Temperature Data Free Download [UPDATED]

Daily weather records come from automated and human-facilitated observation stations in the Global Historical Climatology Network-Daily database. Data from each station are reviewed regularly for quality and consistency: the data have been checked for obvious inaccuracies, but they have not been adjusted to account for the influences of historical changes in instrumentation or observing practices.

Trends in global average surface temperature between 1993 and 2022 in degrees Fahrenheit per decade. Most of the planet is warming (yellow, orange, red). Only a few locations, most of them in Southern Hemisphere oceans, cooled over this time period. NOAA Climate.gov map, based on

Temperature Data Free Download

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Across inaccessible areas that have few measurements, scientists use surrounding temperatures and other information to estimate the missing values. Each value is then used to calculate a global temperature average. This process provides a consistent, reliable method for monitoring changes in Earth's surface temperature over time. Read more about how the global surface temperature record is built in our Climate Data Primer.

Though warming has not been uniform across the planet, the upward trend in the globally averaged temperature shows that more areas are warming than cooling. According to NOAA's 2021 Annual Climate Report the combined land and ocean temperature has increased at an average rate of 0.14 degrees Fahrenheit ( 0.08 degrees Celsius) per decade since 1880; however, the average rate of increase since 1981 has been more than twice as fast: 0.32 F (0.18 C) per decade.

(left) Hypothetical pathways of carbon emissions ("representative concentration pathways," or RCPs) throughout the twenty-first century based on different possible energy policies and economic growth patterns. (right) Projected temperature increase relative to the 1901-1960 average depending on which RCP we eventually follow. Image by Katharine Hayhoe, from the 2017 Climate Science Special Report by the U.S. Global Change Research Program.

According to the 2017 U.S. Climate Science Special Report, if yearly emissions continue to increase rapidly, as they have since 2000, models project that by the end of this century, global temperature will be at least 5 degrees Fahrenheit warmer than the 1901-1960 average, and possibly as much as 10.2 degrees warmer. If annual emissions increase more slowly and begin to decline significantly by 2050, models project temperatures would still be at least 2.4 degrees warmer than the first half of the 20th century, and possibly up to 5.9 degrees warmer.

If you want something that does it for you, and you have a few spare $$, you could look at SensorPush. They sell sensors that measure temperature, humidity and I think Air Pressure and other reading nowadays, which can report directly to your phone / tablet and history recorded locally on the phone / tablet. You can also purchase a gateway that allows for these results to be uploaded to the cloud, if you want to access them remotely.

I personally use both Hubigraph and this 4th option. Hubigraph is super easy to setup, but has its limitations. The Google Sheets option gives you the ability to easily save and access the data long term, but is more work to setup.

I think this applies to most options other then Hubigraphs. You just have to have a always on server that can run an appropriate database engine (ie. mysql, MS SQL, Influxdb) and then an appropriate visualization engine with everyone seems to agree is Grafana.

The daily records summarized here are compiled from a subset of stations in the Global Historical Climatological Network. A station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset.

For a station to be considered for any parameter, it must have a minimum of 30 years of data with more than 182 days complete each year. This is effectively a "30-year record of service" requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not "thread" the separate histories into one record for a city.

This tool provides simplistic counts of records to provide insight into recent climate behavior, but is not a definitive way to identify trends in the number of records set over time. This is particularly true outside the United States, where the number of records may be strongly influenced by station density from country to country and from year to year. These data are raw and have not been assessed for the effects of changing station instrumentation and time of observation.

The summaries below list the number of records broken for several recent periods is summarized in this table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

Use the form below to select the timescale, location, date range, and parameters of your search. If data is available within the selecte date range, a map with station information will be displayed below. Click "Show Records" when selection is complete.

The GISS Surface Temperature Analysis version 4 (GISTEMP v4) is an estimate of global surface temperature change.Graphs and tables are updated around the middle of every month using current data files from NOAA GHCNv4 (meteorological stations) and ERSST v5 (ocean areas), combined asdescribed in our publicationsHansen et al. (2010) andLenssen et al. (2019).These updated files incorporate reports for the previous month and also late reportsand corrections for earlier months.

When referencing the GISTEMP v4 data provided here,please cite both this webpage and also our most recentscholarly publication about the data.In citing the webpage, be sure to include the date of access.

The basic GISS temperature analysis scheme was defined in the late 1970s by James Hansen when a methodof estimating global temperature change was needed for comparison with one-dimensional global climatemodels.The analysis method was fully documented inHansen and Lebedeff (1987).Several papers describing updates to the analysis followed over the following decades,most recently that ofHansen et al. (2010),as well as the uncertainty quantification of Lenssen et al. (2019).

The GISTEMP analysis is updated regularly. Graphs and tables are posted around themiddle of every month using the latest GHCN and ERSST data. The update incorporates reportsfor the previous month as well as late reports and corrections for earlier months.

The EL-SIE-2 and EL-SIE-2+ models are also available to purchase. They are our newest, groundbreaking USB data loggers and are a more powerful alternative to this device. They can store up to a million temperature and humidity readings and come with a crystal clear display for at-a-glance monitoring

Calibration testing carried out on our products provides assurance of the ongoing accuracy of your EasyLog data logger. This is relevant for all users, but is especially important for medical, food and scientific applications, and where audit compliance needs to be demonstrated. We provide a number of standard calibration options suitable for common applications such as monitoring fridges, chilled goods and freezers, but can also provide customised calibration at whatever measurement points you need. Please contact our sales team for details.

The Land Surface Temperature (LST) and Emissivity daily data are retrieved at 1km pixels by the generalized split-window algorithm and at 6km grids by the day/night algorithm. In the split-window algorithm, emissivities in bands 31 and 32 are estimated from land cover types, atmospheric column water vapor and lower boundary air surface temperature are separated into tractable sub-ranges for optimal retrieval. In the day/night algorithm, daytime and nighttime LSTs and surface emissivities are retrieved from pairs of day and night MODIS observations in seven TIR bands. The product is comprised of LSTs, quality assessment, observation time, view angles, and emissivities.

This visualization shows global temperature anomalies along with the underlying seasonal cycle. Temperatures advance from January through December left to right, rising during warmer months and falling during cooler months. The color of each line represents the year, with colder purples for the 1960s and warmer oranges and yellows for more recent years. A long-term warming trend can be seen as the height of each month increases over time, the result of human activities releasing greenhouse gases like carbon dioxide into the atmosphere.

Berkeley Earth provides high-resolution land and ocean time series data and gridded temperature data. Our peer-reviewed methodology incorporates more temperature observations than other available products, and often has better coverage. Global datasets begin in 1850, with some land-only areas reported back to 1750. The newest generation of our products are augmented by machine learning techniques to improve the spatial resolution. This allows Berkeley Earth to provide the most comprehensive, high-resolution instrumental temperature data product available. 2351a5e196