Feature containing Northern Plains regional effective significant tornado parameter values at 23Z on February 8, 2024. Base fields from RAP13 analysis used to compute effective significant tornado parameter values. Feature layer appears in a story map documenting the February 8 severe weather event.

You will learn to work with ArcPy, the Esri-developed site package that integrates Python scripts into ArcGIS Desktop.Goals Create Python scripts to perform geoprocessing tasks. Access lists of datasets and loop through lists to test for a condition. Create dynamic scripts that allow users to interactively specify their own parameter values. Create tools to share your Python scripts.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Web map displaying Northern Plains regional effective significant tornado parameter values at 23Z February 8, 2024. Web map implemented in a story map documenting the February 8, 2024 severe weather event in southern Wisconsin. Web map also appears in an interactive application showcasing the near-storm environment in place over southern Wisconsin on February 8.

Last Revised: February 2016 Map InformationThis nowCOAST™ time-enabled map service provides maps depicting the latest surface weather and marine weather observations at observing sites using the international station model. The station model is a method for representing information collected at an observing station using symbols and numbers. The station model depicts current weather conditions, cloud cover, wind speed, wind direction, visibility, air temperature, dew point temperature, sea surface water temperature, significant wave height, air pressure adjusted to mean sea level, and the change in air pressure over the last 3 hours. The circle in the model is centered over the latitude and longitude coordinates of the station. The total cloud cover is expressed as a fraction of cloud covering the sky and is indicated by the amount of circle filled in; however, all cloud cover values are presently displayed using the "Missing" symbol due to a problem with the source data. Present weather information is also not available for display at this time. Wind speed and direction are represented by a wind barb whose line extends from the cover cloud circle towards the direction from which the wind is blowing. The short lines or flags coming off the end of the long line are called barbs, which indicate wind speed in knots. Each normal barb represents 10 knots, while short barbs indicate 5 knots. A flag represents 50 knots. If there is no wind barb depicted, an outer circle around the cloud cover symbol indicates calm winds.Due to software limitations, the observations included in this map service are organized into three separate group layers: 1) Wind velocity (wind barb) observations, 2) Cloud Cover observations, and 3) All other observations, which are displayed as numerical values (e.g. Air Temperature, Wind Gust, Visibility, Sea Surface Temperature, etc.).Additionally, due to the density of weather/ocean observations in this map service, each of these group data layers has been split into ten individual "Scale Band" layers, with each one visible for a certain range of map scales. Thus, to ensure observations are displayed at any scale, users should make sure to always specify all ten corresponding scale band layers in every map request. This will result in the scale band most appropriate for your present zoom level being shown, resulting in a clean, uncluttered display. As you zoom in, additional observations will appear.The observations in this nowCOAST™ map service are updated approximately every 10 minutes. However, since the reporting frequency varies by network or station, the observations for a particular station may update only once per hour. For more detailed information about layer update frequency and timing, please reference the nowCOAST™ Dataset Update Schedule.Background InformationThe maps of near-real-time surface weather and ocean observations are based on non-restricted data obtained from the NWS Family of Services courtesy of NESDIS/OPSD and also the NWS Meteorological Assimilation Data Ingest System (MADIS). The data includes observations from terrestrial and maritime observing stations from the U.S.A. and other countries. For terrestrial networks, the platforms include but are not limited to ASOS, AWOS, RAWS, non-automated stations, U.S. Climate Reference Networks, many U.S. Geological Survey Stations via NWS HADS, several state DOT Road Weather Information Systems, and U.S. Historical Climatology Network-Modernization. For maritime areas, the platforms include NOS/CO-OPS National Water Level Observation Network (NWLON), NOS/CO-OPS Physical Oceanographic Real-Time System (PORTS), NWS/NDBC Fixed Buoys, NDBC Coastal-Marine Automated Network (C-MAN), drifting buoys, ferries, Regional Ocean Observing System (ROOS) coastal stations and buoys, and ships participating in the Voluntary Ship Observing (VOS) Program. Observations from MADIS are updated approximately every 10 minutes in the map service and those from NESDIS are updated every hour. However, not all stations report that frequently. Many stations only report once per hour sometime between 15 minutes before the hour and 30 minutes past the hour. For these stations, new observations will not appear until approximately 23 minutes past top of the hour for land-based stations and 33 minutes past the top of the hour for maritime stations.Time InformationThis map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:Issue a returnUpdates=true request (ArcGIS REST protocol only) for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of the REST Service page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:validtime: Valid timestamp.starttime: Display start time.endtime: Display end time.reftime: Reference time (sometimes referred to as issuance time, cycle time, or initialization time).projmins: Number of minutes from reference time to valid time.desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.desigprojmins: Number of minutes from designated reference time to valid time.Query the nowCOAST™ LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST™ LayerInfo Help DocumentationReferencesNWS, 2013: Sample Station Plot, NWS/NCEP/WPC, College Park, MD (Available at http://www.wpc.ncep.noaa.gov/html/stationplot.shtml).NWS, 2013: Terminology and Weather Symbols, NWS/NCEP/OPC, College Park, MD (Available at http://www.opc.ncep.noaa.gov/product_description/keyterm.shtml).NWS, 2013: How to read Surface weather maps, JetStream an Online School for Weather (Available at http://www.srh.noaa.gov/jetstream/synoptic/wxmaps.htm).

This layer is deprecated as of April 3, 2023. Use this layer as a replacement: https://noaa.maps.arcgis.com/home/item.html?id=b0cdf263cea24544b0da2fc00fb2b259This nowCOAST time-enabled map service provides maps of NOAA/National Weather Service RIDGE2 mosaics of base reflectivity images across the Continental United States (CONUS) as well as Puerto Rico, Hawaii, Guam and Alaska with a 2 kilometer (1.25 mile) horizontal resolution. The mosaics are compiled by combining regional base reflectivity radar data obtained from 158 Weather Surveillance Radar 1988 Doppler (WSR-88D) also known as NEXt-generation RADar (NEXRAD) sites across the country operated by the NWS and the Dept. of Defense and also from data from Terminal Doppler Weather Radars (TDWR) at major airports. The colors on the map represent the strength of the energy reflected back toward the radar. The reflected intensities (echoes) are measured in dBZ (decibels of z). The color scale is very similar to the one used by the NWS RIDGE2 map viewer. The radar data itself is updated by the NWS every 10 minutes during non-precipitation mode, but every 4-6 minutes during precipitation mode. To ensure nowCOAST is displaying the most recent data possible, the latest mosaics are downloaded every 5 minutes. For more detailed information about the update schedule, see: https://new.nowcoast.noaa.gov/help/#section=updateschedule

Background Information

Reflectivity is related to the power, or intensity, of the reflected radiation that is sensed by the radar antenna. Reflectivity is expressed on a logarithmic scale in units called dBZ. The "dB" in the dBz scale is logarithmic and is unit less, but is used only to express a ratio. The "z" is the ratio of the density of water drops (measured in millimeters, raised to the 6th power) in each cubic meter (mm^6/m^3). When the "z" is large (many drops in a cubic meter), the reflected power is large. A small "z" means little returned energy. In fact, "z" can be less than 1 mm^6/m^3 and since it is logarithmic, dBz values will become negative, as often in the case when the radar is in clear air mode and indicated by earth tone colors. dBZ values are related to the intensity of rainfall. The higher the dBZ, the stronger the rain rate. A value of 20 dBZ is typically the point at which light rain begins. The values of 60 to 65 dBZ is about the level where 3/4 inch hail can occur. However, a value of 60 to 65 dBZ does not mean that severe weather is occurring at that location. The best reflectivity is lowest (1/2 degree elevation angle) reflectivity scan from the radar. The source of the base reflectivity mosaics is the NWS Southern Region Radar Integrated Display with Geospatial Elements (RIDGE2).

Time Information

This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:

Issue a returnUpdates=true request for an individual layer or for
the service itself, which will return the current start and end times of
available data, in epoch time format (milliseconds since 00:00 January 1,
1970). To see an example, click on the "Return Updates" link at the bottom of
this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.


  Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against
  the proper layer corresponding with the target dataset. For raster
  data, this would be the "Image Footprints with Time Attributes" layer
  in the same group as the target "Image" layer being displayed. For
  vector (point, line, or polygon) data, the target layer can be queried
  directly. In either case, the attributes returned for the matching
  raster(s) or vector feature(s) will include the following:


      validtime: Valid timestamp.


      starttime: Display start time.


      endtime: Display end time.


      reftime: Reference time (sometimes reffered to as
      issuance time, cycle time, or initialization time).


      projmins: Number of minutes from reference time to valid
      time.


      desigreftime: Designated reference time; used as a
      common reference time for all items when individual reference
      times do not match.


      desigprojmins: Number of minutes from designated
      reference time to valid time.




  Query the nowCOAST LayerInfo web service, which has been created to
  provide additional information about each data layer in a service,
  including a list of all available "time stops" (i.e. "valid times"),
  individual timestamps, or the valid time of a layer's latest available
  data (i.e. "Product Time"). For more information about the LayerInfo
  web service, including examples of various types of requests, refer to
  the nowCOAST help documentation at:https://new.nowcoast.noaa.gov/help/#section=layerinfo

References

NWS, 2003: NWS Product Description Document for Radar Integrated Display with Geospatial Elements Version 2- RIDGE2, NWS/SRH, Fort Worth, Texas (Available at https://products.weather.gov/PDD/RIDGE_II_PDD_ver2.pdf). NWS, 2013: Radar Images for GIS Software (https://www.srh.noaa.gov/jetstream/doppler/gis.htm).

Summary

Rail Crossings is a spatial file maintained by the Federal Railroad Administration (FRA) for use by States and railroads.

Description

FRA Grade Crossings is a spatial file that originates from the National Highway-Rail Crossing, Inventory Program. The program is to provide information to Federal, State, and local governments, as well as the railroad industry for the improvements of safety at highway-rail crossing.

Credits

Federal Railroad Administration (FRA)

Use limitations

There are no access and use limitations for this item.

Extent

West -79.491008 East -75.178954 North 39.733500 South 38.051719

Scale Range Maximum (zoomed in) 1:5,000 Minimum (zoomed out) 1:150,000,000

ArcGIS Metadata ▼►Topics and Keywords ▼►Themes or categories of the resource  transportation

* Content type  Downloadable Data Export to FGDC CSDGM XML format as Resource Description No

Temporal keywords  2013

Theme keywords  Rail

Theme keywords  Grade Crossing

Theme keywords  Rail Crossings

Citation ▼►Title rr_crossings Creation date 2013-03-15 00:00:00

Presentation formats  * digital map

Citation Contacts ▼►Responsible party  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian

Responsible party  Organization's name Research and Innovative Technology Administration/Bureau of Transportation Statistics Individual's name National Transportation Atlas Database (NTAD) 2013 Contact's position Geospatial Information Systems Contact's role  distributor

Contact information  ▼►Phone  Voice 202-366-DATA

Address  Type  Delivery point 1200 New Jersey Ave. SE City Washington Administrative area DC Postal code 20590 e-mail address answers@BTS.gov

Resource Details ▼►Dataset languages  * English (UNITED STATES) Dataset character set  utf8 - 8 bit UCS Transfer Format

Spatial representation type  * vector

* Processing environment Microsoft Windows 7 Version 6.1 (Build 7600) ; Esri ArcGIS 10.2.0.3348

Credits Federal Railroad Administration (FRA)

ArcGIS item properties  * Name USDOT_RRCROSSINGS_MD * Size 0.047 Location withheld * Access protocol Local Area Network

Extents ▼►Extent  Geographic extent  Bounding rectangle  Extent type  Extent used for searching * West longitude -79.491008 * East longitude -75.178954 * North latitude 39.733500 * South latitude 38.051719 * Extent contains the resource Yes

Extent in the item's coordinate system  * West longitude 611522.170675 * East longitude 1824600.445629 * South latitude 149575.449134 * North latitude 752756.624659 * Extent contains the resource Yes

Resource Points of Contact ▼►Point of contact  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian

Resource Maintenance ▼►Resource maintenance  Update frequency  annually

Resource Constraints ▼►Constraints  Limitations of use There are no access and use limitations for this item.

Spatial Reference ▼►ArcGIS coordinate system  * Type Projected * Geographic coordinate reference GCS_North_American_1983_HARN * Projection NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet * Coordinate reference details  Projected coordinate system  Well-known identifier 2893 X origin -120561100 Y origin -95444400 XY scale 36953082.294548117 Z origin -100000 Z scale 10000 M origin -100000 M scale 10000 XY tolerance 0.0032808333333333331 Z tolerance 0.001 M tolerance 0.001 High precision true Latest well-known identifier 2893 Well-known text PROJCS["NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1312333.333333333],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-77.0],PARAMETER["Standard_Parallel_1",38.3],PARAMETER["Standard_Parallel_2",39.45],PARAMETER["Latitude_Of_Origin",37.66666666666666],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",2893]]

Reference system identifier  * Value 2893 * Codespace EPSG * Version 8.1.1

Spatial Data Properties ▼►Vector  ▼►* Level of topology for this dataset  geometry only

Geometric objects  Feature class name USDOT_RRCROSSINGS_MD * Object type  point * Object count 1749

ArcGIS Feature Class Properties  ▼►Feature class name USDOT_RRCROSSINGS_MD * Feature type Simple * Geometry type Point * Has topology FALSE * Feature count 1749 * Spatial index TRUE * Linear referencing FALSE

Data Quality ▼►Scope of quality information  ▼►Resource level  attribute Scope description  Attributes The States and railroads maintain their own file and get updated to the FRA. The information is reported to the FRA on the U.S. DOT-ARR Crossing inventory form.

Attributes The quality of the inventory can vary because a record of grade crossing location is being maintained by each state and railroad that is responsible for maintaining its respective information.

Lineage ▼►Lineage statement The data was downloaded from the HWY-Rail Crossing Inventory Files. All crossings that were closed or abandon were queried out of the data. All of the crossings with a zero within the latitude or longitude were queried out. Any crossing outside a bounding box of box ((Latitude >= 18 & Latitude <= 72) AND (Longitude >= -171 & Longitude <= -63)) were queried out.

Geoprocessing history ▼►Process  Date 2013-08-14 10:41:15 Tool location c:\program files (x86)\arcgis\desktop10.0\ArcToolbox\Toolboxes\Data Management Tools.tbx\Project Command issued Project RR_CROSSINGS_MD_USDOT \shagbfs\gis_projects\Railroad_Crossings_MD\Railroad_Crossings_MD.gdb\RR_CROSSINGS_MD_USDOT_83FTHARN PROJCS['NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet',GEOGCS['GCS_North_American_1983_HARN',DATUM['D_North_American_1983_HARN',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Conformal_Conic'],PARAMETER['False_Easting',1312333.333333333],PARAMETER['False_Northing',0.0],PARAMETER['Central_Meridian',-77.0],PARAMETER['Standard_Parallel_1',38.3],PARAMETER['Standard_Parallel_2',39.45],PARAMETER['Latitude_Of_Origin',37.66666666666666],UNIT['Foot_US',0.3048006096012192]] WGS_1984_(ITRF00)_To_NAD_1983_HARN GEOGCS['GCS_WGS_1984',DATUM['D_WGS_1984',SPHEROID['WGS_1984',6378137.0,298.25722356]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]] Include in lineage when exporting metadata No

Distribution ▼►Distributor  ▼►Contact information  Individual's name Office of Geospatial Information Systems Organization's name Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS) Contact's role  distributor

Contact information  ▼►Phone  Voice 202-366-DATA

Address  Type  Delivery point 1200 New Jersey Ave. SE City Washington Administrative area DC Postal code 20590 Country US e-mail address answers@bts.gov

Available format  Name Shapefile Version 2013 File decompression technique no compression applied

Ordering process  Instructions Call (202-366-DATA), or E-mail (answers@bts.gov) RITA/BTS to request the National Transportation Atlas Databases (NTAD) 2013 DVD. The NTAD DVD can be ordered from the online bookstore at www.bts.gov. Individual datasets from the NTAD can also be downloaded from the Office of Geospatial Information Systems website at http://www.bts.gov/programs/geographic_information_services/

Transfer options  Transfer size 6.645

Medium of distribution  Medium name  DVD

How data is written  iso9660 (CD-ROM) Recording density 650 Density units of measure Megabytes

Transfer options  Online source  Description  National Transportation Atlas Databases (NTAD) 2013

Distribution format  * Name Shapefile Version 2013

Transfer options  * Transfer size 0.047

Online source  Location http://www.bts.gov/programs/geographic_information_services/

Fields ▼►Details for object USDOT_RRCROSSINGS_MD ▼►* Type Feature Class * Row count 1749

Field FID ▼►* Alias FID * Data type OID * Width 4 * Precision 0 * Scale 0 * Field description Internal feature number.

* Description source ESRI

* Description of values Sequential unique whole numbers that are automatically generated.

Field Shape ▼►* Alias Shape * Data type Geometry * Width 0 * Precision 0 * Scale 0 * Field description Feature geometry.

* Description source ESRI

* Description of values Coordinates defining the features.

Field OBJECTID ▼►* Alias OBJECTID * Data type Integer * Width 9 * Precision 9 * Scale 0

Field CROSSING ▼►* Alias CROSSING * Data type String * Width 7 * Precision 0 * Scale 0 Field description US DOT Valid Crossing ID Number

Description source FRA

Field RAILROAD ▼►* Alias RAILROAD * Data type String * Width 4 * Precision 0 * Scale 0 Field description The

This layer is subset of World Ecological Facets Landform Classes Image Layer. Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This document describes the contents of the Calibration Parameter File (CPF) generated by the Image Assessment System (IAS) for the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The IAS periodically updates the CPF. This file is stamped with applicability dates and is published, allowing systems or individuals who require the file to access it. The CPF is also available to International Ground Stations (IGSs) and customers via the Landsat Mission Web Site (LMWS). The CPF supplies the radiometric and geometric correction parameters and other pertinent parameters required during Level 0 (L0) and Level 1 (L1) processing to create products of uniform consistency. This document also describes the Response Linearization Look Up Table (RLUT). The values contained in the table are a product of the Non-Linear Response Characterization used to correct the non-linear relationship between the input signal and the Digital Number (DN) value at the output of the OLI and TIRS instruments.

Model overviewThe indicative Australian Urban Development Risk Model is based on an assumption that recent-past trends in urban expansion (i.e the transition from non-urban land use to urban land use) will continue linearly, and that parameters associated with past expansion are valid predictors of future expansion. The model is underpinned by a conceptual logic, derived within ERIN, based on known datasets and their reasonable association with patterns of urbanisation. Specifically, we predict a higher urban development risk for non-urban locations with:proximity to existing high urban development areashigh increasing trend in street address densityland uses evidently prone to urbanisation andattractive geomorphology.The model is stratified by Australia’s 109 Significant Urban Areas and eight Greater Capital City Statistical Areas (ABS, 2016) and the model output is limited to these zones.Users should note there are likely to areas of high urban development risk beyond these zones, as discussed in the limitations section below.The model draws on multiple datasets to derive values for the parameters above and then combines them into a single index, with a value for every cell in a 9-second grid (about 1.2 million 250 x 250m cells). Derivation of parameter values is described below, followed by the approaches used to combine them into the index, classifying values for mapping, and combining with non-index masks to make the model spatially complete. Model parameters:The model is based on four parameters or predictor variables. For each parameter the field name (in the GIS data spatial attribute table) is provided in square brackets.1. Proximity to existing high urban development areas [NEAR_DIST]This parameter assumes continuation of 2006-2016 trends in urban development within a given Significant Urban Area or Greater Capital City Statistical Area. Locations close to an urban fringe which had expanded significantly during this period are at higher risk of urbanisation. Identifying past change from non-urban to urban involved comparing 2006 and 2016 ABS mesh blocks data. These datasets use a land use classification comprising 10 categories which were reclassified into urban(commercial, education, medical, industrial, residential, transport) and non-urban(parkland, water, primary production, other). Centre points of all 2016 urbanmesh blocks were compared with 2006 non-urbanmesh blocks to identify new urbanmesh blocks, or those which had changed from non-urbanto urban.These new urbanmesh blocks were used to attribute individual cells in the 9sec grid.Distances were then calculated between each new urbancell and its nearest 2006 urbanmesh block.Means (2006 dist. mean) and standard deviations for these distances were derived within each of Australia’s 109 Significant Urban Areas and eight Greater Capital City Statistical Areas (Areas). Larger mean values indicate greater urban expansion over the period for the Areain question. The standard deviations indicate how variable the expansion was within the Areaand, as shown in the table below, were used to account for uncertainty.To extrapolate a risk rating, all 2016 non-urbancells were converted to points and analysed for their distance to the closest 2016 urbancells. This distance was then compared to the relevant Areamean (2006 dist. mean) as described above. Where a 2016 non-urbancell is closer to an urbancell than the mean distance for conversion in the period 2006-2016, it is rated at higher risk, and particularly so for an Areawhere standard deviations are lower.The following table shows thresholds and parameter values. Conditions for cellParameter Value2016 dist. ≥[2006 dist. mean + standard deviation] 0.12016 dist. ≥[2006 dist. mean] but 0.42016 dist. ≥[2006 dist. mean - standard deviation] 0.72016 dist. –standard deviation 12. Increasing trend in street address density [setGnaf]A density of 60 addresses per 250m cell roughly equates to a ‘quarter acre block’urban landscape. This parameter assumes the continuation of trends in street address densification apparent during 2009-2016. Lower density locations (less than 30/cell) are considered non-urban and not at risk. Moderate density locations demonstrating significant increase during 2009-2016 are considered at high risk of urbanisation.The Geocoded National Address File (GNAF) dataset was used to derive both the number of addresses/cell for February 2016, and the increase from May 2009 to February 2016. The following thresholds and parameter values were applied.Conditions for cellParameter ValueLow density, low densification areas (ie all other than the following)02016 GNAF density ≥30 addresses/cell and density change (2009-2016) ≥2012016 GNAF density ≥40 addresses/cell and density change (2009-2016) ≥1012016 GNAF density ≥60 addresses/cell13.Land uses evidently prone to urbanisation [setLandUse]This parameter builds on the analysis used for Parameter 1. At assumes that past high likelihoods for urbanisation associated with certain land use types in different Areaswill persist. The 2016 new urbancells from Parameter 1, above, were compared to a 9 sec grid of the 2006 land use categories (derived from 2006 mesh blocks). For each land use, in each Area(ie a Significant Urban Areas or Greater Capital City Statistical Areas) the proportion urbanised was calculated as a number between zero and one. This number was directly applied as a parameter value for all 2016 non-urbancells. For example, a non-urbancell on a land use which had demonstrated a 60% chance of conversion to urbanin the period 2006-2016, would be scored at 0.6 for this parameter.4. Attractive geomorphology [setSlope]This parameter assumes that past preferences for urbanisation of lower slope areas will continue, given lower costs associated with developing such sites. Slope was calculated,from Geoscience Australia’s 1sec digital elevation model, as the mean slope across each 9sec cell. The following thresholds and parameter values were applied, and are based on a limited research effort into accessible building codes for new dwellings.Conditions for cellParameter ValueSlope ≥20 0.1Slope ≥12 and 0.4Slope ≥6 and 0.7Slope 1Derivation of the indexParameters were combined with equal weight on the assumption that each makes an equal contribution to our capacity to predict future urban expansion. However, individual parameter values are included in the GIS dataset to allow weights to be adjusted to suit particular analyses.Index Value = 0.25 x (proximity to high urban development) + 0.25 x (street address density) + 0.25 x (urbanising land use) + 0.25 x (attractive geomorphology)Classification of index values The index derives values for all cells between zero and one. These were classified into five equal-sized categories from “very low” to “very high”risk.Derivation of mapping unitsMapping units comprise the five risk categories, masked by non-index values for protected areas and existing-urban areas, as follows:Cells identified as protected, either through their inclusion in the Collaborative Australian Protected Areas Database or as ‘offset’areas in an EPBC Strategic Assessment area are ascribed a value of zero.Cells assessed as 2016 likely-urban for the NEAR_DIST parameter, attributed as ‘residential’in the Mesh Block layer,and with greater than 60 GNAF addresses are predicted to be existing-urban areas and ascribed with a value of 1.Non-index maskIndex valueRisk categorySuggested RGB for map colours: Protected from development38, 115, 00 0.2, Very low56, 148, 00.2 0.4, Low152, 230, 00.4 0.6, Moderate255, 255, 00.6 0.8, High255, 163, 430.8, Very high255, 0, 01, Existing urban239, 228, 190

Last Revised: February 2016

Map Information

This nowCOAST™ time-enabled map service provides maps of lightning strike density data from the NOAA/National Weather Service/NCEP's Ocean Prediction Center (OPC) which emulate (simulate) data from the future NOAA GOES-R Global Lightning Mapper (GLM). The purpose of this product is to provide mariners and others with enhanced "awareness of developing and transitory thunderstorm activity, to give users the ability to determine whether a cloud system is producing lightning and if that activity is increasing or decreasing..." Lightning Strike Density, as opposed to display of individual strikes, highlights the location of lightning cores and trends of increasing and decreasing activity. The maps depict the density of lightning strikes during a 15 minute time period at an 8 km x 8 km spatial resolution. The lightning strike density maps cover the geographic area from 25 degrees South to 80 degrees North latitude and from 110 degrees East to 0 degrees West longitude. The map units are number of strikes per square km per minute multiplied by a scaling factor of 10^3. The strike density is color coded using a color scheme which allows the data to be easily seen when overlaid on GOES imagery and to distinguish areas of low and high density values. The maps are updated on nowCOAST™ approximately every 15 minutes. The latest data depicted on the maps are approximately 12 minutes old (or older). Given the spatial resolution and latency of the data, the data should NOT be used to activite your lightning safety plans. Always follow the safety rule: when you first hear thunder or see lightning in your area, activate your emergency plan. If outdoors, immediately seek shelter in a substantial building or a fully enclosed metal vehicle such as a car, truck or van. Do not resume activities until 30 minutes after the last observed lightning or thunder. For more detailed information about layer update frequency and timing, please reference the
nowCOAST™ Dataset Update Schedule.

Background Information

The source for the data is OPC's gridded lightning strike density data on an 8x8 km grid. The gridded data emulate the spatial resolution of the future Global Lightning Mapper (GLM) instrument to be flown on the NOAA GOES-R series of geostationary satellites, with the first satellite scheduled for launch in late 2016.

The gridded data is based on data from Vaisala's ground based U.S. National Lightning Detection Network (NLDN) and its global lightning detection network referred to as the Global Lightning Dataset (GLD360). These networks are capable of detecting cloud-to-ground strikes, cloud-to-ground flash information and survey level cloud lightning information. According to the National Lightning Safety Institute, NLDN uses radio frequency detectors in the spectrum 1.0 kHz through 400 kHz to measure energy discharges from lightning as well as approximate distance and direction. According to Vaisala, the GLD360 network is capable of a detection efficiency greater than 70% over most of the Northern Hemisphere with a median location accuracy of 5 km or better. OPC's gridded data are coarser than the original source data from Vaisala's networks. The 15-minute gridded source data are updated at OPC every 15 minutes at 10 minutes past the valid time.

The lightning strike density product from NWS/NCEP/OPC is considered a derived product or Level 5 product ("NOAA-generated products using lightning data as input but not displaying the contractor transmitted/provided lightning data") and is appropriate for public distribution.

Time Information

This map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.

When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.

Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:

  Issue a returnUpdates=true request (ArcGIS REST protocol only)
  for an individual layer or for the service itself, which will return
  the current start and end times of available data, in epoch time format
  (milliseconds since 00:00 January 1, 1970). To see an example, click on
  the "Return Updates" link at the bottom of the REST Service page under
  "Supported Operations". Refer to the
  ArcGIS REST API Map Service Documentation
  for more information.


  Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against
  the proper layer corresponding with the target dataset. For raster
  data, this would be the "Image Footprints with Time Attributes" layer
  in the same group as the target "Image" layer being displayed. For
  vector (point, line, or polygon) data, the target layer can be queried
  directly. In either case, the attributes returned for the matching
  raster(s) or vector feature(s) will include the following:


      validtime: Valid timestamp.


      starttime: Display start time.


      endtime: Display end time.


      reftime: Reference time (sometimes referred to as
      issuance time, cycle time, or initialization time).


      projmins: Number of minutes from reference time to valid
      time.


      desigreftime: Designated reference time; used as a
      common reference time for all items when individual reference
      times do not match.


      desigprojmins: Number of minutes from designated
      reference time to valid time.




  Query the nowCOAST™ LayerInfo web service, which has been created to
  provide additional information about each data layer in a service,
  including a list of all available "time stops" (i.e. "valid times"),
  individual timestamps, or the valid time of a layer's latest available
  data (i.e. "Product Time"). For more information about the LayerInfo
  web service, including examples of various types of requests, refer to
  the 
  nowCOAST™ LayerInfo Help Documentation

References

Kithil, 2015: Overview of Lightning Detection Equipment, National
Lightning Safety Institute, Louisville, CO. (Available from
http://www.lightningsafety.com/nsli_ihm/detectors.html).


NASA and NOAA, 2014: Geostationary Lightning Mapper (GLM). (Available at
http://www.goes-r.gov/spacesegment/glm.html).


NWS, 2013: Lightning Strike Density Product Description Document.
NOAA/NWS/NCEP/Ocean Prediction Center, College Park, MD (Available at
http://www.opc.ncep.noaa.gov/lightning/lightning_pdd.php
and http://products.weather.gov/PDD/Experimental%20Lightning%20Strike%20Density%20Product%2020130913.pdf).


NOAA Knows Lightning. NWS, Silver Spring, MD (Available at
http://www.lightningsafety.noaa.gov/resources/lightning3_050714.pdf).


Siebers, A., 2013: Soliciting Comments until June 3, 2014 on an
Experimental Lightning Strike Density product (Offshore Waters). Public
Information Notice, NOAA/NWS Headquarters, Washington, DC (Available at
http://www.nws.noaa.gov/om/notification/pns13lightning_strike_density.htm).

This dataset represents a partition of the world oceans into provinces as defined by Longhurst (1995; 1998; 2006), and are based on the prevailing role of physical forcing as a regulator of phytoplankton distribution. The dataset represents the initial static boundaries developed at the Bedford Institute of Oceanography, Canada. Note that the boundaries of these provinces are not fixed in time and space, but are dynamic and move under seasonal and interannual changes in physical forcing. At the first level of reduction, Longhurst recognised four principal biomes (also referred to as domains in earlier publications): the Polar Biome, the Westerlies Biome, the Trade-Winds Biome, and the Coastal Boundary Zone Biome. These four Biomes are recognisable in every major ocean basin. At the next level of reduction, the ocean basins are partitioned into provinces, roughly ten for each basin. These partitions provide a template for data analysis or for making parameter assignments on a global scale. Please refer to Longhurst's publications when using these shapefiles. A summery table has been prepared by Mathias Taeger and David Lazarus, Museum für Naturkunde, Berlin (2010-03-26). This table makes it easier to relate the classification of Longhurst to the the original quantitative parameters used to create it. Productivity values are from the table in Longhurst, 1995,
Chlorophyll values; photic depth and mixed layer depth originate from graphs in Longhurst, 1998. The sea temperature at 0 and 50 m are from the World Ocean Atlas (2005), average values were calculated in ArcGIS. Each parameter value was set into 5 equal intervals.source: www.marineregions.org

The California Irrigation Management Information System (CIMIS) currently manages over 145 active weather stations throughout the state. Archived data is also available for 85 additional stations that have been disconnected from the network for various reasons. CIMIS stations provide hourly records of solar radiation, precipitation, air temperature, air humidity, and wind speed. Most of the CIMIS stations produce estimates of reference evapotranspiration (ETo) for the station location and their immediate surroundings, often in agricultural areas. The Department of Water Resources operates CIMIS as a free resource to help California to manage water resources more efficiently. CIMIS weather stations collect weather data on a minute-by-minute basis. Hourly data reflects the previous hour's 60 minutes of readings. Hourly and daily values are calculated and stored in the dataloggers. A computer at the DWR headquarters in Sacramento calls every station starting at midnight Pacific Standard Time (PST) and retrieves data at predetermined time intervals. At the time of this writing, CIMIS data is retrieved from the stations every hour. When there is a communication problem between the polling server and any given station, the server skips that station and calls the next station in the list. After all other stations have reported, the polling server again polls the station with the communication problem. The interrogation continues into the next day until all of the station data have been transmitted. CIMIS data processing involves checking the accuracy of the measured weather data for quality, calculating reference evapotranspiration (ETo/ETr) and other intermediate parameters, flagging measured and calculated parameters, and storing the data in the CIMIS database. Evapotranspiration (ET) is a loss of water to the atmosphere by the combined processes of evaporation from soil and plant surfaces and transpiration from plants. Reference evapotranspiration is ET from standardized grass or alfalfa surfaces over which the weather stations are sitting. The standardization of grass or alfalfa surfaces for a weather station is required because ET varies depending on plant (type, density, height) and soil factors and it is difficult, if not impossible, to measure weather parameters under all sets of conditions. Irrigators have to use crop factors, known as crop coefficients (Kc), to convert ET from the standardized reference surfaces into an actual evapotranspiration (ETc) by a specific crop. For more information go to https://cimis.water.ca.gov/. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.3, dated April 13, 2022. DWR makes no warranties or guarantees —either expressed or implied — as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

Grid-based building morphological parameters with global coverage at 30-arc second spatial resolution are currently available in GeoTIFF format. Provided datasets contains three-building morphological parameters (the mean building height Have, plan area density PAD and frontal area density FAD) and two-aerodynamic parameters (aerodynamic roughness length z0 and zero-place displacement d) and sky-view factor (svf).The building morphological datasets were estimated from the global databases such as population, nighttime light, impervious surface area and gross domestic products. Two aerodynamic parameters and sky-view factors are calculated using the empirical equations discussed by Kanda et al. (2013) and Kanda et al. (2005), respectively.1. Raster files: (parameter name)_2013.tifFormat: GeoTIFFProjection: WGS 1984 World Mercator projectionSpatial resolution: 30-arc secondData list: Have_2013.tif, PAD_2013.tif, FAD_2013.tif, d_2013.tif, z0_2013.tif, svf_2013.tif2. Building Original DataFormat: Microsoft Excel WorkbookOriginal_building_data.xlsx contains observed building morphological parameters calculated from three- and two-dimensional building databases, and global databases (impervious surface area ISA and population density adjusted by nighttime light PopdenVIIRS) at each grid code.Validation_analysis.xlsx contains building morphological parameters calculated from three-dimensional building database (observed) and parameters estimated from global databases (predicted) at one-km spatial resolution in Berlin, Singapore and Osaka.Additional_validation_UScities.xlsx contains building morphological parameters at one-km resolution by NUDAPT database (observed) and estimated from global databases (predicted) for 42 US cities. We used this data in the Supplementary Discussion. Megacities_statistic.xlsx contains GDPcity, the maximum, minimum, mean value and standard deviation of each predicted building morphological parameters at 37 megacities. 3. Source CodeProgramming language 1: Python site package in ArcGIS v10.3.1Calculate_parameters.py contains code for calculating observed building morphological parameters from grid-based two- and three-dimensional building database input. We recommend using this script after using the Split By Attributing Tools to convert a fishnet building footprint map into multiple grids.Modifying_population_by_nightlight.py contains code for adjusted population density by nighttime light at each grid.Programming language 2: Python v2.7Converting_grids.py contains code for converting grid-based population density adjusted by nighttime light into a global map. This source code is used after running Modifying_population_by_nightlight.py.

This dataset is stored in a zip file containing includes the input data used in eWater Source, the spatial data for the study site and a spreadsheet model. 1. The input data (stored as CSV files) include meteorological (rainfall and evaporation in mm/day) and streamflow (in ML/day) data, fertilizer and feed input (daily applications in kg/ha), the definition of the functional units used in the model (areas in km^2, with percentage of total area in last column) and the model parameter values. The eWater Source project file is also included. 2. The spatial datasets include ArcGIS shape files of the catchment and subcatchments, land use and waterways. The ArcGIS .mxd file is also included.

Mean values of the microclimate parameters of different types of spaces.

This dataset contains an explanation of data analysis for creating a flood vulnerability map of Samarinda Seberang District. The dataset contains sub-criteria for each flood parameter and its score value. In addition, this dataset contains the weight value of each parameter, flood vulnerability level and its coloring, and the results of calculating the area of each vulnerability level.

The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access, and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and agriculture sustainability. POWER is funded through the NASA Earth Action Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC).---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This annual meteorology service provides time-enabled global Analysis Ready Data (ARD) parameters from 1981 to 2023 for POWER’s communities. Time Interval: AnnualTime Extent: 1981/01/01 to 2023/12/31Time Standard: Local Sidereal Time (LST)Grid Size: 0.5 x 0.5 DegreeProjection: GCS WGS84Extent: GlobalSource: NASA Prediction Of Worldwide Energy Resources (POWER)For questions or issues please email: larc-power-project@mail.nasa.govMeteorology Data Sources:NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – Dec. 31, 2023)Meteorology Data Parameters:CDD10 (Cooling Degree Days Above 10 C): The daily accumulation of degrees when the daily mean temperature is above 10 degrees Celsius.CDD18_3 (Cooling Degree Days Above 18.3 C): The daily accumulation of degrees when the daily mean temperature is above 18.3 degrees Celsius.DISPH (Zero Plane Displacement Height): The height at which the mean velocity is zero due to large obstacles such as buildings/canopy.EVLAND (Evaporation Land): The evaporation over land at the surface of the earth.EVPTRNS (Evapotranspiration Energy Flux): The evapotranspiration energy flux at the surface of the earth.FROST_DAYS (Frost Days): A frost day occurs when the 2m temperature cools to the dew point temperature and both are less than 0 C or 32 F.GWETTOP (Surface Soil Wetness): The percent of soil moisture a value of 0 indicates a completely water-free soil and a value of 1 indicates a completely saturated soil; where surface is the layer from the surface 0 cm to 5 cm below grade.HDD10 (Heating Degree Days Below 10 C): The daily accumulation of degrees when the daily mean temperature is below 10 degrees Celsius.HDD18_3 (Heating Degree Days Below 18.3 C): The daily accumulation of degrees when the daily mean temperature is below 15.3 degrees Celsius.PBLTOP (Planetary Boundary Layer Top Pressure): The pressure at the top of the planet boundary layer.PRECSNOLAND_SUM (Snow Precipitation Land Sum): The snow precipitation sum over land at the surface of the earth.PRECTOTCORR_SUM (Precipitation Corrected Sum): The bias corrected sum of total precipitation at the surface of the earth.PS (Surface Pressure): The average of surface pressure at the surface of the earth.QV10M (Specific Humidity at 10 Meters): The ratio of the mass of water vapor to the total mass of air at 10 meters (kg water/kg total air).QV2M (Specific Humidity at 2 Meters): The ratio of the mass of water vapor to the total mass of air at 2 meters (kg water/kg total air).RH2M (Relative Humidity at 2 Meters): The ratio of actual partial pressure of water vapor to the partial pressure at saturation, expressed in percent.T10M (Temperature at 10 Meters): The air (dry bulb) temperature at 10 meters above the surface of the earth.T2M (Temperature at 2 Meters): The average air (dry bulb) temperature at 2 meters above the surface of the earth.T2MDEW (Dew/Frost Point at 2 Meters): The dew/frost point temperature at 2 meters above the surface of the earth.T2MWET (Wet Bulb Temperature at 2 Meters): The adiabatic saturation temperature which can be measured by a thermometer covered in a water-soaked cloth over which air is passed at 2 meters above the surface of the earth.TO3 (Total Column Ozone): The total amount of ozone in a column extending vertically from the earth's surface to the top of the atmosphere.TQV (Total Column Precipitable Water): The total atmospheric water vapor contained in a vertical column of unit cross-sectional area extending from the surface to the top of the atmosphere.TS (Earth Skin Temperature): The average temperature at the earth's surface.WD10M (Wind Direction at 10 Meters): The average of the wind direction at 10 meters above the surface of the earth.WD2M (Wind Direction at 2 Meters): The average of the wind direction at 2 meters above the surface of the earth.WD50M (Wind Direction at 50 Meters): The average of the wind direction at 50 meters above the surface of the earth.WS10M (Wind Speed at 10 Meters): The average of wind speed at 10 meters above the surface of the earth.WS2M (Wind Speed at 2 Meters): The average of wind speed at 2 meters above the surface of the earth.WS50M (Wind Speed at 50 Meters): The average of wind speed at 50 meters above the surface of the earth.