Metadata

1. About SHELDUS Version 24.0
2. RAW DATA Download/Output
3. AGGREGATED DATA Download/Output
4. SHELDUS Data Sources
5. Raw Data Manipulation
6. Aggregated Data Manipulation
7. SHELDUS Version History
8. How to Reference SHELDUS
9. Contact Information
10. End-user License Agreement, Data Sharing and Publication Restrictions
11. Download Metadata as PDF

1. About SHELDUS Version 24.0

1. Avalanche
2. Coastal
3. Drought
4. Earthquake
5. Flood
6. Fog
7. Hail
8. Heat
9. Hurricane/Tropical Storm
10. Landslide
11. Lightning
12. Severe Thunderstorm
13. Tornado
14. Tsunami/Seiche
15. Volcano
16. Wildfire
17. Wind
18. Winter Weather

1. Air Stagnation
2. Air Turbulence
3. Airburst
4. Animal Incident
5. Ashfall
6. Asian Soybean Rust
7. Avalanche - Debris
8. Avalanche - Snow
9. Bacterial Disease
10. Blizzard
11. Cloudburst
12. Coastal Storm
13. Cold Wave
14. Collision
15. Cyclone - Extratropical
16. Cyclone - Subtropical
17. Cyclone - Unspecified
18. Dam Failure
19. Debris Flow
20. Derecho
21. Disease
22. Downburst
23. Dust Devil
24. Dust Storm
25. Electrical Storm
26. Energetic Particles
27. Erosion - Coastal
28. Erosion - Lakeshore
29. Erosion - Unspecified
30. Expansive Soil
31. Fire - Brush
32. Fire - following Earthquake
33. Fire - Forest
34. Fire - Grass
35. Fire - St Elmo's
36. Fire - Tornado
37. Flood - Coastal
38. Flood - Flash
39. Flood - Ice Jam
40. Flood - Lakeshore
41. Flood - Lowland
42. Flood - Riverine
43. Flood - Rural
44. Flood - Small Stream
45. Flood - Snowmelt
46. Flood - Tidal
47. Flood - Urban
48. Freeze
49. Freezing Fog
50. Freezing Rain
51. Freezing Spray
52. Frost
53. Fungal Disease
54. Funnel
55. Geomagnetic Storm
56. Glacial Lake Outburst
57. Glaze
58. Gustnado
59. Heat Wave
60. High Seas
61. Hyperthermia
62. Hypothermia
63. Ice
64. Ice Breakup
65. Impact
66. Insect Infestation
67. Lahar
68. Landslide following Earthquake
69. Landslide - Slump
70. Lava Flow
71. Liquefaction
72. Macroburst
73. Marine Incident
74. Mass Movement
75. Microburst
76. Mud Flow
77. Mudslide
78. Myotoxin
79. Nor'easter
80. Parasitic Disease
81. Pressure - Change
82. Pressure - High
83. Pressure - Low
84. Prion Disease
85. Pyroclastic Flow
86. Radio Disturbance
87. Rain
88. Rip Current
89. Rip Tide
90. Rock Slide
91. Rockfall
92. Salinity
93. Sandstorm
94. Seiche
95. Shockwave
96. Sinkhole
97. Sleet
98. Smog
99. Smoke
100. Snow
101. Snow - Drifting
102. Snow - Lake Effect
103. Snow - Slide
104. Snow - Squall
105. Space Weather
106. Storm Surge
107. Storm - Convective
108. Storm - Frontal
109. Storm - Midlatitude
110. Storm - Winter
111. Subsidence
112. Surf
113. Swell
114. Temperature - High
115. Temperature - Low
116. Tidal Wave
117. Tide - High
118. Tide - Low
119. Tide - Rip
120. Tropical Depression
121. Tropical Storm
122. Viral Disease
123. Vog
124. Water Damage
125. Waterspout
126. Wave Action
127. Wave - Rogue
128. Wave - Sneaker
129. Wet Spells
130. Wind Chill
131. Wind - Chinook
132. Wind - Gale
133. Wind - Gust
134. Wind - Santa Ana
135. Wind - Squall
136. Wind - Tornadic
137. Wind - Vortex
138. Wintry Mix
139. Tsunami

 2. RAW DATA Download/Output

 3. AGGREGATED DATA Download/Output

 4. SHELDUS Data Sources 

1. Avalanche:
   a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
2. Coastal:
   a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
3. Drought:
   a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
4. Earthquakes :
   a. Stover, Carl W. and Jerry L. Coffman, 1993. Seismicity of the United States, 1568-1989 (revised). U.S. Geological Survey Professional Paper 1527, Washington, D.C.: U.S. Government Printing Office, 418 p.
   b. National Centers for Environmental Information (formerly National Geophysical Data Center), Global Significant Earthquake Database
5. Flood:
   a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
6. Fog:
   a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
7. Hail:
   a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
8. Heat:
   a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
9. Hurricane/Tropical Storm:
   a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
10. Landslide:
    a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
    b. United States Geological Survey Landslide News & Info
    c. United States Department of Agriculture Cost Estimating Guide for Road Construction
    d. National Aeronautics and Space Administration Global Landslide Catalog
    e. Oregon Department of Geology and Mineral Industries Statewide Landslide Information Layer for Oregon
    f. Media reports
11. Lightning:
    a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
12. Severe Thunderstorm:
    a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
13. Tornado:
    a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
14. Tsunami/Seiche:
    a. National Centers for Environmental Information (formerly National Climatic Data Center) Global HIstorical Tsunami Database
15. Volcanic Events:
    a. Blong, RJ 1984. Volcanic Hazards: A Source Book on the Effects of Eruptions. Academic Press.
    b. National Centers for Environmental Information (formerly National Geophysical Data Center) Significant Volcanic Eruptions Database
    c. U.S. Geological Survey
16. Wildfire:
    a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
17. Wind:
    a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
18. Winter Weather:
    a. National Centers for Environmental Information (formerly National Climatic Data Center) Storm Data and Unusual Weather Phenomena, hard copies for 1960-1999 and digital data imports since 2000
19. Named Disasters:
    a. National Centers for Environmental Information (formerly National Climatic Data Center) Billion Dollar Weather and Climate Disasters; SHELDUS includes only those events greater than $1billion or with a significant number of fatalities. Billion dollar events of $1billion, i.e. events that just made the NCEI loss modelling threshold, are generally excluded.
20. GLIDE:
    a. Global Disaster Identifier Number
21. PDD:
    a. Federal Emergency Management Agency, Presidential Disaster Declarations
22. Inflation Adjustments:
    a. U.S. Bureau of Labor Statistics
23. Population Counts:
    a. U.S. Census Bureau
24. Crop Indemnity Payments:
    a. U.S. Department of Agriculture

1. On fatalities and injuries: The determination of direct versus indirect causes of weather-related fatalities or injuries is one of the most difficult aspects of Storm Data preparation. Determining whether a fatality or injury was direct or indirect has to be examined on a case-by-case basis. It is impossible to include all possible cases in this Directive. The preparer should include the word "indirect" in all references to indirect fatalities or injuries in the event narrative.
2. On loss estimation: Property damage estimates should be entered as actual dollar amounts, if a reasonably accurate estimate from an insurance company or other qualified individual is available. If this estimate is not available, then the preparer has two choices: either check the "no information available" box, or make an estimate. The exception is for flood events. The Storm Data preparer must enter monetary damage amounts for flood events, even if it is a "guesstimate." The U.S. Army Corps of Engineers requires the NWS to provide monetary damage amounts (property and/or crop) resulting from any flood event. The Storm Data preparer is encouraged to make a good faith attempt to obtain or estimate the damage. Property damage estimates are very important for many users and should be obtained if at all possible. Estimates can be obtained from emergency managers, U.S. Geological Survey, U.S. Army Corps of Engineers, power utility companies, and newspaper articles.[..] The Storm Data preparer should use the table in Appendix B entitled Property Damage Estimates in determining monetary losses. This table would allow the preparer to estimate monetary amounts for damaged objects when timely communication is not possible with emergency managers or insurance adjusters just prior to Storm Data submission. It is suggested that the Storm Data preparer, in conjunction with local emergency managers, insurance adjusters, utility company representatives, and the U.S. Army Corps of Engineers, enhance the table to more accurately reflect values typically found in the local CWA. Typically, damage refers to damage inflicted to private property (structure s, objects, vegetation) as well as public infrastructure and facilities. [..] Crop damage information may be obtained from reliable sources, such as the U.S. Department of Agriculture (USDA), the county/parish agricultural extension agent, the state department of agriculture, crop insurance agencies, or any other reliable authority. Crop damage amounts may be obtained from the USDA or other similar agencies.
3. On Storm Data sources: Some information appearing in Storm Data may be provided by or gathered from sources outside the National Weather Service (NWS), such as the media, law enforcement and/or other government agencies, private companies, individuals, etc. An effort is made to use the best available information, but because of time and resource constraints, information from these sources may be unverified by the NWS. Accordingly, the NWS does not guarantee the accuracy or validity of the information.

1. On how the Federal crop insurance program works: Federal Crop Insurance Corporation (FCIC) programs are administered by the Risk Management Agency (RMA), which underwrites crop insurance policies for hundreds of crops and livestock in the United States. Crop insurance policies are sold and serviced by private insurance companies. (..) RMA provides policies for more than 100 crops. (This number would be much higher if every insurance plan available for the crops insured in every county were counted.) RMA also conducts studies to determine the feasibility of insuring many other crops and is conducting pilot programs for some new crop policies in selected states and counties. Federal crop insurance policies typically consist of the Common Crop Insurance Policy, the specific crop provisions, and the policy endorsements and special provisions.
2. On the indemnity amount: The total amount of the loss for the designated peril.

 5. SHELDUS Data Manipulation 

1.  
   1.  
      1. Losses reported as a range and not exact dollar values.
         Whenever the original data source reports a loss estimate as a range, SHELDUS uses the lower bound of the range. For example, All NCEI (formerly NCDC) Storm Data estimates prior to 1994/95 were reported in logarithmic categories ($<50, $50-500, $500-5,000, $5,000-50,000, $50,000-500,000, $500,000-$5 Million, $5 Million-50 Million, $50 Million to 500 Million, $500 Million to 5 Billion). For example, for an event with a loss range of $5,000-$50,000, SHELDUS will return an event with a loss of $5,000.
      2. Losses for landslide events.
         Loss estimates were obtained from 2015 NWSI 10-1605 Storm Data, unless noted. If there was no basis for the loss amount, $99 was entered as the property loss, with a note saying the loss amounts were unknown.

         Category	Type	From	To	Based On/Source
         Trees	Tree limb (large) downed	$230	$920
         	Tree destroyed	$575	$1,725
         	Tree on house w/o house damage	$1,750	$7,025
         	Tree on house w/ house damage	$3,500	$8,630
         Power Lines	Power lines downed	$850	$2,300
         	Small transformers	$1,150	$3,450
         	Regular size power pole cost	$350	$1,150
         	Large power pole cost	$850	$1,725
         	Labor cost for pole replacement	$5,000	$172,500
         	Large transmission	$46,000	$92,075
         Roofs	Minor roof damage repair	$2,300	$5,755
         	Major roof damage (truss/roof replace)	$17,250	$34,000
         	Damaged gutters/downspouts	$115	$345
         	Replace brick chimney	$2,300	$5,750
         Buildings	Awning damaged	$285	$1,150
         	Window broken	$230	$1,150
         	Covered porch destroyed	$5,750	$17,250
         	Replace siding, one side average house	$2,300	$5,750
         	1-car garage destroyed	$6,900	$17,250
         	2-car garage destroyed	$17,250	$34,500
         	House destroyed	Value of house + belongings		Use Zillow to estimate house value
         	Mobile home destroyed	$28,780	$57,500
         	Small shed destroyed	$575	$1,725
         	Small pole barn destroyed	$11,500	$34,500
         	Large pole barn destroyed	$28,775	$86,300
         	House basement flooded (minor)	$1,150	$11,500
         	House basement flooded (major)	$11,500	$28,775
         	Electrical damage from lightning	$2,875	$8,630
         	General house damage	10% of house value		Adjust to year
         	Average foundation repair	$3,772	$10,000	HomeAdvisor, 2015 dollars
         Vehicles	Windshield replace	$285	$1,150
         	Minor hail damage	$1,150	$3,450
         	Major hail damage	$2,875	$17,250
         	Car destroyed	Value of car		Adjust to year
         	Semi-trailer overturned	$8,630	$17,250
         	General car damage	20% of car value
         Agriculture	Crop damage	Crop value/acre * acreage
         	Small grain bin destroyed	$8,630	$17,250
         	Large grain bin destroyed	$23,015	$57,500
         	Cow killed	$1,730	$3,450
         	Veal calf	$50	$75
         	Center pivot irrigation system destroyed	$28,775	$57,500
         	General yard damage	$500		Current year
         Infrastructure	County road culvert washed out	$2,875	$57,500
         	County bridge washed out	$28,775	$86,300
         	State-federal bridge washed out	$287,750	$863,000
         	General bridge damage	$5,000		Current year
         	Paving cost	$120,000/lane mile for 2-lane roads	$502,000/lane mile for 4-lane roads	Ohio DOT, 1998 dollars
         	Guardrail	$50/linear foot		NASDAQ
         	Concrete median barrier	$43/ft; $226,180/mi	$72/ft; $378,720/mi	WSDOT, 2014 dollars
         	Clay levee	$4,000/ft
         	Regular levee	$60/ft
         	Retaining wall	$100/sq ft		US DOT Federal Highway Administration
         	Chain-link fence	$3/ft	$6/ft	Cost Helper, 2009 dollars

          
      3. Losses output as per capita values.
         Economic losses (i.e. property and crop losses) are first adjusted for inflation to 2017$ and then divided by the current population of a county. Human losses (i.e. injuries and fatalities) are divided by the current population to arrive at per capita values.
         Note: For Rose Island, American Samoa (population: 0) and Northern Island, Commonwealth of the Northern Marian Islands (population: 0 since 2010), we assume a population of 1 to allow for per county capita calculations.
      4. Events reported for a region and not a singular county.
         SHELDUS distributes the losses for multi-county events equally across the involved counties - this applies also to injuries and fatalities. If there is detailed information on the location of fatalities and injuries, SHELDUS will reflect this and will not distribute the losses equally. For example, if hurricane damage of $1 million was reported for County A and County B together, SHELDUS will show a loss of $500,000 for County A as well as a loss of $500,000 for County B.
      5. Losses by NWS forecast zones and not by county name.
         SHELDUS resolves the forecast zone and translates it into county location. SHELDUS has recreated historic NWS forecast zone information and can accurately resolve forecast zones and changes in zone definitions and boundaries over time. If multiple counties belong to a forecast zone, losses will be distributed equally as outlined under #2.
      6. Losses reported for counties that no longer exist.
         SHELDUS georeferences losses according to the geography at the time of the event. SHELDUS has recreated changes in naming conventions and county zone boundaries. All losses that occurred in a historic county are marked with (*). The following historic counties exist in SHELDUS:

         County Name	State	FIPS	Deletion Year
         *Princess Anne	VA	51810	1963
         *Ormsby	NV	32510	1969
         *Lynn Canal-Icy Straits	AK	02282	1970
         *Palmer-Wasilla-Talkeetna	AK	02170	1970
         *Nansemond	VA	51123	1972
         *Washabaugh	SD	46131	1979
         *Kuskokwim	AK	02160	1980
         *Prince of Wales	AK	02200	1980
         *Skagway-Yakutat	AK	02230	1980
         *Seward	AK	02122	1980
         *Valdez-Chitna-Whittier	AK	02260	1980
         *Upper Yukon	AK	02250	1980
         *Cordova-McCarthy	AK	02065	1980
         *Ketchikan	AK	02130	1980
         *Kenai-Cook Inlet	AK	02120	1980
         *Barrow	AK	02040	1980
         *Outer Ketchikan	AK	02190	1980
         *Angoon	AK	02030	1980
         *Kobuk	AK	02140	1986
         *Aleutians Islands	AK	02013	1987
         *Skagway-Yakutat-Angoon	AK	02231	1992
         *South Boston	VA	51083	1995
         *Dade	FL	12025	1997
         *Yellowstone National Park	MT	30113	1997
         *Clifton Forge	VA	51560	2001
         *Skagway-Yakutat-Angoon	AK	02232	2007
         *Prince of Wales-Outer Ketchikan	AK	02201	2008
         *Wrangell-Petersburg	AK	02280	2008
         *Bedford City	VA	51515	2013
         *Shannon	SD	46113	2015
         *Wade Hampton	AK	02270	2015
         *Valdez Cordova	AK	02261	2019

      7. Insured crop losses reported by month only.
         Whenever the original data source reports an indemnity payment by month only, SHELDUS assigns a time range spanning the entire month meaning the begin date of an event is reported as the first day of the month and the end date of the event is reported as the last day of the month.

 6. Aggregated Data Manipulation 

1.  
   1.  
      1. Aggregation by Geography
         a. State
         All underlying raw data records within a state are summed. The returned loss information are state totals for property losses, crop losses, injuries, and fatalities.This information can be additionally broken down (a) by year (or month) to create annual (or monthly) state loss totals; and/or (b) by hazard type to create totals by hazard type for the state.
         b. County
         Losses are summed across all counties within a state and returned as county totals. All underlying raw data records within a county are summed. The returned loss information are county totals for property losses, crop losses, injuries, and fatalities. This information can be additionally broken down (a) by year (or month) to create annual (or monthly) county loss totals; and/or (b) by hazard type to create totals by hazard type for each county.
      2. Aggregation by Time
         a. Year
         All underlying raw data records within a year are summed. The returned loss information are yearly totals for property losses, crop losses, injuries, and fatalities. This information can be additionally broken down (a) by geography (state or county) to create annual state or county loss totals; and/or (b) by hazard type to create annual totals by hazard type.
         b. Month
         All underlying raw data records within a month are summed. The returned loss information are monthly totals for property losses, crop losses, injuries, and fatalities.This information can be additionally broken down (a) by geography (state or county) to create monthly state or county loss totals; and/or (b) by hazard type to create monthly totals by hazard type.
         c. Quarter
         All underlying raw data records within a quarter are summed. The returned loss information are quarterly totals for property losses, crop losses, injuries, and fatalities. This information can be additionally broken down (a) by geography (state or county) to create quarterly state or county loss totals; and/or (b) by hazard type to create quarterly totals by hazard type.
      3. Aggregation by Hazard Type
         All underlying raw data records are summed across the 18 hazard categories used in SHELDUS. In instances where a raw data event lists multiple hazard types as causal agents, the aggregation will distribute the losses equally among the involved hazards. This categorization of losses may produce hazard types that were not originally select in the database query.

 7. SHELDUS Version History

SHELDUS Version 24.0 (released 2/01/2026 with 996,658 direct loss records and 3,360,088 crop indemnity records): added direct loss estimates as well as crop insurance claims (indemnities) for 2024. The years 2022 and 2023 were fully updated. Volcanic impacts were updated and revised. Minor corrections occurred for years prior. Freely available data encompass all of Arizona (6,889 direct losses; 2,594 crop indemnities), South Carolina (20,126 direct losses; 40,166 crop indemnities), and all drought events nationwide (10,694 direct losses; 1,006,762 crop indemnities). 

SHELDUS Version 23.0 (released 2/19/2025 with 981,528 direct loss records and 3,166,955 crop indemnity records): added direct loss estimates as well as crop insurance claims (indemnities) for 2023. The years 2021 and 2022 were fully updated. Minor corrections occurred for years prior. Freely available data encompass all of Arizona (6,556 direct losses; 2,308 crop indemnities), South Carolina (19,860 direct losses; 35,402 crop indemnities), and all drought events nationwide (10,649 direct losses; 948,258 crop indemnities). 

 8. How to Reference SHELDUS

 9. Contact Information

 10. End User License Agreement, Data Sharing and Publication Restrictions

 11. Download Metadata as PDF