AWIS Weather Services has delivered weather data from our small business in Auburn, Alabama to companies all over the world for over 25 years. We started with a few citrus growing clients in Florida and have expanded to worldwide offerings in both Historical Weather Data and Localized Human Weather Forecasts.

Our Extensive Historical Weather Database is full of 100% quality checked weather data from over 40,000 US zip codes nationwide The data is REAL WEATHER OBSERVATIONS and visually checked by humans each day.

This service is your access to that database as it gets updated.

You choose the variables you need. You choose the cities you need covered. We'll handle the data pulling, updating, and delivery. Most of the time, it's a simple .csv file saved to the Amazon S3 bucket system that only you have access to.

Variables for this Live United States Weather Data Feed available for most locations are

Max Temperature Min Temperature Precipitation Average Wind Speed Average Cloud Cover Max Relative Humidity Min Relative Humidity Evapotranspiration Potential Evapotranspiration Hours of Sunshine Solar Radiation Veg Wetting Max Soil Temperature Min Soil Temperature Avg Soil Temperature

If a variable not listed is needed, contact us, we can likely generate the output from our many ingested inputs stored in our historical databases.

PRICING ESTIMATES: (The number of variables requested could change the price slightly) $1.50 per site, per month if you need less than 1000 zip codes. $1.25 per site, per month if you need 1001-5000 zip codes. $0.75 per site, per month if you need 5001-10000 zip codes. $0.25 per site, per month if you need over 10k zip codes.

Discounts available for long term deals. HISTORICAL DATA available upon request at a reduced rate. Reach out to us for more details and we can provide a targeted proposal within hours.

The purpose of this tool is to estimate daily precipitation patterns for a yearly cycle at any location on the globe. The user input is simply the latitude and longitude of the selected location. There is an embedded Zip Code search routine to find the latitude and longitude for US cities. GlobalRainSIM forecasts the daily rainfall based upon two databases.The first was the average number of days in a month with precipitation (wet days) that were compiled and interpolated by Legates and Willmott (1990a and 1990b) with further improvements by Willmott and Matsuura (1995). The second database was the global average monthly precipitation data collected 1961-1990 and cross-validated by New et al. (1999). These two datasets were then used to establish the monthly precipitation totals and the frequency of precipitation in a month. The average precipitation event was calculated as the monthly mean divided by the number of wet days. This mean value was then randomly assigned to a day of the month looping through the number of wet days. In other words, if the average monthly rainfall was 10 mm/month with 5 average wet days, each rain event was 2 mm. This amount (2 mm) was then randomly assigned to 5 days of that month. The advantage of this tool is that a typical pattern of precipitation can be simulated for any global location arriving at an •average year• as a baseline case for comparison. This tool also outputs the daily rainfall as a file or can be easily embedded within another program. Resources in this dataset:Resource Title: Global RainSIM Verson 1.0. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=227&modecode=50-60-05-00 download page

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, 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 weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in 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. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the 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.

Get access to current conditions and forecasts based on zip code and integrate into your workflows to make better commodity trading decisions.

Regardless of your industry weather can have a dramatic impact on your business. Our clients in agriculture, energy, insurance and commodity trading trust Barchart for timely and accurate weather data.

Weather Data includes: Radar Maps Current Conditions Forecasts (by zip or postal code) Soil moisture Precipitation

This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (nCLIMDIV) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites.

In March 2015, data for thirteen Alaskan climate divisions were added to the nCLIMDIV data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all nCLIMDIV monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska nCLIMDIV data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the nCLIMGRID data set.

As of November 2018, nClimDiv includes county data and additional inventory files.

Description: The Martinique ORSTOM-DIREN Rainfall Dataset is a compiled record of historical rainfall data in Martinique (French West Indies). Spanning from 1956 to 2004, this dataset encapsulates 237,000+ daily rainfall measurements from 27 stations managed by the ORSTOM (now IRD, Institut de Recherche pour le Développement, France) and the DIREN (Direction Régionale de l'Environnement, now DEAL), Martinique. It was rescued from the depths of the internet, specifically from the now-defunct DIREN Martinique website, using a snapshot from the Internet Archive's Wayback Machine dated December 3rd, 2008. Given its original public service provision, we believe in the importance of preserving and sharing this data freely under an open license. Data Sources: The dataset is derived solely from the DIREN Martinique website as archived by the Internet Archive on December 3rd, 2008. This source provides an invaluable historical record of Martinique's climate data. Dataset Preparation Methods: The original Excel files were retrieved and extensively reformatted. Days with imputed values were omitted to ensure the dataset reflects observed values only. This curation process was aimed at maintaining the integrity and reliability of the data for accurate climatological analysis. Discussion: The Martinique ORSTOM-DIREN dataset is a crucial resource for understanding historical rainfall patterns in Martinique. However, users should note that all imputed values have been removed for data purity, which may result in some gaps in the record. Despite this, the dataset remains a rich source of historical climatological data. Description of files:

ORSTOM_DIREN_rainfall.csv: primary dataset file ; it includes comprehensive rainfall data with fields such as station code, station name, year, date, and recorded rainfall in mm. ORSTOM_DIREN_stations.csv: detailed descriptions of the rainfall stations ; includes station codes, names, coordinates reprojected in WGS84, and altitude. salvaged_data.zip: This archive contains all the original data files as they were salvaged. It includes a link to the Wayback Machine snapshot used for data retrieval and a screenshot of the snapshot at the time of data retrieval. Additionally, the archive contains original geopackages in ESRI and MAPINFO formats, providing valuable geographic information system (GIS) data. Access to Dataset: True to the dataset's heritage of open access, the Martinique ORSTOM-DIREN dataset is freely available to all. This open license allows researchers, meteorologists, and interested individuals to explore and utilize this data for various climatological and environmental studies, enriching our understanding of Martinique's environmental history. Authors Contributions:

Original data collection: ORSTOM (now IRD, Institut de recherche pour le développement) and DIREN (Direction Régionale de l'Environnement), Martinique. Data curation: Jérémy Lavarenne Funding: This project was undertaken without specific funding. Changelog:

v1.0.0: Initial compilation of the dataset.

Each annual file contains 21 metrics developed by the CANUE Weather and Climate Team, and calculated by CANUE staff using base data provided by the Canadian Forest Service of Natural Resources Canada.The base data consist of interpolated daily maximum temperature, minimum temperature and total precipitation for all unique DMTI Spatial Inc. postal code locations in use at any time between 1983 and 2015. These were generated using thin-plate smoothing splines, as implemented in the ANUSPLIN climate modeling software. The earliest applications of thin-plate smoothing splines were described by Wahba and Wendelberger (1980) and Hutchinson and Bischof (1983), but the methodology has been further developed into an operational climate mapping tool at the ANU over the last 20 years. ANUSPLIN has become one of the leading technologies in the development of climate models and maps, and has been applied in North America and many regions around the world. ANUSPLIN is essentially a multidimensional “nonparametric” surface fitting method that has been found particularly well suited to the interpolation of various climate parameters, including daily maximum and minimum temperature, precipitation, and solar radiation.The water balance model was developed by Pei-Ling Wang and Dr. Johannes Feddema at the University of Victoria, Geography Department, and implemented by CANUE staff Mahdi Shooshtari. (THESE DATA ARE ALSO AVAILABLE AS MONTHLY METRICS).

Precipitation data were collected on a daily time-scale from the C-1 climate station (3018 m) since 1952. Over time various circumstances have led to days with missing values. These values were estimated from nearby climate stations.

 NOTE: The LTER data portal display does not display important maintenance/log information
  or other EML metadata features. 
  Please be sure to view the EML file (a text file that contains XML tags) which is included in 
  the zip archive (click on "Download zip archive")
  pertaining to each dataset. The EML file name will have the following format:
  knb-lter-nwt.[3 digit dataset number].[version number].xml. Most web browsers can 
  parse the EML so it's easier to read.

This dataset is part of the National Water Census ongoing development of best estimates of daily historical water budgets for over 100,000 hydrologic units across the United States. In this release, estimates of total flow and snowmelt for each hydrologic unit are added to the already released estimates of actual evapotranspiration, snowpack water-equivalent storage, soil moisture, recharge, streamflow, and precipitation. All these estimates are made available per twelve-digit hydrologic unit code watershed as contained in the NHDPlus v2.1 dataset and associated Watershed Boundary Dataset (WBD) snapshot. As this project progresses, it is expected that a complete closed water budget generated from the same water budget model will succeed this data release. Users are advised to ignore the first two years of the simulations to account for model initialization.

For background on source data and generation of these water budget variables, see nhru_hru_outflow.csv and nhru_snowmelt.csv from:

Hay, L.E. and LaFontaine, J.H., 2020, Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), 1980-2016, Daymet Version 3 calibration: U.S. Geological Survey data release, https://doi.org/10.5066/P9PGZE0S

The water budget variables were converted to a HUC12 basis using area-weighted spatial interpolation. The HUC12 version is the one included with WBD snapshot associated withe NHDPlus v2.1. All code used for conversions are published by Blodgett (2020) referenced below.

Summary of files included:
1) hu12_ids.csv.zip - twelve digit hydrologic unit (HUC12) code identifiers used in all files.
2) timesteps.csv.zip - timesteps used in all files.
3) nhm_total_flow_timeseries.csv.zip - One HUC12 per row, one date per column, version of total flow data.
4) nhm_snowmelt_timeseries.csv.zip - One HUC12 per row, one date per column, version of snowmelt data.

This data release compliments the following related data releases:

Actual evapotranspiration and snowpack water equivalent storage:
Blodgett, D.L., 2020, Twelve-digit hydrologic unit actual evapotranspiration and snowpack water equivalent storage from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System 1980-2016: U.S. Geological Survey data release, https://doi.org/10.5066/P9IH7CB8.

Soil moisture and recharge:
Blodgett, D.L., 2019, Twelve digit hydrologic unit soil moisture and recharge from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System: U.S. Geological Survey data release, https://doi.org/10.5066/P9ZZAWK4.

Streamflow:
Russell, A.M., Over, T.M., and Farmer, W.H., 2018, Statistical daily streamflow estimates at HUC12 outlets in the conterminous United States, Water Years 1981-2017: U.S. Geological Survey data release, https://doi.org/10.5066/P9DPSY6G.

Precipitation:
https://www.sciencebase.gov/catalog/item/52a7bed0e4b0de1a6d2dd0fd
Thornton, P.E., Thornton, M.M., Mayer, B.W., Wei, Y., Devarakonda, R., Vose, R.S., and Cook, R.B., 2017, Daymet—Daily surface weather data on a 1-km grid for North America, Version 3: ORNL DAAC website, accessed March 8, 2017, at https://doi.org/10.3334/ORNLDAAC/1328.

We provide in this database median maps at 1 km resolution of the hourly accumulated precipitation over Belgium from 1940 to 2016. We used the analog technique to compute for every day in the past (1940 – 2016) the 25 best analogs selected from the high resolution RMI RADCLIM radar database that is available from 2017 to 2022. The analogs were determined based on ERA5 data from ECMWF. The median was computed based on this 25 analogs.

Please read the LICENSE file for more information on the data licenses.

Citation

If you use this dataset for a publication, please cite the dataset article:

Debrie, E., Demaeyer, J., and Vannitsem, S.: Hourly precipitation series over Belgium based on the Analogue Technique, Earth Syst. Sci. Data Discuss. [preprint], doi:, in review, 2025.

The complete description of the method and performance is provided therein.

Installation

Download the dataset in a given folder, and in a terminal, and still in this folder, enter the following command:

unzip RADCLIM-Analogs-median.zip

This will unpack the dataset. You need at least 40Gb of free space on your disk to perform this operation.

Usage

The database is using Zarr format and is composed of two Zarr archives. One archive contains the database of the 25 best analogue days for each target day between 1940 and 2016. The second one contains all hourly median precipitation fields over Belgium from 1940 upon 2016.

A notebook showing how to load and explore the dataset with the Python language is available here: https://github.com/ElkeDebrie/RADCLIM-Analogs.

Note on the coordinate systems used in the dataset

The original RADCLIM product was set on a grid with the Belgian Lambert 2008 used as projection. The product has a spatial resolution of 1 km with each estimate representing the averaged
precipitation on a square of size 1 km. The product covers an area from 0.3W to 9.7E in longitude and from 47.4N to 53.7N in latitude.

The dataset coordinates x and y are the coordinates on the plane defined by the projection. For convenience, each data points latitude and longitude was also computed and provided in the dataset as latitude and longitude dataset coordinates.

RADCLIM documentation

The original RMI RADCLIM product documentation is available at https://opendata.meteo.be/cases/202107/radclim_userguide.pdf.

This HydroShare resource contains Jupyter Notebooks with instructions and code for accessing and subsetting the NOAA Analysis of Record for Calibration (AORC) dataset. The resource includes two Jupyter Notebooks: 1. AORC_Point_Data_Retrieval.ipynb: Retrieves data for a specific point within the U.S. AORC coverage area, specified using geographic coordinates. 2. AORC_Zone_Data_Retrieval.ipynb: Retrieves data for an area defined by an uploaded polygon shapefile. These notebooks programmatically retrieve the data from Amazon Web Services (https://registry.opendata.aws/noaa-nws-aorc/) , aggregate data at user-defined time scales (which may differ from NOAA’s original time steps), and, in the case of shapefile-based data retrieval, compute the average over the shapes in the given shapefile. The provided notebooks are coded to retrieve data from AORC version 1.1 released in ZARR format in December 2023.

The Analysis Of Record for Calibration (AORC) is a gridded record of near-surface weather conditions covering the continental United States and Alaska and their hydrologically contributing areas (https://registry.opendata.aws/noaa-nws-aorc/). It is defined on a latitude/longitude spatial grid with a mesh length of 30 arc seconds (~800 m), and a temporal resolution of one hour. Elements include hourly total precipitation, temperature, specific humidity, terrain-level pressure, downward longwave and shortwave radiation, and west-east and south-north wind components. It spans the period from 1979 across the Continental U.S. (CONUS) and from 1981 across Alaska, to the near-present (at all locations). This suite of eight variables is sufficient to drive most land-surface and hydrologic models and is used as input to the National Water Model (NWM) retrospective simulation. While the original NOAA process generated AORC data in netCDF format, the data has been post-processed to create a cloud optimized Zarr formatted equivalent that NOAA also disseminates.

CIMIS data is available to the public free of charge via a web Application Programming Interface (API). The CIMIS Web API delivers data over the REST protocol from an enterprise production platform. The system provides reference evapotranspiration (ETo) and weather data from the CIMIS Weather Station Network and the Spatial CIMIS System. Spatial CIMIS provides daily maps of ETo and solar radiation (Rs) data at 2-km grid by coupling remotely sensed satellite data with point measurements from the CIMIS weather stations. In summary, the data provided through the CIMIS Web API is comprised by a) Weather and ETo data registered at the CIMIS Weather Station Network (more than 150 stations located throughout the state of California and b) Spatial CIMIS System data that provides statewide ETo and solar radiation (Rs) data as well as aeraged ETo by zip-codes. The RESTful HTTP services reach a broader range of clients; including Wi-Fi aware irrigation smart controllers as well as browser and mobile applications, all while expanding the delivery options by providing data in either JSON or XML formats.

An uninterrupted data set of 139 annual values of local mean air temperature T, cumulative precipitation depth P, urban area extent A, global mean surface air temperature G, and global CO2 concentration C for the 1881-2019 period of time is shared with the scientific community. The annual time series of T and P are obtained from daily observations collected at the Geophysical Observatory of the University of Modena and Reggio Emilia, Modena, Italy. Observations of A for the town of Modena are available for years 1881, 1940, 1961, 1971, 1981, 1998, 2000, and 2002–2019. Cubic spline interpolation is applied to obtain reconstructed values of A for the other years in the 1881-2019 period of time. The annual time series of G is obtained by adding the NASA GISTEMP temperature change to the average temperature observed in Modena in the 1951–1980 base period so that it can be meaningfully compared to the observed local temperature T. Global CO2 concentration C estimated from ice cores, from 1881 to 1958, and observed in the Mauna Loa Observatory, Hawaii, from 1959 to 2019, are combined to provide the annual time series of C for the 1881-2019 period of time. Matlab code performing a nonlinear analysis of the data and resulting PDF files containing scatter plots are also shared with the scientific community. This bundle contains two datasets:- Table: Time series of T, P, A, G, C- Files: ASCII data tables, PDF files with scatter plots, Matlab code having been used for processing