{"id":7541,"date":"2022-07-18T07:22:34","date_gmt":"2022-07-18T07:22:34","guid":{"rendered":"https:\/\/reuniwatt.com\/de\/?p=7541"},"modified":"2022-07-18T07:25:14","modified_gmt":"2022-07-18T07:25:14","slug":"wrf-solar-eps-eine-neue-probabilistische-vorhersagemethode-fuer-solarenergie","status":"publish","type":"post","link":"https:\/\/reuniwatt.com\/de\/firmennews\/wrf-solar-eps-eine-neue-probabilistische-vorhersagemethode-fuer-solarenergie\/","title":{"rendered":"WRF-Solar EPS: Eine neue, probabilistische Vorhersagemethode f\u00fcr Solarenergie"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1144px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-text fusion-text-1\" style=\"--awb-text-transform:none;\"><p><strong>Unter den vielen existierenden numerischen Wettervorhersagemodellen (NWP models \u00a0\u2013 numerical weather prediction models), ist das open-source Weather Research and Forecasting (WRF) Model das am meisten verbreitete und genutzte. Das erst k\u00fcrzlich entwickelte WRF-Solar Ensemble Prediction System (EPS) erm\u00f6glicht uns nun hoch aufgel\u00f6ste Ensemblevorhersagen der solaren Einstrahlung.<\/strong><\/p>\n<h2>Was ist WRF-Solar?<\/h2>\n<p>Immer mehr Photovoltaikanlagen werden weltweit installiert und damit werden intraday und day-ahead <a href=\"https:\/\/reuniwatt.com\/de\/daycast-day-ahead-solarstromprognose\/\">Solarstromvorhersagen<\/a> auch immer wichtiger. In numerischen Wettervorhersagemodellen ist die kurzwellige Strahlung keine Zustandsvariable, sondern eine diagnostische. Anders ausgedr\u00fcckt: NWP Modelle wurden nicht f\u00fcr solare Einstrahlungsprognosen entwickelt.<\/p>\n<p>WRF-Solar ist eine Weiterentwicklung des WRF-Models um eine bessere Simulation der Wechselwirkung zwischen Aerosolen, Wolken und Strahlung zu erreichen. In den letzten Jahren wurde das Weather Research and Forecasting (WRF) Model laufend verbessert und speziell auf ein pr\u00e4ziseres Modellieren der Wolke-Aerosol-Strahlungs-Interaktionen angepasst <sup>1,2<\/sup>. Zahlreiche Verbesserungen in den Parametrisierungsschemata des WRF-Models wurden implementiert <sup>3,4<\/sup>. Dadurch wurde eine starke Reduktion des Vorhersagefehlers der solaren Einstrahlung erzielt, besonders bei F\u00e4llen mit klarem, wolkenfreiem Himmel.<\/p>\n<h2>Was ist das WRF-Solar Ensemble Prediction System (EPS)?<\/h2>\n<p>WRF-Solar EPS ist eine Erweiterung der WRF-Solar Komponenten des WRF Models. Damit sich stochastische Perturbationen von Parametrisierungsvariablen m\u00f6glich. Gest\u00f6rt werden dabei jene Variablen, die am relevantesten f\u00fcr die den Boden erreichende solare Einstrahlung sind und diese wurden mit einer tangent linear Model identifiziert. Die stochastischen St\u00f6rungen in diesem state-of-art Vorhersagemodel sind speziell ausgew\u00e4hlt um die Unsicherheiten in der Darstellung der Aerosol-, Wolken- und Strahlungsprozesse zu repr\u00e4sentieren <sup>5<\/sup>. Ein hoch-qualitatives Ensemble Vorhersagesystem wie WRF-Solar EPS erlaubt es uns so, solare Energie Ressourcen zu identifizieren und quantifizieren, da jeder Ensemblemember ein m\u00f6gliches Zukunftsszenario darstellt. Der Spread der Ensemblevorhersage kann dabei eine Aussage \u00fcber Minima, Maxima und Unsicherheiten der solaren Energieprognose liefern und helfen die solare Einstrahlungsprognose nachhaltig zu verbessern.<\/p>\n<p>Diese interaktive Grafik zeigt beispielhaft die von WRF-Solar EPS vorhergesagte Globalstrahlung (GHI) f\u00fcr den 4. M\u00e4rz 2022 in Lindenberg (Deutschland). WRF berechnet die Vorhersage dabei in zwei Domains: Eine mit einem horizontalen Gitterabstand von 9km und eine zweite mit einem Gitterabstand von 3km. Der Spread des 5-Member-Ensembles unterscheidet sich von einer Domain zur anderen. Die Vergleichsbeobachtungsdaten stammen von aus dem DWD Open Data Portal.<\/p>\n<\/div><div>                        window.PlotlyConfig = {MathJaxConfig: 'local'};\n                        <div id=\"9a15e8b4-d6b5-497d-a92b-988e517a3d76\" class=\"plotly-graph-div\" style=\"height:100%;width:100%\"><\/div>                                                window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"9a15e8b4-d6b5-497d-a92b-988e517a3d76\")) {                    Plotly.newPlot(                        \"9a15e8b4-d6b5-497d-a92b-988e517a3d76\",                        [{\"mode\": \"lines\", \"name\": \"WRF-Solar EPS mean 3km, RMSE = 22\", \"type\": \"scatter\", \"x\": [\"2022-03-04T00:00:00+00:00\", \"2022-03-04T00:15:00+00:00\", \"2022-03-04T00:30:00+00:00\", \"2022-03-04T00:45:00+00:00\", \"2022-03-04T01:00:00+00:00\", \"2022-03-04T01:15:00+00:00\", \"2022-03-04T01:30:00+00:00\", \"2022-03-04T01:45:00+00:00\", 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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"WRFv4.4 GHI forecast at Lindenberg<br><span style=\"font-size: 12px\">20220304 initialized at 0 UTC, WRF-Solar EPS running with 5 ensemble members<\/span>\"}, \"xaxis\": {\"title\": {\"font\": {\"size\": 15}, \"text\": \"Time (UTC)\"}}, \"yaxis\": {\"title\": {\"font\": {\"size\": 15}, \"text\": \"GHI (W\/m2)\"}}},                        {\"responsive\": true}                    )                };                                    <\/div><div class=\"fusion-text fusion-text-2\" style=\"--awb-text-transform:none;\"><h2>Multi-layer Wolkenvorhersagen<\/h2>\n<p>Jedes NWP Model ist eine \u00dcbersetzung von physikalischen Gleichungen in Computercode. Da Prozesse in der Atmosph\u00e4re sich von Sekunden bis Tagen und Nanometern bis Kilometern abspielen, besteht jedes Wettermodel aus vielen Sch\u00e4tzungen und N\u00e4herungen. Das Model ist auch immer limitiert in seiner Aufl\u00f6sung, die die kleinsten und fl\u00fcchtigsten aufgel\u00f6sten Prozesse definiert. WRF erm\u00f6glicht es, Domains zu \u201anesten\u2018. Dabei wird eine Domain mit sehr hoher Aufl\u00f6sung in einer mit gr\u00f6beren Aufl\u00f6sung initialisiert. Dadurch, und im Vergleich mit wenig Rechenleistung, kann eine regional hochaufgel\u00f6ste Prognose erzielt werden und sogar feinskalige Prozesse k\u00f6nnen von dem Model berechnet werden.<\/p>\n<p>Diese Animation zeigt beispielhaft eine WRF Vorhersage der Wolken-Bedeckung (hohe, mittlere und tiefe Wolken) in Zentraleuropa mit dem bekannten Zwei-Domain-Setup. Die rechte Domain ist dabei in die linke \u201agenested\u2018 und daher auch nicht nur ein einfaches Hineinzoomen. Alle physikalischen Prozesse werden auch in der h\u00f6her aufgel\u00f6sten Domain berechnet, mit den Randbedingungen der gr\u00f6beren Domain. Die Anfangsbedingungen f\u00fcr diese stammen dabei aus dem Global Forecast System (GFS), das von NOAA betrieben wird. Man beachte die Unterschiede in den kleinskaligen Wolken zwischen den beiden Domains. Die Verbesserungen in den Parametrisierungen von WRF-Solar f\u00fchren hier zu realistischeren hochaufgel\u00f6sten Wolkenfeldern und -formationen.<\/p>\n<\/div><div class=\"fusion-video fusion-youtube\" style=\"--awb-max-width:1000px;--awb-max-height:563px;--awb-align-self:center;--awb-width:100%;\"><div class=\"video-shortcode\"><div class=\"fluid-width-video-wrapper\" style=\"padding-top:56.3%;\" ><iframe title=\"YouTube video player 1\" src=\"https:\/\/www.youtube.com\/embed\/cHU1JayE_cw?wmode=transparent&autoplay=0\" width=\"1000\" height=\"563\" allowfullscreen allow=\"autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture;\"><\/iframe><\/div><\/div><\/div><div class=\"fusion-text fusion-text-3\" style=\"--awb-text-transform:none;\"><h2>Vergleich der Wolkenmaske zwischen WRF und Satellit<\/h2>\n<p>Verifikation ist das Absch\u00e4tzen der F\u00e4higkeit eines Vorhersagemodels einen zuk\u00fcnftigen Zustand korrekt zu prognostizieren. Bezogen auf Wolkenvorhersagen ist es besonders wichtig den sogenannten forecast skill eines Models r\u00e4umlich zu quantifizieren. Hier bei Reuniwatt wird daf\u00fcr eine Wolkenmaske (Cloud Mask) von WRF- und WRF-Solar-Prognosen erstellt und mit einer Cloud Mask basierend auf Satellitenmessungen verglichen. Diese Fernerkundungsdaten helfen so eine Hit-or-Miss-Statistik zu erstellen, auf der wir unterschiedliche Metriken und forecast skill Berechnungen aufbauen k\u00f6nnen.<\/p>\n<p>Diese Grafik zeigt ein Beispiel eine von WRF abgeleitete Cloud Mask mit 3 km Gitterabstand im Vergleich mit den Satellitendaten. Der Differenzplot verdeutlicht Fl\u00e4chen, in denen beide Male bedeckter oder klarer Himmel gegeben wurde, genauso wie Bereiche, in denen nur in WRF oder im Satellit eine Wolke gegeben wurde.<\/p>\n<\/div><div class=\"fusion-image-element\" style=\"text-align:center;--awb-caption-title-font-family:var(--h2_typography-font-family);--awb-caption-title-font-weight:var(--h2_typography-font-weight);--awb-caption-title-font-style:var(--h2_typography-font-style);--awb-caption-title-size:var(--h2_typography-font-size);--awb-caption-title-transform:var(--h2_typography-text-transform);--awb-caption-title-line-height:var(--h2_typography-line-height);--awb-caption-title-letter-spacing:var(--h2_typography-letter-spacing);\"><span class=\" fusion-imageframe imageframe-none imageframe-1 hover-type-none\"><img decoding=\"async\" width=\"700\" height=\"1023\" alt=\"WRF-Solar Bedeckung Vorhersage\" title=\"WRF-Solar Bedeckung Vorhersage\" src=\"https:\/\/reuniwatt.com\/de\/wp-content\/uploads\/sites\/4\/2022\/07\/WRF-Solar_Bedeckung_Vorhersage.jpg\" class=\"img-responsive wp-image-7543\" srcset=\"https:\/\/reuniwatt.com\/de\/wp-content\/uploads\/sites\/4\/2022\/07\/WRF-Solar_Bedeckung_Vorhersage-200x292.jpg 200w, https:\/\/reuniwatt.com\/de\/wp-content\/uploads\/sites\/4\/2022\/07\/WRF-Solar_Bedeckung_Vorhersage-400x585.jpg 400w, https:\/\/reuniwatt.com\/de\/wp-content\/uploads\/sites\/4\/2022\/07\/WRF-Solar_Bedeckung_Vorhersage-600x877.jpg 600w, https:\/\/reuniwatt.com\/de\/wp-content\/uploads\/sites\/4\/2022\/07\/WRF-Solar_Bedeckung_Vorhersage.jpg 700w\" sizes=\"(max-width: 640px) 100vw, 700px\" \/><\/span><\/div><div class=\"fusion-text fusion-text-4\" style=\"--awb-text-transform:none;\"><h3>Quellen<\/h3>\n<ol>\n<li>Haupt, S. E., Kosovic, B., Jensen, T. L., Lee, J., Jimenez Munoz, P., Lazo, J. K., \u2026 Hinkleman, L. (2016). <em>The Sun4Cast\u00ae Solar Power Forecasting System: The Result of the Public-Private-Academic Partnership to Advance Solar Power Forecasting<\/em> (No. NCAR\/TN-526+STR). doi:10.5065\/D6N58JR2.<\/li>\n<li>Jimenez, P.A., J. P. Hacker, J. Dudhia, S. E. Haupt, J. A. Ruiz-Arias, C. A. Gueymard, G. Thompson, T. Eidhammer and A. Deng, 2016a: WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction. <em>Bull. Amer. Met. Soc.<\/em>.\u00a0<strong>97<\/strong>, 1249-1264. doi:10.1175\/BAMS-D-14-00279.1.<\/li>\n<li>Ruiz-Arias, J.A., J. Dudhia and C. A. Gueymard, 2014: A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model.\u00a0<em>Geosci., Model Devel.<\/em>,\u00a0<strong>7<\/strong>, 1159-1174.<\/li>\n<li>Xie, Yu, M. Sengupta, J. Dudhia, 2016: A Fast-sky Radiation model for Solar applications (FARMS): Algorithm and performance evaluation.\u00a0<em>Solar Energy<\/em>,\u00a0<strong>135<\/strong>, 435-445.<\/li>\n<li>J. -H. Kim\u00a0<em>et al<\/em>., &#8222;The WRF-Solar Ensemble Prediction System to Provide Solar Irradiance Probabilistic Forecasts,&#8220; in\u00a0<em>IEEE Journal of Photovoltaics<\/em>, vol. 12, no. 1, pp. 141-144, Jan. 2022, doi: 10.1109\/JPHOTOV.2021.3117904.<\/li>\n<\/ol>\n<\/div><div style=\"text-align:center;\"><a class=\"fusion-button button-flat fusion-button-default-size button-default fusion-button-default button-1 fusion-button-default-span fusion-button-default-type\" target=\"_self\" href=\"https:\/\/reuniwatt.com\/de\/kontakt\/\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Kontaktieren Sie uns<\/span><\/a><\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":12,"featured_media":7542,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[5],"tags":[],"class_list":["post-7541","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-firmennews"],"_links":{"self":[{"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/posts\/7541","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/comments?post=7541"}],"version-history":[{"count":3,"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/posts\/7541\/revisions"}],"predecessor-version":[{"id":7546,"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/posts\/7541\/revisions\/7546"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/media\/7542"}],"wp:attachment":[{"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/media?parent=7541"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/categories?post=7541"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/reuniwatt.com\/de\/wp-json\/wp\/v2\/tags?post=7541"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}