{"version":"1.0","provider_name":"\u0421\u0430\u0439\u0442 \u0436\u0443\u0440\u043d\u0430\u043b\u0443 \u00ab\u0413\u0435\u043e\u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430\u00bb","provider_url":"http:\/\/www.geology.com.ua\/en","author_name":"\u0410\u0434\u043c\u0456\u043d\u0456\u0441\u0442\u0440\u0430\u0442\u043e\u0440","author_url":"http:\/\/www.geology.com.ua\/en\/blog\/author\/andriy\/","title":"- \u0421\u0430\u0439\u0442 \u0436\u0443\u0440\u043d\u0430\u043b\u0443 \u00ab\u0413\u0435\u043e\u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430\u00bb","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"RfjMNTeI0O\"><a href=\"http:\/\/www.geology.com.ua\/en\/8332-2\/\"><\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"http:\/\/www.geology.com.ua\/en\/8332-2\/embed\/#?secret=RfjMNTeI0O\" width=\"600\" height=\"338\" title=\"&#8220;&#8221; &#8212; \u0421\u0430\u0439\u0442 \u0436\u0443\u0440\u043d\u0430\u043b\u0443 \u00ab\u0413\u0435\u043e\u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430\u00bb\" data-secret=\"RfjMNTeI0O\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=http:\/\/www.geology.com.ua\/wp-includes\/js\/wp-embed.min.js\n\/* ]]> *\/\n<\/script>\n","description":"Geoinformatika 2018; 4(68) : 93-101 \u0423\u0414\u041a 551.583.16:551.509.33-19\/20-477 THE SKILL OF \u00abNON-METHODOLOGICAL\u00bb LONG-TERM AIR TEMPERATURE AND PRECIPITATION FORECASTS IN UKRAINE O.A. Shchehlov krainian Hydrometeorological Institute of State Service of Emergencies and National Academy of Sciences of Ukraine, Nayki ave. 37, Kyiv, 03028, Ukraine, e-mail: aleshcheglov@gmail.com Purpose.\u00a0 The purpose of the article is to estimate the forecast skill of \u00abnon-methodological\u00bb (persistence, random and climatological) forecasts of monthly mean air temperature and monthly precipitation over the territory of Ukraine according to the official methodology adopted in Ukraine. The aim is also to assess whether the methodology equally estimates the climatological forecast skill over a much larger area with the presence of different types of climate (within Eastern Europe). Design\/ methodology\/ approach. The official methodology adopted in Ukraine was used for assessing the forecast skill. The forecast skill was estimated only on a monthly time scale. Findings.\u00a0The research has shown that random and persistence forecasts for the territory of Ukraine are much worse than climatological. The climatological forecast is the best alternative in case of absence of a methodological long-term forecast of both air temperature and precipitation. The average annual forecast skill according to the official methodology is 60,9 % for the air temperature climatological forecast. The mean absolute error of the climatological forecasts is 1,88 \u00b0C. The forecast skill of monthly precipitation is also the highest for the climatological forecast (63,4 %). The official methodology has a drawback, which is in the usage of fixed temperature gradations in a calculation. As a result, the failure to take into account the regional climate features (namely different standard deviation) leads to a different estimation of the forecasts skill. Using the NCEP\/NCAR reanalysis data, it has been shown that the official methodology indicates the superiority of forecast skill over the seas and coastal areas. With an exception of points above the seas, the mean absolute error for the territory of Eastern Europe is 3,01 \u00b0C, and the forecast skill is 45,2 %. In case of inclusion the grid points over the seas \u2014 2,78 \u00b0C and 48,6 % respectively. Practical value\/implications. \u00a0The obtained forecast skills of \u00abnon-methodological\u00bb forecasts are needed for estimation of the relative effectiveness of monthly forecasts based on a certain methodology (model). The ratio of the certain model forecast skill to the climatological forecast skill &gt;1,0 will indicate the practical usefulness of the model, while the ratio of &lt;1,0 will indicate the need for an adjustment of the model or the refusal to use such a model. &nbsp; The full text of papers &nbsp; References Vil\u2019fand, R., Martazinova, V., Tsepelev, V., Khan, V., Mironicheva, N., Eliseev, G., Ivanova, E., Tishchenko, V., Utkuzova, D. Integration of synoptic and hydrodynamic monthly air temperature forecasts. Russian Meteorology &amp; Hydrology. 2017. Vol. 42, issue 8. P. 485\u2014493. Manual on the Global Observing System Volume I \u2014 Global Aspects. Annex II-8 to the WMO Technical Regulations. Available at: https:\/\/www.wmo.int\/pages\/prog\/www\/DPS\/Publications\/WMO _485_Vol_I.pdf (Accessed 30 October 2018). Nastanova po sluzhbi prohnoziv ta poperedzhen pro nebezpechni ta stykhiyni yavyshcham pohody. Kyiv: Derzhavna. hidrometeorolohichna sluzhba, 2003. 30 p. [in Ukrainian]. Ugryumov A.I. Long-term meteorological forecasts. A manual. St. Petersburg: RSHU Publishers, 2006. 84 p. Khandozhko L.A. Ekonomicheskaya effektivnost\u2019 meteorologicheskikh prognozov. Obninsk: VNIIGMI, 2008. 145 p. [in Russian]. Kalnay E. et al. The NCEP\/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society. 1996. Vol. 77. 437471. Kim G., Ahn J.-B., Kryjov V.N., Sohn S.-J., Yun W.-T., Graham R., Kolli R.K., Kumar A., Ceron J.-P. Global and regional skill of the seasonal predictions by WMO Lead Centre for Long-Range Forecast Multi-Model Ensemble. Int. J. Climatol. 2016. Vol. 36. P. 1657\u20141675. doi:10.1002\/ joc.4449. Lorenz E.N., Some aspects of atmospheric predictability. Problems and Prospects in Long and Medium Range Weather Forecasting (D.M. Burridge and E. Killn. Eds). Berlin: SpringerVerlag, 1984. P. 1\u201420. Mariotti A., Ruti P.M., Rixen M. Progress in subseasonal to seasonal prediction through a joint weather and climate community effort. Climate and Atmospheric Science. 2018. Vol. 1, doi:10.1038\/s41612-018-0014-z. Mason S.J., Graham N.E., Areas beneath the relative operating characteristics (ROC), and relative operating levels (ROL) curves: Statistical significance and interpretation. Q.J.R. Meteorological Society. 2002. Vol. 128. P. 2145\u20142166."}