Topics


![]()
![]()
Ensemble Applications in Winter
This lesson provides an introduction to ensemble forecast systems using an operational case study of the Blizzard of 2013 in Southern Ontario. The module uses models available to forecasters in the Meteorological Service of Canada, including Canadian and U.S. global and regional ensembles. After briefly discussing the rationale for ensemble forecasting, the module presents small lessons on probabilistic ensemble products useful in winter weather forecasting, immediately followed by forecast applications to a southern Ontario case. The learner makes forecasts for the Ontario Storm Prediction Ce ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1073
Published by: The University Corporation for Atmospheric Research ; 2014
This lesson provides an introduction to ensemble forecast systems using an operational case study of the Blizzard of 2013 in Southern Ontario. The module uses models available to forecasters in the Meteorological Service of Canada, including Canadian and U.S. global and regional ensembles. After briefly discussing the rationale for ensemble forecasting, the module presents small lessons on probabilistic ensemble products useful in winter weather forecasting, immediately followed by forecast applications to a southern Ontario case. The learner makes forecasts for the Ontario Storm Prediction Center area and, in the short range, for the Toronto metropolitan area. An additional section applies a probabilistic aviation product to forecasts for Toronto Pearson International Airport.
Disclaimer regarding 3rd party resources: WMO endeavours to ensure, but cannot and does not guarantee the accuracy, accessibility, integrity and timeliness of the information available on its website. WMO may make changes to the content of this website at any time without notice.
The responsibility for opinions expressed in articles, publications, studies and other contributions rests solely with their authors, and their posting on this website does not constitute an endorsement by WMO of the opinion expressed therein.
WMO shall not be liable for any damages incurred as a result of the use of its website. Please do not misuse our website.Language(s): English
Format: Digital (Standard Copyright)Tags: Statistics ; Weather forecasting ; Numerical weather prediction ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists
Add tag
No review, please log in to add yours !
![]()
![]()
Introduction to Ensembles: Forecasting Hurricane Sandy
This module provides an introduction to ensemble forecast systems with an operational case study of Hurricane Sandy. The module concentrates on models from NCEP and FNMOC available to forecasters in the U.S. Navy, including NAEFS (North American Ensemble Forecast System), and NUOPC (National Unified Operational Prediction Capability). Probabilistic forecasts of winds and waves developed from these ensemble forecast systems are applied to a ship transit and coastal resource protection. Lessons integrated in the case study provide information on ensemble statistics, products, bias correction and ...
Available online: https://www.meted.ucar.edu/training_module.php?id=1029
Published by: The University Corporation for Atmospheric Research ; 2013
This module provides an introduction to ensemble forecast systems with an operational case study of Hurricane Sandy. The module concentrates on models from NCEP and FNMOC available to forecasters in the U.S. Navy, including NAEFS (North American Ensemble Forecast System), and NUOPC (National Unified Operational Prediction Capability). Probabilistic forecasts of winds and waves developed from these ensemble forecast systems are applied to a ship transit and coastal resource protection. Lessons integrated in the case study provide information on ensemble statistics, products, bias correction and verification. Additional lessons address multimodel ensembles, extreme events, and automated forecasting.
Disclaimer regarding 3rd party resources: WMO endeavours to ensure, but cannot and does not guarantee the accuracy, accessibility, integrity and timeliness of the information available on its website. WMO may make changes to the content of this website at any time without notice.
The responsibility for opinions expressed in articles, publications, studies and other contributions rests solely with their authors, and their posting on this website does not constitute an endorsement by WMO of the opinion expressed therein.
WMO shall not be liable for any damages incurred as a result of the use of its website. Please do not misuse our website.Language(s): English
Format: Digital (Standard Copyright)Tags: Statistics ; Weather forecasting ; Wind ; Numerical weather prediction ; Lesson/ Tutorial ; NWP Skills and Knowledge for Operational Meteorologists
Add tag
No review, please log in to add yours !
![]()
![]()
Topics geo natural catastrophes 2012: analyses, assessments, positions
Munich-Re, 2013This report shows that the natural catastrophe statistics for 2012 were largely dominated by atmospheric events, with no catastrophic earthquakes. Due to a number of major weather-related catastrophes, including severe tornado outbreaks in the spring and a record drought in the US Midwest, the USA accounted for an exceptionally high proportion of natural catastrophes. However, Russia also experienced unusually hot, dry conditions, and vast tracts of land were devastated by wildfires.
![]()
Available online: http://preventionweb.net/go/33975
Published by: Munich-Re ; 2013
This report shows that the natural catastrophe statistics for 2012 were largely dominated by atmospheric events, with no catastrophic earthquakes. Due to a number of major weather-related catastrophes, including severe tornado outbreaks in the spring and a record drought in the US Midwest, the USA accounted for an exceptionally high proportion of natural catastrophes. However, Russia also experienced unusually hot, dry conditions, and vast tracts of land were devastated by wildfires.
Language(s): English
Format: Digital (Free)Tags: Natural hazards ; Climate change ; Severe cold ; Tropical cyclone ; Drought ; Earthquake ; Flood ; Heat wave ; Landslide ; Tornado ; Tsunami ; Volcanic Eruption ; Wildfire ; Statistics
Add tag
No review, please log in to add yours !
![]()
![]()
Global Estimates 2012: People displaced by disasters
Over five years from 2008 to 2012, around 144 million people were forced from their homes in 125 countries. In 2012, an estimated 32.4 million people in 82 countries were newly displaced by disasters associated with natural hazards triggered by climate- and weather-related events (98 per cent of all displacement in 2012; 83 per cent over five years), with flood disasters in India and Nigeria accounting for 41 per cent of global displacement in 2012. In India, monsoon floods displaced 6.9 million and in Nigeria 6.1 million people were newly displaced. The Global Estimates report determines that ...
![]()
Available online: http://www.internal-displacement.org/publications/global-estimates-2012-people-d [...]
M. Yonetani ; Internal Displacement Monitoring Centre ; Norwegian Refugee Council
Over five years from 2008 to 2012, around 144 million people were forced from their homes in 125 countries. In 2012, an estimated 32.4 million people in 82 countries were newly displaced by disasters associated with natural hazards triggered by climate- and weather-related events (98 per cent of all displacement in 2012; 83 per cent over five years), with flood disasters in India and Nigeria accounting for 41 per cent of global displacement in 2012. In India, monsoon floods displaced 6.9 million and in Nigeria 6.1 million people were newly displaced. The Global Estimates report determines that while over the past five years 81 per cent of global displacement has occurred in Asia, in 2012 Africa had a record high for the region of 8.2 million people newly displaced, over four times more than in any of the previous four years. The report concludes that the systematic collection, analysis and sharing of data is critical to inform policy and measures where they are most needed.
Language(s): English
Format: Digital (Free)Tags: Natural hazards ; Social aspects ; Statistics ; Case/ Case study
Add tag
No review, please log in to add yours !
![]()
![]()
2012 disasters in numbers in Asia
UN/ISDR, 2012An early view of disaster trends in 2012 across Asia, the world's most disaster-prone region, shows that mortality from flood events continues to decline but economic losses remain a major cause of concern.
![]()
Available online: http://www.unisdr.org/files/30026_confpress2012asia.pdf
United Nations International Strategy for Disaster Reduction
Published by: UN/ISDR ; 2012An early view of disaster trends in 2012 across Asia, the world's most disaster-prone region, shows that mortality from flood events continues to decline but economic losses remain a major cause of concern.
Language(s): English
Format: Digital (Free) (ill., charts)Tags: Natural hazards ; Multi-hazard Early Warning Systems (MHEWS) ; Statistics ; Region II - Asia
Add tag
No review, please log in to add yours !
![]()
![]()
![]()
People displaced by natural hazard-induced disasters: global estimates 2011
This study presents global estimates for the number of people newly displaced in 2011 by disasters induced by both weather-related and geophysical hazards, and makes comparisons with findings from 2008, 2009 and 2010. It provides evidence of the scale and location of displacement associated with natural hazard-induced disasters, and is aimed to serve as a contribution to the knowledge required to inform policy and practice, as well as to prevent and prepare for future events.
The study observes that a relatively small number of large and mega-disasters have been responsible for ...
Permalink![]()
![]()
![]()
Impacts of Disasters since the 1992 Rio de Janeiro Earth Summit
UN/ISDR, 2012Here’s a look at the impact of disasters since the Earth Summit (1992-2012).
Permalink![]()
![]()
![]()
The Little Green Data Book 2012
World Bank, 2012The Little Green Data Book is a pocket-sized ready reference on key environmental data for over 200 economies. Key indicators are organized under the headings of agriculture, forestry, biodiversity, energy, emission and pollution, and water and sanitation.
Permalink![]()
![]()
![]()
Managing water under uncertainty and risk: from the United Nations World Water Development Report 4 (WWDR4) - facts and figures
UNESCO, 2012This document gathers the main statistics and analysis from the UN world water development report 4 (WWDR4) related to water demand and its link to energy crisis, industry and human activities. It also provides facts and figures on water quality and related hazard risks, water management and capacity development, social and environmental benefits, and regional challenges and global governance and impacts.
Permalink![]()
![]()
![]()
2011 disasters in numbers
UN/ISDR, 2012For two consecutive years the long-term disasters trend has been bucked by major earthquakes which claimed thousands of lives and affected millions in both 2010 and 2011, according to new statistics published today by CRED and the UN office for disaster risk reduction, UNISDR.
Permalink![]()
![]()
![]()
WCDMP, 72. Guidelines on Analysis of extremes in a changing climate in support of informed decisions for adaptation
World Meteorological Organization (WMO) ; Zwiers Francis W.; Zhang Xuebin - WMO, 2009 (WMO/TD-No. 1500)
Permalink![]()
![]()
![]()
Introduction to Verification of Hydrologic Forecasts
This module offers a comprehensive description of a set of common verification measures for hydrologic forecasts, both deterministic and probabilistic. Through use of rich illustrations, animations, and interactions, this module explains how these verification measures can provide valuable information to users with varying needs. In addition to providing a measure of how well a forecast matches observations, verification measures can be used to help forecasters and users learn about the strengths and weaknesses of a forecast.
Permalink![]()
![]()
![]()
Introduction to Statistics for Climatology
The effective use of climate data and products requires an understanding of what the statistical parameters mean and which parameters best summarize the data for particular climate variables. This module addresses both concerns, taking a two-pronged approach: 1) focusing on the statistical parameters (mean, median, mode, extreme values, percent frequency of occurrence and time, range, standard deviation, and data anomalies), defining what they mean and how they are calculated using climate data as examples, and 2) focusing on weather and climate variables, identifying the statistical parameter ...
Permalink![]()
![]()
![]()
Report of the Secretariat on information provided by the parties in accordance with Article 7 and 9 of the Montreal Protocol
UNEP, 1999
PermalinkPermalink