Climate Data Online (CDO) provides free access to NCDC's archive of global historical weather and climate data in addition to station history information. These data include quality controlled daily, monthly, seasonal, and yearly measurements of temperature, precipitation, wind, and degree days as well as radar data and 30-year Climate Normals.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Other Collections. The data include parameters of database with a geographic location of . The time period coverage is from Unavailable begin date to Unavailable end date in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).

Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.

Terrestrial CDRs are composed of sensor data that have been improved and quality controlled over time, together with ancillary calibration data.

NOAA's National Climatic Data Center hosts the Climate Data Online (CDO) system. The web application system has been providing world wide climate data since 1998. Services using Resource Oriented Architecture (ROA) and Open Geospatial Consortium (OGC) standards are also available.

This Global Summaries dataset, known as GSOY for Yearly, contains a yearly resolution of meteorological elements from 1763 to present with updates applied weekly. The major parameters are: – average annual temperature, average annual minimum and maximum temperatures; total annual precipitation and snowfall; departure from normal of the mean temperature and total precipitation; heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; extreme annual minimum and maximum temperatures; number of days with fog; and number of days with thunderstorms. The primary input data source is the Global Historical Climatology Network - Daily (GHCN-Daily) dataset. The Global Summaries datasets also include a monthly resolution of meteorological elements in the GSOM (for Monthly) dataset. See associated resources for more information. These datasets are not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which are not available in GSOM and GSOY. The GSOM and GSOY datasets replace the legacy U.S. COOP Summaries (DSI-3220), and have been expanded to include non-U.S. (global) stations. U.S. COOP Summaries (DSI-3220) only includes National Weather Service (NWS) COOP Published, or "Published in CD", sites.

This dataset contains Raleigh Durham International Airport weather data pulled from the NOAA web service described at Climate Data Online: Web Services Documentation. We have pulled this data and converted it to commonly used units. This dataset is an archive - it is not being updated.

If you're looking for weather datasets, there are several reputable sources where you can access comprehensive weather data for various applications, including research, machine learning, and more. Here are some popular options:

1. National Centers for Environmental Information (NCEI):

   • The NCEI, part of NOAA, offers a wide range of climate and weather data. You can find historical weather data, global climate data, and more.
   • NCEI Weather Data

2. OpenWeatherMap:

   • Provides current weather data, forecasts, and historical data. They offer free and paid plans depending on the level of access and detail needed.
   • OpenWeatherMap API

3. Weather Underground:

   • Offers a rich set of weather data including current conditions, forecasts, and historical weather data.
   • Weather Underground API

4. European Centre for Medium-Range Weather Forecasts (ECMWF):

   • ECMWF provides datasets including ERA-Interim, ERA5, and seasonal forecasts. They focus on global weather and climate data.
   • ECMWF Data

5. The Weather Company (IBM):

   • Offers a range of weather data services, including historical weather data, forecasts, and more through their APIs.
   • The Weather Company API

6. NASA Earth Observing System Data and Information System (EOSDIS):

   • Provides access to a vast array of global climate data, satellite imagery, and other environmental data.
   • NASA EOSDIS

7. Global Surface Summary of the Day (GSOD):

   • A dataset that includes daily weather summaries from global stations, available through the National Centers for Environmental Information.
   • GSOD Data

8. Climate Data Online (CDO):

   • Another resource from NOAA, offering access to a variety of climate data, including daily and monthly summaries, storm data, and more.
   • NOAA CDO

9. Meteostat:

   • Provides free access to historical weather and climate data, focusing on quality-controlled and easy-to-use datasets.
   • Meteostat

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

The Indiana State Climate Office (INClimate) is the state archive of official daily and hourly weather observations recorded throughout Indiana. INClimate maintains an online archive of many recent daily and hourly observations from both manual and automated networks. Older observations are being converted to an online database as part of an ongoing national effort.

INClimate was established in 1956 to document and study the climate of Indiana. Ever since, it has been catering to the needs of different users, namely individuals, businesses, and government agencies. INClimate not only assists in providing climate observations and summaries but also interprets and applies this data to solve climate related problems at hand.

Primary users of Climate data belong to sectors such as agriculture, attorneys, construction, environmental monitoring, forensics, government insurance, news media, research, education and utilities.

The global climate data analysis market is projected to grow from $926.1 million in 2025 to $5,028.9 million by 2033, at a CAGR of 20.1%. Climate data analysis is the process of collecting, analyzing, and interpreting large datasets to gain insights into climate patterns and trends. This market growth is driven by the increasing need for accurate and timely climate information for various applications such as weather forecasting, climate modelling, and agricultural planning. The rise in climate-related disasters, such as floods, droughts, and wildfires, has also highlighted the importance of climate data analysis for risk assessment and disaster preparedness. The key segments of the climate data analysis market include type and application. By type, the market is segmented into climate model evaluation, climate data processing and visualization, climate data formats, and statistical methods. By application, the market is segmented into government agencies and research institutions, energy sectors, agriculture and food industry, insurance and risk management, and transportation and logistics. The major regions covered in the report include North America, South America, Europe, Middle East & Africa, and Asia Pacific. The report provides an in-depth analysis of the competitive landscape and profiles of key players such as The Climate Corporation, IBM Corporation, Schneider Electric, AccuWeather, Inc., Risk Management Solutions, Inc., Earth Networks, Inc., Vaisala, Weather Source LLC, Meteomatics AG, Climatempo Meteorologia, Climate Data Online, Climeworks AG.

The IRI Data Library is a powerful and freely accessible online data repository and analysis tool that allows a user to view, manipulate, and download over 400 climate-related data sets through a standard web browser. The Data Library contains a wide variety of publicly available data sets, including station and gridded atmospheric and oceanic observations and analyses, model-based analyses and forecasts, and land surface and vegetation data sets, from a range of sources. It includes a flexible, interactive data viewer that allows a user to visualize. multi-dimensional data sets in several combinations, create animations, and customize and download plots and maps in a variety of image formats. The Data Library is also a powerful computational engine that can perform analyses of varying complexity using an extensive array of statistical analysis tools. Online tutorials and function documentation are available to aid the user in applying these tools to the holdings available in the Data Library. Data sets and the results of any calculations performed by the user can be downloaded in a wide variety of file formats, from simple ascii text to GIS-compatible files to fully self-describing formats, or transferred directly to software applications that use the OPeNDAP protocol. This flexibility allows the Data Library to be used as a collaborative tool among different disciplines and to build new data discovery and analysis tools.

This resource demonstrates the workflow developed to prepare downscaled GCM data for input to Model My Watershed (ModelMyWatershed.org). GCM data for the Delaware River Basin was assembled from 19 GCMs including each model's RCP4.5 and RCP8.5; this was performed by Dr. Tim Hawkins, Shippensburg University (http://www.ship.edu/geo-ess/). Downscaled precipitation data from global climate models (GCM) does not accurately retain the magnitude and frequency of individual storm events for a given location. This lack of predictive resolution of event magnitude and frequency limits realism of rainfall-runoff models used to for predicting watershed hydrology under future climate scenarios. To address this problem, Maimone et al (2019) developed a method for summarizing the statistical distribution of precipitation event magnitude and frequency that could be applied to downscaled GCM precipitation predictions. Application of the methods here to down-scaled GCM scenarios requires that the those predictions do not include an increase in the number of days of precipitation per year. Maimone et al (2019) state this requirement: "Because GCM projections for the Philadelphia region do not indicate an increase in the number of wet days per year, future increases in precipitation are the result of the existing number and distribution of wet days becoming more intense."

I developed a workflow to replicate Maimone et al's methods and provide an example of it in this Resource. There are three sections of the R Markdown document. The first section seeks to replicate the synthetic weather generator developed by Maimone et al (2019) using an example dataset. The second section applies those methods to the downscaled GCM ensemble average conditions for the Delaware River Basin provided by Dr. Hawkins. The third section develops depth-duration-frequency statistics for the 24 hour storm event relevant to the 2080-2100 predictions. To open the R Markdown document and execute the workflow yourself, find the Open With dropdown list in the upper right hand corner of this Resource and select CUAHSI JupyterHub.

The first section uses an example precipitation dataset from the Philadelphia Airport for the period 01 January 1995 through 31 December 2013. The data were downloaded from NOAA's Climate Data Online Search portal: https://www.ncdc.noaa.gov/cdo-web/search.

The downloaded data and metadata for this NOAA Climate Data are available on Hydroshare here: http://www.hydroshare.org/resource/60058ceda8334e68be141516c5b8de3f. Additional data on precipitation frequency at the Philadelphia Airport was downloaded from the NOAA Hydrometeorological Design Studies Center: https://hdsc.nws.noaa.gov/hdsc/pfds/index.html.

An example of working with this type of NOAA Climate Data is provided on the NEON website here: https://www.neonscience.org/da-viz-coop-precip-data-R.

References: Maimone, M., S. Malter, J. Rockwell, and V. Raj. 2019. Transforming Global Climate Model Precipitation Output for Use in Urban Stormwater Applications. Journal of Water Resources Planning and Management 145:04019021.

The GPCP Daily analysis is a companion to the GPCP Monthly analysis, and provides globally complete precipitation estimates at a spatial resolution of one degree latitude-longitude and daily time scale from October 1996 to the present. The data set is part of World Climate Research Program (WCRP) and GEWEX activities, being part of the array of data sets describing the water and energy cycles of the planet under the auspices of the GEWEX Data and Assessment Panel (GDAP).

The New Jersey State Climatologist office offers New Jersey historical climatic data and climate norm data for the state of New Jersey, USA. All data sets are available via World Wide Web from the Rutgers University Home Page.

Frequently Updated Climate Data:

Winter 2019–2020 Snow Event Totals Monthly Statewide/Divisional/County (1895-Present) Monthly Station Monthly Maps A selected New Jersey station history is also available.

[Summary adapted from the New Jersey State Climatologist home page]

Contains global weather station locations with data for monthly means from 1981 through 2010 for: Daily Mean Temperature °C Daily Maximum Temperature °C Daily Minimum Temperature °C Precipitation in mm Highest Daily Temperature °C Lowest Daily Temperature °C Additional monthly fields containing the equivalent values in °F and inches are available at the far right of the attribute table. GHCND stations were included if there were at least fifteen average daily values available in each month for all twelve months of the year, and for at least ten years between 1981 and 2010. 3,197 of the 7,480 stations did not collect or lacked sufficient precipitation data. These data are compiled from archived station values which have not undergone rigorous curation, and thus, there may be unexpected values, particularly in the daily extreme high and low fields. Esri is working to further curate this layer and will make updates as improvements are found. If your area of study is within the United States, we recommend using the U.S. Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer because the data in that service were compiled from web services produced by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc., thus the data in the U.S. service is of higher quality. Revision History: Initially Published: 6 Feb 2019 Updated: 12 Feb 2019 - Improved initial extraction algorithm to remove stations with extreme values. This included values higher than the highest temperature ever recorded on Earth, or those with mean values that were considerably different than adjacent neighboring stations.Updated: 18 Feb 2019 - Updated after finding an error in initial processing that excluded a 2,870 stations. Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 635 of 7,452 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as:

Esri, 2019: World Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed April 2019. https://www.arcgis.com/home/item.html?id=ed59d3b4a8c44100914458dd722f054f Source Data: Station locations compiled from: Initially compiled using station locations from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24 Amended to use the most recent station locations from Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. Access Date: April 10, 2019 doi:10.7289/V5NV9G8D. Station Monthly Means compiled from Daily Data: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24

This point layer contains monthly summaries of daily temperatures (means, minimums, and maximums) and precipitation levels (sum, lowest, and highest) for the period January 1981 through December 2010 for weather stations in the Global Historical Climate Network Daily (GHCND). Data in this service were obtained from web services hosted by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc. The data were compiled at Esri from publicly available sources hosted and administered by NOAA. Because the ACIS data is updated and corrected on an ongoing basis, the date of collection for this layer was Jan 23, 2019. The following process was used to produce this dataset:Download the most current list of stations from ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt. Import this into Microsoft Excel and save as CSV. In ArcGIS, import the CSV as a geodatabase table and use the XY Event layer tool to locate each point. Using a detailed U.S. boundary extract the points that fall within the 50 U.S. States, the District of Columbia, and Puerto Rico. Using Python with DA.UpdateCursor and urllib2 access the ACIS Web Services API to determine whether each station had at least 50 monthly values of temperature data for each station. Delete the other stations. Using Python add the necessary field names and acquire all monthly values for the remaining stations. Thus, there are stations that have some missing data. Using Python Add fields and convert the standard values to metric values so both would be present. Thus, there are four sets of monthly data in this dataset: Monthly means, mins, and maxes of daily temperatures - degrees Fahrenheit. Monthly mean of monthly sums of precipitation and the level of precipitation that was the minimum and maximum during the period 1981 to 2010 - mm. Temperatures in 3a. in degrees Celcius. Precipitation levels in 3b in Inches. After initially publishing these data in a different service, it was learned that more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer these most precise coordinates are used. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. If your study area includes areas outside of the U.S., use the World Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer. The data in this layer come from the same source archive, however, they are not curated by the ACIS staff and may contain errors. Revision History: Initially Published: 23 Jan 2019 Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 3,222 of 9,636 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR data is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as: Esri, 2019: U.S. Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed

Surface temperatures and thickness-derived temperatures from a 54-station, globally distributed radiosonde network have been used to estimate global, hemispheric, and zonal annual and seasonal temperature deviations. Most of the temperature values used were column-mean temperatures, obtained from the differences in height (thickness) between constant-pressure surfaces at individual radiosonde stations. The pressure-height data before 1980 were obtained from published values in Monthly Climatic Data for the World. Between 1980 and 1990, Angell used data from both the Climatic Data for the World and the Global Telecommunications System (GTS) Network received at the National Meteorological Center. Between 1990 and 1995, the data were obtained only from GTS, and since 1995 the data have been obtained from National Center for Atmospheric Research files. The data are evaluated as deviations from the mean based on the interval 1961-1990. Time series for the earth's surface, and the 850-300mb, 300-100mb and 100-50mb layers are presented for north polar (60-90N), north temperate (30-60N), tropical (30S-30N), south temperate (30-60S) and south polar (60-90S) climate zones, as well as for the Northern and Southern hemispheres and the globe. The data presentation is more compact than in the case of Angell's 63-station network, with two fewer layers and three fewer climate zones, for a total of eight time series.CITE AS: Angell, J.K. 2012. Global, hemispheric, and zonal temperature deviations derived from a 54-station radiosonde network. In Trends Online: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A. doi: 10.3334/CDIAC/cli.005

As part of this national strategy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) had launched a 5-year (FY2007 - 2011) initiative called the Innovative Program of Climate Change Projection for the 21st Century (KAKUSHIN Program), using the Earth Simulator (ES) to address emerging research challenges, such as those derived from the outcomes of the MEXT's Kyosei Project (FY2002 - 2006), that had made substantial contributions to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The KAKUSHIN Program was expected to further contribute to the Fifth Assessment Report (AR5).

The research items include the advancement and forecasting of global warming models, the quantification and reduction of model uncertainty, and the evaluation of the impacts of natural disasters based on forecast information. Much of the data submitted to CMIP5 from Japan were generated under this KAKUSHIN program using the global climate models and the Earth system models developed in Japan. This dataset is the result of using the Non-hydrostatic Atmospheric Circulation Model NICAM-09.

All CMIP5 data are collected, managed, and published by the Earth System Grid Federation (ESGF), and DIAS serves as an ESGF node. All public datasets, including this dataset, are available from ESGF. For information on how to use these datasets, including this dataset, see "CMIP5 Data - Getting Started" (URL is available in the online information below). Please note that an ESGF account is required to download the CMIP5 data.

Because the terms of use for CMIP5 data are different from CMIP6 in many respects, please check the following Terms of Use carefully: https://pcmdi.llnl.gov/mips/cmip5/terms-of-use.html Currently, all CMIP5 data, including this dataset, is classified as "unrestricted" within it.

The State of the Climate is a collection of periodic summaries recapping climate-related occurrences on both a global and national scale. The State of the Climate Monthly Overview - Global Hazards highlights significant weather-related hazards and disasters across the world. The reports focus on the following events: drought and wildfire, excessive temperatures, flooding, severe storms, tropical cyclones, extratropical cyclones, severe winter weather, polar events and sea ice, and ecosystem impacts. The information provided is a compilation of media news and other reports accompanied by relevant images, graphs, and documents. Material provided in this report is chosen subjectively and included at the discretion of the National Climatic Data Center (NCDC). The ability to report on a given event is limited by the amount of information available to NCDC at the time of publication. Inclusion of a particular event does not constitute a greater importance in comparison with an event that has not been incorporated into the discussion. Following a summer season summary and an annual report issued in 1998, the Global Hazards report was issued monthly (from 1999) until the final report issued in March 2013. All previously issued reports will be maintained online.