The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Q: What are the chances for various temperature conditions over the next three months? A: Shaded areas show where average temperature has an increased chance of being warmer or cooler than usual. The darker the shading, the greater the chance for the indicated condition. White areas have equal chances for average temperatures below, near, or above the long-term average for the month. Q: What data do experts use to develop these forecasts? A: Climate scientists base future climate outlooks on current patterns in the ocean and atmosphere. They examine projections from climate and weather models and consider recent trends. They also check historical records to see what temperature conditions resulted from similar patterns in the past. Q: What do the colors mean? A: Colors on the map show experts’ level of confidence in their forecasts for above- or below-average temperatures. Each location on the map has some chance to experience average temperatures that rank in the bottom, middle, or top of records from the previous three decades. White areas have equal chances for all three conditions. Colors show where the odds for one of the conditions are higher than for the other two. A common mistake is to interpret these maps as predicted temperatures. However, dark orange areas are not predicted to be warmer than light orange areas. The dark orange areas simply have a higher likelihood for above-average temperatures than the light orange areas do. Similarly, dark blue areas are not predicted to be cooler than light blue areas. Keep in mind that outlooks show the most likely condition for each region, not the only possible outcome. Q: Why do these data matter? A: Energy companies want to know how much energy people will need in the next three months. Temperature outlooks can inform them when they should prepare to meet high demand for energy. Outlooks can also help them choose the best time to schedule maintenance procedures. Forestry managers also check temperature outlooks for the upcoming season. When they see increased chances for warmer-than-usual weather, they may take extra measures to prepare for more wildfires. Managers in agricultural industries also want to know if temperatures are likely to be warmer or cooler than usual. This information can help them optimize food production. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's Climate Prediction Center (CPC) produces the source images for monthly temperature outlooks. To produce our images, we run a set of scripts that access mapping layers from CPC, re-project them into desired projections at various sizes, and output them with a custom color bar. References One-Month to Three-Month Climate Outlooks. http://www.cpc.ncep.noaa.gov/products/forecasts/ Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-three-month-outlookThis upload includes two additional files:* Temperature - Three-Month Outlook _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-three-month-outlook)* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

Context

Who among us doesn't talk a little about the weather now and then? Will it rain tomorrow and get so cold to shake your chin or will it make that cracking sun? Does global warming exist?

With this dataset, you can apply machine learning tools to predict the average temperature of Detroit city based on historical data collected over 5 years.

Content

The given data set was produced from the Historical Hourly Weather Data [https://www.kaggle.com/selfishgene/historical-hourly-weather-data], which consists of about 5 years of hourly measurements of various weather attributes (eg. temperature, humidity, air pressure) from 30 US and Canadian cities.

From this rich database, a cutout was made by selecting only the city of Detroit (USA), highlighting only the temperature, converting it to Celsius degrees and keeping only one value for each date (corresponding to the average daytime temperature - from 9am to 5pm).

In addition, temperature values ​​were artificially and gradually increased by a few Celsius degrees over the available period. This will simulate a small global warming (or is it local?)...

In summary, the available dataset contains the average daily temperatures (collected during the day), artificially increased by a certain value, for the city of Detroit from October 2012 to November 2017.

The purpose of this dataset is to apply forecasting models in order to predict the value of the artificially warmed average daily temperature of Detroit.

See graph in the following image: black dots refer to the actual data and the blue line represents the predictive model (including a confidence area).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3089313%2Faf9614514242dfb6164a08c013bf6e35%2Fplot-ts2.png?generation=1567827710930876&alt=media" alt="">

Acknowledgements

This dataset wouldn't be possible without the previous work in Historical Hourly Weather Data.

Inspiration

What are the best forecasting models to address this particular problem? TBATS, ARIMA, Prophet? You tell me!

Climate Data Products at Environment Canada comprise of four different datasets: Almanac Averages and Extremes, Monthly Climate Summaries, Canadian Climate Normals, and Canadian Historical Weather Radar. Almanac Averages and Extremes provides average and extreme temperature and precipitation values for a particular station over its entire period of record. Monthly Climate Summaries contains values of various climatic parameters, including monthly averages and extremes of temperature, precipitation amounts, degree days, sunshine hours, days without precipitation, etc. Canadian Climate Normals are used to summarize or describe the average climatic conditions of a particular location. Data is available for stations with at least 15 years of data between the periods of 1961-1990, 1971-2000 and 1981-2010. Canadian Historical Weather Radar compirses of historical images from the radar network providing a national overview of where percipitation is occuring.

Q: Was the month cooler or warmer than usual? A: Colors show where and by how much the monthly average temperature differed from the month’s long-term average temperature from 1991-2020. Red areas were warmer than the 30-year average for the month, and blue areas were cooler. White and very light areas had temperatures close to the long-term average. Q: Where do these measurements come from? A: Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments collect the highest and lowest temperature of the day at each station over the entire month, and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly average of daily mean temperatures, then plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). To calculate the difference-from-average temperatures shown on these maps—also called temperature anomalies—NCEI scientists take the average temperature in each 5x5 km grid box for a single month and year, and subtract its 1991-2020 average for the same month. If the result is a positive number, the region was warmer than average. A negative result means the region was cooler than usual. Q: What do the colors mean? A: Shades of blue show places where average monthly temperatures were below their long-term average for the month. Areas shown in shades of pink to red had average temperatures that were warmer than usual. The darker the shade of red or blue, the larger the difference from the long-term average temperature. White and very light areas show where average monthly temperature was the same as or very close to the long-term average. Q: Why do these data matter? A: Comparing an area’s recent temperature to its long-term average can tell how warm or how cool the area is compared to usual. Temperature anomalies also give us a frame of reference to better compare locations. For example, two areas might have each had recent temperatures near 70°F, but 70°F could be above average for one location while below average for another. Knowing an area is much warmer or much cooler than usual can encourage people to pay close attention to on-the-ground conditions that affect daily life and decisions. People check maps like this to judge crop progress, estimate energy use, consider snow and lake ice melt; and to understand impacts on wildfire regimes. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on NClimGrid climate data produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Q: Data Format Description A: NetCDF (Version: 4) Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Average Temperature NClimGrid Temperature Normals References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions NCEI Monthly National Analysis Cl

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Q: What was the average temperature for the month? A: Colors show the average monthly temperature across the contiguous United States. White and very light areas had average temperatures near 50°F. Blue areas on the map were cooler than 50°F; the darker the blue, the cooler the average temperature. Orange to red areas were warmer than 50°F; the darker the shade, the warmer the monthly average temperature. Q: Where do these measurements come from? A: Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments collect the highest and lowest temperature of the day at each station over the entire month, and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly average of daily mean temperatures, then plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). Q: What do the colors mean? A: Shades of blue show areas that had monthly average temperatures below 50°F. The darker the shade of blue, the lower the average temperature. Areas shown in shades of orange and red had average temperatures above 50°F. The darker the shade of orange or red, the higher the average temperature. White or very light colors show areas where the average temperature was near 50°F. Q: Why do these data matter? A: The 5x5km NClimGrid data allow scientists to report on recent temperature conditions and track long-term trends at a variety of spatial scales. The gridded cells are used to create statewide, regional and national snapshots of climate conditions. Energy companies use this information to estimate demand for heating and air conditioning. Agricultural businesses also use these data to optimize timing of planting, harvesting, and putting livestock to pasture. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on NClimGrid climate data produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Average Temperature References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions) NCEI Monthly National Analysis) Climate at a Glance - Data Information) NCEI Climate Monitoring - All Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-us-monthly-averageThis upload includes two additional files:* Temperature - US Monthly Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots.* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

We can do any location (hyper-local through to city wide) in most countries globally.

Each weather record holds detailed day average records for items such as temperature, cloud cover through to humidity

Where days are missing we will highlight those and either back- or forward-fill using rolling mean averages

Layer Information: -Weather Events: Convection in Las Cruces and the Significant Flood Event of 2006 in El Paso are displayed. Clicking on the icon can give information of the phenomena or event. - Major Cities: Major cities and populations are mapped. The bigger the circle the bigger the population of that city. - Observation/ Data collection sites: This layer contains the location of where atmospheric soundings launched from the surface and where in-stu surface observations are gathered. The later includes Weather, Ocean, Lake, River, Water Quality, and Air Quality. - Köppen-Geiger Climate Divisions: General temperatures, precipitation, and latitude define these climate classes. The World Meteorological Organization (WMO) defined a classic climate record to be 30 years, so this current map is based of off the average weather an area has experienced from 1981 to 2010. New normals will be calculated in 2021. To read more click here. -National Weather Service Forecast Offices (WFO): Locations of the continental United States weather forecast offices, including office contact information. App Information: How to use it: Zooming in and out will turn on and off different layers. A zoomed out map will show the global Koppen climate classification. Zooming in will turn off the climate layer, while enabling the National Weather Service (NWS) Offices, Weather Events and other layers. Clicking on a Weather event or NWS office in the map will bring up a window with more information. - The legends and layers are shown by toggling the menus on via the icons at the bottom of the map.

Q: Where is severe weather likely at this time of year? A: Shading on each map reflects how often severe weather occurred within 25 miles during a 30-year base period. The darker the shading, the higher the number of severe weather reports near that date. For this map, severe weather encompasses tornadoes, thunderstorm winds over 58 miles per hour, and hail larger than three-quarters of an inch in diameter. Q: How were these maps produced? A: For each day of the year, scientists plotted reports of severe weather from 1982 to 2011 on a gridded map. To reveal the long-term patterns of these events, they applied mathematical filters to smooth the counts in time and space. Keep in mind that severe weather is possible at any location on any day of the year. Q: What do the colors mean? A: Shaded areas show the historical probability of severe weather occurring within 25 miles. Meteorologists estimated these probabilities from severe weather reports submitted from 1982-2011. For each day of the year, scientists plotted reports of severe events onto a map marked with grid cells 50 miles on a side. For each grid cell, they counted the number of years with at least one report, and divided by the total number of years. To reveal the long-term patterns suggested by this relatively small dataset, they used statistical methods to smooth the data. For instance, to smooth clusters of events in time, a mathematical filter replaced the value in every grid cell with a 15-day average. Another filter extended report locations over a 25-mile-wide circle to indicate the probability that the event could have occurred at other points within that area. Q: Why do these data matter? A: Knowing when and where severe weather tends to occur through the year promotes preparedness. Residents who are alert to the possibility of severe weather are better able to respond in ways that keep them safe. These data can also help emergency response personnel plan for when and where their services may be necessary. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's National Weather Service Storm Prediction Center produced the Severe Weather Climatology files. To produce our images, we obtained the climatology data as a numpy array, and ran a set of scripts to display the mapped areas on our base maps with a custom color bar. Additional information Data for these images represents an update and extension of work first put forth by Dr. Harold Brooks of the National Severe Storms Laboratory. References Brooks, H. E., C. A. Doswell, III, and M. P. Kay, (2003) Climatological estimates of local daily tornado probability, Wea. Forecasting, 18, 626-640.Source: https://www.climate.gov/maps-data/data-snapshots/data-source/historic-probability-severe-weather This upload includes two additional files:* Historic Probability of Severe Weather _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/historic-probability-severe-weather )* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

CustomWeather's flood risk data forecasts are based on past, current, and future precipitation compared to the typical precipitation expected during the period. We look at the past three days and the next three days of precipitation and then compare it to the expected during the 5-day period ahead (dynamically moving the typical precipitation amounts day by day). Files can be delivered as kmz/kml, map tile, or geotiff/ shape files. Map Data.

CustomWeather's Approach: Historical and forecast precipitation is compared to typical precipitation and rainfall statistics such as IDF and Rainfall Return to determine whether recent rain and forecast rain will cause flooding. Past rainfall is based on satellite derived precipitation (10 Km / hourly resolution). Recent days are considered as well as longer term rainfall (14 day / 30 day) considered as input into flood potential model. Rain vs snow is considered – snow fall will not cause flooding. Rain on snow or snow melt is considered – rain on snow will increase flood potential and constant snow melt due to warm temperatures will influence flood potential

70 N to 70 S / 180 W to 180 E / 0.1 degree resolution

Updated 1 x day (can be updated 4 x day dependent on requirements. Analysis and daily forecasts out to five days – can be adjusted based on requirements.

Definitions of Flood Potential: (Tiff value and definition) 0: No Flooding: No flooding expected 1: Possible Flooding: Rainfall and/or snow melt may cause flooding 2: Probable Flooding: Rainfall and/or snow melt likely to cause flooding 3: Flooding: Flooding expected in favorable areas 4: Significant Flooding: Flooding expected in favorable areas and more widespread 5: More Significant Flooding: Flooding expected to be widespread

NOTE: definitions can be adjusted as needed. Current definitions coordinated with USACE and based on typical rainfall and extreme rainfall statistics.

This flood risk data represents part of CustomWeather's comprehensive data offerings, covering the entire life cycle of weather - past, present, and future.

This CustomWeather map data serves the following categories: Global Weather Data, Place Data, Precipitation Data, Rainfall Data, Surface Data, Storm Data, Risk Data, Weather Forecasts, Natural Disasters Data, and Severe Weather Data.

These rasters provide the local mean annual extreme low temperature from 1991 to 2020 in an 800m x 800m grid covering the USA (including Puerto Rico) based on interpolation of data from more than a thousand weather stations. Each location's Plant Hardiness Zone is calculated based on classifying that temperature into 5 degree bands.The classified rasters are then used to create print and interactive maps.Temperature station data for the 2023 edition of the USDA Plant Hardiness Zone Map (PHZM) came from many different sources. In the eastern and central United States, Puerto Rico, and Hawaii, data came primarily from weather stations of the National Weather Service and several state networks. In the western United States and Alaska, data from stations maintained by USDA Natural Resources Conservation Service, USDA Forest Service, U.S. Department of the Interior (DOI) Bureau of Reclamation, and DOI Bureau of Land Management also helped to better define hardiness zones in mountainous areas. Environment Canada provided data from Canadian stations, and data from Mexican stations came from the Mexico National Weather Service and the Global Historical Climate Network. The USDA PHZM was produced with PRISM, a highly sophisticated climate mapping technology developed at Oregon State University. The map was produced from a digital computer grid, with each cell measuring about a half mile on a side. PRISM estimated the mean annual extreme minimum temperature for each grid cell (or pixel on the map) by examining data from nearby stations; determining how the temperature changed with elevation; and accounting for possible coastal effects, temperature inversions, and the type of topography (ridge top, hill slope, or valley bottom). Information on PRISM can be obtained from the PRISM Climate Group website https://prism.oregonstate.edu. Once a draft of the map was completed, it was reviewed by a team of climatologists, agricultural meteorologists, and horticultural experts. If the zone for an area appeared anomalous to these expert reviewers, experts doublechecked the draft maps for errors or biases. A detailed explanation of the mapmaking process and a discussion of the horticultural applications of the 2012 PHZM (similar to 2023) are available from the articles listed below. Daly, C., M.P. Widrlechner, M.D. Halbleib, J.I. Smith, and W.P. Gibson. 2012. Development of a new USDA Plant Hardiness Zone Map for the United States. Journal of Applied Meteorology and Climatology, 51: 242-264.Widrlechner, M.P., C. Daly, M. Keller, and K. Kaplan. 2012. Horticultural Applications of a Newly Revised USDA Plant Hardiness Zone Map. HortTechnology, 22: 6-19.

Q: What are the chances for various temperature conditions next month? A: Shaded areas show where average temperature has an increased chance of being warmer or cooler than usual. The darker the shading, the greater the chance for the indicated condition. White areas have equal chances for average temperatures that are below, near, or above the long-term average for the month. Q: What data do experts use to develop these forecasts? A: Climate scientists base future climate outlooks on current patterns in the ocean and atmosphere. They examine projections from climate and weather models and consider recent trends. They also check historical records to see what temperature conditions resulted from similar patterns in the past. Q: What do the colors mean? A: Colors on the map show experts’ level of confidence in their forecasts for above- or below-average temperatures. Each location on the map has some chance to experience average temperatures that rank in the bottom, middle, or top of records from the previous three decades. White areas have equal chances for all three conditions. Colors show where the odds for one of the conditions are higher than for the other two. A common mistake is to interpret these maps as predicted temperatures. However, dark orange or red areas are not predicted to be warmer than light orange areas. The darker orange areas simply have a higher likelihood for above-average temperatures than the lighter orange areas do. Similarly, dark blue areas are not predicted to be cooler than light blue areas. Keep in mind that outlooks show the most likely condition for each region, not the only possible outcome. You can visit the Data Snapshots interface to view previous temperature outlooks and compare them to monthly temperature observations. Q: Why do these data matter? A: Energy companies want to know how much energy people will need in the next month. Temperature outlooks can inform them when they should prepare to meet high demand for energy. Outlooks can also help them choose the best time to schedule maintenance procedures. Forestry managers also check temperature outlooks. When they see increased chances for warmer-than-usual weather, they prepare for more wildfires. Managers in agricultural industries also want to know if temperatures are likely to be warmer or cooler than usual. This information can help them optimize food production. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's Climate Prediction Center (CPC) produces the source images for monthly temperature outlooks. To produce our images, we run a set of scripts that access mapping layers from CPC, re-project them into desired projections at various sizes, and output them with a custom color bar. Additional information CPC issues monthly outlooks one-half month before the beginning of the month of interest. On the day before the new month begins, experts update the outlook for the upcoming month. Each monthly outlook in Data Snapshots shows the date the outlook was issued. Outlooks that include Alaska are available: while displaying an outlook of interest, click the Download button, select Full Resolution Assets, and then click OK References One-Month to Three-Month Climate Outlooks. http://www.cpc.ncep.noaa.gov/products/forecasts/ Current Outlook Discussion http://www.cpc.ncep.noaa.gov/products/predictions/long_range/fxus07.html Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-monthly-outlook This upload includes two additional files:* Temperature - Monthly Outlook _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-so

This map shows the coldest days of the year, on average, throughout the United States based on the latest (1991-2020) U.S. Climate Normals from NOAA's National Centers for Environmental Information. The Normals are 30-year averages of climate conditions from weather station data across the country, including the average low temperature for each day. From these values, scientists can identify which day of the year, on average, has the lowest minimum temperature, a.k.a. the “coldest day." If the lowest minimum temperature occurs on several days in a row, the map shows the central day of the range. The lightest colors on the map show places where the coldest day of the year occurs early in winter, starting at the beginning of December. The darker purples show places where the coldest day occurs later in the season, all the way through the end of March. The dots on the map show the station-based observations. The underlying map shows estimated ("interpolated") values for areas between stations. While the map shows the coldest days of the year on average, this year’s actual conditions may vary widely based on weather and climate patterns. For a prediction of your actual local daily temperature, and to see how it matches up with the Climate Normals, check out your local forecast office on Weather.gov.

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data. Download this data or get more informationThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This entry covers global ocean data aggregated to a monthly time resolution. The catalogue entry includes temperature and salinity characteristics of the upper oceans and complements the other seasonal forecast catalogue entries for the land and atmospheric variables. Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The seasonal forecast data is grouped in several catalogue entries (CDS datasets), currently defined by the model component and type of variable: outputs from the ocean component or the atmospheric one (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

The highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.

Marcus Weather Mapping (MWM) is an online, global weather / data mapping, visualization application that offers some unique features that no other current weather mapping system provides.

Below we highlight some features of MWM:

• Weather forecast and observational information updated every 6 hours • Non-static mapping - the ability to pan and zoom (to expose the highest level of station detail), a globally unique feature to Marcus Weather Mapping • Display preset areas OR build your own custom regions – again a feature unique to Marcus Weather Mapping
• Mapping variables include total precipitation, % normal precipitation, precipitation climatology, average/maximum/minimum temperature/temperature departures, GDDs, HDDs and CDDs (and departures) + others • Custom or pre-selected calendar dates (such as 5/10 days forward or 60/30 days back) up to a 180 day window • Historical Data selection - currently available from 2010, but will soon be adding data back to 2000 • The Yearly Comparison Tool, the ability to compare a weather variable for a user selected time period, against the same time period from a selected year – showing the difference between years • The Forecast Comparison Tool, the ability to compare forecast data from a previous forecast, to the current forecast, showing how the forecast has changed • Other mapping options include, map build speed, display density, choice of unit designation, coloring options, map contours, weather overlay opacity and map base layer options • A screenshot button for the current map created, weather fixed or zoomed • Satellite Imagery, Including: Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Thermal Condition Index (TCI) and Moisture Condition Index (VCI). Map Satellite Images for both preset AND user defined mapping areas. • Global Surface Soil Moisture, Root Zone Soil Moisture, Surface Soil Temperature, 10cm Subsurface Soil Temperature, 20cm Subsurface Soil Temperature. • Satellite Imagery Comparison Tool (SICT) – Compare any satellite image to another from a different time period, assessing change between the two satellite images. The SICT comes in two presentation modes, color change and Improve/Deteriorate View • MWM twitter, keeping users up to date of changes, improvements, bugs and other announcements – the twitter feedback be found here: MWM Twitter - https://twitter.com/MWMapping

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

Service Records and Retention System (SRRS) is historical digital data set DSI-9949, a collection of products created by the U.S. National Weather Service (NWS) and archived at the National Centers for Environmental Information (NCEI) [formerly National Climatic Data Center (NCDC)]. SRRS was a network of computers and associated hardware whose purpose was to transmit and store a large number of NWS products and make them available as needed. Basic meteorological and hydrological data, analyses, forecasts, and warnings are distributed among NWS offices over the AFOS (Automation of Field Operations and Services) communications system since 1978. These include PIREP (aircraft reports from pilots), AIRMET (aeronautical meteorological bulletins), SIGMET (significant meteorological information), surface and upper air plotted unanalyzed maps, air stagnation, precipitable water, Forecasts such as wind and temperature aloft, thickness and analysis, fire weather, area, local, zone, state, agricultural advisory, and terminal; and Warnings such as marine, severe weather, hurricane and tornado. The AFOS system was developed to increase the productivity and effectiveness of NWS personnel and to increase the timeliness and quality of their warning and forecasting services. This format version of the SRRS data was archived at NCEI from 1983 to 2001 (when a new format was created). The NCEI can service requests for products from the SRRS; two types of products are available to the user: 1) graphic displays of meteorological analyses and forecast charts (limited), and 2) alphanumeric displays of narrative summaries and meteorological/hydrological data. The following is a partial list of historical SRRS products available through the NCDC: rawinsonde data above 100 MB; AIREPS buoy reports; coastal flood warning; Coast Guard surface report; climatological report (daily and misc, incl monthly reports); weather advisory Coastal Waters Forecast Center (CWSU); weather statement; 3- to 5-day extended forecast; average 6- to 10-day weather outlook (local and national); aviation area forecast winds aloft forecast; flash flood statements, watches and warnings; flood statement; flood warning forecast; medium range guidance; FOUS relative humidity/temperature guidance; FOUS prog max/min temp/POP guidance; FOUS wind/cloud guidance; Great Lakes forecast; hurricane local statement; high seas forecast; international aviation observations; local forecast; local storm report; rawinsonde observation - mandatory levels;, METAR formatted surface weather observation; marine weather statement; short term rorecast; non-precipitation warnings/watches/advisories; nearshore marine forecast (Great Lakes only), offshore aviation area forecast; offshore forecast; other marine products, other surface weather observations, pilot report plain language, ship report, state pilot report, collective recreational report; narrative radar summary radar observation; hydrology-meteorology data report; river summary; river forecast; miscellaneous river product; river recreation statement; ; regional weather summary; surface aviation observation; preliminary notice of watch and canc msg SVR; local storm watch and warning; cancelation msg SELS watch; point information message; state forecast discussion ; state forecast rawinsonde observation - significant levels; surface ship report at intermediate synoptic time; surface ship report at non-synoptic time; surface ship report at synoptic time; special weather statement international; SIGMET severe local storm watch and area outline; special marine warning; intermediate surface synoptic observation; main surface synoptic observation; severe thunderstorm warning; severe weather statement; severe storm outlook; narrative state weather summary; terminal forecast; tropical cyclone discussion; marine/aviation tropical cyclone advisory; public tropical cyclone advisory; tornado warning; transcribed weather broadcast; tropical weather discussion; tropical weather outlook and summary; AIRMET SIGMET zone forecast; terminal forecast (prior to 7/1/96); winter weather warnings, watches, advisories; marine advisory/warning; special marine warning; miscellaneous product convective SIGMET ; local ice forecast; area forecast discussion; public information statement. SRRS (DSI-9949) by the Gateway SRRS (DSI-9957; C00583). NWS products after 2001 can be obtained from those systems, from NCEI.