000214\n", - "(99.0, 63.0):\n", - "\t129.506, 130.040516955\n", - "\tDifference: -0.5345169549999866\n", - "(99.0, 64.0):\n", - "\t130.726, 131.09387353099999\n", - "\tDifference: -0.36787353099998654\n", - "(99.0, 65.0):\n", - "\t131.965, 132.160238603\n", - "\tDifference: -0.19523860300000706\n", - "(99.0, 66.0):\n", - "\t133.225, 133.23961217100003\n", - "\tDifference: -0.014612171000038643\n", - "(99.0, 67.0):\n", - "\t134.504, 134.33199423499997\n", - "\tDifference: 0.17200576500002285\n", - "(99.0, 68.0):\n", - "\t135.804, 135.4373847949999\n", - "\tDifference: 0.3666152050001017\n", - "(99.0, 69.0):\n", - "\t137.125, 136.55578385100003\n", - "\tDifference: 0.5692161489999705\n", - "(99.0, 70.0):\n", - "\t138.465, 137.68719140300007\n", - "\tDifference: 0.7778085969999324\n", - "(99.0, 71.0):\n", - "\t139.826, 138.83160745099994\n", - "\tDifference: 0.9943925490000538\n", - "(99.0, 72.0):\n", - "\t141.207, 139.98903199499998\n", - "\tDifference: 1.2179680050000172\n", - "(99.0, 73.0):\n", - "\t142.608, 141.159465035\n", - "\tDifference: 1.4485349649999932\n", - "(99.0, 74.0):\n", - "\t144.029, 142.34290657099993\n", - "\tDifference: 1.686093429000067\n", - "(99.0, 75.0):\n", - "\t145.471, 143.53935660300002\n", - "\tDifference: 1.9316433969999878\n", - "(99.0, 76.0):\n", - "\t146.932, 144.74881513100004\n", - "\tDifference: 2.1831848689999447\n", - "(99.0, 77.0):\n", - "\t148.414, 145.97128215500013\n", - "\tDifference: 2.442717844999862\n", - "(99.0, 78.0):\n", - "\t149.916, 147.20675767499995\n", - "\tDifference: 2.709242325000048\n", - "(99.0, 79.0):\n", - "\t151.439, 148.45524169100003\n", - "\tDifference: 2.983758308999967\n", - "(99.0, 80.0):\n", - "\t152.981, 149.71673420300007\n", - "\tDifference: 3.264265796999922\n", - "(99.0, 81.0):\n", - "\t154.544, 150.991235211\n", - "\tDifference: 3.5527647890000082\n", - "(99.0, 82.0):\n", - "\t156.127, 152.27874471500007\n", - "\tDifference: 3.8482552849999365\n", - "(99.0, 83.0):\n", - "\t157.731, 153.579262715\n", - "\tDifference: 4.151737284999996\n", - "(99.0, 84.0):\n", - "\t159.354, 154.89278921099992\n", - "\tDifference: 4.461210789000091\n", - "(99.0, 85.0):\n", - "\t160.998, 156.21932420299996\n", - "\tDifference: 4.778675797000034\n", - "(99.0, 86.0):\n", - "\t162.662, 157.5588676910001\n", - "\tDifference: 5.103132308999903\n", - "(99.0, 87.0):\n", - "\t164.346, 158.91141967500002\n", - "\tDifference: 5.434580324999985\n", - "(99.0, 88.0):\n", - "\t166.05, 160.27698015499993\n", - "\tDifference: 5.7730198450000785\n", - "(99.0, 89.0):\n", - "\t167.774, 161.65554913100004\n", - "\tDifference: 6.1184508689999575\n", - "(99.0, 90.0):\n", - "\t169.519, 163.04712660299995\n", - "\tDifference: 6.471873397000053\n", - "(99.0, 91.0):\n", - "\t171.284, 164.45171257099994\n", - "\tDifference: 6.832287429000047\n", - "(99.0, 92.0):\n", - "\t173.069, 165.86930703500008\n", - "\tDifference: 7.199692964999912\n", - "(99.0, 93.0):\n", - "\t174.875, 167.29990999500006\n", - "\tDifference: 7.575090004999936\n", - "(99.0, 94.0):\n", - "\t176.7, 168.74352145099982\n", - "\tDifference: 7.956478549000167\n", - "(99.0, 95.0):\n", - "\t178.546, 170.20014140300006\n", - "\tDifference: 8.345858596999932\n", - "(99.0, 96.0):\n", - "\t180.412, 171.66976985100004\n", - "\tDifference: 8.742230148999965\n", - "(99.0, 97.0):\n", - "\t182.299, 173.1524067950001\n", - "\tDifference: 9.146593204999903\n", - "(99.0, 98.0):\n", - "\t184.205, 174.64805223500008\n", - "\tDifference: 9.556947764999933\n", - "(99.0, 99.0):\n", - "\t186.132, 176.15670617099994\n", - "\tDifference: 9.975293829000066\n", - "(99.0, 100.0):\n", - "\t188.079, 177.67836860300002\n", - "\tDifference: 10.400631396999984\n", - "(100.0, 40.0):\n", - "\t109.256, 111.57542827599994\n", - "\tDifference: -2.3194282759999396\n", - "(100.0, 41.0):\n", - "\t110.067, 112.37467097300006\n", - "\tDifference: -2.3076709730000715\n", - "(100.0, 42.0):\n", - "\t110.899, 113.18747207400003\n", - "\tDifference: -2.288472074000026\n", - "(100.0, 43.0):\n", - "\t111.752, 114.01383157900003\n", - "\tDifference: -2.261831579000031\n", - "(100.0, 44.0):\n", - "\t112.626, 114.853749488\n", - "\tDifference: -2.2277494880000006\n", - "(100.0, 45.0):\n", - "\t113.522, 115.70722580099995\n", - "\tDifference: -2.1852258009999446\n", - "(100.0, 46.0):\n", - "\t114.438, 116.57426051800002\n", - "\tDifference: -2.1362605180000145\n", - "(100.0, 47.0):\n", - "\t115.376, 117.45485363899998\n", - "\tDifference: -2.0788536389999734\n", - "(100.0, 48.0):\n", - "\t116.335, 118.34900516399998\n", - "\tDifference: -2.0140051639999825\n", - "(100.0, 49.0):\n", - "\t117.315, 119.25671509299998\n", - "\tDifference: -1.941715092999985\n", - "(100.0, 50.0):\n", - "\t118.316, 120.17798342599993\n", - "\tDifference: -1.8619834259999237\n", - "(100.0, 51.0):\n", - "\t119.338, 121.11281016299998\n", - "\tDifference: -1.7748101629999837\n", - "(100.0, 52.0):\n", - "\t120.381, 122.06119530399991\n", - "\tDifference: -1.6801953039999091\n", - "(100.0, 53.0):\n", - "\t121.446, 123.02313884899995\n", - "\tDifference: -1.577138848999951\n", - "(100.0, 54.0):\n", - "\t122.532, 123.99864079800004\n", - "\tDifference: -1.4666407980000429\n", - "(100.0, 55.0):\n", - "\t123.638, 124.98770115100007\n", - "\tDifference: -1.3497011510000618\n", - "(100.0, 56.0):\n", - "\t124.766, 125.99031990800015\n", - "\tDifference: -1.2243199080001403\n", - "(100.0, 57.0):\n", - "\t125.915, 127.00649706900005\n", - "\tDifference: -1.0914970690000416\n", - "(100.0, 58.0):\n", - "\t127.085, 128.03623263400007\n", - "\tDifference: -0.9512326340000783\n", - "(100.0, 59.0):\n", - "\t128.277, 129.0795266030001\n", - "\tDifference: -0.8025266030001035\n", - "(100.0, 60.0):\n", - "\t129.489, 130.1363789760001\n", - "\tDifference: -0.6473789760000841\n", - "(100.0, 61.0):\n", - "\t130.723, 131.20678975300007\n", - "\tDifference: -0.4837897530000532\n", - "(100.0, 62.0):\n", - "\t131.977, 132.29075893400002\n", - "\tDifference: -0.3137589340000204\n", - "(100.0, 63.0):\n", - "\t133.253, 133.38828651900002\n", - "\tDifference: -0.13528651900003297\n", - "(100.0, 64.0):\n", - "\t134.55, 134.49937250800002\n", - "\tDifference: 0.050627491999989616\n", - "(100.0, 65.0):\n", - "\t135.868, 135.624016901\n", - "\tDifference: 0.2439830989999905\n", - "(100.0, 66.0):\n", - "\t137.207, 136.76221969800002\n", - "\tDifference: 0.44478030199996965\n", - "(100.0, 67.0):\n", - "\t138.567, 137.91398089899994\n", - "\tDifference: 0.6530191010000692\n", - "(100.0, 68.0):\n", - "\t139.949, 139.07930050399997\n", - "\tDifference: 0.8696994960000382\n", - "(100.0, 69.0):\n", - "\t141.352, 140.258178513\n", - "\tDifference: 1.0938214870000138\n", - "(100.0, 70.0):\n", - "\t142.775, 141.45061492600001\n", - "\tDifference: 1.3243850739999914\n", - "(100.0, 71.0):\n", - "\t144.22, 142.65660974299996\n", - "\tDifference: 1.5633902570000373\n", - "(100.0, 72.0):\n", - "\t145.686, 143.87616296399992\n", - "\tDifference: 1.80983703600009\n", - "(100.0, 73.0):\n", - "\t147.173, 145.10927458899997\n", - "\tDifference: 2.0637254110000356\n", - "(100.0, 74.0):\n", - "\t148.681, 146.35594461799997\n", - "\tDifference: 2.325055382000045\n", - "(100.0, 75.0):\n", - "\t150.211, 147.61617305100006\n", - "\tDifference: 2.594826948999952\n", - "(100.0, 76.0):\n", - "\t151.761, 148.889959888\n", - "\tDifference: 2.871040112000003\n", - "(100.0, 77.0):\n", - "\t153.333, 150.17730512900005\n", - "\tDifference: 3.1556948709999517\n", - "(100.0, 78.0):\n", - "\t154.925, 151.47820877399997\n", - "\tDifference: 3.4467912260000446\n", - "(100.0, 79.0):\n", - "\t156.539, 152.79267082300012\n", - "\tDifference: 3.746329176999865\n", - "(100.0, 80.0):\n", - "\t158.174, 154.12069127600003\n", - "\tDifference: 4.053308723999976\n", - "(100.0, 81.0):\n", - "\t159.831, 155.46227013300003\n", - "\tDifference: 4.368729866999956\n", - "(100.0, 82.0):\n", - "\t161.508, 156.81740739400007\n", - "\tDifference: 4.690592605999939\n", - "(100.0, 83.0):\n", - "\t163.206, 158.18610305899995\n", - "\tDifference: 5.019896941000042\n", - "(100.0, 84.0):\n", - "\t164.926, 159.5683571280001\n", - "\tDifference: 5.3576428719999\n", - "(100.0, 85.0):\n", - "\t166.666, 160.96416960099998\n", - "\tDifference: 5.701830399000016\n", - "(100.0, 86.0):\n", - "\t168.428, 162.37354047800008\n", - "\tDifference: 6.054459521999917\n", - "(100.0, 87.0):\n", - "\t170.211, 163.79646975899996\n", - "\tDifference: 6.414530241000051\n", - "(100.0, 88.0):\n", - "\t172.015, 165.23295744400005\n", - "\tDifference: 6.782042555999936\n", - "(100.0, 89.0):\n", - "\t173.84, 166.68300353299992\n", - "\tDifference: 7.156996467000084\n", - "(100.0, 90.0):\n", - "\t175.687, 168.1466080259999\n", - "\tDifference: 7.5403919740001015\n", - "(100.0, 91.0):\n", - "\t177.554, 169.62377092299988\n", - "\tDifference: 7.930229077000121\n", - "(100.0, 92.0):\n", - "\t179.442, 171.1144922240001\n", - "\tDifference: 8.32750777599992\n", - "(100.0, 93.0):\n", - "\t181.352, 172.61877192899993\n", - "\tDifference: 8.73322807100007\n", - "(100.0, 94.0):\n", - "\t183.283, 174.1366100379999\n", - "\tDifference: 9.146389962000086\n", - "(100.0, 95.0):\n", - "\t185.235, 175.66800655099996\n", - "\tDifference: 9.566993449000051\n", - "(100.0, 96.0):\n", - "\t187.208, 177.21296146800003\n", - "\tDifference: 9.995038531999967\n", - "(100.0, 97.0):\n", - "\t189.202, 178.77147478899997\n", - "\tDifference: 10.430525211000031\n", - "(100.0, 98.0):\n", - "\t191.217, 180.3435465139999\n", - "\tDifference: 10.873453486000102\n", - "(100.0, 99.0):\n", - "\t193.254, 181.92917664299995\n", - "\tDifference: 11.324823357000042\n", - "(100.0, 100.0):\n", - "\t195.312, 183.5283651759998\n", - "\tDifference: 11.783634824000217\n" + "Mismatch between NCL and GeoCAT greater than 2° = 6.349137931034483% (1473/23200)\n" ] } ], "source": [ - "for pair in ncl_heat_index.keys():\n", + "difference_from_ncl = 0\n", + "diff_0 = []\n", + "diff_1 = []\n", + "coeff_type = []\n", + "for key in ncl_heat_index.keys():\n", + " temp, rh, alt_coeffs = key\n", " try:\n", - " assert math.isclose(\n", - " ncl_heat_index[pair], geocat_heat_index[pair].flatten()[0], rel_tol=1e-05\n", - " ) # within 5 decimal points\n", + " assert ncl_heat_index[key] == geocat_heat_index[key]\n", " except Exception:\n", - " print(f\"{pair}:\")\n", - " print(f\"\\t{ncl_heat_index[pair]}, {geocat_heat_index[pair].flatten()[0]}\")\n", - " print(\n", - " f\"\\tDifference: {ncl_heat_index[pair] - geocat_heat_index[pair].flatten()[0]}\"\n", - " )" + " try:\n", + " assert ncl_heat_index[key] - geocat_heat_index[key] <= 2\n", + " except Exception:\n", + " difference_from_ncl += 1\n", + " if alt_coeffs == 0:\n", + " diff_0.append(ncl_heat_index[key] - geocat_heat_index[key])\n", + " if alt_coeffs == 1:\n", + " diff_1.append(ncl_heat_index[key] - geocat_heat_index[key])\n", + " coeff_type.append(alt_coeffs)\n", + "print(\n", + " f\"Mismatch between NCL and GeoCAT greater than 2° = {difference_from_ncl/len(ncl_heat_index)*100}% ({difference_from_ncl}/{len(ncl_heat_index)})\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "2e9d9048-a69c-4025-bb1c-5c1edcb913c5", + "execution_count": 19, + "id": "46dd5eee-6bd5-4e2d-94af-2613b6045bc4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0, 0, '48'), Text(0, 0, '1425')]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots()\n", + "counts, edges, bars = plt.hist(coeff_type, range=[0, 1], bins=2)\n", + "plt.title(\"Greater than 2° (0/1 - True/False Alternate Coeffs)\")\n", + "plt.bar_label(bars)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ee425065-9371-4544-ae37-de7a303c61fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0, 0, '5'),\n", + " Text(0, 0, '5'),\n", + " Text(0, 0, '5'),\n", + " Text(0, 0, '4'),\n", + " Text(0, 0, '5'),\n", + " Text(0, 0, '5'),\n", + " Text(0, 0, '4'),\n", + " Text(0, 0, '5'),\n", + " Text(0, 0, '5'),\n", + " Text(0, 0, '5')]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "counts, edges, bars = plt.hist(diff_0)\n", + "plt.title(\"Difference from NCL for Default Coefficients\")\n", + "plt.bar_label(bars)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f3d65e9c-0f01-48be-9871-d8aa8eec6337", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0, 0, '872'),\n", + " Text(0, 0, '166'),\n", + " Text(0, 0, '119'),\n", + " Text(0, 0, '85'),\n", + " Text(0, 0, '63'),\n", + " Text(0, 0, '45'),\n", + " Text(0, 0, '33'),\n", + " Text(0, 0, '22'),\n", + " Text(0, 0, '14'),\n", + " Text(0, 0, '6')]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "ncl_heat_index.keys()" + "fig, ax = plt.subplots()\n", + "counts, edges, bars = plt.hist(diff_1)\n", + "plt.title(\"Difference from NCL for Alternate Coefficients\")\n", + "plt.bar_label(bars)" ] }, { "cell_type": "code", "execution_count": null, - "id": "3ca33016-1d57-4954-b033-1d3edead0522", + "id": "d00b3b3b-7b91-442b-8145-7155519f5d4a", "metadata": {}, "outputs": [], "source": [] diff --git a/references.bib b/references.bib index 0b4df723..129cac95 100644 --- a/references.bib +++ b/references.bib @@ -131,3 +131,11 @@ @misc{nws_heat_index organization = {National Weather Service - Weather Prediction Center}, title = {The Heat Index Equation} } + +@misc{rothfusz_heat_index, + title = {The Heat Index "Equation" (or, More Than You Ever Wanted to Know About Heat Index)}, + url = {https://www.weather.gov/media/ffc/ta_htindx.PDF}, + year = {1990}, + author={Rothfusz, Lans P}, + year={1990} +} From 4263a4fa7c1d726a1d19c218d1bedd2dcc3f67db Mon Sep 17 00:00:00 2001 From: cyschneck <22159116+cyschneck@users.noreply.github.com> Date: Thu, 24 Oct 2024 16:18:28 -0600 Subject: [PATCH 5/8] heat index Rothfusz reference --- references.bib | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/references.bib b/references.bib index 129cac95..2f2dfcc6 100644 --- a/references.bib +++ b/references.bib @@ -136,6 +136,5 @@ @misc{rothfusz_heat_index title = {The Heat Index "Equation" (or, More Than You Ever Wanted to Know About Heat Index)}, url = {https://www.weather.gov/media/ffc/ta_htindx.PDF}, year = {1990}, - author={Rothfusz, Lans P}, - year={1990} + author={Rothfusz, Lans P} } From d8e654ae71e7a5e2d512c29262c19dda9d26f0c8 Mon Sep 17 00:00:00 2001 From: cyschneck <22159116+cyschneck@users.noreply.github.com> Date: Thu, 31 Oct 2024 11:54:28 -0600 Subject: [PATCH 6/8] example graphs --- ncl/receipts/meteorology.ipynb | 131 +++++++++++---------------------- 1 file changed, 43 insertions(+), 88 deletions(-) diff --git a/ncl/receipts/meteorology.ipynb b/ncl/receipts/meteorology.ipynb index 87c8d551..e2baf477 100644 --- a/ncl/receipts/meteorology.ipynb +++ b/ncl/receipts/meteorology.ipynb @@ -162,7 +162,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/user/miniconda3/envs/wavelet_tutorial/lib/python3.11/site-packages/geocat/comp/meteorology.py:1058: UserWarning: WARNING: max_daylight has limited validity for abs(lat) > 55 \n", + "/home/cs/miniconda3/envs/geocat-applications/lib/python3.12/site-packages/geocat/comp/meteorology.py:1058: UserWarning: WARNING: max_daylight has limited validity for abs(lat) > 55 \n", " warnings.warn(\n" ] } @@ -231,7 +231,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/user/miniconda3/envs/wavelet_tutorial/lib/python3.11/site-packages/geocat/comp/meteorology.py:125: RuntimeWarning: invalid value encountered in sqrt\n", + "/home/cs/miniconda3/envs/geocat-applications/lib/python3.12/site-packages/geocat/comp/meteorology.py:125: RuntimeWarning: invalid value encountered in sqrt\n", " heatindex - ((13 - relative_humidity) / 4) * np.sqrt(\n" ] } @@ -273,70 +273,70 @@ "name": "stdout", "output_type": "stream", "text": [ - "(274.0, 94.0):\n", + "(np.float64(274.0), np.float64(94.0)):\n", "\t-0.0055542, -0.005570036309563875\n", "\tDifference: 1.5836309563875377e-05\n", - "(275.0, 87.0):\n", + "(np.float64(275.0), np.float64(87.0)):\n", "\t-0.0839233, -0.08393959272956408\n", "\tDifference: 1.6292729564076902e-05\n", - "(275.0, 88.0):\n", + "(np.float64(275.0), np.float64(88.0)):\n", "\t0.073761, 0.07374617196580857\n", "\tDifference: 1.482803419142198e-05\n", - "(277.0, 76.0):\n", + "(np.float64(277.0), np.float64(76.0)):\n", "\t0.00259399, 0.0025903565796738803\n", "\tDifference: 3.633420326119678e-06\n", - "(278.0, 71.0):\n", + "(np.float64(278.0), np.float64(71.0)):\n", "\t0.0261536, 0.026158530424595483\n", "\tDifference: -4.930424595483984e-06\n", - "(279.0, 66.0):\n", + "(np.float64(279.0), np.float64(66.0)):\n", "\t-0.0278931, -0.027897415813697535\n", "\tDifference: 4.3158136975342265e-06\n", - "(281.0, 58.0):\n", + "(np.float64(281.0), np.float64(58.0)):\n", "\t0.0712891, 0.07128017477202775\n", "\tDifference: 8.925227972245153e-06\n", - "(282.0, 54.0):\n", + "(np.float64(282.0), np.float64(54.0)):\n", "\t0.0129395, 0.01291856405322278\n", "\tDifference: 2.0935946777218828e-05\n", - "(283.0, 50.0):\n", + "(np.float64(283.0), np.float64(50.0)):\n", "\t-0.130432, -0.13044636060385528\n", "\tDifference: 1.4360603855290144e-05\n", - "(283.0, 51.0):\n", + "(np.float64(283.0), np.float64(51.0)):\n", "\t0.144928, 0.14490829422311435\n", "\tDifference: 1.9705776885647897e-05\n", - "(284.0, 47.0):\n", + "(np.float64(284.0), np.float64(47.0)):\n", "\t-0.0726318, -0.07262340593854333\n", "\tDifference: -8.39406145666799e-06\n", - "(285.0, 44.0):\n", + "(np.float64(285.0), np.float64(44.0)):\n", "\t-0.0798035, -0.07982300528288988\n", "\tDifference: 1.950528288988118e-05\n", - "(286.0, 41.0):\n", + "(np.float64(286.0), np.float64(41.0)):\n", "\t-0.160156, -0.1601734585760255\n", "\tDifference: 1.7458576025503048e-05\n", - "(287.0, 39.0):\n", + "(np.float64(287.0), np.float64(39.0)):\n", "\t0.0386658, 0.0386467450997543\n", "\tDifference: 1.905490024570189e-05\n", - "(290.0, 32.0):\n", + "(np.float64(290.0), np.float64(32.0)):\n", "\t-0.0795288, -0.07954541765832346\n", "\tDifference: 1.6617658323461737e-05\n", - "(291.0, 30.0):\n", + "(np.float64(291.0), np.float64(30.0)):\n", "\t-0.115204, -0.11521555297190389\n", "\tDifference: 1.1552971903888709e-05\n", - "(294.0, 25.0):\n", + "(np.float64(294.0), np.float64(25.0)):\n", "\t-0.110413, -0.11042499197856159\n", "\tDifference: 1.1991978561595729e-05\n", - "(295.0, 24.0):\n", + "(np.float64(295.0), np.float64(24.0)):\n", "\t0.156677, 0.15665929836796977\n", "\tDifference: 1.7701632030242553e-05\n", - "(297.0, 21.0):\n", + "(np.float64(297.0), np.float64(21.0)):\n", "\t-0.0614929, -0.06151243710161225\n", "\tDifference: 1.953710161224642e-05\n", - "(299.0, 19.0):\n", + "(np.float64(299.0), np.float64(19.0)):\n", "\t0.162537, 0.16251872398265732\n", "\tDifference: 1.8276017342666595e-05\n", - "(302.0, 16.0):\n", + "(np.float64(302.0), np.float64(16.0)):\n", "\t0.138275, 0.1382598702660971\n", "\tDifference: 1.5129733902913278e-05\n", - "(303.0, 15.0):\n", + "(np.float64(303.0), np.float64(15.0)):\n", "\t0.0130615, 0.013067891707237322\n", "\tDifference: -6.391707237322214e-06\n" ] @@ -390,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "id": "df9425e3-0c44-46b6-97ab-ce01a4d97dc1", "metadata": {}, "outputs": [ @@ -398,7 +398,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mismatch between NCL and GeoCAT greater than 2° = 6.349137931034483% (1473/23200)\n" + "Greater than 2° Difference between NCL and GeoCAT = 6.349137931034483% (1473/23200)\n" ] } ], @@ -422,29 +422,19 @@ " diff_1.append(ncl_heat_index[key] - geocat_heat_index[key])\n", " coeff_type.append(alt_coeffs)\n", "print(\n", - " f\"Mismatch between NCL and GeoCAT greater than 2° = {difference_from_ncl/len(ncl_heat_index)*100}% ({difference_from_ncl}/{len(ncl_heat_index)})\"\n", + " f\"Greater than 2° Difference between NCL and GeoCAT = {difference_from_ncl/len(ncl_heat_index)*100}% ({difference_from_ncl}/{len(ncl_heat_index)})\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "id": "46dd5eee-6bd5-4e2d-94af-2613b6045bc4", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "[Text(0, 0, '48'), Text(0, 0, '1425')]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -459,37 +449,19 @@ "fig, ax = plt.subplots()\n", "counts, edges, bars = plt.hist(coeff_type, range=[0, 1], bins=2)\n", "plt.title(\"Greater than 2° (0/1 - True/False Alternate Coeffs)\")\n", - "plt.bar_label(bars)" + "plt.bar_label(bars)\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "id": "ee425065-9371-4544-ae37-de7a303c61fb", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "[Text(0, 0, '5'),\n", - " Text(0, 0, '5'),\n", - " Text(0, 0, '5'),\n", - " Text(0, 0, '4'),\n", - " Text(0, 0, '5'),\n", - " Text(0, 0, '5'),\n", - " Text(0, 0, '4'),\n", - " Text(0, 0, '5'),\n", - " Text(0, 0, '5'),\n", - " Text(0, 0, '5')]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -501,38 +473,20 @@ "source": [ "fig, ax = plt.subplots()\n", "counts, edges, bars = plt.hist(diff_0)\n", - "plt.title(\"Difference from NCL for Default Coefficients\")\n", - "plt.bar_label(bars)" + "plt.title(\"Degree Difference from NCL for Default Coefficients\")\n", + "plt.bar_label(bars)\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "id": "f3d65e9c-0f01-48be-9871-d8aa8eec6337", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "[Text(0, 0, '872'),\n", - " Text(0, 0, '166'),\n", - " Text(0, 0, '119'),\n", - " Text(0, 0, '85'),\n", - " Text(0, 0, '63'),\n", - " Text(0, 0, '45'),\n", - " Text(0, 0, '33'),\n", - " Text(0, 0, '22'),\n", - " Text(0, 0, '14'),\n", - " Text(0, 0, '6')]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" ] @@ -544,14 +498,15 @@ "source": [ "fig, ax = plt.subplots()\n", "counts, edges, bars = plt.hist(diff_1)\n", - "plt.title(\"Difference from NCL for Alternate Coefficients\")\n", - "plt.bar_label(bars)" + "plt.title(\"Degree Difference from NCL for Alternate Coefficients\")\n", + "plt.bar_label(bars)\n", + "plt.show()" ] }, { "cell_type": "code", "execution_count": null, - "id": "d00b3b3b-7b91-442b-8145-7155519f5d4a", + "id": "2bcc1d0e-d14f-40f5-a9f6-ce3e7053c5f0", "metadata": {}, "outputs": [], "source": [] @@ -573,7 +528,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.12.7" } }, "nbformat": 4, From 8e38fcde11e73a56dbee1876be6d302f99bef323 Mon Sep 17 00:00:00 2001 From: cyschneck <22159116+cyschneck@users.noreply.github.com> Date: Thu, 31 Oct 2024 12:10:50 -0600 Subject: [PATCH 7/8] update heat_index to full range --- ncl/ncl_entries/heat_index_nws_output.txt | 5 -- ncl/receipts/heat_index_nws.ncl | 8 ++-- ncl/receipts/meteorology.ipynb | 57 +++++++++++++---------- 3 files changed, 36 insertions(+), 34 deletions(-) delete mode 100644 ncl/ncl_entries/heat_index_nws_output.txt diff --git a/ncl/ncl_entries/heat_index_nws_output.txt b/ncl/ncl_entries/heat_index_nws_output.txt deleted file mode 100644 index 88a1f393..00000000 --- a/ncl/ncl_entries/heat_index_nws_output.txt +++ /dev/null @@ -1,5 +0,0 @@ - Copyright (C) 1995-2019 - All Rights Reserved - University Corporation for Atmospheric Research - NCAR Command Language Version 6.6.2 - The use of this software is governed by a License Agreement. - See http://www.ncl.ucar.edu/ for more details. diff --git a/ncl/receipts/heat_index_nws.ncl b/ncl/receipts/heat_index_nws.ncl index d1ccc8c7..510d831d 100644 --- a/ncl/receipts/heat_index_nws.ncl +++ b/ncl/receipts/heat_index_nws.ncl @@ -1,6 +1,6 @@ print("Temperature (F), Relative Humidity (%), Alternate Coefs (True/False), Heat Index (F)") -do tf=80,115 - do rh=40,100 +do tf=0, 115 + do rh=1,100 begin heatindex = heat_index_nws(tf,rh,(/2,2/), False) print (tf +","+ rh +"," + "0" + "," + heatindex) @@ -8,8 +8,8 @@ do tf=80,115 end do end do -do tf=70,115 - do rh=1,80 +do tf=0,115 + do rh=1,100 begin heatindex = heat_index_nws(tf,rh,(/2,2/), True) print (tf +","+ rh +"," + "1" + "," + heatindex) diff --git a/ncl/receipts/meteorology.ipynb b/ncl/receipts/meteorology.ipynb index e2baf477..027ddf84 100644 --- a/ncl/receipts/meteorology.ipynb +++ b/ncl/receipts/meteorology.ipynb @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 27, "id": "7af6f81c-2498-4aea-b250-579ec3fd31c8", "metadata": {}, "outputs": [], @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 28, "id": "3166c583-1290-4300-b96b-beab81cd033d", "metadata": {}, "outputs": [], @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 29, "id": "39fd494c-609b-4dd0-83fd-b08d0daf6aaa", "metadata": {}, "outputs": [], @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 30, "id": "69028bd0-ae29-43d6-9824-e80b4d6d8f44", "metadata": {}, "outputs": [], @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 31, "id": "128f13cb-b22f-4b4e-b144-268db532aa78", "metadata": {}, "outputs": [], @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 32, "id": "ed9351f8-4ff7-4585-b957-d491ace5bdb3", "metadata": {}, "outputs": [ @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 33, "id": "c5011a50-2837-494e-8e46-4d682e33f7aa", "metadata": {}, "outputs": [], @@ -209,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 34, "id": "23f1e229-c1c5-4336-b33a-61d35b423e4c", "metadata": {}, "outputs": [], @@ -223,19 +223,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 35, "id": "cb5cf657-2b96-4a76-8d85-c6bcface8f76", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/cs/miniconda3/envs/geocat-applications/lib/python3.12/site-packages/geocat/comp/meteorology.py:125: RuntimeWarning: invalid value encountered in sqrt\n", - " heatindex - ((13 - relative_humidity) / 4) * np.sqrt(\n" - ] - } - ], + "outputs": [], "source": [ "### Calculate GeoCAT-Comp `heat_index_nws` value and temp/rh\n", "from geocat.comp import heat_index\n", @@ -265,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 36, "id": "74362fd9-0e9f-4cf9-91da-08cd81be625c", "metadata": {}, "outputs": [ @@ -369,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 37, "id": "840d88ab-7ecc-485a-9aaf-d8de846196a9", "metadata": {}, "outputs": [], @@ -390,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 38, "id": "df9425e3-0c44-46b6-97ab-ce01a4d97dc1", "metadata": {}, "outputs": [ @@ -428,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 39, "id": "46dd5eee-6bd5-4e2d-94af-2613b6045bc4", "metadata": {}, "outputs": [ @@ -455,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 40, "id": "ee425065-9371-4544-ae37-de7a303c61fb", "metadata": {}, "outputs": [ @@ -480,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 41, "id": "f3d65e9c-0f01-48be-9871-d8aa8eec6337", "metadata": {}, "outputs": [ @@ -510,6 +501,22 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d2cf8ec-8743-45e2-870d-ae2d81b0518c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8cf5065-8554-4379-8378-f9f460eb0248", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From e5b3502aa9e6ec5f4eb0a7088700613c7c7dfb19 Mon Sep 17 00:00:00 2001 From: cyschneck <22159116+cyschneck@users.noreply.github.com> Date: Thu, 31 Oct 2024 12:13:49 -0600 Subject: [PATCH 8/8] update ncl raw range --- ncl/ncl_raw/meteorology.ncl | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/ncl/ncl_raw/meteorology.ncl b/ncl/ncl_raw/meteorology.ncl index 7ccbfbbd..c763735c 100644 --- a/ncl/ncl_raw/meteorology.ncl +++ b/ncl/ncl_raw/meteorology.ncl @@ -33,21 +33,21 @@ end do ; ncl -n heat_index_nws.ncl >> heat_index_nws_output.txt -print("Temperature (F), Relative Humidity (%), Heat Index (F)") -do tf=0,79 - do rh=1,40 +print("Temperature (F), Relative Humidity (%), Alternate Coefs (True/False), Heat Index (F)") +do tf=0, 115 + do rh=1,100 begin heatindex = heat_index_nws(tf,rh,(/2,2/), False) - print (tf +","+ rh +","+ heatindex) + print (tf +","+ rh +"," + "0" + "," + heatindex) end end do end do -do tf=79,100 - do rh=40,100 +do tf=0,115 + do rh=1,100 begin heatindex = heat_index_nws(tf,rh,(/2,2/), True) - print (tf +","+ rh +","+ heatindex) + print (tf +","+ rh +"," + "1" + "," + heatindex) end end do end do