The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates fitted projected future precipitation depths derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. A maximum likelihood approach is used to fit the projected future extreme precipitation depths to extreme projected future precipitation data estimated using a statistical scaling approach. The return levels are modified to account for changes in the future frequency of large-scale meteorological factors conducive to precipitation by means of an analog resampling approach. The projected future extreme precipitation depths are tabulated by duration (1 day) and return period (5, 10, 25, 50, 100, and 200 years).

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2068-72 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates change factors derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical precipitation depths fitted to extreme-precipitation data. The change factors are tabulated by duration (1 day) and return period (5, 10, 25, 50, 100, 200, and 500 years).

Weather data from two weather stations at Stuttgart Rice Research and Extension center are archived. Current air temperature, relative humidity, wind speed, solar radiation and soil temperature data are provided by station and are displayed and archived either hourly or daily. Historical weather data goes back to 2008. Resources in this dataset:Resource Title: Weather Station Data. File Name: Web Page, url: https://www.ars.usda.gov/southeast-area/stuttgart-ar/dale-bumpers-national-rice-research-center/docs/weather-station-data/

The weather station on the campus of Loughborough University, in the East Midlands of the UK, had fallen into disuse and disrepair by the mid-2000s, but in 2007 the availability of infrastructure funding made it possible to re-establish regular weather observation with new equipment. The meteorological dataset subsequently collected at this facility between 2008 and 2021 is archived here. The dataset comes as fourteen Excel (.xlsx) files of annual data, with explanatory notes in each.Site descriptionThe campus weather station is located at latitude 52.7632°, longitude -1.235° and 68 m a.s.l., in a dedicated paddock on a green space near the centre-east boundary of the campus. A cabin, which houses power and network points, sits 10 m to the northeast of the main meteorological instrument tower. The paddock is otherwise mostly open on an arc from the northwest to the northeast, but on the other sides there are fruit trees (mainly varieties of prunus domestica) at distances of 13–16 m, forming part of the university's "Fruit Routes" biodiversity initiative.Data collectionInstruments were fixed to a 3 m lattice mast which is concreted into the ground in the centre of the paddock described above. Up to late July 2013, the instruments were controlled by a solar-charged, battery-powered Campbell Scientific CR1000 data logger, and periodically manually downloaded. From early November 2013, this logger was replaced with a Campbell Scientific CR3000, run from the mains power supply from the cabin and connected to the campus network by ethernet. At the same time, the station's Young 01503 Wind Monitor was replaced by a Gill WindSonic ultrasonic anemometer. This combination remained in place for the rest of the measurement period described here. Frustratingly, the CS215 temperature/relative humidity sensor failed shortly before the peak of the 2018 heatwave, and had to be replaced with another CS215. Likewise, the ARG100 rain gauge was replaced in 2011 and 2016. The main cause of data gaps is the unreliable power supply from the cabin, particularly in 2013 and 2021 (the latter leading to the complete replacement of the cabin and all other equipment). Furthermore, even though the post-2013 CR3000 logger had a backup battery, it sometimes failed to restart after mains power was lost, yielding data gaps until it was manually restarted. Nevertheless, out of 136 instrument-years deployment, only 36 are less than 90% complete, and 21 less than 75% complete.Data processingData retrieved manually or downloaded remotely were filtered for invalid measurements. The 15-minute data were then processed to daily and monthly values, using the pivot table function in Microsoft Excel. Most variables could be output simply as midnight-to-midnight daily means (e.g. solar and net radiation, wind speed). However, certain variables needed to be referred to the UK and Ireland standard ‘Climatological Day’ (Burt, 2012:272), 0900-0900: namely, air temperature minimum and maximum, plus rainfall total. The procedure for this follows Burt (2012; https://www.measuringtheweather.net/) and requires the insertion of additional date columns into the spreadsheet, to define two further, separate ‘Climate Dates’ for maximum temperature and rainfall total (the 24 hours commencing at 0900 on the date given, ‘ClimateDateMax’), and for minimum temperatures (24 hours ending at 0900 on the date given, ‘ClimateDateMin’). For the archived data, in the spreadsheet tabs labelled ‘Output - Daily 09-09 minima’, the pivot table function derives daily minimum temperatures by the correct 0900-0900 date, given by the ClimateDateMin variable. Similarly, in the tabs labelled ‘Output - Daily 09-09 maxima’, the pivot table function derives daily maximum temperatures and daily rainfall totals by the correct 0900-0900 date, given by the ClimateDateMax variable. Then in the tabs labelled ‘Output - Daily 00-00 means’, variables with midnight-to-midnight means use the unmodified date variable. To take into account the effect of missing data, the tab ‘Completeness’ again uses a pivot table to count the numbers of daily and monthly observations where the 15-minute data are not at least 99.99% complete. Values are only entered into the ‘Daily data’ tab of the archived spreadsheets where 15-minute data are at least 75% complete; values are only entered into ‘Monthly data’ tabs where daily data are at least 75% complete.Wind directions are particularly important in UK meteorology because they indicate the origin of air masses with potentially contrasting characteristics. But wind directions are not averaged in the same way as other variables, as they are measured on a circular scale. Instead, 15-minute wind direction data in degrees are converted to 16 compass points (the formula is included in the spreadsheets), and a pivot table is used to summarise these into wind speed categories, giving the frequency and strength of winds by compass point.In order to evaluate the reliability of the collected dataset, it was compared to equivalent variables from the HadUK-Grid dataset (Hollis et al., 2019). HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations, which have been interpolated from meteorological station data onto a uniform grid to provide coherent coverage across the UK at 1 km x 1 km resolution. Daily and monthly air temperature and rainfall variables from the HadUK-Grid v1.1.0.0 Met Office (2022) were downloaded from the Centre for Environmental Data Analysis (CEDA) archive (https://catalogue.ceda.ac.uk/uuid/bbca3267dc7d4219af484976734c9527/). Then the grid square containing the campus weather station was identified using the Point Subset Tool of the NOAA Weather and Climate Toolkit (https://www.ncdc.noaa.gov/wct/index.php) in order to retrieve data from that specific location. Daily and monthly HadUK-grid data are included in the spreadsheets for convenience.Campus temperatures are slightly, but consistently, higher than those indicated by HadUK-grid, while HadUK-Grid rainfall is on average almost 10% higher than that recorded on the campus. Trend-free statistical relationships between campus and HadUK-grid data implies that there is unlikely to be any significant temporal bias in the campus dataset.ReferencesBurt, S. (2012). The Weather Observer's Handbook. Cambridge University Press, https://doi.org/10.1017/CBO9781139152167.Hollis, D, McCarthy, M, Kendon, M., Legg, T., Simpson, I. (2019). HadUK‐Grid—A new UK dataset of gridded climate observations. Geoscience Data Journal 6, 151–159, https://doi.org/10.1002/gdj3.78.Met Office; Hollis, D.; McCarthy, M.; Kendon, M.; Legg, T. (2022). HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.1.0.0 (1836-2021). NERC EDS Centre for Environmental Data Analysis, https://dx.doi.org/10.5285/bbca3267dc7d4219af484976734c9527.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates fitted historical precipitation depths derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. The historical extreme precipitation depths are fitted to extreme precipitation data using a maximum likelihood approach and tabulated by duration (1 day) and return period (5, 10, 25, 50, 100, and 200 years).

To provide an online archive of daily information, with long-term data for NOAA climate stations

Stores both current and historical daily weather data for approximately 350 weather stations throughout California. It allows the user to: display daily data for one station, over a range of dates, request a data file for use with DDU (Degree-Day Utility), TRAP or CALEX, request a comma-delimited data file for use with spreadsheet software, or find out more about a station's characteristics.

Data can be searched online at: "http://axp.ipm.ucdavis.edu/WEATHER/wxretrieve.html"

Analysis of ‘USDA-ARS Colorado Maize Water Productivity Dataset 2012-2013’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/49b608ca-0d73-49d6-95e6-15697d8350c7 on 12 February 2022.

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

The USDA-Agricultural Research Service carried out an experiment on water productivity in response to seasonal timing of irrigation of maize (Zea mays L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado (40°26’ N, 104°38’ W) starting in 2012. Twelve treatments involved different water availability targeted at specific growth-stages. This dataset includes data from the first two years, which were complete years with intact treatments. Data includes canopy growth and development (canopy height, canopy cover and LAI), irrigation, precipitation, and soil water storage measured periodically through the season; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use, harvest index and crop yield. Hourly and daily weather data are also provided from the CoAgMET, Colorado’s network of meteorological information (https://coagmet.colostate.edu/ ; GLY04 station). Additional soil data can be found in a previous dataset (USDA-ARS Colorado Maize Water Productivity Dataset 2008-2011) also available from the Ag Data Commons. This previous dataset included six targeted treatments that were generally uniform through the season. This new dataset can be used to further validate and refine maize crop models.

The data are presented in a spreadsheet format in individual sheets within one workbook. The first sheet in the work book provides a list of data descriptions. Two sheets (one sheet for each of the two years) provide the hourly weather data, with the exception of the precipitation data, which is included in the sheet with daily data per treatment. The weather data is from a weather station on site. Another sheet provides plot level data (harvest index, yield, annual ET, maximum LAI, stand density, total aboveground biomass) taken annually by plot (four plots per treatment). Another sheet provides LAI measured four times over each season per plot. The final sheet provides daily data per treatment over each season, including data needed to compute daily water balance. This sheet has LAI, crop growth stage, plant height, estimated root depth, interpolated canopy cover, ET coefficients, precipitation, and estimated deep percolation, evaporation, and soil water deficit at four soil depths.

List of files:
LIRF small plots map 2012-2013
LIRF maize annual_daily_hourly data 2012-2013

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

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2068-72 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates projected future precipitation depths derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. A maximum likelihood approach is used to fit the projected future extreme-precipitation depths to extreme projected future precipitation data estimated using a statistical scaling approach. The return levels are modified to account for changes in the future frequency of large-scale meteorological factors conducive to precipitation by means of an analog resampling approach. The projected future extreme-precipitation depths are tabulated by duration (1 day) and return period (5, 10, 25, 50, 100, 200, and 500 years).

Climate variable. AEMET provides a daily Excel spreadsheet showing the minimum, average and maximum meteorological risk of forest fires per municipality. Forest fire risk levels are generated automatically based on meteorological data and numerical weather prediction models, and they are divided into 5 categories (low, moderate, high, very high and extreme); these are indicators of the probability of a fire, as well as the extent and intensity of the fire.This table sets this indicator as a percentage of the days in which the maximum meteorological risk was rated as very high or extreme in each municipality, and this is monitored monthly and annually. The daily level of meteorological risk of forest fires is based on the Canadian system and is calculated using the data from AEMET weather stations and the outcomes of a numerical weather prediction model. The input variables for the risk estimation model are: dry air temperature T (°C), relative humidity in the air Hr (%), wind speed Vv (km/h) and precipitation recorded in the last 24 hours Pp (mm). The analysis and prognostic data refer to the 12 UTCs, with the aim of determining the maximum daily risk value, which occurs around midday, although its value is valid for several hours before until several hours after for the 12 UTCs. At AEMET, the data involved in the calculation for risk levels comes from its network of synoptic and automatic weather stations, and the HIRLAM 0.05 model (spatial resolution of 0.05° and an operating window of 47,367 grid points). Each grid point is located in the centre of a square or pixel of 5km on each side, and therefore the calculation variables are representative for an area of 25 km2 or 2500 ha.Monitoring: Monthly and annually, per municipality

Information about weather-related conditions in Sweden during the period 1500-1870 has been extracted from various historical documents. The information is presented as cited text, together with the date and geographical region for which the information is relevant.

Since the database essentially consists of excerpts from different historical documentary sources of various kinds (Institutional chronicles, accountings, private weather diaries etc) the language is Swedish, though citations of original texts are occasionally given in other languages whenever relevant and when other languages were originally used.

See the Swedish description for more information.

The database contains a large number of contemporary descriptions for the period 1500–1870 from various types of documents — direct observations in diaries, administrative notes on activities that have been affected by weather conditions, letter collections, newspaper articles, etc. — of weather conditions in Sweden within current borders.

** Database file structure and content:

The database is collected in a spreadsheet (xlsx). The same information is also presented in a semicolon-separated text file (csv) (character set: Western Europe, ISO-8859-15 / EURO). File size: 1.6 MB (xlsx) and 4.1 MB (csv). The number of file rows, including the title row, is 20896.

In addition to the data file itself, the dataset also contains a source list in xlsx format. The file has two pages: "Otryckta källor" (unprinted sources) and "Bibliografi" (bibliography). The same information is also presented in two comma-separated csv files (character set: Western Europe, ISO-8859-15 / EURO).

The main database file contains information in eight columns with the following headings (here also translated to English):

• År (year)
• Månad (month)
• Dag (day)
• Annan tidsangivelse (other time indication)
• Område (area)
• Väder (weather)
• Källa (source)
• Ytterligare hänvisning/information (additional reference / information)

The database main language is Swedish. Quotations of writings in old language are generally preserved as in their original spellings.

For a more detailed description of the database content, please see the Swedish data description.

** Main sources and collection method:

The data collection was performed by systematically reading through available archive material and literature relevant to the subject. Information that was considered to be of value for climate history was entered into the database, either as a quotation or in the form of comments, together with an indication of the source material for each individual item (row) in the database. Each such item refers to a more or less specified geographical location and either a specific date or an approximate time period.

Data have been collected along three main channels: unprinted archive material, printed sources and literature.

Unprinted archive material has been retrieved mainly from the National Archives. For the period 1500–1540, data comes mainly from the database of the "Svenskt diplomatariums huvudkartotek" (Sdhk) (Swedish diplomatarium's main file). For the time thereafter, data from, among others, the "Riksregistraturet" (national registry), a collection of copies of letters issued by the "kungliga kansliet" (royal chancellery), has been used. Several unprinted letters, diaries, accounts and reports have also been searched.

A particularly extensive individual source is Märta Helena Reenstierna's (known as the Årsta lady) diaries from Årsta Gård in Stockholm, written during the period 1793–1839 and kept in the Nordic Museum's archives. These diaries contain a large number of notes on local weather conditions. More than half of all individual entries in the database originate from the Årsta diaries.

Printed sources include editions of source publications such as Gustav Vasa's (King Gustav I of Sweden) letters in 29 volumes. There are also the Royal Swedish Academy of Sciences' Transactions which, among other things, contain meteorological observations.

The category literature contains a number of local historical presentations. There are also early attempts at climate historical overviews and interpretations. Corporate history and military history literature have also been used.

** The roles of primary researchers during the construction of the database

The main part of the work with building up the database was done during the period 2006–2010 by Johan Söderberg, Lotta Leijonhufvud, Dag Retsö and Ulrica Söderlind at the Department of Economic History, Stockholm University, under the leadership of Johan Söderberg. Curation of the database prior to publication in SND was carried out during 2019–2020 by Lotta Leijonhufvud in collaboration with Anders Moberg, Department of Physical Geography, Stockholm University.

Previous, unpublished, versions of the database have been used in the following studies (see list of publications):

• Edvinsson et al. (2009). Väder, skördar och priser i Sverige.
• Leijonhufuvud et al. (2010). Five centuries of Stockholm winter/spring temperatures reconstructed from documentary evidence and instrumental observations.
• Wetter et al. (2014). The year-long unprecedented European heat and drought of 1540 – a worst case.
• Retsö (2015). Documentary evidence of historical floods and extreme rainfall events in Sweden 1400–1800.

Daily averaged surface energy balance (SEB) components and meteorological quantities at 19 locations on the Greenland ice sheet. Three stations are located in the accumulation zone (S10, KAN_U and S21), while the other locations are in the ablation zone. Seven stations are part of the IMAU network , while the other stations are from the PROMICE network. The longest time series span between 2003 and 2023 (S5, S6 and S9, all located along the K-transect in West Greenland). The data contains modelled and measured turbulent heat fluxes, all four radiation components, daily melt energy, subsurface heat flux, air temperature, wind and humidity corrected at standard heights above the surface, height of the sensor, snow accumulation measured by a sonic height ranger, ice/snow ablation measured by a draw-wire, a pressure transducer depth assembly, or acoustic ranger mounted on a separate stake. Days with at least one missing sample are removed from the dataset.

The NYC Parks outdoor pool season typically runs from late June to the Sunday after Labor Day. During the season, Parks' staff record data via a mobile app survey at the end of each pool session. The survey includes questions on attendance, staffing, meals, issues, weather conditions, and closures for that specific session.

NYC Parks operates two sessions at each pool every day of the pool season. First Session is from 11:00am - 3:00pm. Second Session is from 4:00pm - 7:00pm, with the requirement for Olympic / Intermediate pools to stay open for Extended Second Session from 7:00pm - 8:00pm when the City Heat Emergency Plan is activated.

For each pool season, every pool will have at least two survey submissions per day - one submission for the first session, and one submission for the second session. A pool will have a third submission if it stays open for an extended second session.

Data Dictionary: https://docs.google.com/spreadsheets/d/15lHSZF76W1cZnjwlWRSn7tzLh6EqVZVeZ2vwDFqXHMM/edit?usp=sharing

For reference, pool geography from Open Data can be found here: https://data.cityofnewyork.us/City-Government/Pools/3vjv-6tf5

Abstract: An automatic weather station was operated on the McMurdo Ice Shelf near Pegasus Air Strip for 365 days from 24 January 2016 to 22 January 2017. The sensors consisted of temperature/RH at 2 m and 8 m (above surface), wind speed at 2 m and 8 m, 4-component radiometer, and wind direction. Time series provides averages for every 30 minutes of a 30 second sample scheme.

The USDA-Agricultural Research Service carried out a water productivity field trial for irrigated maize (Zea mays L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado in 2008 through 2011. The dataset includes daily measurements of irrigation, precipitation, soil water storage, and plant growth; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use and crop yield. Soil parameters and hourly and daily weather data are also provided. The dataset can be useful to validate and refine maize crop models. The data are presented in spreadsheet format. The primary data files are the four annual LIRF Maize 20xx.xlsx files that include the daily water balance and phenology, final yield and biomass data, and crop management logs. Annual LIRF Weather 20xx.xlsx files provide hourly and daily weather parameters including reference evapotranspiration. The LIRF Soils.xlsx file gives soil parameters. Each spreadsheet contains a Data Descriptions worksheet that provides worksheet or column specific information. Comments are embedded in cells with specific information. A LIRF photos.pdf file provides images of the experimental area, measurement processes and crop conditions. Photo credit Peggy Greb, ARS; copyright-free, public domain copyright policy. Resources in this dataset:Resource Title: LIRF Weather 2008. File Name: LIRF Weather 2008.xlsxResource Description: LIRF hourly and daily weather data for 2008Resource Title: LIRF Weather 2009. File Name: LIRF Weather 2009.xlsxResource Description: LIRF hourly and daily weather data for 2009Resource Title: LIRF Weather 2010. File Name: LIRF Weather 2010.xlsxResource Description: LIRF hourly and daily weather data for 2010Resource Title: LIRF Weather 2011. File Name: LIRF Weather 2011.xlsxResource Description: LIRF hourly and daily weather data for 2011Resource Title: LIRF Soils. File Name: LIRF Soils.xlsxResource Description: LIRF soil maps, soil texture, moisture retention, and chemical constituentsResource Title: LIRF Photo Log. File Name: LIRF Photo Log.pdfResource Description: Photos of the LIRF Water Productivity field trials and instrumentation.Resource Title: Data Dictionaries. File Name: DataDictionary r1.xlsxResource Description: Data descriptions of all the data resources (also included in their respective data files).Resource Title: LIRF Methodology. File Name: LIRF Methodology r1.pdfResource Description: Description of data files, data, and data collection methodology for the LIRF 2008-2011 Water Productivity field trials.Resource Title: LIRF Maize 2008. File Name: LIRF Maize 2008 r1.xlsxResource Description: Water balance and yield data for 2008 LIRF field trialResource Title: LIRF Maize 2009. File Name: LIRF Maize 2009 r1.xlsxResource Description: Water balance and yield data for 2009 LIRF field trialResource Title: LIRF Maize 2010. File Name: LIRF Maize 2010 r1.xlsxResource Description: Water balance and yield data for 2010 LIRF field trialResource Title: LIRF Maize 2011. File Name: LIRF Maize 2011 r1.xlsxResource Description: Water balance and yield data for 2011 LIRF field trial

This data set includes daily, monthly, and yearly mean surface air temperatures for four interior West Antarctic sites between 1978 and 1997. Data include air surface temperatures measured at the Byrd, Lettau, Lynn, and Siple Station automatic weather stations. In addition, because weather stations in Antarctica are difficult to maintain, and resulting multi-decade records are often incomplete, the investigators also calculated surface temperatures from satellite passive microwave brightness temperatures. Calibration of 37-GHz vertically polarized brightness temperature data during periods of known air temperature, using emissivity modeling, allowed the investigators to replace data gaps with calibrated brightness temperatures. MS Excel data files and GIF images derived from the data are available via ftp from the National Snow and Ice Data Center.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period.
A Microsoft Excel workbook is provided which tabulates change factors derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical precipitation depths fitted to extreme precipitation data. The change factors are tabulated by duration (1 day) and return period (5, 10, 25, 50, 100, and 200 years).

This dataset provides Daymet Version 4 R1 monthly climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Monthly averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and monthly totals are provided for the precipitation variable. Each data file is yearly by variable with 12 monthly time steps and covers the same period of record as the Daymet V4 R1 daily data. The monthly climatology files are derived from the larger datasets of daily weather parameters produced on a 1 km x 1 km grid for North America, Hawaii, and Puerto Rico. Separate monthly files are provided for the land areas of continental North America (Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Data are distributed in standardized Climate and Forecast (CF)-compliant netCDF (.nc) and Cloud-Optimized GeoTIFF (.tif) formats. In Version 4 R1 (ver 4.1), all 2020 and 2021 files (60 total) were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.

The Meteorological Service of Canada (MSC) is Canada's source for meteorological information. The Service monitors water quantities, provides information and conducts research on climate, atmospheric science, air quality, ice and other environmental issues, making it an important source of expertise in these areas.