From 3804bd09f952f96e54523d28853f88af409067ec Mon Sep 17 00:00:00 2001 From: malmans2 Date: Mon, 14 Oct 2024 09:18:41 +0200 Subject: [PATCH] minor changes --- ...ope_climate-and-weather-extremes_q02.ipynb | 293 ++++++++++-------- 1 file changed, 164 insertions(+), 129 deletions(-) diff --git a/In_Situ/insitu_insitu-gridded-observations-europe_climate-and-weather-extremes_q02.ipynb b/In_Situ/insitu_insitu-gridded-observations-europe_climate-and-weather-extremes_q02.ipynb index c9b2c8f6..510df1ae 100644 --- a/In_Situ/insitu_insitu-gridded-observations-europe_climate-and-weather-extremes_q02.ipynb +++ b/In_Situ/insitu_insitu-gridded-observations-europe_climate-and-weather-extremes_q02.ipynb @@ -5,31 +5,65 @@ "id": "0", "metadata": {}, "source": [ - "# Insitu Air Temperature trends assessment for climate monitoring\n", - "\n", - "Production date: 05/09/2024\n", - "\n", - "Produced by: Ana Oliveira (CoLAB +ATLANTIC)\n", - "\n", - "## Use Case: Adaptation to Climate Extremes.\n", + "```{note}\n", + "If anything is unclear with the templates, or if you encounter any problems, please report them at [GH164](https://github.com/ecmwf-projects/c3s2-eqc-quality-assessment/issues/164).\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "# Insitu Air Temperature trends assessment for climate monitoring" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "Production date: 10/10/2024\n", "\n", - "## Quality assessment questions\n", + "Produced by: Ana Oliveira (CoLAB +ATLANTIC)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "## 🌍 Use case: Adaptation to Climate Extremes." + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "## ❓ Quality assessment questions\n", "* **User Question: How well does gridded data derived from observations represent local exposure to heatwaves?**\n", - "* **How do EOBS air temperature extremes compare to those derived from reanalysis?**\n", - "\n", + "* **How do EOBS air temperature extremes compare to those derived from reanalysis?**" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ "In this Use Case we will access the E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations (henceforth, E-OBS) data from the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S) and analyse the spatial consistency between E-OBS and ERA5 regarding the amount of hot days (TX90p) and the amount of warm nights (TN90p), over a given Area of Interest (AoI), showcasing a regional example of European State of the Climate 2023 diagnostics accross two datasets. These climate indices are calculated according to the recommendations of the World Meteorological Organization (WMO)'s Expert Team on Sector-Specific Climate Indices (ET-SCI), in conjunction with sector experts. " ] }, { "cell_type": "markdown", - "id": "1", + "id": "6", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ - "## Quality Statements of this Use Case are:\n", + "## 📱 Quality assessment statement\n", "\n", "* The study examined annual trends in heatrelated indices based on daily minimum and maximum temperatures (TN and TX, respectively) across the Iberian Peninsula using E-OBS and ERA5 datasets, comparing their fitness in capturing the number of warm nights (TN90p) and hot days (TX90p), following the guidelines of the World Meteorological Organization (WMO) Expert Team on Sector-specific Climate Indices (ET-SCI) changes over the complete time series length.\n", "\n", @@ -50,7 +84,7 @@ } }, "cell_type": "markdown", - "id": "2", + "id": "7", "metadata": {}, "source": [ "![Trens_TX_EOBS-1.png](attachment:Trens_TX_EOBS-1.png)" @@ -58,10 +92,10 @@ }, { "cell_type": "markdown", - "id": "3", + "id": "8", "metadata": {}, "source": [ - "## Methodology\n", + "## 📋 Methodology\n", "\n", "**[](C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-1)** \n", "**[](C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-1.1)** \n", @@ -80,15 +114,15 @@ }, { "cell_type": "markdown", - "id": "4", + "id": "9", "metadata": {}, "source": [ - "## Analysis and results" + "## 📈 Analysis and results" ] }, { "cell_type": "markdown", - "id": "5", + "id": "10", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-1)=\n", @@ -97,7 +131,7 @@ }, { "cell_type": "markdown", - "id": "6", + "id": "11", "metadata": {}, "source": [ "#### Import all the libraries/packages\n", @@ -108,7 +142,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "7", + "id": "12", "metadata": {}, "outputs": [], "source": [ @@ -131,7 +165,7 @@ }, { "cell_type": "markdown", - "id": "8", + "id": "13", "metadata": {}, "source": [ "#### Data Overview\n", @@ -167,7 +201,7 @@ }, { "cell_type": "markdown", - "id": "9", + "id": "14", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-1.1)=\n", @@ -177,7 +211,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "10", + "id": "15", "metadata": {}, "outputs": [], "source": [ @@ -203,7 +237,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "11", + "id": "16", "metadata": {}, "outputs": [ { @@ -725,7 +759,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "12", + "id": "17", "metadata": {}, "outputs": [ { @@ -1255,7 +1289,7 @@ }, { "cell_type": "markdown", - "id": "13", + "id": "18", "metadata": {}, "source": [ "Download and prepare E-OBS daily maximum air temperature data (TX)" @@ -1264,7 +1298,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "14", + "id": "19", "metadata": {}, "outputs": [], "source": [ @@ -1288,7 +1322,7 @@ }, { "cell_type": "markdown", - "id": "15", + "id": "20", "metadata": {}, "source": [ "Now that we have downloaded the data, we can inspect it. We have requested the data in NetCDF format. This is a commonly used format for array-oriented scientific data. To read and process this data we will make use of the Xarray library. Xarray is an open source project and Python package that makes working with labelled multi-dimensional arrays simple and efficient. We will read the data from our NetCDF file into an xarra dataset. We will print both TX and TN." @@ -1296,7 +1330,7 @@ }, { "cell_type": "markdown", - "id": "16", + "id": "21", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-1.2)=\n", @@ -1308,7 +1342,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "17", + "id": "22", "metadata": {}, "outputs": [ { @@ -1830,7 +1864,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "18", + "id": "23", "metadata": {}, "outputs": [ { @@ -1880,7 +1914,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "19", + "id": "24", "metadata": {}, "outputs": [ { @@ -2410,7 +2444,7 @@ }, { "cell_type": "markdown", - "id": "20", + "id": "25", "metadata": {}, "source": [ "We can see from the data structure that our variables, TN and TX, are already stored in a four-dimensional array with dimensions: 25933 days in 'time', 21 steps in 'latitude', and 13 steps in 'longitude'." @@ -2418,7 +2452,7 @@ }, { "cell_type": "markdown", - "id": "21", + "id": "26", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-1.3)=\n", @@ -2427,7 +2461,7 @@ }, { "cell_type": "markdown", - "id": "22", + "id": "27", "metadata": {}, "source": [ "Download and prepare ERA5 hourly air temperature data. Due to the data size, it is best to split the request by decades. We will also inspect the data following the previous steps." @@ -2436,7 +2470,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "23", + "id": "28", "metadata": {}, "outputs": [ { @@ -2473,7 +2507,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "24", + "id": "29", "metadata": {}, "outputs": [ { @@ -2510,7 +2544,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "25", + "id": "30", "metadata": {}, "outputs": [ { @@ -2547,7 +2581,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "26", + "id": "31", "metadata": {}, "outputs": [ { @@ -2584,7 +2618,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "27", + "id": "32", "metadata": {}, "outputs": [ { @@ -2621,7 +2655,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "28", + "id": "33", "metadata": {}, "outputs": [ { @@ -2660,7 +2694,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "29", + "id": "34", "metadata": {}, "outputs": [ { @@ -3212,7 +3246,7 @@ }, { "cell_type": "markdown", - "id": "30", + "id": "35", "metadata": {}, "source": [ "We can see from the data structure that our variable, hourly T2m, is also stored in a four-dimensional array, but has time steps: 25933 hours in 'time', 17 steps in 'latitude', and 23 steps in 'longitude'. Also, air temperature is shown in Kelvin, and needs to be converted to Celsius degrees, as follows." @@ -3221,7 +3255,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "31", + "id": "36", "metadata": {}, "outputs": [], "source": [ @@ -3231,7 +3265,7 @@ }, { "cell_type": "markdown", - "id": "32", + "id": "37", "metadata": {}, "source": [ "\n", @@ -3241,7 +3275,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "33", + "id": "38", "metadata": {}, "outputs": [], "source": [ @@ -3251,7 +3285,7 @@ }, { "cell_type": "markdown", - "id": "34", + "id": "39", "metadata": {}, "source": [ "Having done this, we will resample ERA5 hourly data to get the daily minimum and maximum air temperatures (TN and TX, respectively)." @@ -3260,7 +3294,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "35", + "id": "40", "metadata": {}, "outputs": [ { @@ -3805,7 +3839,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "36", + "id": "41", "metadata": {}, "outputs": [ { @@ -4357,7 +4391,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "37", + "id": "42", "metadata": {}, "outputs": [ { @@ -4909,7 +4943,7 @@ }, { "cell_type": "markdown", - "id": "38", + "id": "43", "metadata": {}, "source": [ "We can see from the data structure that our variables, TN and TX, are then stored in a four-dimensional array, but spatial dimensions differ from E-OBS: 25933 days in 'time', 17 steps in 'latitude', and 23 steps in 'longitude'.\n", @@ -4920,7 +4954,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "39", + "id": "44", "metadata": {}, "outputs": [ { @@ -5435,7 +5469,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "40", + "id": "45", "metadata": {}, "outputs": [ { @@ -5949,7 +5983,7 @@ }, { "cell_type": "markdown", - "id": "41", + "id": "46", "metadata": {}, "source": [ "We can see that now the dimensions are consistent between both datasets, and may proceed with merging TN and TX." @@ -5958,7 +5992,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "42", + "id": "47", "metadata": {}, "outputs": [], "source": [ @@ -5976,19 +6010,19 @@ }, { "cell_type": "markdown", - "id": "43", + "id": "48", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-2)=\n", - "## 2. Inspect and view data" + "### 2. Inspect and view data" ] }, { "cell_type": "markdown", - "id": "44", + "id": "49", "metadata": {}, "source": [ - "### Create a set of weights based on latitude values\n", + "#### Create a set of weights based on latitude values\n", "The weighted method is used to create a weighted dataset. Any subsequent aggregation operations (such as mean, sum, etc.) will take these weights into account.\n", "These weights can be used to account for the varying area of grid cells in latitude-longitude grids to ensure that calculations properly account for varying areas represented by grid cells at different latitudes. \n", "\n", @@ -5998,7 +6032,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "45", + "id": "50", "metadata": {}, "outputs": [], "source": [ @@ -6012,7 +6046,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "46", + "id": "51", "metadata": {}, "outputs": [ { @@ -6047,7 +6081,7 @@ }, { "cell_type": "markdown", - "id": "47", + "id": "52", "metadata": {}, "source": [ "### Merge the two TX datasets using latitude and longitude as coordinates\n" @@ -6056,7 +6090,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "48", + "id": "53", "metadata": {}, "outputs": [], "source": [ @@ -6070,7 +6104,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "49", + "id": "54", "metadata": {}, "outputs": [ { @@ -6105,7 +6139,7 @@ }, { "cell_type": "markdown", - "id": "50", + "id": "55", "metadata": {}, "source": [ "From both plots, results suggest that E-OBS temperatures reach more extreme values: i.e., TN is lowest and TX is highest in E-OBS, compared to ERA5. This agrees with previous results that suggest some smoothing in reanalysis, compared to observations. \n", @@ -6116,7 +6150,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "51", + "id": "56", "metadata": {}, "outputs": [ { @@ -6141,7 +6175,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "52", + "id": "57", "metadata": {}, "outputs": [ { @@ -6165,7 +6199,7 @@ }, { "cell_type": "markdown", - "id": "53", + "id": "58", "metadata": {}, "source": [ "We can now subset and plot the selected years, as follows." @@ -6174,7 +6208,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "54", + "id": "59", "metadata": {}, "outputs": [ { @@ -6214,7 +6248,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "55", + "id": "60", "metadata": {}, "outputs": [ { @@ -6253,7 +6287,7 @@ }, { "cell_type": "markdown", - "id": "56", + "id": "61", "metadata": {}, "source": [ "To facilitate any further time series analysis, let's save the data into .csv and .nc formats" @@ -6262,7 +6296,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "57", + "id": "62", "metadata": {}, "outputs": [], "source": [ @@ -6275,16 +6309,16 @@ }, { "cell_type": "markdown", - "id": "58", + "id": "63", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-3)=\n", - "## 3. Calculation of 90-Percentile for E-OBS and ERA5\n" + "### 3. Calculation of 90-Percentile for E-OBS and ERA5\n" ] }, { "cell_type": "markdown", - "id": "59", + "id": "64", "metadata": {}, "source": [ "Following the guidelines from the WMO's ET-SCI, the amount of hot days (TX90p) and amount of warm nights (TN90p) indices are based on daily differences form the 90th percentile of TX and TN, respectively, using the 1961-1990 climatology, which allows for a better intercomparison with existing studies.\n", @@ -6295,7 +6329,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "60", + "id": "65", "metadata": {}, "outputs": [ { @@ -6962,7 +6996,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "61", + "id": "66", "metadata": {}, "outputs": [ { @@ -7616,7 +7650,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "62", + "id": "67", "metadata": {}, "outputs": [ { @@ -7639,7 +7673,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "63", + "id": "68", "metadata": {}, "outputs": [ { @@ -8191,7 +8225,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "64", + "id": "69", "metadata": {}, "outputs": [ { @@ -8227,7 +8261,7 @@ }, { "cell_type": "markdown", - "id": "65", + "id": "70", "metadata": {}, "source": [ "We can see from this plot that the reference 90th threshold from the ERA5 has lower values. As a next step, we can smooth the climatological reference by using a moving average. Here, we employ a 7-days sliding window." @@ -8236,7 +8270,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "66", + "id": "71", "metadata": {}, "outputs": [], "source": [ @@ -8262,7 +8296,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "67", + "id": "72", "metadata": {}, "outputs": [ { @@ -8297,7 +8331,7 @@ }, { "cell_type": "markdown", - "id": "68", + "id": "73", "metadata": {}, "source": [ "During winter, TN90p is up to 2ÂșC cooler in E-OBS, compared to ERA5. This difference is smaller during the summer, at approximately 1ÂșC, which indicates the importance of seasonality on comparing both datasets regarding nocturnal extremes.\n", @@ -8308,7 +8342,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "69", + "id": "74", "metadata": {}, "outputs": [ { @@ -8975,7 +9009,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "70", + "id": "75", "metadata": {}, "outputs": [ { @@ -9629,7 +9663,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "71", + "id": "76", "metadata": {}, "outputs": [ { @@ -9652,7 +9686,7 @@ { "cell_type": "code", "execution_count": 44, - "id": "72", + "id": "77", "metadata": {}, "outputs": [ { @@ -10204,7 +10238,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "73", + "id": "78", "metadata": {}, "outputs": [ { @@ -10239,7 +10273,7 @@ }, { "cell_type": "markdown", - "id": "74", + "id": "79", "metadata": {}, "source": [ "As with TN, the TX is shown to have highest values on E-OBS, specially during the summer season. Like before, we will smooth the climatological reference by using a moving average. Here, we employ a 7-days sliding window." @@ -10248,7 +10282,7 @@ { "cell_type": "code", "execution_count": 46, - "id": "75", + "id": "80", "metadata": {}, "outputs": [], "source": [ @@ -10274,7 +10308,7 @@ { "cell_type": "code", "execution_count": 47, - "id": "76", + "id": "81", "metadata": {}, "outputs": [ { @@ -10309,7 +10343,7 @@ }, { "cell_type": "markdown", - "id": "77", + "id": "82", "metadata": {}, "source": [ "During summer, TX90p is more than 2ÂșC warmer in E-OBS, compared to ERA5. This difference is smaller during the winter, lower than 1ÂșC, which indicates the importance of seasonality on comparing both datasets regarding diurnal extremes." @@ -10317,26 +10351,26 @@ }, { "cell_type": "markdown", - "id": "78", + "id": "83", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-4)=\n", - "## 4. Trend Analysis and Intercomparison\n", + "### 4. Trend Analysis and Intercomparison\n", "As shown before, E-OBS and ERA5 showcase different temperature ranges in absolute and relative values. To check whether this influences the quantification of hot days and amount of warm nights, and its changes, we need to calculate the amount of days above the 90th percentiles calculated before. The resulting differences, or anomalies, are than agregated by year to produce descriptive statistics and linear trend analysis." ] }, { "cell_type": "markdown", - "id": "79", + "id": "84", "metadata": {}, "source": [ - "### Calculation of the daily deviations from the 90th percentiles" + "#### Calculation of the daily deviations from the 90th percentiles" ] }, { "cell_type": "code", "execution_count": 48, - "id": "80", + "id": "85", "metadata": {}, "outputs": [], "source": [ @@ -10363,7 +10397,7 @@ { "cell_type": "code", "execution_count": 49, - "id": "81", + "id": "86", "metadata": {}, "outputs": [], "source": [ @@ -10387,7 +10421,7 @@ { "cell_type": "code", "execution_count": 50, - "id": "82", + "id": "87", "metadata": {}, "outputs": [ { @@ -10529,7 +10563,7 @@ { "cell_type": "code", "execution_count": 51, - "id": "83", + "id": "88", "metadata": {}, "outputs": [], "source": [ @@ -10544,7 +10578,7 @@ { "cell_type": "code", "execution_count": 52, - "id": "84", + "id": "89", "metadata": {}, "outputs": [], "source": [ @@ -10555,7 +10589,7 @@ }, { "cell_type": "markdown", - "id": "85", + "id": "90", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-4.1)=\n", @@ -10570,7 +10604,7 @@ { "cell_type": "code", "execution_count": 53, - "id": "86", + "id": "91", "metadata": {}, "outputs": [], "source": [ @@ -10645,7 +10679,7 @@ }, { "cell_type": "markdown", - "id": "87", + "id": "92", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-4.2)=\n", @@ -10655,7 +10689,7 @@ { "cell_type": "code", "execution_count": 54, - "id": "88", + "id": "93", "metadata": {}, "outputs": [ { @@ -10689,7 +10723,7 @@ }, { "cell_type": "markdown", - "id": "89", + "id": "94", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-4.3)=\n", @@ -10699,7 +10733,7 @@ { "cell_type": "code", "execution_count": 55, - "id": "90", + "id": "95", "metadata": {}, "outputs": [], "source": [ @@ -10722,7 +10756,7 @@ { "cell_type": "code", "execution_count": 56, - "id": "91", + "id": "96", "metadata": {}, "outputs": [], "source": [ @@ -10746,7 +10780,7 @@ { "cell_type": "code", "execution_count": 57, - "id": "92", + "id": "97", "metadata": {}, "outputs": [ { @@ -10780,7 +10814,7 @@ }, { "cell_type": "markdown", - "id": "93", + "id": "98", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-4.4)=\n", @@ -10790,7 +10824,7 @@ { "cell_type": "code", "execution_count": 58, - "id": "94", + "id": "99", "metadata": {}, "outputs": [], "source": [ @@ -10813,7 +10847,7 @@ { "cell_type": "code", "execution_count": 59, - "id": "95", + "id": "100", "metadata": {}, "outputs": [], "source": [ @@ -10837,7 +10871,7 @@ { "cell_type": "code", "execution_count": 60, - "id": "96", + "id": "101", "metadata": {}, "outputs": [ { @@ -10871,7 +10905,7 @@ }, { "cell_type": "markdown", - "id": "97", + "id": "102", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-4.5)=\n", @@ -10881,7 +10915,7 @@ { "cell_type": "code", "execution_count": 61, - "id": "98", + "id": "103", "metadata": {}, "outputs": [], "source": [ @@ -10904,7 +10938,7 @@ { "cell_type": "code", "execution_count": 62, - "id": "99", + "id": "104", "metadata": {}, "outputs": [], "source": [ @@ -10928,7 +10962,7 @@ { "cell_type": "code", "execution_count": 63, - "id": "100", + "id": "105", "metadata": {}, "outputs": [ { @@ -10963,7 +10997,7 @@ { "cell_type": "code", "execution_count": 64, - "id": "101", + "id": "106", "metadata": {}, "outputs": [ { @@ -11513,7 +11547,7 @@ { "cell_type": "code", "execution_count": 66, - "id": "102", + "id": "107", "metadata": {}, "outputs": [], "source": [ @@ -11531,7 +11565,7 @@ { "cell_type": "code", "execution_count": 68, - "id": "103", + "id": "108", "metadata": {}, "outputs": [], "source": [ @@ -11583,7 +11617,7 @@ { "cell_type": "code", "execution_count": 69, - "id": "104", + "id": "109", "metadata": {}, "outputs": [ { @@ -11642,7 +11676,7 @@ }, { "cell_type": "markdown", - "id": "105", + "id": "110", "metadata": {}, "source": [ "(C3S2_D520.5.3.14b_Quality_Assessment_User_Questions_EOBS_UQ2_v1.13:code-section-5)=\n", @@ -11651,7 +11685,7 @@ }, { "cell_type": "markdown", - "id": "106", + "id": "111", "metadata": {}, "source": [ "- The study analyzed annual minimum and maximum temperatures over the Iberian Peninsula using E-OBS and ERA5 datasets for specified time periods.\n", @@ -11669,10 +11703,10 @@ }, { "cell_type": "markdown", - "id": "107", + "id": "112", "metadata": {}, "source": [ - "## If you want to know more\n", + "## â„č If you want to know more\n", "\n", "### Key resources\n", "\n", @@ -11680,13 +11714,14 @@ "\n", "The CDS catalogue entries for the data used were:\n", "* E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations:\n", - " https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview\n", - "* ERA5 hourly data on single levels from 1940 to present: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview \n", + " https://cds.climate.copernicus.eu/datasets/insitu-gridded-observations-europe?tab=overview\n", + "* ERA5 hourly data on single levels from 1940 to present: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview \n", "\n", "\n", "Code libraries used:\n", "* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`, prepared by [BOpen](https://www.bopen.eu/)\n", "\n", + "### References\n", "\n", "[[1]](https://library.wmo.int/index.php?lvl=notice_display&id=20130) World Meteorological Organization (WMO), Guidelines on the Calculation of Climate Normals.\n", "\n", @@ -11694,7 +11729,7 @@ "\n", "[[3]](https://confluence.ecmwf.int/display/CKB/E-OBS+daily+gridded+observations+for+Europe+from+1950+to+present%3A+Product+user+guide) E-OBS daily gridded observations for Europe from 1950 to present: Product user guide.\n", "\n", - "[[4]] ( https://doi.org/10.1029/2017JD028200) Cornes, R., G. van der Schrier, E.J.M. van den Besselaar, and P.D. Jones. 2018: An Ensemble Version of the E-OBS Temperature and Precipitation Datasets, J. Geophys. Res. (Atmospheres), 123.\n", + "[[4]]( https://doi.org/10.1029/2017JD028200) Cornes, R., G. van der Schrier, E.J.M. van den Besselaar, and P.D. Jones. 2018: An Ensemble Version of the E-OBS Temperature and Precipitation Datasets, J. Geophys. Res. (Atmospheres), 123.\n", "\n", "[[5]](https://doi.org/10.1002/QJ.4174)Bell, Bill, Hans Hersbach, Adrian Simmons, Paul Berrisford, Per Dahlgren, AndrĂĄs HorĂĄnyi, JoaquĂ­n Muñoz-Sabater, et al. 2021. “The ERA5 Global Reanalysis: Preliminary Extension to 1950.” Quarterly Journal of the Royal Meteorological Society 147 (741): 4186–4227. \n", "\n",