{"id":3051,"date":"2026-05-19T09:47:04","date_gmt":"2026-05-19T09:47:04","guid":{"rendered":"https:\/\/mistral.upc.es\/inline-platform\/?page_id=3051"},"modified":"2026-06-26T10:31:47","modified_gmt":"2026-06-26T10:31:47","slug":"explanation","status":"publish","type":"page","link":"https:\/\/mistral.upc.es\/inline-platform\/explanation\/","title":{"rendered":"Explanation"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"3051\" class=\"elementor elementor-3051\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d6fabab elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d6fabab\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2f7c6a1\" data-id=\"2f7c6a1\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e1166df elementor-widget elementor-widget-spacer\" data-id=\"e1166df\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t<div class=\"elementor-element elementor-element-2335bcf e-flex e-con-boxed e-con e-parent\" data-id=\"2335bcf\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-38169f3 e-n-tabs-mobile elementor-widget elementor-widget-n-tabs\" data-id=\"38169f3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"nested-tabs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"e-n-tabs\" data-widget-number=\"58812915\" aria-label=\"Tabs. Open items with Enter or Space, close with Escape and navigate using the Arrow keys.\">\n\t\t\t<div class=\"e-n-tabs-heading\" role=\"tablist\">\n\t\t\t\t\t<button id=\"radar-nowcasting\" data-tab-title-id=\"e-n-tab-title-588129151\" class=\"e-n-tab-title\" aria-selected=\"true\" data-tab-index=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"e-n-tab-content-588129151\" style=\"--n-tabs-title-order: 1;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tRadar Nowcasting for Pluvial Flood Hazard\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"storm-impact\" data-tab-title-id=\"e-n-tab-title-588129152\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-588129152\" style=\"--n-tabs-title-order: 2;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tStorm Impact Forecasting\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"river-flash-flood\" data-tab-title-id=\"e-n-tab-title-588129153\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"3\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-588129153\" style=\"--n-tabs-title-order: 3;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tRiver Flash Flood Impact Forecasting\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t\t<\/div>\n\t\t\t<div class=\"e-n-tabs-content\">\n\t\t\t\t<div id=\"e-n-tab-content-588129151\" role=\"tabpanel\" aria-labelledby=\"radar-nowcasting\" data-tab-index=\"1\" style=\"--n-tabs-title-order: 1;\" class=\"e-active elementor-element elementor-element-0b8c9bf e-con-full e-flex e-con e-child\" data-id=\"0b8c9bf\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-e1dd25d e-flex e-con-boxed e-con e-child\" data-id=\"e1dd25d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8290b78 elementor-widget elementor-widget-heading\" data-id=\"8290b78\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Explanation - Methodologies to Create the Layers<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6499a99 elementor-widget elementor-widget-heading\" data-id=\"6499a99\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Radar Nowcasting for Pluvial Flood Hazard<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f6b190a elementor-widget elementor-widget-text-editor\" data-id=\"f6b190a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Reflectivity observations from the pan-European EUMETNET OPERA composite are used to generate nowcasts of rainfall up to 5-hours ahead using the SBMCast model. The nowcasts are used to compute the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#pluvial-hazard1\">pluvial flood hazard in urban areas layers<\/a>, they are also one input to the computation of the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/explanation\/#river-flash-flood\">River Flash Flood Impact<\/a> layers.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4f163d8 elementor-widget elementor-widget-heading\" data-id=\"4f163d8\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"radar-observation\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Radar Observations<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7feaf76 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"7feaf76\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ea32bcd elementor-widget elementor-widget-text-editor\" data-id=\"ea32bcd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Reflectivity observations (units of dBZ) are obtained from the <a href=\"https:\/\/www.eumetnet.eu\/observations\/opera-radar-animation\/v\" target=\"_blank\" rel=\"noopener\">EUMETNET OPERA<\/a> pan-European composite. Data are available every 15-minutes at a spatial resolution of 2-km. Reflectivity values are transformed into rainfall using a Z-R conversion.<\/p><p>The converted rainfall values are bias corrected to ensure the values closely match those observed at gauging stations.The bias adjustment is done by multiplying the radar rainfall data by a correction factor, which is computed as the difference between the previous 30-days accumulated between the gauged value and the radar value. This computation is performed separately at each gauging station across a pan-European network, shown in the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#efas-24h\">EFAS Raingauge accum. (24h)<\/a> layer. The correction factors at all gauge station locations are then interpolated onto the same 2-km grid as the OPERA composite. This procedure to compute the correction factor is done once per day at 00 UTC. The gridded correction factor is multiplied to all radar rainfall values over the following 24-hour period to compute the bias corrected values. More details about the methodology can be found in <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0022169419303221?via%3Dihub\" target=\"_blank\" rel=\"noopener\">Park et al., 2019<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59db895 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"59db895\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"750\" height=\"171\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/52_rain_bias_adj-1024x233.png\" class=\"attachment-large size-large wp-image-4865\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/52_rain_bias_adj-1024x233.png 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/52_rain_bias_adj-300x68.png 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/52_rain_bias_adj-768x174.png 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/52_rain_bias_adj-1536x349.png 1536w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/52_rain_bias_adj.png 1893w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a783f41 elementor-widget elementor-widget-heading\" data-id=\"a783f41\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"radar-rainfall-nowcasting\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Radar Rainfall Nowcasting<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1178a76 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"1178a76\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d9e083e elementor-widget elementor-widget-text-editor\" data-id=\"d9e083e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The bias corrected radar rainfall estimates are used to create a nowcast of rainfall up to 5-hours ahead with 15-minute time steps. The nowcast is created using the SBMCast model (<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0022169411002940?via%3Dihub\" target=\"_blank\" rel=\"noopener\">Berenguer et al., 2011<\/a>) which applies Lagrangian persistence to the previous 6-hours of bias corrected radar rainfall values. New nowcasts are made every 15-minutes when new radar observations become available.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a97338d elementor-widget elementor-widget-heading\" data-id=\"a97338d\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"pluvial-hazard\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Computation of Pluvial Hazard in Urban Areas<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-049ecfe elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"049ecfe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8d0cbd3 elementor-widget elementor-widget-text-editor\" data-id=\"8d0cbd3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#pluvial-hazard\">pluvial flood hazard in urban areas<\/a> layer is computed using the radar rainfall nowcasts computed in the previous step. The layer is only computed in urban areas, which are defined as 2-km grid cells in which \u226520% of the cell area is covered by urban or highly impermeable surfaces, defined from the <a href=\"https:\/\/sdi.eea.europa.eu\/catalogue\/srv\/api\/records\/71fc9d1b-479f-4da1-aa66-662a2fff2cf7?language=all\" target=\"_blank\" rel=\"noopener\">CORINE land cover dataset<\/a>. Within the 2-km grid cells identified as urban areas, the hourly rainfall accumulation in each 15-minute time step of the nowcast is compared against climatological thresholds associated with the 2-, 5- and 20-year return periods. These thresholds are computed from historical hourly rainfall accumulations from 8-years of bias corrected radar rainfall estimates. If the nowcasted rainfall in a given timestep and urban grid cell is expected to exceed one of the return period thresholds (Table 1), it is assigned to the corresponding hazard level.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba545de elementor-widget elementor-widget-text-editor\" data-id=\"ba545de\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<style>\n  \/* Base table setup *\/\n  table.hestia-nested-table {\n    width: 100%;\n    border-collapse: collapse;\n    margin: 15px 0;\n  }\n\n  table.hestia-nested-table th, \n  table.hestia-nested-table td {\n    border: 1px solid #cccccc !important;\n    padding: 12px 15px;\n    text-align: left;\n    font-size: 15px;\n  }\n\n  \/* Table Header Styling *\/\n  table.hestia-nested-table th {\n    font-weight: bold;\n    background-color: #e0e0e0 !important; \/* Slightly darker grey for header contrast *\/\n    color: #333333 !important;\n  }\n\n  \/* Explicit Grey Cells for the Return Periods *\/\n  table.hestia-nested-table tbody tr td.bg-grey {\n    background-color: #f5f5f5 !important; \/* Light Grey background *\/\n    color: #333333 !important;\n    font-weight: normal !important;        \/* Forces normal text weight *\/\n  }\n\n  \/* Specific Hazard Level Cell Colors *\/\n  table.hestia-nested-table tbody tr td.hazard-low-yellow {\n    background-color: #ffeb3b !important; \/* Bright Yellow *\/\n    color: #333333 !important;\n    font-weight: normal !important;        \/* Forces normal text weight *\/\n  }\n\n  table.hestia-nested-table tbody tr td.hazard-med-orange {\n    background-color: #ff9800 !important; \/* Bright Orange *\/\n    color: #333333 !important;\n  }\n\n  table.hestia-nested-table tbody tr td.hazard-high-red {\n    background-color: #f44336 !important; \/* Bright Red *\/\n    color: #ffffff !important;\n  }\n<\/style>\n\n<table class=\"hestia-nested-table\">\n  <thead>\n    <tr>\n      <th>Return Period<\/th>\n      <th>Hazard Level<\/th>\n    <\/tr>\n  <\/thead>\n  <tbody>\n    <tr>\n      <td class=\"bg-grey\">2-year<\/td>\n      <td class=\"hazard-low-yellow\">Low<\/td>\n    <\/tr>\n    <tr>\n      <td class=\"bg-grey\">5-year<\/td>\n      <td class=\"hazard-med-orange\">Medium<\/td>\n    <\/tr>\n    <tr>\n      <td class=\"bg-grey\">20-year<\/td>\n      <td class=\"hazard-high-red\">High<\/td>\n    <\/tr>\n  <\/tbody>\n  <tfoot>\n    <tr>\n      <td colspan=\"2\">Table 1: Return period thresholds associated with each hazard level of the pluvial hazard in urban areas layer<\/td>\n    <\/tr>\n  <\/tfoot>\n<\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-122cbf8 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"122cbf8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"750\" height=\"800\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/40_pluvial_hazard-960x1024.png\" class=\"attachment-large size-large wp-image-4897\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/40_pluvial_hazard-960x1024.png 960w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/40_pluvial_hazard-281x300.png 281w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/40_pluvial_hazard-768x819.png 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/40_pluvial_hazard-1440x1536.png 1440w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/40_pluvial_hazard.png 1778w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">An example of the pluvial hazard in urban areas layer in Valencia.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1bd98f7 elementor-widget elementor-widget-heading\" data-id=\"1bd98f7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">References<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-736038d elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"736038d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9172896 elementor-widget elementor-widget-text-editor\" data-id=\"9172896\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Berenguer, M., Sempere-Torres, D., Pegram, G.G.S. 2011. SBMcast \u2013 An ensemble nowcasting technique to assess the uncertainty in rainfall forecasts by Lagrangian extrapolation. Journal of Hydrology 404 (3-4), pp 226-240, <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0022169411002940?via%3Dihub\" target=\"_blank\" rel=\"noopener\">10.1016\/j.jhydrol.2011.04.033<\/a><\/li><li>Park, S., Berenguer, M., Sempere-Torres, D., &amp; Von Lerber, A. 2024. The EUMETNET OPERA radar network: European-wide precipitation composites supporting rainfall-induced flash flood emergency management. In Responding to Extreme Weather Events (pp. 105\u2013118). Wiley, <a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/9781119741374.ch5\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1002\/9781119741374.ch5<\/a><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-588129152\" role=\"tabpanel\" aria-labelledby=\"storm-impact\" data-tab-index=\"2\" style=\"--n-tabs-title-order: 2;\" class=\" elementor-element elementor-element-8500d66 e-con-full e-flex e-con e-child\" data-id=\"8500d66\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-21c4ea1 e-flex e-con-boxed e-con e-child\" data-id=\"21c4ea1\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d2ffd83 elementor-widget elementor-widget-heading\" data-id=\"d2ffd83\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Storm Impact Forecasting<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-845893c elementor-widget elementor-widget-text-editor\" data-id=\"845893c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span>The storm impact layers show lightning, wind and precipitation hazard and risk forecasted for the next three hours with 15-minute time steps. The products are shown in the same grid with the OPERA radar composites at spatial resolution of 2 kilometers.<\/span><\/p><p><span>Storm hazard levels shown in the layers are divided into three classes having colours yellow (low), orange (moderate) and red (severe). The ranges of these classes are listed in Table 1. Precipitation below the minimum threshold of 1 mm\/h or missing values are not shown.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73a17b9 elementor-widget elementor-widget-text-editor\" data-id=\"73a17b9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<table class=\"hestia-nested-table\">\n<tbody>\n<tr>\n<td><\/td>\n<td>Lightning (flashes \/ 6*6 km \/ 30 minutes)<\/td>\n<td>Precipitation (mm\/h)<\/td>\n<td>Wind (m\/s)<\/td>\n<\/tr>\n<tr class=\"red-cell\">\n<td>High<\/td>\n<td>&gt; 50<\/td>\n<td>&gt; 10<\/td>\n<td>&gt; 40<\/td>\n<\/tr>\n<tr class=\"orange-cell\">\n<td>Moderate<\/td>\n<td>10-50<\/td>\n<td>5-10<\/td>\n<td>30-40<\/td>\n<\/tr>\n<tr class=\"yellow-cell\">\n<td>Low<\/td>\n<td>1-10<\/td>\n<td>1-5<\/td>\n<td>20-30<\/td>\n<\/tr>\n<tr>\n<td>No hazard<\/td>\n<td>&lt; 1<\/td>\n<td>&lt; 1<\/td>\n<td>&lt; 20<\/td>\n<\/tr>\n<\/tbody>\n<tfoot>\n<tr>\n<td colspan=\"4\">Table 1: Ranges of hazard classes corresponding to the predicted meteorological quantities.<\/td>\n<\/tr>\n<\/tfoot>\n<\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bc4cdc1 elementor-widget elementor-widget-text-editor\" data-id=\"bc4cdc1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span>The risk level is assigned based on exposure. In the storm risk products the exposure information is population density divided into classes listed in Table 2.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b44c2b3 elementor-widget elementor-widget-text-editor\" data-id=\"b44c2b3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<style>\n  .hestia-nested-table {\n    width: 100%;\n    border-collapse: collapse;\n    margin: 15px 0;\n  }\n\n  .hestia-nested-table td {\n    border: 1px solid #cccccc;\n    padding: 12px 15px;\n    text-align: left;\n    font-size: 15px;\n    color: #333333;\n    background-color: #ffffff;\n  }\n\n  \/* Bold header row *\/\n  .hestia-nested-table tr:first-child td {\n    font-weight: bold;\n    background-color: #f2f2f2;\n  }\n\n  \/* Colored rows (data rows only) *\/\n  .hestia-nested-table tr:nth-child(2) td {\n    background-color: #f44336; \/* red *\/\n    color: #ffffff;\n  }\n\n  .hestia-nested-table tr:nth-child(3) td {\n    background-color: #ff9800; \/* orange *\/\n  }\n\n  .hestia-nested-table tr:nth-child(4) td {\n    background-color: #ffeb3b; \/* yellow *\/\n  }\n<\/style>\n\n<table class=\"hestia-nested-table\">\n  <tbody>\n    <tr>\n      <td>Exposure<\/td>\n      <td>Population (people \/ km2)<\/td>\n    <\/tr>\n    <tr>\n      <td>High<\/td>\n      <td>> 50<\/td>\n    <\/tr>\n    <tr>\n      <td>Moderate<\/td>\n      <td>10-50<\/td>\n    <\/tr>\n    <tr>\n      <td>Low<\/td>\n      <td>1-10<\/td>\n    <\/tr>\n    <tr>\n      <td>No exposure<\/td>\n      <td>< 1<\/td>\n    <\/tr>\n  <\/tbody>\n\n  <tfoot>\n    <tr>\n      <td colspan=\"2\">\n        Table 2: Ranges for exposure classes based on population.\n      <\/td>\n    <\/tr>\n  <\/tfoot>\n<\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f2d1fc6 elementor-widget elementor-widget-text-editor\" data-id=\"f2d1fc6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Storm risk is calculated from storm hazard by multiplying hazard level (0-3) with exposure level (0-3). This results into a risk level in the range between 0 (no risk) and 9 (severe) shown in yellow-to-red colours, as illustrated in Figures 1 and\u00a0 2.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c40f001 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"c40f001\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"750\" height=\"526\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/54_fmi_risk_matrix.png\" class=\"attachment-large size-large wp-image-5043\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/54_fmi_risk_matrix.png 813w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/54_fmi_risk_matrix-300x210.png 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/54_fmi_risk_matrix-768x538.png 768w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 1: Risk levels from combinations of different hazard and exposure levels.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4368567 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"4368567\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"198\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/55_fmi_risk_calculation-1024x271.png\" class=\"attachment-large size-large wp-image-5044\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/55_fmi_risk_calculation-1024x271.png 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/55_fmi_risk_calculation-300x79.png 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/55_fmi_risk_calculation-768x203.png 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/55_fmi_risk_calculation-1536x406.png 1536w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/55_fmi_risk_calculation-2048x541.png 2048w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 2: One-hour precipitation hazard nowcast at 2024-05-31 07:00 (left), the corresponding exposure (population, middle) and the risk nowcast produced by multiplying the predicted hazard levels with exposure levels.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab7c8c3 elementor-widget elementor-widget-heading\" data-id=\"ab7c8c3\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"precipitation-hazard-risk\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Precipitation hazard and risk<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b43ebb4 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"b43ebb4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fba9f34 elementor-widget elementor-widget-text-editor\" data-id=\"fba9f34\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Nowcasts of precipitation hazard and risk are produced by using a convolutional neural network. The network architecture used here is Simpler yet Better Video Prediction (SimVP, Tan et al. 2025). Post-processing is applied to ensure that the predictions are consistent with the observed precipitation (Pulkkinen et al. 2019) and to generate ensemble nowcasts (Pulkkinen et al. 2021). Differently to the extrapolation method used in the meteorological layers group, the neural network is not only moving the precipitation but also predicting intensity changes. However, it generally cannot predict initiation of new storm cells. The neural network can also indicate uncertainty that increases with forecast length. This is illustrated in Figure 3 showing the changing intensity and smoothing to filter small-scale precipitation that cannot be predicted reliably. The corresponding risk nowcast is shown in Figure 4.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2a32e5a elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"2a32e5a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"464\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/56_precip_hazard_nowcast-1024x634.png\" class=\"attachment-large size-large wp-image-5045\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/56_precip_hazard_nowcast-1024x634.png 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/56_precip_hazard_nowcast-300x186.png 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/56_precip_hazard_nowcast-768x476.png 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/56_precip_hazard_nowcast.png 1095w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 3: Precipitation hazard nowcasts at 2026-03-14 07:15 UTC with lead times of 15 minutes (left) and 3 hours (right). The hazard nowcasts are shown on top of radar reflectivity layer, where precipitation is moved by extrapolation.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-566847e elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"566847e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"528\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/57_precip_risk_example.png\" class=\"attachment-large size-large wp-image-5046\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/57_precip_risk_example.png 994w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/57_precip_risk_example-300x211.png 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/57_precip_risk_example-768x541.png 768w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 4: Precipitation risk nowcast at 2026-03-14 07:15 UTC.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce93d59 elementor-widget elementor-widget-text-editor\" data-id=\"ce93d59\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The precipitation hazard levels are estimated from an ensemble of nowcasts representing alternative scenarios. For each hazard level displayed in Table 1, the exceedance probability of each level in each grid cell is estimated from the ensemble. The probabilities are converted into a \u201cyes\u201d or \u201cno\u201d prediction for the exceedance. The conversion is done by using probability thresholds that minimize false alarms and maximize hits. Then a maximum hazard level with a \u201cyes\u201d prediction is taken. This is illustrated in Figure 5. However, the probability information is not currently shown to the user. This will be implemented in a future version.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-70247cb elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"70247cb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"257\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/58_neural_net_method-1024x351.png\" class=\"attachment-large size-large wp-image-5047\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/58_neural_net_method-1024x351.png 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/58_neural_net_method-300x103.png 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/58_neural_net_method-768x264.png 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/58_neural_net_method-1536x527.png 1536w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/58_neural_net_method-2048x703.png 2048w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 5: Ensemble precipitation nowcast from neural network (left), optimal probability thresholds that minimize false alarms and maximize hits when probabilities are converted into \u201cyes\u201d or \u201cno\u201d predictions (middle) and taking the maximum hazard level with \u201cyes\u201d prediction to obtain the final hazard level.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c5f0cc6 elementor-widget elementor-widget-heading\" data-id=\"c5f0cc6\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"lightning-hazard-risk\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Lightning hazard and risk<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-36a47a4 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"36a47a4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-91cbe8d elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"91cbe8d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"480\" height=\"544\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/59_nordlis_network.png\" class=\"attachment-large size-large wp-image-5048\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/59_nordlis_network.png 480w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/59_nordlis_network-265x300.png 265w\" sizes=\"(max-width: 480px) 100vw, 480px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 6: Coverage of the NORDLIS flash observation network.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62db8cb elementor-widget elementor-widget-text-editor\" data-id=\"62db8cb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span>Lightning hazard and risk products are implemented in a limited area. Cloud-to-ground flash observations are used from the NORDLIS observation network shown in <\/span>Figure <span>6. The observations are gridded into flash density, as illustrated in <\/span>Figure <span>7. Each grid cell in the flash density contains the number of flashes in a 6 x 6 km spatial window centered at the cell and in a time window containing the previous 30 minutes. Nowcasts of the flash density are produced by using the extrapolation method implemented in pySTEPS (Pulkkinen et al. 2019) without predicting intensity changes. Then the method described above is applied to assign hazard and risk levels. This is done from the deterministic nowcast without generating an ensemble. Due to limitations of the extrapolation method, it is advised to use this product only when looking at the next 30 minutes. Predicting intensification or weakening of lightning activity will be implemented in a future version.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-025b437 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"025b437\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"507\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/60_lightning_obs_example-1024x692.png\" class=\"attachment-large size-large wp-image-5049\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/60_lightning_obs_example-1024x692.png 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/60_lightning_obs_example-300x203.png 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/60_lightning_obs_example-768x519.png 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/60_lightning_obs_example.png 1107w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 7: Example of lightning flash observations in a 30-minute time window and flash density calculated from the point observations. The selected time is at 2025-07-28 11:30 UTC.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b77d178 elementor-widget elementor-widget-heading\" data-id=\"b77d178\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"wind-hazard-risk\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Wind hazard and risk<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6542884 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"6542884\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b5afb09 elementor-widget elementor-widget-text-editor\" data-id=\"b5afb09\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span>The wind hazard and risk products are based on wind u- and v-components from ECMWF\u2019s Integrated Forecasting System (IFS) model. Deterministic HRES forecasts at height of 10 meters above ground level are used. The forecasts are queried from the ECMWF data feed via the API provided by the FMI. In this way new forecasts are available every 12 hours with 3-hour time steps. The u- and v-components are processed into wind speed (m\/s). Interpolation is applied to estimate the wind speed at 15-minute time steps shown on the platform in the 2 x 2 km OPERA grid (the original grid is in latitude-longitude projection with resolution of 0.1<sup>o<\/sup>). The resulting hazard and risk products by applying the above method are illustrated in Figure 8.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c38b7da elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"c38b7da\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"208\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/61_wind_risk_example-1024x284.png\" class=\"attachment-large size-large wp-image-5050\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/61_wind_risk_example-1024x284.png 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/61_wind_risk_example-300x83.png 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/61_wind_risk_example-768x213.png 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/61_wind_risk_example.png 1146w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 8: Example of wind hazard (left) and risk products (right) shown in the platform at 2026-01-08 21:00 UTC.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-db9569e elementor-widget elementor-widget-heading\" data-id=\"db9569e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">References<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1236206 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"1236206\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-29999de elementor-widget elementor-widget-text-editor\" data-id=\"29999de\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Pulkkinen S., Chandrasekar, V., and Niemi, T., 2021, Lagrangian Integro-Difference Equation Model for Precipitation Nowcasting, J. Atmos. Ocean. Technol., 38, 2125-2145, <a href=\"https:\/\/doi.org\/10.1175\/JTECH-D-21-0013.1\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1175\/JTECH-D-21-0013.1<\/a><\/li><li>Pulkkinen, S., Nerini, D., P\u00e9rez Hortal A. A., Velasco-Forero, C., Seed, A., Germann, U. and Foresti, L., 2019, Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0), Geosci. Model Dev., 12, 4185-4219, <a href=\"https:\/\/doi.org\/10.5194\/gmd-12-4185-2019\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.5194\/gmd-12-4185-2019<\/a><\/li><li>Tan, C., Gao, Z., Li, S. and Li, S. Z., 2025, SimVPv2: Towards Simple Yet Powerful Spatiotemporal Predictive Learning, IEEE Trans. Multimedia, 27, 5170 &#8211; 5184, <a href=\"https:\/\/doi.org\/10.1109\/TMM.2025.3543051\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1109\/TMM.2025.3543051<\/a><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-588129153\" role=\"tabpanel\" aria-labelledby=\"river-flash-flood\" data-tab-index=\"3\" style=\"--n-tabs-title-order: 3;\" class=\" elementor-element elementor-element-1860d8c e-con-full e-flex e-con e-child\" data-id=\"1860d8c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-4b60cf0 e-flex e-con-boxed e-con e-child\" data-id=\"4b60cf0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e41ba9d elementor-widget elementor-widget-heading\" data-id=\"e41ba9d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">River Flash Flood Impact Forecasting<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cc763d5 elementor-widget elementor-widget-text-editor\" data-id=\"cc763d5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span>The river flash flood impact product identifies rivers, with an upstream drainage area greater than 50 km<sup>2<\/sup> and less than 2000 km<sup>2<\/sup>, where there is a risk of flash flood impacts over the next 5-days. An animated example of this product is shown below in Figure 1.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0de6e12 elementor-widget elementor-widget-html\" data-id=\"0de6e12\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div style=\"max-width: 100%; \">\n  <img decoding=\"async\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/htbewgqee6tnld8z8dvb.gif\" style=\"max-width: 100%; height: auto; display: block;\">\n<\/div>\n\n<div style=\"text-align: left; margin-top: 12px; padding-left: 2px;\">\n  <span style=\"font-size: 15px; color: #777777; font-style: italic; font-family: 'Roboto', sans-serif; font-weight: 400; display: block;\">\n    Figure 1: Example of the river flash flood impact forecast layers in northern France and Belgium at 19:00 UTC on the 10th February 2025.\n  <\/span>\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9cabac0 elementor-widget elementor-widget-text-editor\" data-id=\"9cabac0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The product is created in three steps (Figure 2):<\/p><ol><li>Creating a blended ensemble precipitation forecast by combining radar nowcasts and ECMWF IFS NWP.<\/li><li>Calculating the flash flood hazard in the river network, expressed as the probability of exceeding the 2-year return period threshold.<\/li><li>Converting the forecasted flash flood hazard into potential impacts by combining with a combined exposure layer (which accounts for population and critical infrastructure).<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d6c0368 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"d6c0368\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"271\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/63_fImpact_workflow-1024x370.jpg\" class=\"attachment-large size-large wp-image-3648\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/63_fImpact_workflow-1024x370.jpg 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/63_fImpact_workflow-300x108.jpg 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/63_fImpact_workflow-768x277.jpg 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/63_fImpact_workflow-1536x554.jpg 1536w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/63_fImpact_workflow-2048x739.jpg 2048w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 2: Workflow used to compute the flash flood impact forecast layers.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eab8883 elementor-widget elementor-widget-heading\" data-id=\"eab8883\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"blended-precipitation\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Blended Ensemble Precipitation Forecasting<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8d2b46a elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"8d2b46a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e409811 elementor-widget elementor-widget-text-editor\" data-id=\"e409811\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>For the first 5-hours lead time, an hourly ensemble forecast is created by blending the radar nowcast and the ECMWF IFS. Beyond 5-hours lead time, the precipitation forecast is obtained only from the ECMWF IFS ensemble at 6-hourly timesteps up to 120-hours lead time. The 80th percentile from the ensemble forecast is computed and used to create the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#precipitation-seamless\">Seamless Precipitation Accumulation<\/a> and <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#precipitation-80\">Hourly precipitation 80th percentile products.<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-813e956 elementor-widget elementor-widget-text-editor\" data-id=\"813e956\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Blended Precipitation for Lead Times 0-5 hours<\/strong><\/p><p>For the first 5-hours of the forecast lead time, the precipitation accumulations have been obtained by the blending of the:<\/p><ul><li>20-member ensemble nowcast of 1-hour accumulated precipitation, generated using SBMcast (Berenguer et al., 2011) using the gauge-adjusted rainfall estimates from the pan-European OPERA radar composite (Park et al. 2019). These have a spatial resolution of 2 km.<\/li><li>51-member ensemble forecast of 1-hour accumulated precipitation generated by Numerical Weather Prediction (NWP) from the ECMWF Ensemble Prediction System (EPS). These data have a spatial resolution of 9 km, they are interpolated onto the same 2 km resolution as the nowcast data using a nearest neighbour method.<\/li><\/ul><p>The blending technique (Wong et al. 2009) is applied for the first 5-hours and involves 4 steps (Figure 3):<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f39af90 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"f39af90\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"268\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/64_blending_workflow-1024x366.jpg\" class=\"attachment-large size-large wp-image-3655\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/64_blending_workflow-1024x366.jpg 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/64_blending_workflow-300x107.jpg 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/64_blending_workflow-768x275.jpg 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/64_blending_workflow-1536x549.jpg 1536w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/64_blending_workflow-2048x732.jpg 2048w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 3: Workflow to compute the blended rainfall forecasts using radar and NWP data.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5bc8bd elementor-widget elementor-widget-text-editor\" data-id=\"d5bc8bd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ol>\n    <li style=\"margin-bottom: 25px;\">\n        <div style=\"margin-bottom: 12px;\">Phase shifting and bias correcting the NWP forecasted precipitation to ensure a closer between the locations of rain in the NWP and radar nowcasts<\/div>\n        <ul>\n            <li>For each member of the NWP forecast, an optical flow method is used to compute the phase shift in the first 1-hour time step between the forecasted precipitation in the NWP ensemble member and the first ensemble member of the nowcast<\/li>\n            <li>The computed phase shift is applied to the accumulated precipitation forecasted by the NWP for each hourly time step up to a lead time of 6 hours<\/li>\n            <li>For each member of the NWP forecast, a scale factor is computed in the first 1-hour time step in each grid cell between the accumulated precipitation from the first member of the ensemble nowcast and the NWP forecast and the first member of the ensemble nowcast at the first time step is computed\n                <ul style=\"list-style-type: square;\">\n                    <li>A Gaussian filter is applied to the scale factor grid which was computed above<\/li>\n                    <li>The phase shift corrected accumulated precipitation forecasted by the NWP in each time step, up to a maximum lead time of 6 hours, is multiplied by the scale factor<\/li>\n                <\/ul>\n            <\/li>\n        <\/ul>\n    <\/li>\n\n    <li style=\"margin-bottom: 25px;\">\n        <div style=\"margin-bottom: 12px;\">A spatial weighting factor, <em>w<sub>s<\/sub><\/em>, is computed by applying a hyperbolic tangent function to the area within the EUMETNET OPERA radar coverage, to give greater weight to the radar nowcast data in areas which are closer to the locations of radar stations<\/div>\n        <ul>\n            <li>A temporal weighting factor, <em>w<sub>t<\/sub><\/em>, is computed for each 1-hour time step of the 6-hour forecast lead time using a hyperbolic tangent function. This gives more weight to the accumulated precipitation from the NWP forecast at longer lead times<\/li>\n        <\/ul>\n    <\/li>\n\n    <li style=\"margin-bottom: 25px;\">Ensemble member matching of each member of the phase shifted and bias corrected NWP ensemble forecast (see the first two steps) to the member of the radar-based nowcast which has the smallest difference &#8211; computed as the sum of the square differences divided by the sum of the normalised square differences<\/li>\n\n    <li>Compute blended forecast, <em>Tp<sub>Blend<\/sub><\/em>, by adding each ensemble member, <em>m<\/em>, of the phase shifted and bias corrected NWP precipitation forecast, <em>Tp<sub>NWP<\/sub><\/em>, to the precipitation from the closest matching member of the radar-based nowcast, <em>Tp<sub>Nowcast<\/sub><\/em>. Spatial and temporal weighting factors (<em>w<sub>s<\/sub><\/em> and <em>w<sub>t<\/sub><\/em> respectively) are multiplied to both the NWP and nowcast precipitation forecasts. This produces a blended forecast of accumulated precipitation at each 1-hour time step, <em>t<\/em>, for the first 6 hours lead time:<\/li>\n<\/ol>\n\n<div style=\"text-align: center; margin: 30px 0 20px 0; font-family: 'Times New Roman', serif; font-size: 1.15em;\">\n    <em>Tp<sub>Blend t,m<\/sub><\/em> = <em>Tp<sub>Nowcast t,m<\/sub><\/em> * <em>w<sub>s<\/sub><\/em> * <em>w<sub>t<\/sub><\/em> + <em>Tp<sub>NWP t,m<\/sub><\/em> * (1 &#8211; <em>w<sub>s<\/sub><\/em> * <em>w<sub>t<\/sub><\/em>)\n<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b6dd0aa elementor-widget elementor-widget-text-editor\" data-id=\"b6dd0aa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>NWP Precipitation for Lead Times 6-120 hours<\/strong><\/p><p>For lead times from 6-120 hours, the accumulated precipitation is only from the 51-members ECMWF ensemble NWP forecast. This dataset has a spatial resolution of 9 km, it is regridded onto the same 2 km grid as the radar-based nowcasts using a nearest neighbour method. The forecasts at this lead time range have a time step of 6-hours.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c862faa elementor-widget elementor-widget-text-editor\" data-id=\"c862faa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Calculating the 80th Percentile<\/strong><\/p><p>At each 1-hour time step for the first 5-hours lead time, the accumulated precipitation associated with the 80th percentile is computed from the 51-member blended forecast and is displayed in the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#precipitation-80\">Hourly Precipitation 80th percentile layer<\/a>. For each 6-hour time step for lead times from 6 to 120-hours, the accumulated precipitation associated with the 80th percentile is computed from the 51-member ECMWF NWP forecasts. The total precipitation associated with the 80th percentile is accumulated over lead time windows of 0-6, 6-24, 24-48 and 48-120 hours and is displayed in the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#precipitation-seamless\">Seamless Precipitation Accumulation<\/a> layer.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9b00e1f elementor-widget elementor-widget-heading\" data-id=\"9b00e1f\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"ff-impact-river\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Computation of Flash Flood Impacts on the River Network<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9682d75 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"9682d75\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba032e5 elementor-widget elementor-widget-text-editor\" data-id=\"ba032e5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Flash flood impacts on the river network are computed in two steps: 1) a river flash flood hazard is computed by accumulating rainfall on the river network and computing the exceedance probability of the 2-year return period, 2) the exceedance probability is then overlaid with exposure information to estimate the flash flood impact level.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0097de9 elementor-widget elementor-widget-heading\" data-id=\"0097de9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">River Flash Flood Hazard Forecasts<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e9f004c elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"e9f004c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-13e2b01 elementor-widget elementor-widget-text-editor\" data-id=\"13e2b01\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Flash flood hazard is computed in three steps (Figure 4): 1) rainfall is accumulated on a channel network, 2) the accumulated rainfall is compared against the 2-year return period climatological threshold to identify grid cells where it is exceeded, 3) the 2-year return period exceedance is computed for all forecast ensemble members to compute the probability of exceedance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f083ef6 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"f083ef6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"313\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/65_fHazard_workflow-1024x428.jpg\" class=\"attachment-large size-large wp-image-4134\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/65_fHazard_workflow-1024x428.jpg 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/65_fHazard_workflow-300x125.jpg 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/65_fHazard_workflow-768x321.jpg 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/65_fHazard_workflow-1536x642.jpg 1536w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/65_fHazard_workflow-2048x857.jpg 2048w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 4: Workflow used to compute the river flash flood hazard by accumulating rainfall on the river network and comparing against climatological thresholds.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e418782 elementor-widget elementor-widget-text-editor\" data-id=\"e418782\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"margin-bottom: 25px;\">At each time step of the forecast, at each grid cell with an upstream drainage area &ge;50 km<sup>2<\/sup> and &le;2000 km<sup>2<\/sup>, the rainfall is summarised as the total amount which was forecasted to fall across the area upstream of the grid cell over the preceding time period equal to the river&#8217;s time of concentration, <em>T<sub>c<\/sub><\/em>. The time of concentration refers to the time between rain falling and the river reaching its peak, it was computed at each grid cell using the method of Giandotti, 1934 which was derived for small rural catchments:<\/p>\n\n<div style=\"text-align: center; margin: 25px 0; font-family: 'Times New Roman', serif; font-size: 1.15em;\">\n    <em>Tc<\/em> = (4&radic;<em>A<\/em> + 1.5<em>L<\/em>) \/ 0.8 &radic;<em>H<\/em>\n<\/div>\n\n<p style=\"margin-bottom: 25px;\">where <em>A<\/em> = upstream drainage area (km<sup>2<\/sup>), <em>L<\/em> = the slope length (km) and <em>H<\/em> = difference between the mean elevation in the area upstream of the grid cell and the elevation of the grid cell (m)<\/p>\n\n<div style=\"margin-bottom: 25px;\">\n    <div style=\"margin-bottom: 12px;\">The accumulated rainfall is compared against a climatological threshold, which is the summarised rainfall associated with the 2-year return period. This climatological threshold is derived from the following datasets:<\/div>\n    <ul>\n        <li style=\"margin-bottom: 8px;\">8-year gauge-adjusted OPERA radar rainfall data<\/li>\n        <li>A dataset of 20-year reforecasts obtained with ECMWF Integrated Forecasting System (IFS)<\/li>\n    <\/ul>\n<\/div>\n\n<p style=\"margin-top: 25px;\">At each location on the river network and each forecast time step, the total number of ensemble members which exceed the summarised rainfall associated with the 2-year return period is computed and then divided by the total number of ensemble members (51) to give a probability of exceedance (%).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f38159 elementor-widget elementor-widget-heading\" data-id=\"2f38159\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"ff-impact-prediction\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">River Flash Flood Impact Prediction<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-786ea1e elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"786ea1e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f7f2512 elementor-widget elementor-widget-text-editor\" data-id=\"f7f2512\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>River flash flood impact level is computed by intersecting the flash flood hazard, computed in the previous step, with a static dataset of combined population and critical infrastructure exposure (Figure 5).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb705e6 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"cb705e6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"327\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/66_fImpact_workflow-1024x447.jpg\" class=\"attachment-large size-large wp-image-4147\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/66_fImpact_workflow-1024x447.jpg 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/66_fImpact_workflow-300x131.jpg 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/66_fImpact_workflow-768x335.jpg 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/66_fImpact_workflow.jpg 1224w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 5: Workflow to compute the river flash flood impact forecast, by combining the flash flood hazard forecast and exposure information within an impact matrix.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7f4f5b3 elementor-widget elementor-widget-text-editor\" data-id=\"7f4f5b3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The exposure data accounts for population and critical infrastructure in the form of health, education, transport, and energy generation facilities. Population data were obtained from the GHSL (<a href=\"https:\/\/human-settlement.emergency.copernicus.eu\/\" target=\"_blank\" rel=\"noopener\">Global Human Settlement Layer<\/a>), the critical infrastructure data were from <a href=\"https:\/\/www.nature.com\/articles\/s41597-019-0135-1\" target=\"_blank\" rel=\"noopener\">HARCI-EU<\/a> within EU member states and OpenStreetMap for non-EU member states. Data for each of these five categories was harmonised and combined with equal weighting to create a combined exposure layer whose values ranged from 1.0-2.0. The resulting dataset is available on the web platform as the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#static-layers\">Exposure<\/a> layer.<\/p><p>The river flash flood impact level is computed at each time step by intersecting the river flash flood hazard forecast with the combined exposure dataset. The low, medium, and high values for flash flood hazard likelihood on the y-axis of the impact matrix indicate where there is a 5%-50%, 50%-80%, and &gt;80% probability of exceeding the 2-year return period threshold.<\/p><p>The categories for low, medium, and high exposure, on the x-axis of the impact matrix, relate to combined exposure values of 1.0-1.3, 1.3-1.6 and 1.6-2.0 respectively. These categories were chosen based on the statistical distribution of exposure values across the EFAS domain, and consequently 81.0% of the exposure values are classified as low exposure, 8.0% as medium exposure and 1.2% as high exposure. This reflects the reality that most grid points in Europe have a low population density and with few exposed critical infrastructures<\/p><p>This combination of flash flood hazard level with exposure gives impact levels for each grid cell on the river network at each time step of the 120 hour forecast period. The data for the first 5-hours lead time are visualised on the web platform as the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#ff-impact-river\">River flash flood impact over river network<\/a> layer.<\/p><p>The forecasted river flash flood impact level is summarised onto river sub-catchments by computing the 90th percentile of the flash flood impact level across all grid cells within each sub-catchment. The 90th percentile was chosen rather than the maximum to prevent sub-catchments being assigned an impact level based upon a single grid cell value. The sub-catchment summary is displayed on the web platform as the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#ff-impact-sub-catchment\">River flash flood impact over sub-catchment<\/a> layer.<\/p><p>Flash flood impacts in each sub-catchment are also summarised over lead time windows of 0-6, 6-24, 24-48 and 48-120 hours, by computing the maximum flash flood impact level in each grid cell in each lead time window. Then the 90th percentile of the flash flood impact level for each lead time window is computed in each sub-catchment. The results are shown in the <a href=\"https:\/\/mistral.upc.es\/inline-platform\/layers-reference\/#flash-flood-sub-catchment-destino\">Flash flood impact over sub-catchment<\/a> layer (Figure 6). When this layer is clicked in the web platform, a pop out window appears containing additional information about the total affected population and the critical infrastructure assets. These are computed by summarising the total population and infrastructure assets in grid cells where the forecasted flash flood impact level is low or above. The time of the peak is computed by identifying the lead time when the maximum impact level is forecasted.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c25c3f9 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"c25c3f9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"750\" height=\"277\" src=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/67_fImpact_example-1024x378.jpg\" class=\"attachment-large size-large wp-image-4163\" alt=\"\" srcset=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/67_fImpact_example-1024x378.jpg 1024w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/67_fImpact_example-300x111.jpg 300w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/67_fImpact_example-768x284.jpg 768w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/67_fImpact_example-1536x567.jpg 1536w, https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/05\/67_fImpact_example-2048x756.jpg 2048w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Figure 6: Example of the river flash flood impact forecast summary over sub-catchments for 0-6 hours lead time in France near Grenoble at 20:00 UTC on the 29th October 2025.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fc36e04 elementor-widget elementor-widget-heading\" data-id=\"fc36e04\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">References<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e14aa2 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"3e14aa2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ffbf99a elementor-widget elementor-widget-text-editor\" data-id=\"ffbf99a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Berenguer, M., Sempere-Torres, D. and Pegram, G, 2011: SBMcast &#8211; An ensemble nowcasting technique to assess the uncertainty in rainfall forecasts by Lagrangian extrapolation. Journal of Hydrology, 404(3), 226-240<\/li><li>Park, S., Berenguer, M. and Sempere-Torres, D, 2019: Long-term analysis of gauge-adjusted radar rainfall accumulations at European Scale. Journal of Hydrology, 573, 768-777<\/li><li>Wong, W., Yeung, L., Wang, Y. and Chen, M, 2009: <a href=\"https:\/\/www.hko.gov.hk\/en\/hkonews\/files\/r844.pdf\" target=\"_blank\" rel=\"noopener\">Towards the Blending of NWP with Nowcast &#8211; Operation Experience in B08FDP<\/a>. WMO Symposium on Nowcasting, 30 Aug- 4 Sep, Whistler, Canada.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1be3e1c elementor-widget elementor-widget-html\" data-id=\"1be3e1c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<script>\ndocument.addEventListener('DOMContentLoaded', function() {\n    function handleFlexibleTabAnchor() {\n        var hash = window.location.hash;\n        if (!hash) return;\n\n        \/\/ 1. 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[&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/template-fullwidth.php","meta":{"footnotes":""},"class_list":["post-3051","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Explanation - INLINE Platform Documentation<\/title>\n<meta name=\"description\" content=\"Find out all the information about the upcoming meetings and workshops organised by EDERA project.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mistral.upc.es\/inline-platform\/explanation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Explanation - INLINE Platform Documentation\" \/>\n<meta property=\"og:description\" content=\"Find out all the information about the upcoming meetings and workshops organised by EDERA project.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/mistral.upc.es\/inline-platform\/explanation\/\" \/>\n<meta property=\"og:site_name\" content=\"INLINE Platform Documentation\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-26T10:31:47+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mistral.upc.es\/inline-platform\/wp-content\/uploads\/2026\/06\/52_rain_bias_adj.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1893\" \/>\n\t<meta property=\"og:image:height\" content=\"430\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"38 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/mistral.upc.es\\\/inline-platform\\\/explanation\\\/\",\"url\":\"https:\\\/\\\/mistral.upc.es\\\/inline-platform\\\/explanation\\\/\",\"name\":\"Explanation - 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