Integrated Global Radiosonde Archive (IGRA) Version 2 consists of quality-controlled radiosonde observations of temperature, humidity, and wind at stations across all continents. Data are drawn from more than 30 different sources. The earliest year of data is 1905, and the data are updated on a daily basis. Record length, vertical extent and resolution, and availability of variables varies among stations and over time. In addition to the merged and quality-controlled set of soundings, several supplementary products are included: sounding-derived moisture and stability parameters for each suitable sounding; monthly means at mandatory pressure levels; the Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) in which post-1997 data are based on IGRA 2; and station history information derived from documented changes in instruments and observing practice as well as from instrument codes received along with the sounding data. The change to Version 2.2 includes two additional data streams which permits further updating of the IGRA data records that use the new BUFR format. Version 2.2 began in 2023.

SondeHub Radiosonde telemetry contains global radiosonde (weather balloon) data captured by SondeHub from our participating radiosonde_auto_rx receiving stations. radiosonde_auto_rx is a open source project aimed at receiving and decoding telemetry from airborne radiosondes using software-defined-radio techniques, enabling study of the telemetry and sometimes recovery of the radiosonde itself. Currently 313 receiver stations are providing data for an average of 384 radiosondes a day. The data within this repository contains received telemetry frames, including radiosonde type, gps position, and for some radiosondes atmospheric sensor data (temperature, humidity, pressure). As the downlinked telemetry does not always contain calibration information, any atmospheric sensor data should be considered to be uncalibrated. Note that radiosonde_auto_rx does not have sensor data support for all radiosonde types.

This data set contains rawinsonde profiles from 63 National Weather Service (NWS) upper-air sites archived during the North American Monsoon Experiment (NAME) for the tier-3 area. During NAME, rawinsondes were released twice daily at 00 and 12 UTC with more released during the IOPs. The data files consist of vertical profiles of temperature, dew point, relative humidity, u and v wind components, total wind speed, wind direction, and altitude. The vertical resolution is six seconds. This data set has been quality controlled by The Joint Office of Science Support (JOSS). Consult the README for more information. NOTE: This data set has been corrected as of 23 March 2010 for a significant dry bias. Only the following 5 stations were corrected: Amarillo, TX (KAMA), El Paso, TX/Santa Teresa, NM (KEPZ), Flagstaff, AZ (KFGZ), Midland, TX (KMAF), and Tucson, AZ (KTUS). See the documentation file for details on the correction(s) applied to this data set. Also see Ciesielski et al 2009 in JTECH.

This is a dataset of high resolution upper air soundings collected by European Centre for Medium-Range Weather Forecasts (ECMWF) in the World Meteorological Organization's (WMO) Binary Universal Form for the Representation of meteorological data (BUFR) format. The parameters and metadata captured at each level contain time displacement, latitude displacement, longitude displacement, geopotential height, pressure, temperature, dew point temperature, wind speed, wind direction, level significance (flags). Many reports are at 2-second resolution ~3500 levels for a full ascent, some are at 1-second resolution: ~7000 levels (but we have seen up to 14500 levels). Few observations in this data set are at low resolution (standard+significant levels). That data are from Oct 2, 2014 - present, updated monthly. In 2003, the WMO members approved a migration from traditional alphanumeric codes (TAC)to table driven code forms (TDCF) BUFR for data distribution on the Global Telecommunications System (GTS). The TDCF BUFR, also known as just BUFR, is a binary data format maintained by the World Meteorological Organization (WMO). Compared with the traditional alphanumeric codes (TAC), the BUFR offers great advantages of flexibility and expandability, allowing for the dissemination of much higher vertical resolution with the reporting of the time and position at each level and extra metadata. The Commission agreed on the deadline of November 2014 to stop the parallel distribution of TAC and BUFR data. The European Centre for Medium-Range Weather Forecasts (ECMWF) has maintained an archive of global radiosonde BUFR data since October of 2014, which will complement NCEI's real-time archiving of National Weather Service (NWS) BUFR stream commencing in May of 2017.

The radiosonde takes measurements at intervals of approximately 2 seconds. The high resolution data files contain all such data. The standard resolution data files contain measurements taken at standard and significant pressure levels of the atmosphere. Standard global radiosonde data is available from 1997 onwards.

          The data consists of vertical profiles of pressure, temperature,
          relative humidity, humidity mixing ratio, sonde position, wind speed
          and wind direction. Measurements are taken at 2 second intervals and
          the ascents extend to heights of approximately 20-30km. Two subsets of
          data are avaliable.

          Data from Aberporth (on the west coast of Wales) is available from
          April 1990 - present - At least one ascent per day up until April
          1996, 4 ascents per day thereafter. This data was obtained to support
          the work with the MST radar at Aberystwyth.

          Data from other UK stations is starting to arrive. Generally there are
          4 ascents per day from each station. The archive will have around 10
          stations with data from the 1990's.

          Link to the data set home page:
          http://badc.nerc.ac.uk/home/index.html

          [Summary Extracted from the BADC Home Page]

No description found

This catalogue entry provides access to vertical profiles of standard meteorological variables. It includes two archives. The first is version 2 of the Integrated Global Radiosounding Archive (IGRA) from 1978 which incorporates global radiosounding profiles of temperature, humidity and wind from a large number of data sources, which is 30% larger than the previous version 1. IGRA v2 is the result of quality assurance procedures applied to the radiosoundings data which can be grouped into eight categories: fundamental “sanity” checks, checks on the plausibility and temporal consistency of surface elevation, internal consistency checks, checks for the repetition of values, checks for gross position errors in ship tracks, climatology-based checks, checks on the vertical and temporal consistency of temperature, and data completeness checks. No uncertainty estimation for the IGRA data is available. The second is the Radiosounding HARMonization (RHARM) dataset where values of temperature, relative humidity and wind are homogenized to remove systematic effects (such as change in the measurement sensors, solar radiation biases, sonde time-lag, calibration drifts, station relocation etc.) at 700 IGRA radiosounding stations and radiosoundings from ships. RHARM includes twice daily (0000 and 1200 UTC) radiosonde data at the mandatory levels (listed below). At levels with pressure lower than 10 hPa, homogenized temperatures are not provided because of the paucity of available observations and the issues affecting the measurements at those levels. Relative humidity homogenization and data provision are limited to 250 hPa owing to pervasive sensor performance issues at greater altitudes. Moreover, an estimation of the measurement uncertainty is provided for each value of the time series. The radiosounding significance levels are also homogenized per interpolation along with the corresponding uncertainties. The RHARM dataset thus inherits the IGRA quality assurance procedures, and additional quality checks are then applied, performing tests on physical plausibility, accuracy of the homogenization (bias adjustment), presence of outliers, and coherency check for the homogenization applied at the significant levels. The IGRA dataset is provided by NOAA's National Centers for Environmental Information (NCEI) also through the IGRA data portal. The RHARM dataset has been specifically developed for the Copernicus Climate Change Service (C3S). The dataset can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files.

These are raw radiosonde and pilot balloon observations taken from various locations at various times around the globe transmitted through the National Weather Service Telecommunications Gateway (NWSTG) in a World Meteorological Organization (WMO) Binary Universal Form for the Representation of meteorological data (BUFR) format beginning in May 2017. Variables include Temperature, humidity, Wind direction and speed, pressure, height, elapsed time and position displacement since launch, and some metadata. Vertical and temporal resolution varies.

The GPS RO technique has a number of advantages over the traditional RS such as its global coverage, high vertical resolution, 24 hour availability, high accuracy, all weather capability and lack of bias effects. The RO data contains high resolution height, temperature, pressure, refractivity, bending angle and water vapour content at the tangent point locations. There are a number of satellite constellations that are capable of deriving RO measurements. In this project we utilised data from the FORMOSAT-3 (Taiwan's Formosa Satellite Missions # 3)/ COSMIC (Constellation Observing System for Meteorology Ionosphere and Climate) and CHAMP (CHAllenging Minisatellite Payload) satellites. These constellations are capable of producing many measurements daily, the Cosmic Data Analysis and Archive Centre (CDAAC) provides around 1800 neutral atmospheric profiles per day.

RS measurements have been the dominant method for the acquisition of upper air atmospheric information for the last 70 years. The RS monitoring technique measures atmospheric profiles of pressure, temperature and humidity using sensors attached to balloons. The data collected by the sensors is transmitted to the ground based weather station. The usual operational frequency is two times per day (0000 and 1200 UT). A global RS network of approximately 1500 stations is currently in operation. The RS monitoring method has a limited coverage, low spatial and temporal resolution and is normally restricted to land masses.

In the Antarctic region there are only 16 weather stations mainly distributed along the coastal fringe due to the environmental harshness and costs involved. As such this RS network is far from ideal for studying the atmosphere, meteorology and climatology in the Antarctic region. It does however provide excellent reference stations to test and validate the RO technique as a suitable meteorological data type in the Antarctic region.

These data were downloaded from the CDAAC: COSMIC Data Analysis and Archive Centre website.
http://cdaac-www.cosmic.ucar.edu/cdaac/index.html

These data are freely available.

We downloaded and used data from the CHAMP and COSMIC wetPrf, atmPrf and sonPrf data files.

The GPS RO data was tested against co-located radiosonde measurements from 16 radiosonde weather stations located in Antarctica. We investigated the spatial and temporal buffer required for a large and accurate data set. We found that a spatial and temporal buffer set of 300km and 3 hours to be appropriate to test the RO data sets. The RO data sets were found to match well with the radiosonde measurements in the Antarctic region.

We then used these data sets to investigate annual, bimonthly temperature trends at various heights (pressure levels) and at various locations.

These data were collected by the CDAAC: COSMIC Data Analysis and Archive Centre.
We used COSMIC data collected from 1st January 2007 to 31st December 2014.
We used CHAMP data collected from 2003 to 2008.

NCEP ADP Global Upper Air Observational Weather Data are composed of global upper air weather reports operationally collected by the National Centers for Environmental Prediction (NCEP). These include radiosondes, pibals and aircraft reports from the Global Telecommunications System (GTS), and satellite data from the National Environmental Satellite Data and Information Service (NESDIS). The reports can include pressure, geopotential height, air temperature, dew point temperature, wind direction and speed. Data may be available at up to 20 mandatory levels from 1000 millibars to 1 millibar, plus a few significant levels. Report intervals range from hourly to 12 hourly. These data are the primary input to the NCEP Global Data Assimilation System (GDAS), which is used to create the NCEP Final Tropospheric Analyses (FNL).

Full daily data can be downloaded in BUFR or LITTLE_R format. Spatial subsets decoded into ASCII can also be selected by latitude/longitude or station ID.

NCEP ADP Global Upper Air and Surface Weather Observations (PREPBUFR format) are composed of a global set of surface and upper air reports operationally collected by the National Centers for Environmental Prediction (NCEP). These include land surface, marine surface, radiosonde, pibal and aircraft reports from the Global Telecommunications System (GTS), profiler and US radar derived winds, SSM/I oceanic winds and TCW retrievals, and satellite wind data from the National Environmental Satellite Data and Information Service (NESDIS). The reports can include pressure, geopotential height, temperature, dew point temperature, wind direction and speed. Report time intervals range from hourly to 12 hourly.

These data are the output from the PREPBUFR processing performed at NCEP, which is the final step in preparing the majority of conventional observational data for assimilation into the various NCEP analyses including the North American Model (NAM) and NAM Data Assimilation System (NDAS) unified grid-point statistical interpolation (GSI) analysis (the "NAM" and "NDAS" networks), the Global Forecast System (GFS) and Global Data Assimilation System (GDAS) unified grid-point statistical interpolation (GSI) analysis (the "GFS" and "GDAS" networks), the Rapid Refresh (RAP) unified grid-point statistical interpolation (GSI) analysis (the "RAP" network), the Real Time Mesoscale Analysis (RTMA) unified grid-point statistical interpolation (GSI) analysis (the "RTMA" network), and the Climate Data Assimilation System (CDAS) spectral statistical interpolation (SSI) analysis (the "CDAS" network).

This step involves the execution of series of programs designed to assemble observations dumped from a number of on-line decoder databases, encode information about the observational error for each data type as well the background (first guess) interpolated to each data location, perform both rudimentary multi-platform quality control and more complex platform-specific quality control, and store the output in a monolithic BUFR file, known as PREPBUFR. The background guess information is used by certain quality control programs while the observation error is used by the analysis to weigh the observations. The structure of the BUFR file is such that each PREPBUFR processing step which changes a datum (either the observation itself, or its quality marker) records the change as an "event" with a program code and a reason code. Each time an event is stored, the previous events for the datum are "pushed down" in the stack. In this way, the PREPBUFR file contains a complete history of changes to the data throughout all of the PREPBUFR processing. The most recent changes are always at the top of the stack and are thus read first by any subsequent data decoder routine. It is expected that the data at the top of the stack are of the highest quality.

The data provided here are also available in NetCDF and ASCII formats, which can be accessed by following the "Get a subset" link on the ds337.0 data access page [https://rda.ucar.edu/datasets/ds337.0/#!access]. The NetCDF datafiles are converted from PREPBUFR format using the pb2nc utility in the Model Evaluation Tools (MET) software package.

Raw data for polarimetric backscatter sondes launched from Davis station. Included are accompanying radiosonde data. A ReadMe file is included with the data which gives information on the encoding of the polarsonde information by the radiosonde telemetry system. It also give information on dealing with the different time stamps put on the backscatter sonde data by the radiosonde system.

Polarimetric backscatter sonde profiles - Davis Station 2018 This dataset comprises measurements of backscatter intensity from polarimetric backscatter sondes launched at Davis Station in November and December 2019. They were flown with weather balloons using radiosondes (Vaisala RS92SGP-D) to provide temperature, pressure and humidity data as well as telemetry. The data are organised into folders for each flight, the name of which includes the UTC launch time.

The Vaisala Digicora system has a quirk in that the timestamps for the special sensor data (the polarsonde) start from when the radiosonde is connected to the ground check station. The radiosonde data timestamps start either from when the pressure sensor detects the launch (EDT file) or have multiple starts during the groundcheck process (FRAWPTU file). Thus time calibration data must be supplied with every launch. This is given as the timestamp in the SPECSENSORS file at which the launch occurred.

All data are in ASCII text format, but the special sensors file is further encoded. A MatLab code snippet showing how the lines in the SPECSENSORS file are decoded is at the end of this file.

The raw data are affected by a background signal (due to internal pickup on the circuit board) that is measured prior to each launch by covering the photodetectors with opaque black foam, and recording data for ca 10 - 15 min, while the polarsonde/radiosonde package is left outside to come to ambient temperature.

In terms of the index (separation = 1 second) of the SPECSENSORS data, the following table gives the index corresponding to launch, and the range of indices used to establish the background (in each channel). The location and time are the same as used in the filenames. The time is the launch time in UTC. The index rather than the timestamp is given because sometimes the sequence of timestamps restarts.

More information, including MatLab code are available in the readme file in the download.

After many years of research and technical preparation, the production of a new ECMWF climate reanalysis to replace ERA-Interim is in progress. ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, which started with the FGGE reanalyses produced in the 1980s, followed by ERA-15, ERA-40 and most recently ERA-Interim. ERA5 will cover the period January 1950 to near real time.

ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (12 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. There are a number of forecast parameters (for example, mean rates and accumulations) that are not available from the analyses.

Improvements to ERA5, compared to ERA-Interim, include use of HadISST.2, reprocessed ECMWF climate data records (CDR), and implementation of RTTOV11 radiative transfer. Variational bias corrections have not only been applied to satellite radiances, but also ozone retrievals, aircraft observations, surface pressure, and radiosonde profiles.

This dataset comprises measurements of backscatter intensity from polarimetric backscatter sondes launched at Macquarie Is. from January 2017 to March 2017. They were flown with weather balloons using radiosondes (Vaisala RS92SGP-D) to provide temperature, pressure and humidity data as well as telemetry.

The data are organised into folders for each flight, the name of which includes the UTC launch time. Within each folder is a calibration file which provides corrections referred to below. All data are in ASCII text format, but the special sensors file is further encoded - a code snippet below indicates how this is decoded.

The raw data are affected by a background signal (due to internal pickup on the circuit board) that is measured prior to each launch by covering the photodetectors with opaque black foam, and recording data for ca 15 min, while the polarsonde/radiosonde package is left outside to come to ambient temperature.

The Vaisala Digicora system has a quirk in that the timestamps for the special sensor data (the polarsonde) start from when the radiosonde is connected to the ground check station. The radiosonde data timestamps start either from when the pressure sensor detects the launch (EDT file) or have multiple starts during the groundcheck process (FRAWPTU file). Thus time calibration data must be supplied with every launch. This is given as the timestamp in the SPECSENSORS file at which the launch occurred.

A MatLab code snippet showing how the lines in the SPECSENSORS file are decoded is available in the download file.