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Bulygina Olga All-Russian Research Institute of Hydrometeorological Information, Obninsk, Russia First Technical Workshop.

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Presentation on theme: "Bulygina Olga All-Russian Research Institute of Hydrometeorological Information, Obninsk, Russia First Technical Workshop."— Presentation transcript:

1 Bulygina Olga All-Russian Research Institute of Hydrometeorological Information, Obninsk, Russia First Technical Workshop on Standards for Hazard Monitoring, Databases, Metadata and Analysis Techniques to Support Risk Assessment 10 to 14 June 2013 WMO,Geneva Switzerland Hazard Monitoring, Databases, Metadata and Analysis at RUSSIA

2 According Government resolutions the Federal Service for Hydrometeorology and Environmental Monitoring (Roshydromet) have detect and predict for meteorological, hydrological and climate-related hazards. The Ministry of the Russian Federation for Civil Defense, Emergencies and Elimination of Consequences of Natural Disasters

3 METEOROLOGICAL, HYDROLOGICAL AND CLIMATE-RELATED HAZARDS monitoring detecting maintaining databases analytical techniques Guidelines for Preparation of Country Documents for the First Technical Workshop on Standards for Hazard Monitoring, Databases, Metadata and Analysis Techniques to Support Risk Assessment :“Please kindly prepare a document that describes practices and approaches in your country to monitoring, detecting, maintaining databases and analytical techniques (statistical and forecasting) for primary meteorological, hydrological and climate-related hazards.”

4  Hazards events are detecting by network of hydrometeorological stations. Russian defined hazard’s criteria is in “List of natural hazards” On the territory of Russia, where there is a great variety of climate conditions, more than 30 hazards occur. To detect and predict these hazards the (Roshydromet) has conducted regular hydrometeorological observations for many years (in accordance with the approved programme and manuals). The phenomena with the intensity exceeding critical values for a given region or season are referred to as hazards. These phenomena are registered by hydrometeorolgical stations. Hydrometeorological stations just register hazards on the basis of the specified and approved criteria and do not take into account their consequences and impacts on economy and population. Data on hazards are collected and stored on the machine-readable media in the State Fund (National Database) of Environmental Data hosted by the Research Institute of Hydrometeorological Information – World Data Centre (RIHMI-WDC) Hazard Definition and Detection

5 Meteorological stations in Russia

6 Meteorological stations 1627

7  Additional information (satellite and other) have use to detect the hazards events State Research Center "Planeta" SRC "Planeta" is a leading organization in exploiting and development of national satellite Earth observation systems for hydrometeorological, oceanographic, heliogeophysical and environmental purpose and also in receiving and processing data from foreign satellites in cooperation with national hydrometeorological services and space agencies of more than 30 countries  Social and economic damage are estimating by regional government commission. Information about hazards that led to economic losses are sending to different organizations and to RIHMI-WDC.  Roshydromet (regional hydrometeorological departments and Hydrometcentre of Russia) prepares warnings for hydrometeorological hazards; Hazard Definition and Detection

8 Hazards databases  Data on hazards (without economical information) are include in the State Fund (National Database) of Environmental Data (RIHMI-WDC)  Since 1997 RIHMI-WDC has been developing and maintaining a specialized set of data on hazards that led to economic losses. Currently this set contains information on all the hazards and adverse weather occurrences that caused economic damage in the period of (The early information is not reliable) The main principle of developing and maintaining this data set is that it includes information on all the hazards and adverse weather occurrences that caused social and economic damage irrespective of values and characteristics of the related hydrometeorological parameters.

9 Alexander Korshunov – Head of Department of Applied and System Research (RIHMI-WDC) Hazards data base Archival frequency- Regular Data archival standards- Microsoft Accese information is free available

10 All hazardous events have parameters:  Date  Type (1 – strong wind, hurricane, squall, spout. dust storm; 2 – blizzard, heavy snowfall, a mix of snow and rain, slush build-up, glaze, glazed frost; 3 – heavy rain, long-lasting rain, shower, hail, thunderstorm; 4 – severe frost, freezing, strong heat, sharp temperature change; 5 – spring flood, rain flood, flooding, surge, ice jam; 6- avalanche, mudflow; 7 – atmospheric and soil drought, hot dry wind; 8 – fire hazard emergency; 9 – other)  Meteorological and hydrological characteristics  Location (WMO index of station, region..)  Damage Hazards data base

11 Hazards Monitoring Roshydromet are preparing national monitoring products (monthly, seasonal and yearly) to assess the actual state of the climate. Information about hazards are include to these products. “ A REPORT ON CLIMATE FEATURES ON THE TERRITORY OF THE RUSSIAN FEDERATION” (http://www.meteorf.ru ).http://www.meteorf.ru

12 Distribution of hazardous weather events that caused social and economic damage in the period by years. Russia

13 Distribution of hazardous events in the period of by seasons (cold seasons: November and December of the previous year; January, February and March of the current year)

14 Average monthly number of hazardous events, that cause economic damage, in the period of compared with that in 2010

15 Number of hazardous events (by types) in 2010 compared with the monthly average in the period (1 – strong wind, hurricane, squall, spout. dust storm; 2 – blizzard, heavy snowfall, a mix of snow and rain, slush build-up, glaze, glazed frost; 3 – heavy rain, long-lasting rain, shower, hail, thunderstorm; 4 – severe frost, freezing, strong heat, sharp temperature change; 5 – spring flood, rain flood, flooding, surge, ice jam; 6-avalanche, mudflow; 7 – atmospheric and soil drought, hot dry wind; 8 – fire hazard emergency; 9 – other)

16 Some results from climate research of extreme events, hazards

17 Linear trend estimates in the time series of the number of days with icing at meteorological stations (days/decade) (indicated by color) and averaged (indicated by numerals) over quasi-homogeneous regions (%/decade) ( ). Bulygina O.,Arzhanova N., Groisman, P. Ya., 2013.Icing conditions in the northern extratropics in changing climate. JpGU Meeting 2013

18 a)Linear trend coefficient (days/10 years) in the time series of days with abnormally low air temperatures (minimum temperatures lower than the 5th percentile) in winter (December–February). Time period1971–2010. b)Linear trend coefficient (days/10 years) in the time series of days with abnormally high air temperatures (maximum temperatures higher than the 95th percentile) in summer (June–August). Time period 1971–2010. (Bulygina O.N., Razuvaev V.N.,Korshunova N.N., Groisman, P. Ya.,2007: Climate variations and changes in extreme climate events in Russia., Environ. Res. Lett. 2 N4 (October-December 2007)045020)

19 Left. Regional trends (% per 10 years ) that are presented only for the regions where during the period they were statistically significant at the 0.05 or higher level. Right. Annual variability of the percent of stations where the DER events were observed in the Atlantic Sector of the Russian Arctic. DER, dangerous events for reindeer husbandry: ice crust above 5 mm during 10 consequent days over the “field” routes (O. Bulygina, P. Groisman, V. Razuvaev, and V. Radionov Snow cover basal ice layer changes over Northern Eurasia since Environ. Res. Lett doi: / /5/1/015004) / /5/1/015004

20 Left. Regional trends (% per 10 years ) that are presented only for the regions where during the period they were statistically significant at the 0.05 or higher level. Right. Annual variability of the percent of stations where the DEC events were observed in the southwest of European Russia. DEC, dangerous event for winter crops: ice crust above 20 mm over the “field” routes

21 Linear trend coefficient (days/10 years; 5% significance level) in the time series of days with extreme (wind speed more than the 95th percentile) wind speed. O. Bulygina, V.Razuvaev and N.Korshunova, CHANGES IN THE WIND CONDITIONS OVER NORTHERN EURASIA. AGU Meeting 2012

22 Air temperature anomalies (deviations from averages). Mean monthly air temperatures Daily (mean, maximum) air temperatures

23 - Tmin > 23,9 °C in SUMMER Tmin > 23,9 °C in SUMMER 2010 and Nday (with Tmin > 23,9 °C ) in 2010 > Nday on all period of observation Sochi Oktjabr’skij Gorodok “HOT NIGHTS” Saint-Petersburg. For the first time since 1881 Tmin has exceeded 23.9 C (14, 16, 29 July 2010). Moscow. For the first time Tmin has exceeded 23.9 C (28 and 29 July, 4 August 2010).

24 THANK YOU!

25 Name of hazardCharacteristics, criteria or definition of hazard A.1 Meteorological hazards A.1.1 Very strong wind Wind speed with gusts of no less than 25 m/s or mean wind speed of no less than 20 m/s; on sea coasts and in mountain regions wind speed (not gusts) attains 30 m/s and above* A.1.2 Windstorm (hurricane) Wind speed attains 33 m/s and above A.1.3 SquallAbrupt short (for one to several minutes) strengthening of wind to 25 m/s and above* A.1.4 SandstormStrong low-scale whirlwind in the form of column or funnel which is directed from cloud to underlying surface A.1.5 Heavy shower Heavy rain shower with amount of precipitation no less than 30 mm for no more than one hour* List of natural hazards (examples)

26 A.1.6 Very heavy rain (very heavy rain and snow, very heavy sleet, very heavy snow and rain) Heavy rainfall or mixed precipitation (rain, shower, rain and snow, sleet) with amount of precipitation no less than 50 mm (30 mm in shower and mudflow dangerous mountain regions) for no more than 12 hours* A.1.7 Very heavy snow Heavy solid precipitation (snow, snow shower) with amount of precipitation no less than 20 m/s for no more than 12 hours A.1.8 Long-lasting heavy rain Rain with short intervals (no more than one hour) with amount of precipitation no less than 100 mm (no less than 60 mm in shower dangerous regions) for more than 12 hours and less than 48 hours or with 120 mm of precipitation for more than two days A.1.9 Large hailHail of diameter 20 mm and above A.1.10 Heavy snowstorm Transfer of snow from underlying surface (often accompanied by snowfall) with strong wind (mean wind speed no less than 15 m/s) at meteorological optical range no more than 500 m for no less than 12 hours* A.1.11 Severe dust/sand storm Transfer of dust or sand with strong wind (mean wind speed no less than 15 m/s) at meteorological optical range no more than 500 m for no less than 12 hours*

27 A.1.12 Heavy fog/mist Heavy atmospheric turbidity due to aggregate of finest particles of water, dust or combustion products at meteorological optical range no more than 50 m for no less than 12 hours* A.1.13 Severe glaze- ice and rime deposition Diameter of deposition on ice accretion indicator: no less than 20 mm for glaze; no less than 35 mm for complex deposition or wet snow; no less than 50 mm for rime A.1.14 Heavy frostIn the period November-March, minimum air temperatures attain or drop below the value that is considered dangerous for a given area* A.1.15 Abnormally cold weather From October through March, mean daily air temperatures are 7°C and more below normal for five days and above* A.1.16 Intense heatFrom May through August, maximum air temperatures attain or exceed the value that is considered dangerous for a given area* A.1.17 Abnormally hot weather From April through September, mean daily air temperatures are 7°C and more above normal for five days and above* A.1.18 Extreme fire hazard Fire hazard index refers to class 5 (10 000°C by Nesterov formula) A.1.19 AvalanchingAvalanching that causes significant damage to economic entities or poses hazard to human settlements


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