The Environmental Data WebPortal provides access to a range of environmental monitoring data from across the three regions of Affinity Water; the Central, the East and the Southeast. Map shows locations and their values overlaid on a map. Choose parameters, values and intervals. List shows a grid format of what appears on the map - locations and their values. Location enables you to search for a particular location. Once a location is selected it will display a list of data sets available. Data Set provides a summary of each dataset available and is further broken up into sub-tabs of Summary, Chart, Grid and Statistics. Export enables you to export data from the system.

Agricultural Land Classification Grade and Job number for post-1988 ALC surveys for England Only. Polygons showing 5 classes of agricultural land plus classifications for urban and non-agricultural land. Grade one is best quality and grade five is poorest quality. A number of consistent criteria were used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness). Full metadata can be viewed on data.gov.uk.

Agricultural Land Classification Grade and Job number for post-1988 ALC surveys for England Only. Polygons showing 5 classes of agricultural land plus classifications for urban and non-agricultural land. Grade one is best quality and grade five is poorest quality. A number of consistent criteria were used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness). Full metadata can be viewed on data.gov.uk.

The Annual Energy Outlook presents longterm annual projections of energy supply, demand, and prices focused on the U.S. through 2050, based on results from EIA's National Energy Modeling System (NEMS). NEMS enables EIA to make projections under alternative, internally-consistent sets of assumptions, the results of which are presented as cases. The analysis in AEO2014 focuses on five primary cases: a Reference case, Low and High Economic Growth cases, and Low and High Oil Price cases. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

The Annual Energy Outlook API presents long-term annual projections of energy supply, demand, and prices through 2040. The projections, focused on U.S. energy markets, are based on results from EIA’s National Energy Modeling System (NEMS). NEMS enables EIA to make projections under alternative, internally-consistent sets of assumptions, the results of which are presented as cases. The projections cover natural gas, petroleum, coal, electricity and renewable fuels by sector (residential, commercial, industrial, electric generation, and transportation) and by region (census). Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

Each time a rain, hail or snow storm passes, volunteers take measurements of precipitation from as many locations as possible. These precipitation reports are then recorded on our Web site www.cocorahs.org. The data are then displayed and organized for many of our end users to analyze and apply to daily situations ranging from water resource analysis and severe storm warnings to neighbors comparing how much rain fell in their backyards.

The Global Wind Atlas is a free, web-based application developed to help policymakers, planners, and investors identify high-wind areas for wind power generation virtually anywhere in the world, and then perform preliminary calculations. The Global Wind Atlas facilitates online queries and provides freely downloadable datasets based on the latest input data and modeling methodologies. Users can additionally download high-resolution maps of the wind resource potential, for use in GIS tools, at the global, country, and first-administrative unit (State/Province/Etc.) level in the Download section. Information on the datasets and methodology used to create the Global Wind Atlas can be found in the Methodology and Datasets sections.

This datasets contains local weather data collected by Council's maintained weather stations.

Find out more about how the environment may be affecting your health with this easy to use tool that lets you see health and environmental information in one place. Learn about environmental health issues in your community and what you can do to protect yourself and your family. Use this website to answer questions about air quality, drinking water, cancer, and a wide variety of other topics.

Office of the New Mexico State Climatologist provides weather and climate information to our community members. Contains the most current observations from the ZiaMet weather station network, the current CoCoRaHS precipitation map, and the US Drought Monitor map for New Mexico.

A collection of shapefiles created and compiled by the National Oceanic and Atmospheric Administration. The data is intended to help people understand and predict weather patterns - in particular to plan for potentially dangerous weather conditions such as storms and droughts. From the site: "The National Weather Service produces short-term warnings to protect lives and property. Four types of warnings (Tornado, Severe Thunderstorm, Flash Flood, and Special Marine) include polygon information at the bottom of the warning, highlighting the primary threat area for the warning. Data from these warnings are collected and databased into a real-time set of GIS shapefiles. These files can be downloaded from this website in order to be used real-time in other Geographic Information Systems applications."

A collection of shapefiles created and compiled by the National Oceanic and Atmospheric Administration. The data is intended to help people understand and predict weather patterns - in particular to plan for potentially dangerous weather conditions such as storms and droughts.

Links to NWS GIS data resources / GIS Viewer web application and BTV (Burlington, VT forecast office) 24-Hour Daily Climate Maps.

NCEI offers several types of climate information generated from examination of the data in the archives. These types of information include record temperatures, record precipitation and snowfall, climate extremes statistics, and other derived climate products.

NOAA weather and atmosphere information; many of the National Weather Service data sets are available in formats that are able to be imported directly into Geographic Information Systems (GIS). Data formats include downloadable shapefiles, web services and even KML files.

To increase customer understanding of weather-related energy issues in New England, EIA released an interactive dashboard showing energy market conditions in that region. The dashboard will help analysts and interested participants examine many key aspects of the New England energy market such as fuel diversification, wholesale price volatility, energy delivery dynamics, the effect of weather on operations, the effect of fuel prices on electricity prices, regional and onsite fuel stocks.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

Provisional Agricultural Land Classification Grade. Agricultural land classified into five grades. Grade one is best quality and grade five is poorest quality. A number of consistent criteria used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness) for England only. Digitised from the published 1:250,000 map which was in turn compiled from the 1 inch to the mile maps.More information about the Agricultural Land Classification can be found at the following links:http://webarchive.nationalarchives.gov.uk/20130402200910/http://archive.defra.gov.uk/foodfarm/landmanage/land-use/documents/alc-guidelines-1988.pdfhttp://publications.naturalengland.org.uk/publication/35012.Full metadata can be viewed on data.gov.uk.

Provisional Agricultural Land Classification Grade. Agricultural land classified into five grades. Grade one is best quality and grade five is poorest quality. A number of consistent criteria used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness) for England only. Digitised from the published 1:250,000 map which was in turn compiled from the 1 inch to the mile maps.More information about the Agricultural Land Classification can be found at the following links:http://webarchive.nationalarchives.gov.uk/20130402200910/http://archive.defra.gov.uk/foodfarm/landmanage/land-use/documents/alc-guidelines-1988.pdfhttp://publications.naturalengland.org.uk/publication/35012.Full metadata can be viewed on data.gov.uk.

Rain Forecast for Maitland from BOM

Abstract:Rain on Snow is a statewide coverage of rain-on-snow zones.  Rain-on-snow zones are based on average amounts of snow on the ground in early January, relative to the amount of snow that could reasonably be melted during a model storm event.  Five Rain on Snow zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation.  Rain on Snow was digitized from 1:250,000 USGS quads.Purpose:The Rain-on-snow coverage was created as a screening tool to identify forest practice applications that may be in a significant rain-on-snow zone (WAC 222-22-100).Description:Five ROS zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on snow is a process that exhibits spatial and temporal variation under natural conditions, with the effects of vegetation on snow accumulation and melt adding additional complications in prediction. There is no map that shows the magnitude and frequency of water inputs to be expected from rain on snow events, so we have attempted to create an index map based on what we know about the process controls and their effects in the various climatic zones. If we assume that, averaged over many years, the seasonal storm tracks that bring warm, wet cyclonic storms to the Northwest have access to all parts of Washington , then the main factors controlling and/or reflecting the occurrence and magnitude of a R/S event in any particular place are: 1) Climatic region: especially the differences between windward and leeward sides of major mountain ranges, which control seasonal climatic patterns;2) Elevation: controls temperature, thus the likelihood and amount of snow on the ground, and affects orographic enhancement of storm precipitation; 3) Latitude: affects temperature, thus snow;4) Aspect: affects insolation and temperature (especially in winter), thus melting of snow; 5) Vegetation: the species composing forest communities can reflect the climate of an area (tolerance of warmth or cold, wet or dry conditions, deep and/or long lived snowpacks); the height and density of vegetation also partly controls the amount of snow on the ground. As natural vegetation integrates the effects of all of these controls, we tried to find or adapt floral indicators of the various zones of water input. We designed the precipitation zones to reflect the amount of snow likely to be on the ground at the beginning of a storm. We assumed that some middle elevation area would experience the greatest water input due to Rain on Snow, because the amount of snow available would be likely to be approximately the amount that could be melted. Higher and lower elevation zones would bear diminished effects, but for opposite reasons (no snow to melt, vs too cold to melt much). These considerations suggested a three or five zone system. We chose to designate five zones because a larger number of classes reduces the importance of the dividing lines, and thus of the inherent uncertainties of those lines. The average snow water equivalents (SWE) for the early January measurements at about 100 snow courses and snow pillows were compiled; snow depths for the first week in January at about 85 weather stations were converted into SWE. For each region (western North Cascades, Blue Mountains, etc.), the snow amounts were sorted by station elevation to derive a rough indicator of the relationship between snow accumulation and elevation. (Sub regional differences in snow accumulation patterns were also recognized.) After trying various combinations of ratios for areas where the snow hydrology is relatively well known, we adopted the following designations: 5. Highlands: >4 5 times ideal SWE; high elevation, with little likelihood of significant water input to the ground during storms (precipitation likely to be snow, and liquid water probably refreezes in a deep snow pack); effects of harvest on snow accumulation are minor; 4. Snow dominated zone: from "1.25 1.5 ideal SWE, up to "4; melt occurs during R/S (especially during early season storms), but effects can be mitigated by the lag time of percolation through the snowpack; 3. Peak rain on snow zone: "0.5 0.75 up to "1.25 ideal SWE; middle elevations: shallow snow packs are common in winter, so likelihood and effects of R/S in heavy rainstorms are greatest; typically more snow accumulation in clearings than in forest; 2. Rain dominated zone: "0.1 0.5 ideal SWE; areas at lower elevations, where rain occasionally falls on small amounts of snow; 1. Lowlands: <0.1 ideal SWE; coastal, low elevation, and rain shadow areas; lower rainfall intensities, and significant snow depths are rare. Precipitation zones were mapped on mylar overlays on 1:250,000 scale topographic maps. Because snow depth is affected by many factors, the correlation between snow and elevation is crude, and it was not possible to simply pick out contour markers for the boundaries. Ranges of elevations were chosen for each region, but allowance was made for the effects of sub regional climates, aspect, vegetative indicators of snow depth, etc. Thus, a particular boundary would be mapped somewhat lower on the north side of a ridge or in a cool valley (e.g. below a glacier), reflecting greater snow accumulations in such places. The same boundary would be mapped higher on the south side of the ridge, where inter-storm sunshine could reduce snow accumulation. Conditions at the weather stations and snow courses were used to check the mapping; but in areas where measurements are scarce, interpolation had to be performed. The boundaries of the precipitation zones were entered in the DNR's GIS. Because of the small scale of the original mapping and the imprecision of the digitizing process, some errors were introduced. It should not be expected that GIS images can be projected to large scales to define knife edge zone boundaries (which don't exist, anyway), but they are good enough to locate areas tens of acres in size. Some apparent anomalies in the map require explanation. Much of western Washington is mapped in the lowland or highland zones. This does not mean that R/S does not occur in those areas; it does, but on average with less frequency and hydrologic significance than in the middle three zones. Most of central and eastern Washington is mapped in the rain dominated zone, despite meager precipitation there; this means only that the amount of snow likely to be on the ground is small, and storm water inputs are composed dominantly of the rain itself, without much contribution from snow melt. Much of northeastern Washington is mapped in the peak Rain Snow zone, despite the fact that such events are less common there than in western Washington. This is due to the fact that there is less increase in snow depth with elevation (i.e. the snow wedge is less steep), so a wider elevation band has appropriate snow amounts; plus, much of that region lies within that elevation band where the 'ideal' amount of snow is liable to be on the ground when a model Rain Snow event occurs. This does not reflect the lower frequency of such storms in that area.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The API provides data back to 1990 and projections annually, monthly, and quarterly for 18 months. Summarizes the outlook for demand, supply and prices for petroleum, natural gas, electricity and coal as well as projections of carbon dioxide emissions from the production of fossil fuels, and a discussion of price forecast uncertainty. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

The EDP Open Data hub shares operational data from EDP assets

This endpoint provides daily average GB temperature data (in Celsius). This average data is calculated by National Grid ESO from the data retrieved from 6 weather stations around Britain. NGESO use this data as part of the electricity demand forecasting process

USGS GIS data of the United States water resources maps, soil, hydrology, and weather. Contains multiple links to useful and informative websites.

Spatial model of Vermont tornado climatology. Models Vermont tornado events per long-term data collection (data date-range is January 1950 - February 2019). Provides access to Vermont tornado-event information.Data-source credit: NCEI (National Centers for Environmental Information) (https://www.ncei.noaa.gov/).Downloaded tornado-event data--in CSV format--from NCEI database on 06/06/2019. Data period is 01/1950-02/2019. Imported data to a geodatabase. Used beginning latitude/longitude values to spatially enable the data; 1 record was missing a beginning latitude/longitude (record w/ EVENT_ID = 10355004)--estimated beginning latitude/longitude of that event by referencing its EVENT_NARRATIVE. Removed fields so that fields focus on core event-info. Projected data to Vermont State Plane NAD83 meters. Moved narrative fields (EVENT_NARRATIVE and EPISODE_NARRATIVE) fields to a separate non-spatial table; those fields have lengthy contents that exceed the shapefile text-field limit--intention is to make them available in open-data portal as CSV table that is joinable to the feature class (via EVENT_ID field).Feature-Class Climate_VTTORNADOS_point FIELD DESCRIPTIONS:EVENT_ID: Unique ID assigned by NWS to note a single, small part that goes into a specific storm episode.BEGIN_DATE: Beginning date.TOR_F_SCALE: Enhanced Fujita Scale describes the strength of the tornado based on the amount and type of damage caused by the tornado. The F-scale of damage will vary in the destruction area; therefore, the highest value of the F-scale is recorded for each event.DEATHS_DIRECT: The number of deaths directly related to the weather event.INJURIES_DIRECT: The number of injuries directly related to the weather event.DAMAGE_PROPERTY_NUM: The estimated amount of damage to property incurred by the weather event. (e.g. 10.00K = $10,000; 10.00M = $10,000,000)DAMAGE_CROPS_NUM: The estimated amount of damage to crops incurred by the weather event. (e.g. 10.00K = $10,000; 10.00M = $10,000,000)TOR_LENGTH: Length of the tornado or tornado segment while on the ground (minimal of tenths of miles)TOR_WIDTH: Width of the tornado or tornado segment while on the ground (in feet)ENDING_LAT: Ending latitude (not available in all records).ENDING_LON: Ending longitude (not available in all records).Table Table_VTTORNADOS_Narratives FIELD DESCRIPTIONS:EVENT_ID: Unique ID assigned by NWS to note a single, small part that goes into a specific storm episode. Can join to EVENT_ID field of Climate_VTTORNADOS_point.EVENT_NARRATIVE: The event narrative provides more specific details of the individual event. The event narrative is provided by NWS.EPISODE_NARRATIVE: The episode narrative depicting the general nature and overall activity of the episode. The narrative is created by NWS. Ex: A strong upper level system over the southern Rockies lifted northeast across the plains causing an intense surface low pressure system and attendant warm front to lift into Nebraska.VCGI and the State of VT make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.

This data is provided by TfNSW Roads and Maritime and provides live webcam vision of coastal bars and alpine waters to help boaters and skippers prepare for a safe trip. The information provided covers the location of the coastal bar, condition of the entrance and bar and any cautions that need to be taken to safely cross coastal bars. To access the link, click Go to Resource.