This dataset includes an aggregated and event-correlated analysis of power outages in the United States, synthesized by integrating three data sources: the Environment for the Analysis of Geo-Located Energy Information (EAGLE-I), the Electric Emergency Incident Disturbance Report (DOE-417), and Annual Estimates of the Resident Population for Counties 2024 (CO-EST2024-POP). The EAGLE-I dataset, spanning from 2014 to 2023, encompasses over 146 million customers and offers county-level outage information at 15-minute intervals. The data has been processed, filtered, and aggregated to deliver an enhanced perspective on power outages, which are then correlated with DOE-417 data based on geographic location as well as the start and end times of events. For each major disturbance documented in DOE-417, essential metrics are defined to quantify the outages associated with the event. This dataset supports researchers in examining outages triggered by major disturbances like extreme weather and physical disruptions, thereby aiding studies on power system resilience. Links to the raw data for generating the correlated dataset are included below as "DOE-417", "EAGLE-I", and "CO-EST2024-POP" resources. Acknowledgement: This work is funded by the Laboratory Directed Research and Development (LDRD) at the Pacific Northwest National Laboratory (PNNL) as part of the Resilience Through Data-Driven, Intelligently Designed Control (RD2C) Initiative.

The power outages in this layer are pulled directly from the utility public power outage maps and is automatically updated every 15 minutes. This dataset represents only the most recent power outages and does not contain any historical data. The following utility companies are included:Pacific Gas and Electric (PG&E)Southern California Edison (SCE)San Diego Gas and Electric (SDG&E)Sacramento Municipal Utility District (SMUD)Los Angeles Water & Power (LAWP)Layers included in this dataset:Power Outage Incidents - Point layer that shows data from all of the utilities and is best for showing a general location of the outage and driving any numbers in dashboards.Power Outage Areas - Polygon layer that shows rough power outage areas from PG&E only (They are the only company that feeds this out publicly). With in the PG&E territory this layer is useful to show the general area out of power. The accuracy is limited by how the areas are drawn, but is it good for a visual of the impacted area.Power Outages by County - This layer summaries the total impacted customers by county. This layer is good for showing where outages are on a statewide scale.If you have any questions about this dataset please email GIS@caloes.ca.gov

The electric companies file their emergency response plan (ERP) reports, which list information about historic power outages. It includes information such as city or town where the outage occurred, number of customers affected, outage duration, time the outage occurred, and reason for outage.Note: To access the spreadsheet with historic power outage information, please visit the online "file room" and type the docket number, replacing the first two digits in a case number with the relevant year’s last two digits. For example, to find power outages in 2016 in Unitil's service territory, use docket number "17-ERP-08". Power outage information is filed the following year it happened with the DPU and 2016 is the earliest year that the data is available in Excel spreadsheets.

Hosted view containing outage data by Maryland zip code for MDEM Power Outage Application.

The dataset provides comprehensive fault logs from a power grid system, capturing a variety of fault events that occurred across different locations within the grid. It includes critical information such as timestamps, specific locations (e.g., substations, transformers, transmission lines), fault descriptions, and the underlying causes of the faults, which may range from equipment malfunctions, overload conditions, voltage fluctuations, to external factors like weather events or human errors. Each entry in the dataset records detailed actions taken in response to the faults, such as isolating affected components, activating backup systems, dispatching maintenance teams, or rerouting power lines to minimize downtime and ensure continued service. Additionally, the dataset provides valuable context, such as the type of equipment involved (e.g., transformers, power distribution units), which can be instrumental in identifying recurring issues, understanding fault patterns, and optimizing grid management strategies. This dataset is crucial for developing advanced predictive maintenance models, enabling utilities to proactively identify potential faults before they occur, reduce unplanned outages, and ultimately improve the resilience and efficiency of power grid systems. Through analysis of this dataset, researchers can enhance fault detection, system reliability, and overall operational efficiency within the power grid, contributing to more sustainable energy manageme

This dataset reports on the operation state of traffic signals in Austin, TX. Traffic signals enter flash mode when something is preventing the signal from operating normally. This is typically the result of a power surge, power outage, or damage to signal equipment. A signal may also be intentionally placed into flash mode for maintenance purposes or be scheduled to flash overnight.

You can view an interactive map of flashing traffic signals here:

https://data.mobility.austin.gov/signal-monitor

Approximately 90% of the City’s signals communicate with our Advanced Transportation Management System. When these signals go on flash, they will be reported in this dataset. Although we are extending communications to all signals, approximately 10% are not currently captured in this dataset. It also occasionally happens that the event that disables a traffic signal also disables network communication to the signal, in which case the signal outage will not be reported here.

In this dataset the distinction between scheduled and unscheduled flash is identified by the 'operation state' column. A signal that is in unscheduled flash mode will have a status of 2 or 7. A signal that is in in scheduled flash mode will have a status of 1.

This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of traffic signals.

Note: This is a real-time dataset. If you do not see any data on the map, there may not be an event taking place. The Atlantic hurricane season begins on June 1 and ends on November 30, and the eastern Pacific hurricane season begins on May 15 and ends on November 30.Hurricanes, also known as typhoons and cyclones, fall under the scientific term tropical cyclone. Tropical cyclones that develop over the Atlantic and eastern Pacific Ocean are considered hurricanes.Meteorologists have classified the development of a tropical cyclone into four stages: tropical disturbance, tropical depression, tropical storm, and tropical cyclone. Tropical cyclones begin as small tropical disturbances where rain clouds build over warm ocean waters. Eventually, the clouds grow large enough to develop a pattern, where the wind begins to circulate around a center point. As winds are drawn higher, increasing air pressure causes the rising thunderstorms to disperse from the center of the storm. This creates an area of rotating thunderstorms called a tropical depression with winds 62 kmph (38 mph) or less. Systems with wind speeds between 63 kmph (39 mph) and 118 kmph (73 mph) are considered tropical storms. If the winds of the tropical storm hit 119 kmph (74 mph), the storm is classified as a hurricane. Tropical cyclones need two primary ingredients to form: warm water and constant wind directions. Warm ocean waters of at least 26 degrees Celsius (74 degrees Fahrenheit) provide the energy needed for the storm to become a hurricane. Hurricanes can maintain winds in a constant direction at increasing speeds as air rotates about and gathers into the hurricane’s center. This inward and upward spiral prevents the storm from ripping itself apart. Hurricanes have distinctive parts: the eye, eyewall, and rain bands. The eye is the calm center of the hurricane where the cooler drier air sinks back down to the surface of the water. Here, winds are tranquil, and skies are partly cloudy, sometimes even clear. The eyewall is composed of the strongest ring of thunderstorms and surrounds the eye. This is where rain and winds are the strongest and heaviest. Rain bands are stretches of rain clouds that go far beyond the hurricane’s eyewall, usually hundreds of kilometers. Scientists typically use the Saffir-Simpson Hurricane Wind Scale to measure the strength of a hurricane’s winds and intensity. This scale gives a 1 to 5 rating based on the hurricane’s maximum sustained winds. Hurricanes rated category 3 or higher are recognized as major hurricanes. Category 1: Wind speeds are between 119 and 153 kmph (74 and 95 mph). Although this is the lowest category of hurricane, category 1 hurricanes still produce dangerous winds and could result in damaged roofs, power lines, or fallen tree branches. Category 2: Wind speeds are between 154 and 177 kmph (96 and 110 mph). These dangerous winds are likely to cause moderate damage; enough to snap or uproot small trees, destroy roofs, and cause power outages. Category 3: Wind speeds are between 178 and 208 kmph (111 and 129 mph). At this strength, extensive damage may occur. Well-built homes could incur damage to their exterior and many trees will likely be snapped or uprooted. Water and electricity could be unavailable for at least several days after the hurricane passes. Category 4: Wind speeds are between 209 and 251 kmph (130 and 156 mph). Extreme damage will occur. Most of the area will be uninhabitable for weeks or months after the hurricane. Well-built homes could sustain major damage to their exterior, most trees may be snapped or uprooted, and power outages could last weeks to months. Category 5: Wind speeds are 252 kmph (157 mph) or higher. Catastrophic damage will occur. Most of the area will be uninhabitable for weeks or months after the hurricane. A significant amount of well-built, framed homes will likely be destroyed, uprooted trees may isolate residential areas, and power outages could last weeks to months. This map is built with data from the NOAA National Hurricane Center (NHC) and the Joint Typhoon Warning Center (JTWC). The map shows recent, observed, and forecasted hurricane tracks and positions, uncertainties, wind speeds, and associated storm watches and warnings. This is a real-time dataset that is programed to check for updates from the NHC and JTWC every 15 minutes. If you are in an area experiencing a tropical cyclone, tune into local sources for more up-to-date information and important safety instructions. This map includes the following information: Forecast position points: These points mark the locations where the NHC predict the tropical cyclone will be at 12, 24, 36, 48, 72, 96, and 120 hours in the future.Observed position points: These points mark the locations where the tropical cyclone has been.Forecast track: This is the line that connects the forecast points and marks the expected path of the hurricane.Observed track: This line marks the path the tropical cyclone has already taken.Cone of uncertainty: Due to the complexity of ocean atmospheric interactions, there are many different factors that can influence the path of a hurricane. This uncertainty is represented on the map by a cone. The further into the future the forecast is, the wider the cone due to the greater uncertainty in the precise path of the storm. Remember rain, wind, and storm surge from the hurricane will likely impact areas outside the cone of uncertainty. This broader impact of wind can be seen if you turn on or off Tropical Storm Force (34 Knots) 5-Day Wind Probability, Strong Tropical Storm Force (50 Knots) 5-Day Wind Probability, or Hurricane Force (64 Knots) 5-Day Wind Probability map layers.Watches and warnings: Storm watches or warnings depend on the strength and distance from the location of the forecasted event. Watches indicate an increased risk for severe weather, while a warning means you should immediately move to a safe space.Tropical storm watch: The NHC issues this for areas that might be impacted by tropical cyclones with wind speeds of 34 to 63 knots (63 to 119 kilometers per hour or 39 to 74 miles per hour) in the next 48 hours. In addition to high winds, the region may experience storm surge or flooding.Tropical storm warning: The NHC issues this for places that will be impacted by hurricanes with wind speeds of 34 to 63 knots (63 to 119 kilometers per hour or 39 to 74 miles per hour) in the next 36 hours. As with the watch, the area may also experience storm surge or flooding.Hurricane watch: The NHC issues this watch for areas where a tropical cyclone with sustained wind speeds of 64 knots (119 kilometers per hour or 74 miles per hour) or greater in the next 48 hours may be possible. In addition to high winds, the region may experience storm surge or flooding.Hurricane warning: The NHC issues this warning for areas where hurricanes with sustained wind speeds of 64 knots (119 kilometers per hour or 74 miles per hour) or greater in the next 36 hours are expected. As with the watch, the region may experience storm surge or flooding. This warning is also posted when dangerously high water and waves continue even after wind speeds have fallen below 64 knots.Recent hurricanes: These points and tracks mark tropical cyclones that have occurred this year but are no longer active.

Want to learn more about how hurricanes form? Check out Forces of Nature or explore The Ten Most Damaging Hurricanes in U.S. History story.

Analysis of ‘Traffic Signals Status’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/15a2b0ac-3a77-4893-8433-4391674798e4 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset reports on the operation state of traffic signals in Austin, TX. Traffic signals enter flash mode when something is preventing the signal from operating normally. This is typically the result of a power surge, power outage, or damage to signal equipment. A signal may also be intentionally placed into flash mode for maintenance purposes or be scheduled to flash overnight.

You can view an interactive map of flashing traffic signals here: http://transportation.austintexas.io/signals-on-flash

Approximately 90% of the City’s signals communicate with our Advanced Trasnportation Management System. When these signals go on flash, they will be reported in this dataset. Although we are extending communications to all signals, approximately 10% are not currently captured in this dataset. It also occasionally happens that the event that disables a traffic signal also disables network communication to the signal, in which case the signal outage will not be reported here.

In this dataset the distinction between scheduled and unscheduled flash is identified by the 'operation state' column. A signal that is in unscheduled flash mode will have a status of 2 or 7. A signal that is in in scheduled flash mode will have a status of 1.

This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of traffic signals.

--- Original source retains full ownership of the source dataset ---

Introduction This dataset records all curtailment events experienced by curtailable-connection customers. About Curtailment When a generation customer requests a firm connection under a congested part of our network, there may be a requirement to reinforce the network to accommodate the connection. The reinforcement works take time to complete which increases the lead time to connect for the customer. Furthermore, the customer may need to contribute to the cost of the reinforcement works.UK Power Networks offers curtailable-connections as an alternative solution for our customers. It allows customers to connect to the distribution network as soon as possible rather than waiting, and potentially paying, for network reinforcement. This is possible because under a curtailable connection, the customer agrees that their access to the network can be controlled when congestion is high. These fast-tracked curtailable-connections can transition to firm connections once the reinforcement activity has taken place. Curtailable connections have enabled faster and cheaper connection of renewable energy generation to the distribution network owned and operated by UK Power Networks.The Distribution System Operator (DSO) team has developed the Distributed Energy Resource Management System (DERMS) that monitors curtailable-connection generators as well as associated constraints on the network. When a constraint reaches a critical threshold, an export access reduction signal may be sent to generators associated with that constraint so that the network can be kept safe, secure, and reliable.This dataset contains a record of curtailment actions we have taken and the resultant access reduction experienced by our curtailment-connections customers. Access reduction is calculated as the MW access reduction from maximum × duration of access reduction in hours (MW×h). The dataset categorises curtailment actions into 2 categories: Constraint-driven curtailment: when a constraint is breached, we aggregate the access reduction of all customers associated with that constraint. A constraint breach occurs when the network load exceeds the safe limit. Non-constraint driven curtailment: this covers all curtailment which is not directly related to a constraint breach on the network. It includes customer comms failures, non-compliance trips (where the customer has not complied with a curtailment instruction), planned outages and unplanned outages Each row in the dataset details the start and end times, durations and customer access reduction associated with a curtailment actions. We also provide the associated grid supply point (GSP) and nominal voltage to provide greater aggregation capabilities. By virtue of being able to track curtailment across our network in granular detail, we have managed to significantly reduce curtailment of our curtailable-connections customers. Methodological Approach A Remote Terminal Unit (RTU) is installed at each curtailable-connection site providing live telemetry data into the DERMS. It measures communications status, generator output and mode of operation. RTUs are also installed at constraint locations (physical parts of the network, e.g., transformers, cables which may become overloaded under certain conditions). These are identified through planning power load studies. These RTUs monitor current at the constraint and communications status. The DERMS design integrates network topology information. This maps constraints to associated curtailable connections under different network running conditions, including the sensitivity of the constraints to each curtailable connection. In general, a 1MW reduction in generation of a customer will cause <1MW reduction at the constraint. Each constraint is registered to a GSP.DERMS monitors constraints against the associated breach limit. When a constraint limit is breached, DERMS calculates the amount of access reduction required from curtailable connections linked to the constraint to alleviate the breach. This calculation factors in the real-time level of generation of each customer and the sensitivity of the constraint to each generator. Access reduction is issued to each curtailable-connection via the RTU until the constraint limit breach is mitigated. Multiple constraints can apply to a curtailable-connection and constraint breaches can occur simultaneously. Where multiple constraint breaches act upon a single curtailable-connection, we apportion the access reduction of that connection to the constraint breaches depending on the relative magnitude of the breaches. Where customer curtailment occurs without any associated constraint breach, we categorise the curtailment as non-constraint driven. Future developments will include the reason for non-constraint driven curtailment. Quality Control Statement The dataset is derived from data recorded by RTUs located at customer sites and constraint locations across our network. UKPN’s Ops Telecoms team monitors and maintains these RTUs to ensure they are providing accurate customer/network data. An alarms system notifies the team of communications failures which are attended to by our engineers as quickly as possible. RTUs can store telemetry data for prolonged periods during communications outages and then transmit data once communications are reinstated. These measures ensure we have a continuous stream of accurate data with minimal gaps. On the rare instances where there are issues with the raw data received from DERMS, we employ simple data cleaning algorithms such as forward filling. RTU measurements of access reduction update on change or every 30-mins in absence of change. We also minimise postprocessing of RTU data (e.g. we do not time average data). Using the raw data allows us to ascertain event start and end times of curtailment actions exactly and accurately determine access reductions experienced by our customers. Assurance Statement The dataset is generated and updated by a script which is scheduled to run daily. The script was developed by the DSO Data Science team in conjunction with the DSO Network Access team, the DSO Operations team and the UKPN Ops Telecoms team to ensure correct interpretation of the RTU data streams. The underlying script logic has been cross-referenced with the developers and maintainers of the DERMS scheme to ensure that the data reflects how DERMS operates. The outputs of the script were independently checked by the DSO Network Access team for accuracy of the curtailment event timings and access reduction prior to first publication on the Open Data Portal (ODP). The DSO Operations team conduct an ongoing review of the data as it is updated daily to verify that the operational expectations are reflected in the data. The Data Science team have implemented automated logging which notifies the team of any issues when the script runs. This allows the Data Science to investigate and debug any errors/warnings as soon as they happen.

Other

Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

Scheduled energy interruptions aggregated by postal code.1. The information made available by E-REDES constitutes an approximation to the values taken from the system and is based on the moment in which it is collected. Given that the connection points, the electricity distribution network, and the consumption and production values themselves are naturally very dynamic, it is safeguarded that the information made available may be subject to subsequent changes and updates, with the exception of any omissions and/or occasional inaccuracies of location that the information may contain.2. In this way, E-REDES is not liable to third parties, namely, partners, service providers, contractors, users and customers, for damages that may arise as a result, direct or indirect, of the use of this Information, in particular when carrying out interventions, calculations and/or estimates, without confirming the accuracy and updating of the data, whereby it is duly noted that the consultation of this information does not affect the duty to promote a direct consultation with E-REDES in order to obtain updated information.The data provided by the E-REDES Open Data Portal is covered by open licenses (CC BY 4.0). There are no restrictions on access, under the commitment that data users cite the publisher. Therefore, we suggest that you cite the Open Data E-REDES Portal as:E-REDES – Distribuição de Eletricidade, “E-REDES Open Data Portal”. Accessed in “Data”. [Online] Available at https://e-redes.opendatasoft.com/pages/homepage/If you share on social media, please add #PortalOpenData_E_REDES

Introduction When a generation customer requests a firm connection under a congested part of our network, there may be a requirement to reinforce the network to accommodate the connection. The reinforcement works take time to complete which increases the lead time to connect for the customer. Furthermore, the customer may need to contribute to the cost of the reinforcement works. UK Power Networks offers curtailable-connections as an alternative solution for our customers. It allows customers to connect to the distribution network as soon as possible rather than waiting, and potentially paying, for network reinforcement. This is possible because under a curtailable connection, the customer agrees that their access to the network can be controlled when congestion is high. These fast-tracked curtailable-connections can transition to firm connections once the reinforcement activity has taken place. Curtailable connections have enabled faster and cheaper connection of renewable energy generation to the distribution network owned and operated by UK Power Networks. The Distribution System Operator (DSO) team has developed the Distributed Energy Resource Management System (DERMS) that monitors curtailable-connection generators as well as associated constraints on the network. When a constraint reaches a critical threshold, an export access reduction signal may be sent to generators associated with that constraint so that the network can be kept safe, secure, and reliable. This dataset contains a record of curtailment events and the associated access reduction experienced by DERs with curtailable connections. Access reduction is calculated as the MW access reduction from maximum × duration of access reduction in hours (MW×h). The dataset categorises curtailment actions into two categories:

  Constraint-driven curtailment: when a constraint is breached, we aggregate the access reduction of all customers associated with that constraint. A constraint breach occurs when the network load exceeds the safe limit; and
  Non-constraint driven curtailment: this covers all curtailment which is not directly related to a constraint breach on the network. It includes customer comms failures, non-compliance trips (where the customer has not complied with a curtailment instruction), planned outages, and unplanned outages.

Each row in the dataset is a curtailment event, meaning a continuous period of access reduction, with associated start and end times, volume of access reduction, estimated energy reduction, and likely curtailment driver. We also provide the associated grid supply point (GSP) and nominal voltage to provide greater aggregation capabilities.
Energy reduction has been estimated using a recent history baseline. Future enhancements will look at using more sophisticated baseline estimation methodologies.
The curtailment driver column represents UK Power Networks' best view of the likely driver of the curtailment. Future improvements may remap drivers and provide a more detailed breakdown of drivers.
By virtue of being able to track curtailment across our network in granular detail, we have managed to significantly reduce curtailment of our curtailable-connections customers.

Methodological Approach

  A Remote Terminal Unit (RTU) is installed at each curtailable-connection site providing live telemetry data into the DERMS. It measures communications status, generator output, and mode of operation.
  RTUs are also installed at constraint locations (physical parts of the network, e.g., transformers, cables which may become overloaded under certain conditions). These are identified through planning power load studies. These RTUs monitor current at the constraint and communications status.
  The DERMS design integrates network topology information. This maps constraints to associated curtailable connections under different network running conditions, including the sensitivity of the constraints to each curtailable connection. In general, a 1MW reduction in generation of a customer will cause <1MW reduction at the constraint. Each constraint is registered to a GSP.
  DERMS monitors constraints against the associated breach limit. When a constraint limit is breached, DERMS calculates the amount of access reduction required from curtailable connections linked to the constraint to alleviate the breach. This calculation factors in the real-time level of generation of each customer and the sensitivity of the constraint to each generator.
  Access reduction is issued to each curtailable-connection via the RTU until the constraint limit breach is mitigated.
  Multiple constraints can apply to a curtailable-connection and constraint breaches can occur simultaneously.
  Where multiple constraint breaches act upon a single curtailable-connection, we apportion the access reduction of that connection to the constraint breaches depending on the relative magnitude of the breaches.
  Where customer curtailment occurs without any associated constraint breach, we categorise the curtailment as non-constraint driven.
  Future developments will include the reason for non-constraint driven curtailment.


Quality Control Statement
The dataset is derived from data recorded by RTUs located at customer sites and constraint locations across our network. UKPN’s Ops Telecoms team monitors and maintains these RTUs to ensure they are providing accurate customer/network data. An alarms system notifies the team of communications failures which are attended to by our engineers as quickly as possible. RTUs can store telemetry data for prolonged periods during communications outages and then transmit data once communications are reinstated. These measures ensure we have a continuous stream of accurate data with minimal gaps. On the rare instances where there are issues with the raw data received from DERMS, we employ simple data cleaning algorithms such as forward filling.
RTU measurements of access reduction update on change or every 30 minutes in the absence of change. We also minimise post-processing of RTU data (e.g., we do not time average data). Using the raw data allows us to ascertain event start and end times of curtailment actions exactly and accurately determine access reductions experienced by our customers.

Assurance Statement
The dataset is generated and updated by a script which is scheduled to run daily. The script was developed by the DSO Data Science team in conjunction with the DSO Network Access team, the DSO Operations team, and the UKPN Ops Telecoms team to ensure correct interpretation of the RTU data streams. The underlying script logic has been cross-referenced with the developers and maintainers of the DERMS scheme to ensure that the data reflects how DERMS operates.
The outputs of the script were independently checked by the DSO Network Access team for accuracy of the curtailment event timings and access reduction prior to first publication on the Open Data Portal (ODP). The DSO Operations team conducts an ongoing review of the data as it is updated daily to verify that the operational expectations are reflected in the data.
The Data Science team has implemented automated logging which notifies the team of any issues when the script runs. This allows the Data Science team to investigate and debug any errors/warnings as soon as they happen.

Other
Download dataset information: Metadata (JSON)
Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: Open Data Portal Glossary
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Purpose: This is a web map used for a situational awareness viewer. Click on links below for more information, this is just a summary of the layers in this map as of 09/14/2018.Live Data Live Feed - Storm Reports (NOAA) - This map contains continuously updated U.S. tornado reports, wind storm reports and hail storm reports. You can click on each to receive information about the specific location and read a short description about the issue. Live Feed - Observed Weather (NOAA METAR) - Current wind and weather conditions at all METAR stations.Live Feed: Open Shelters (FEMA / Red Cross National Shelter System) - his web service displays data from the FEMA National Shelter System database. The FEMA NSS database is synchronized every morning with the American Red Cross shelter database. After this daily refresh, FEMA GIS connects every 20 minutes to the FEMA NSS database looking for any shelter updates that occur throughout the day in the the FEMA NSS.Live Feed: Active Hurricanes - Hurricane tracks and positions provide information on where the storm has been, where is it going, where it is currently located and the category as defined by wind speed. This data is provided by NOAA National Hurricane Center (NHC).Live Feed Action Level Stream Gauges (USGS) - This map service shows those gauges from the Live Stream Gauge layer that are currently flooding. It only includes those gauges where flood stages have been defined by the contributing agencies. Action stage represents the river depth at which the agency begins preparing for a flood and taking mitigative action.Live Feed: USA Short-Term Weather Warnings - This layer presents continuously updated US weather warnings. You can click on each to receive information about the specific location and read a short description about the issue. Each layer is updated every minute with data provided by NOAA’s National Weather Service - http://www.nws.noaa.gov/regsci/gis/shapefiles/.Live Feed: Power Outages - Current power outage data reported by the EARSS system.Live Feed: Radar (NOAA) - Quality Controlled 1km x 1km CONUS Radar Base Reflectivity. This data is provided by Mutil-Radar-Multi-Sensor (MRMS) algorithm.Flood Prediction / Simulation (Created on 09/13 by Pacific Northwest National Laboratory RIFT Model) - Pacific Northwest National Laboratory RIFT Model: The simulations, based on NOAA weather forecasts, are used to improve understanding of the storm and its potential flood impacts. The simulations were created with PNNL's Rapid Inundation Flood Tool, a two-dimensional hydrodynamic computer model.Base Data - FEMA National Flood Hazard Layer - The National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). Base Data - Storm Surge Scenarios (NOAA) - This mapping service displays near worst case storm surge flooding (inundation) scenarios for the Gulf and Atlantic coasts. This map service was derived from an experimental storm surge data product developed by the National Hurricane Center (NHC).