Last modified on Nov 12, 2025 10:45
Contributors: F. Ambrogi, M. Blaschek,L. Haimberger, U. Voggenberger

Issued by: UNIVIE / Leopold Haimberger

Issued Date: 30/06/2022

Ref: DC3S311c_Lot2.1.5.2_Product User Guide

Official refence number service contract: 2019/C3S_311c_Lot2_UNIVIE/SC1 and 2021/C3S2_311_Lot2_CNR-IMAA

History of modifications

Acronyms

Product description

The In situ Comprehensive Upper-Air Observation Network (CUON) serves the most comprehensive harmonised public collection of conventional radiosonde and pilot balloon data available to date. It contains measurements of the ECVs temperature, humidity (relative, specific, dewpoint, dewpoint depression) and wind (speed and direction, u and v components). The network consists of more than 5400 individual upper-air observing stations at fixed location, and ca. 9000 mobile platforms. While the majority of records contain only a limited number of observations, around 1000 observing stations provide data spanning several decades. Besides the measured values and the most basic metadata (position, time), it provides observation minus background and observation minus analysis departure values from ERA5 during assimilation. CUON contains homogeneity adjustments for temperature and several wind as well as humidity variables that can be applied to the raw observations, together with observation error estimates derived from the departure statistics based on ERA5 back to 1940. Metadata regarding the instrumentation type information is also provided. All the information adhere to the Common Data Model for observations https://github.com/ecmwf-projects/cdm-obs/blob/master/ and more specifically to its subset of variables, the CDM-OBS-core .
Data are provided in binary (netCDF) as well as compressed CSV format, which are best read by standard python packages (e.g. xarray or pandas, h5py). Requests can be created via the CDS forms or using simple python scripts. A few example scripts that demonstrate basic usage are given below. 

With version 1.1.0, the database contains records up to 31 December 2024.

Updates are planned yearly, with the aim to move to monthly updates in the future. The following sections inform about data sources and coverage. Sections 7-11 inform about the content of the retrieved data and give example how to use it for data analysis. 

Figure 1: Location of fixed-based station records that contain at least one ascent. Notes: (1) "ECMWF" is the union of several data collections (see section 5, identifiers "ERA5 1" , "ERA5 2", "ERA5 1759" and "ERA5 1761"); (2) Station records may consist of ascents from different sources, and colours are plotted over the previous colours in the order listed in the legend.

Data and Metadata Sources

The following data sorces were used to build the CUON "merged" database. References describing the
sources and the data can be found in the reference section of the download tab.

As can be seen from Fig. 2, most of the data served by CUON come from the  ERA5_1, ERA5_2 and IGRA2 collections. The ERA5 observation data collections are accessible for ECMWF member state users only via the MARS archive, due to restrictions on certain observation types (other than radiosondes). The collection of radiosonde intercomparison data, both single radiosonde launches and aggregated data, which is described in detail by Imfeld et al. (2021) is part of CUON but is not currently served via the CDS.

The data come from the sources in various formats. As a first step, all source data have been aligned with the Common Data Model and then saved in netCDF4 format. From these source data, a merged data set was created.

The merging serves three purposes:

• Concatenate pieces of the same station record found in different sources. Quite often, station records come in pieces, sometimes with different identifiers. Identifying those pieces can be challenging. A consistent and comprehensive station inventory is essential.
• Identify and remove duplicates. The data sources noted above show a high degree of redundancy. Users usually do not want to choose themselves which data source to use and rely on the data provider to make appropriate choices. In case of the present data sets the following rules are applied
  • selection of the record with measurements recorded in the uppermost layer of the atmosphere. If multiple sources are available, the next selections are applied.
  • If ERA5 2 or ERA5 1 data is available, use these data, since for those also background departure feedback information is present.
  • if not, use the record from the data source with the largest amount of valid data.
  • if the 'largest size' of the records from different sources is identical, we give preference to IGRA version 2.
  • For finding duplicates, certain thresholds have to be used, e.g. the launch time difference must be less than 2 hours and the spherical distance between the coordinates of the observation stations must be less than 0.3 deg.
  • The stationID of the merged record is not necessarily the same as the stationID in the chosen data set. It is first tried to identify the station with the radiosonde station IDs of the WMO OSCAR data base. If a match is found, also the WIGOS ID of the station record is clear. In case no match is found, it is tried to find the WMO ID of the station and to coin a temporary WIGOS-formatted ID starting with 0-20000-0-WMOID. For a few stations, other temporary IDs which are WIGOS-formatted but not globally valid had to be generated. These IDs will be superseded once a Copernicus facility able to coin permanent WIGOS IDs is in operations. Station lat/lons are normally kept as reported for each ascent, since this allows to keep track of station relocation. Only in cases where the lat/lon has poor quality (e.g. lost minus sign in latitude), it is corrected.

• Verify the consistency of latitude and longitude for records with the same station ID. If there are significant discrepancies in location, such as jumps greater than 1 degree (data shows cases sometimes exceeding 10 degrees), these records should be separated into distinct entries. This step applies to approximately 200 station IDs. Special consideration should be given to quasi-stationary weather ship stations, identified by WMO IDs starting with "99," as they represent a unique case and may require different handling.

Metadata on the sensors used has been extracted from a database described by S.Schroeder ( https://ams.confex.com/ams/pdfpapers/54950.pdf) up to the year 2013 if no other instrumentation metadata was available. Other instrumentation metadata is retrieved directly from the above sources where it was reported using the WMO manual on codes https://library.wmo.int/doc_num.php?explnum_id=10235). More details on the specific codes used are given in the Appendix. 

Data Processing

The original merged data set contains a total of 5408 merged netCDF files for fixed-position station, and 9634 for mobile stations. The total amount of records exceeds 50 million.

Figure 2: – Stacked distribution of the record counts per data sources of the entire data set version 1. It contains also the non-public data.

To improve the usability of the dataset, additional processing steps are performed.

First, the original merged records are augmented with additional derived climate variables. For example, an ascent, especially if it has never been assimilated, may have only temperature, dew point, wind speed and wind direction information. In this case, relative humidity, specific humidity, dew point depression and the u and v components of the wind are calculated and added to the records. Due to this procedure, unnecessary data gaps for certain parameters are avoided. If mandatory pressure levels are missing in the data, those pressure levels are interpolated linearly in log(p), provided that the specified resolution and close measurements permit the implementation of this procedure. 

In the subsequent step, the balloon drift is calculated based on the wind observations, with the method being described in detail by Voggenberger et al. (2024). This approach enables the updating of positions for each observation. Due to the balloon drift, the columns latitude/longitude and latitude|header_table and longitude|header_table differ, with the former containing the position of the observation and the latter the position of the balloon launch. The users can therefore decide whether to assume a vertical or a slanted ascent.

Some efforts went into making available the time of the actual launch of the radiosonde or PILOT balloons. The column record_timestamp contains the nominal time of the ascent (often given at full hours in TEMP messages) whereas the column report_timestamp contains the actual launch time. They typically differ by less than an hour. If the actual launch time was not available, it has been tried to estimate it from periods where it was available, assuming the launch practice has not changed. If the profile originates from modern high resolution BUFR records, launch time and nominal time are identical.

The launch time may differ substantially from the actual time of the observation, since a typical ascent lasts about 2 hours. The time since launch is not specified in most historical reports and is also not given by CUON. If really needed it can be, however, relatively accurately reconstructed from the geopotential height of the balloon, assuming a constant ascent speed of 5 m/sec (e.g. Voggenberger et al. 2024). 

Furthermore, typically required for ascents not assimilated by ERA5, background departures and analysis departures are calculated offline by interpolating gridded data to the ascent location and time. For this procedure the beforehand calculated drift information is considered to minimize the spatial distance between observation and background field. The CDM variable "conversion_flag" in the "observations_table" tracks whether the observation value was available already in the source data or if it was calculated during merging.

The sequence of the previous steps results in the full version of the merged CUON dataset, and constitute the starting point for additional processing, namely adding bias adjustments and observation uncertainty estimates. The calculated bias estimates and the uncertainty estimates are stored in the CDM tables "advanced_homogenisation" and "advanced_uncertainty", respectively.

For a detailed description how those are calculated, we refer to the ATBDs, which accompany this document.

Access and Examples

Users can retrieve CUON data with requests generated by the web form or by using the CDS API.

The following keywords are valid for the CDS API and the CDS:

For data retrieval, run the code provided in the API request section at the bottom of the web form. This will only work with the correct credentials set in the .cdsapi file or the shell environment. The returned output file (netCDF or CSV) contains tables with the columns listed below. Some columns contain numeric code values defined in the CDM-OBS data model (see list of tables at https://github.com/ecmwf-projects/cdm-obs/blob/master/tables/)

observation_id	

report_id	

data_policy_licence	

z_coordinate	

z_coordinate_type

observed_variable	

observation_value	

units	

sensor_id	

source_id	

station_name	

platform_type	

primary_station_id	

height_of_station_above_sea_level	

report_timestamp	

record_timestamp	

city	

an_depar@body

fg_depar@body

fg_depar@offline

uncertainty_type1

uncertainty_units1

uncertainty_value1

longitude

longitude|header_table

latitude|header_table

In its current version CUON allows only subarea selection and time windows of at least one day. This means that the downloaded data often contain more data than actually needed. Therefore some additional processing is usually needed to get the desired information. Data also come as "ragged" arrays, i.e. profiles for different ascents may have different lengths, and they may contain missing values. This makes them more challenging to use than gridded data. 

There are many ways to deal with this. We show here examples of reading the downloaded file into a pandas dataframe, using python. Once in dataframe format, one can squeeze out unnecessary information using the  pandas selection tools. Other tools such as duckdb also allow performing sub-selections, using SQL like syntax (such an example is provided later). 

Let's start with a simple request. Note that we specify a target file name, which is not strictly necessary but convenient.

import cdsapi

dataset = "insitu-comprehensive-upper-air-observation-network"
request = {
    'variable': ['air_temperature', 'air_dewpoint'],
    'year': ['2010'],
    'month': ['07'],
    'day': ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31'],
    'format': 'netcdf',
    'area': [50, 15, 45, 20],
}

target='download.nc'
client = cdsapi.Client()
client.retrieve(dataset, request, target)

Executing this code yields output like this (afterwards it is buffered for some time):

2025-05-06 11:39:57,754 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.
2025-05-06 11:39:57,755 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the "Show API request code" tool on the dataset Download Form to check you are using the correct syntax for your API request.
2025-05-06 11:39:58,829 INFO Request ID is ee08852f-d416-4ca1-bf2a-51417a874ad6
2025-05-06 11:39:59,020 INFO status has been updated to accepted
2025-05-06 11:40:00,207 INFO status has been updated to running
2025-05-06 11:40:02,401 INFO status has been updated to successful
'download.nc'

Executing the code above creates a netCDF file download.nc. It can be read with different tools or python modules. For example, one can use the python package xarray and pandas to have a look at the data. The code below prints the first row of each column. 

import xarray as xr
import pandas as pd
df=xr.open_dataset(target).to_dataframe()
print('size:',os.path.getsize(target)//1024,'kB')
for d in df.columns:
    print(f'{d: >35}\t{df[d][0]}')

size: 822 kB
                     observation_id	b'20100000000000060865'
                          report_id	b'20100000000000000176'
                data_policy_licence	0
                       z_coordinate	4410.0
                  z_coordinate_type	1
                  observed_variable	b'air_temperature'
                  observation_value	219.10000610351562
                              units	b'5'
                          sensor_id	b'79'
                          source_id	b'era5_1'
                        report_type	-2147483648
                       station_name	b'GRAZ-THALERHOF-FLUGHAFEN'
                       station_type	1
                      platform_type	0
                 primary_station_id	b'0-20000-0-11240'
  height_of_station_above_sea_level	378.79998779296875
                   report_timestamp	2010-07-01 02:30:00
                    report_duration	-2147483648
                   record_timestamp	2010-07-01 03:00:00
                       secondary_id	b'11239,11240,11241,5682,AUM0001'
                               city	b'Graz'
                      an_depar@body	-0.19339817762374878
                      fg_depar@body	-0.44553908705711365
                   fg_depar@offline	nan
                  uncertainty_type1	1
                 uncertainty_units1	b'5'
                 uncertainty_value1	nan
                          longitude	15.556254386901855
                           latitude	46.903221130371094
             longitude|header_table	15.430000305175781
              latitude|header_table	47.0

Note that strings are byte strings. They can be converted to unicode strings using the decode function, e.g. df['units'].str.decode('utf-8'). If data are read as csv, string variables are automatically decoded. 

The data will always include the above columns and they are available also in other upper-air data sets. This helps reaching interoperability between different observation datasets.

If format': 'netcdf'is replaced by 'format': 'csv', the data will be provided in zipped CSV format:
Executing the  retrieval code again, with different format,  creates a zipped CSV file download.zip.

import cdsapi

dataset = "insitu-comprehensive-upper-air-observation-network"
request = {
    'variable': ['air_temperature', 'air_dewpoint'],
    'year': ['2010'],
    'month': ['07'],
    'day': ['f{d:0>2}' for d in range(1,32) ],
    'format': 'csv',
    'area': [50, 15, 45, 20],
}

target='download.zip'
client = cdsapi.Client()
client.retrieve(dataset, request, target)

This file is much larger (factor ~10) than the netcdf file. It can be read with different tools or python modules. pandas can read the content of the zip file directly with one line of code, but let's read the zip file as text first. This helps finding the number of header lines.

import zipfile
with zipfile.ZipFile(target) as z:
    for n in z.namelist():
        with z.open(n) as f:
            l=-1
            h=next(f).decode()
            while len(h)<2 or '#' in h:
                print(h,end='')
                h=next(f).decode()
                l+=1  		
	
df=pd.read_csv(target,header=l)

The header explains the contents of the file:

########################################################################################
# This file contains data retrieved from the CDS https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-comprehensive-upper-air-observation-network
# This is a C3S product under the following licences:
# 20180314_Copernicus_License_V1.1
# This is a CSV file following the CDS convention cdm-obs
# Data source: CUON
# Time extent: 20100701 - 20100731
# Geographic area (minlat/maxlat/minlon/maxlon): 45.17/49.92/15.19/19.87
# Variables selected and units:
# air_temperature [5]
# air_dewpoint [5]
# Uncertainty legend:
# uncertainty_value1 Random uncertainty
########################################################################################

It is important to consider that the data are stored in the CDS object store as flat netCDF4 files, where each file contains data from one day and from a certain longitudinal slice. The appropriate time and longitudinal slices are chosen automatically. Currently only rectangular lat/lon boxes and days can be queried. It is not possible to query for primary_ids or a specific launch time using the CDSAPI.

One can select a certain station, however, by specifying the bounding box as tight around the station coordinates as possible. This helps for fixed stations. If one is interested in ship platforms, a considerably larger domain is likely needed to catch all reports within a certain time interval. 

In order to get only a specific ascent or data from just one primary id, it will be often necessary to do a more fine grained query on the downloaded dataframe. This can be implemented using e.g. the xarray, pandas or duckdb packages. 

For the files from the above queries we can get the included stations (primary_station_id values) as follows:

import numpy as np
print(np.unique(df['primary_station_id']))

['0-20000-0-11240' '0-20000-0-11747' '0-20000-0-12843' '0-20000-0-14240'
 '0-20001-0-11035']

This can be used to query e.g. for station Vienna (11035) using duckdb:

import duckdb
import datetime
print(duckdb.query(f"select observed_variable,observation_value from df \
where record_timestamp=='2010-07-01 12:00:00' \
and primary_station_id=='0-20001-0-11035' \
and z_coordinate==50000"))

which yields temperature and dewpoint values at the 500 hPa level at midday nominal launch time:
┌───────────────────┬───────────────────┐
│ observed_variable │ observation_value │
│      varchar      │      double       │
├───────────────────┼───────────────────┤
│ air_temperature   │             258.9 │
│ air_dewpoint      │             235.9 │
└───────────────────┴───────────────────┘

Alternatively one can do a similar query using pandas syntax:

df[(df.record_timestamp=='2010-07-01 12:00:00') & (df.primary_station_id=='0-20001-0-11035') & (df.z_coordinate==50000)].filter(['z_coordinate','observed_variable','observation_value'])

With this filter pattern, a rudimentary T-logp plot for one ascent can be generated with the following code:

tlogp=df[(df.record_timestamp=='2010-07-01 12:00:00') & (df.primary_station_id=='0-20001-0-11035')].\
filter(['z_coordinate','observed_variable','observation_value'])
%matplotlib inline
import matplotlib.pyplot as plt
for p in 'air_temperature','air_dewpoint':
    plt.semilogy(tlogp.observation_value[df.observed_variable==p],tlogp.z_coordinate[df.observed_variable==p],label=p)
plt.ylim(100000,1000)
plt.ylabel('hPa')
plt.xlabel('K')
plt.title('0-20001-0-11035 '+'2010-07-01 12:00:00')
plt.legend()

Figure 3: – Temperature and dewpoint at station Vienna on 1st July 2010, 12GMT, using the code above (left panel), and using metpy (right panel). See https://gitlab.phaidra.org/ulrichv94/CUON/-/blob/master/CUON/public/testing/PUG_1.1.ipynb for details to reproduce the second plot. 

More fancy displays like skewed T-log diagrams can be plotted using e,g, metpy. This package expects aligned numpy arrays for humidity and temperature observations. Since the columns of CUON output are organised as dense packed arrays, avoiding all-nan rows, one may have to expand the humidity variables who do not reach as high up as temperature values, and to fill gaps with NaNs.  

Time series at standard pressure levels can be made e.g. as follows:

request = {
    'variable': ['air_temperature'],
    'year': [f'{m}' for m in range(2000,2010)],
    'month': [f'{m:0>2}' for m in range(1,13)],
    'day': ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31'],
    'format': 'netcdf',
    'area': [50, 15, 45, 20],
}

target='download.nc'
client.retrieve(dataset, request, target)

df=xr.open_dataset(target).to_dataframe()
ts=df[(df.primary_station_id==b'0-20001-0-11035')&(df.observed_variable==b'air_temperature')&\
(df.z_coordinate==50000)].\
filter(['record_timestamp','observation_value'])
print(len(df),len(ts))
plt.plot(ts.record_timestamp,ts.observation_value)
plt.ylabel('K')
plt.title('Temperature at 500 hPa for station Vienna')

Figure 4: Example retrieval of a multi-year temperature observation time series from station Vienna.  

Please be aware that retrievals of time series at this stage (June 2025) take some time (~5 min) and may fail due to memory limitations if more than 5 years are retrieved. In this case it may be necessary to split requests into shorter time slices and then concatenate the series. 

How to use additional metadata

CUON records contain sensor metadata collected offline by various researchers in the past (e.g. Gaffen, 1994, Schroeder, 2013). Since several years, this information has been reported also in standard TEMP and BUFR messages, using WMO manual on codes 1.2:  https://library.wmo.int/records/item/35625-manual-on-codes-volume-i-2-international-codes. In CUON the instrument codes of Schroeder (2013) and the WMO have been harmonised to be able to provide metadata from early years as well as for recent ascents in one data column (sensor_id). This column can be used to filter stations using certain radiosonde types. 

As an example, a map of ascents using Vaisala RS41 sensors on 1 December 2024 is plotted, showing the value of the available instrumentation metadata.  Such retrievals take some time (20 minutes), but are expected to become faster in the future. 

request = {
    'variable': ['air_temperature', 'air_dewpoint'],
    'year': ['2024'],
    'month': ['12'],
    'day': ['01'],
    'format': 'netcdf',
}

mtarget='download1.nc'
client.retrieve(dataset, request, target)

import cartopy.crs as ccrs
import cartopy.feature as cfeature

df=xr.open_dataset(mtarget).to_dataframe()
ax = plt.axes([0, 0, 1, 1], projection=ccrs.PlateCarree())
ax.add_feature(cfeature.OCEAN, zorder=0)
ax.coastlines()
_,idx=np.unique(df['longitude|header_table'],return_index=True)
idy=np.where(np.isin(df['sensor_id'][idx] ,(b'123',b'124',b'125',b'141',b'142')))[0]
longitudes = list(df['longitude|header_table'][idx[idy]])
latitudes = list(df['latitude|header_table'][idx[idy]])
plt.scatter(longitudes, latitudes, s=40, alpha=1,
                edgecolor='k',)
x=plt.title('2024-12-01 number of stations with Vaisala RS41 sensors: '+str(len(idy)))

Figure 5: – Location of radiosonde stations using flavors of the Vaisala RS41 radiosonde on 1 December 2024.

CUON contains not only instrumentation metadata but also information about balloon drift. While radiosonde ascents have been assumed to be vertical in many operational applications in the past, the collocation errors made through this assumption cannot be tolerated given the current accuracy of models and data assimilation systems. The code below generates a figure which gives an impression of the drift to be expected in midlatitudes (in this case station Vienna).

request = {
    'variable': ['air_temperature', 'air_dewpoint'],
    'year': [f'{m}' for m in range(1980,1983)],
    'month': [f'{m:0>2}' for m in range(1,13)],
    'day': ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31'],
    'format': 'netcdf',
    'area': [50, 15, 45, 20],
}

tstarget='tsdownload.nc'
client.retrieve(dataset, request, tstarget)

df=xr.open_dataset(tstarget).to_dataframe()

ax = plt.axes([0, 0, 1, 1], projection=ccrs.PlateCarree())
ax.set_extent([12,22,46,51])
ax.add_feature(cfeature.OCEAN, zorder=0)
ax.add_feature(cfeature.BORDERS, linestyle='-', alpha=1)
ax.coastlines()
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
                  linewidth=2, color='gray', alpha=0.5, linestyle='--')

idx=np.where(df['primary_station_id']==b'0-20001-0-11035')[0]
idy=np.where(df['z_coordinate'][idx]==30000)[0]
plt.scatter(df['longitude'].values[idx], df['latitude'].values[idx], s=10, alpha=1,
                color='r',)
plt.scatter(df['longitude'].values[idx[idy]], df['latitude'].values[idx[idy]], s=10, alpha=1,
                color='y',)
plt.scatter(df['longitude'].values[idx][-2:], df['latitude'].values[idx][-2:], s=40, alpha=1,
                color='k',)
x=plt.title('All Positions of radiosondes launched from Vienna 1980-1982')

Figure 6: Drift of balloons at station Vienna during the period 1980-1982 (ca. 2000 ascents). Red: All positions reached, Yellow: Position reached at or below the 300 hPa level, Black: Position of launch site. 

How to work with background departures and bias adjustments

Bias adjustments are available for all temperature, humidity and wind variables except wind speed. They are automatically retrieved together with the observation values and other columns. Owing to the method used for calculating them, they are usually set to zero if the available time series for a record is shorter than 6 months. Breakpoints and bias adjustments were calculated through analysis of the variable fg_depar@offline in the case of wind (see the corresponding ATBD document). To get adjusted observation time series one needs to subtract them from the observation_values. Users should do that when calculating statistics such as trends from the observation time series, when comparing observations e.g. with satellite radiances, or when calculating gridded observation products. 

The effect of the adjustments is often small compared to the overall variance of the observation values and thus not easy to see. It can be better seen when subtracting the adjustments from the fg_depar@offline columns, which contain obs-bg differences. The code below shows the effect of adjustments on a station in the Tropical Pacific. A break in the time series of fg_depar@body is clearly visible in 1995. A change in instrumentation is the likely reason, indicated by the available metadata., where "AH" means a change from US radiosonde types ("ZKB" is VIZ-B) to Vaisala RS80. After adjustment the standard deviation of the departures from ERA5 is clearly reduced both for temperature at 50 hPa and the relative humidity at 200 hPa levels, respectively. Note that balloon drift has been taken into account when calculating fg_depar@body.  

Figure 7: .Time series of fg_depar@offline, without adjustment (blue) and with adjustmend (fg_depar adj), for radiosonde station 91408 (KOROR, in the tropical Pacific). Left: for relative humidity at 200hPa, right for temperature at 50 hPa. The shift in 1995 is clearly indicated. For details on the calculation of the adjustments, see the ATBD on temperature and humidity adjustments (to be published soon). 

For expert users: Please note that it makes no sense to subtract the adjustments from fg_depar@body and an_depar@body for temperature, since these have been calculated AFTER subtracting the bias adjustments used when assimilating ERA5. The situation is different for humidity and wind adjustments, since humidity and wind were not adjusted when assimilating ERA5. 

How to retrieve uncertainty estimates

Observation uncertainty estimates have been calculated for records where the data source was ERA5_1 or ERA5_2 (about 76% of records) from the archived fg_depar@body and an_depar@body values, using the Desroziers (2005) method, using an averaging interval of 30 days. Note that these estimates contain
both measurement and representation errors, and it was assumed that the assimilation process was bias-free. This assumption is justifiable for most of the temperature and wind data, for humidity data it is questionable since these are known to have relatively large unadjusted biases.
Still we consider these estimates a quite useful tool to show the improvement in the insitu upper-air observation input over time. 

# tstarget is the output file already retrieved for the timeseries plot at the end of section 7

df=xr.open_dataset(tstarget).to_dataframe()
ts=df[(df.primary_station_id==b'0-20001-0-11035')&(df.observed_variable==b'air_temperature')&\
(df.z_coordinate==50000)].\
filter(['record_timestamp','uncertainty_value1'])


plt.plot(ts.record_timestamp,ts.uncertainty_value1,'.')
plt.ylabel('K')
plt.title('Temperature observation uncertainty estimate at 500 hPa for station Vienna')

Figure 8: .Time series of fg_depar@offline, without adjustment (blue) and with adjustmend (fg_depar adj), for radiosonde station 91408 (KOROR, in the tropical Pacific). Left: for relative humidity at 200hPa, right for temperature at 50 hPa. The shift in 1995 is clearly indicated. For details on the calculation of the adjustments, see the ATBD on temperature and humidity adjustments (to be published soon). 

How to request the intercomparison data

CUON contains ascents from many international radiosonde intercomparisons, such as the one in Vacoas, Mauritius, in 2010. This information will be released at a later stage. Please look for updates of this document in the forthcoming months. 

User support

The service team provides tier 2 support with a latency of one week. Queries should be submitted as JIRA requests via the CDS. They will be directed to us. We will try to maintain an FAQ.

Acknowledgements

The authors wish to thank the C3S team and collaborators for their continuous support, particularly Paul Poli, Markel Garcia-Diez and Edward Comyn-Platt.

References (other than data sources)

Desroziers, G., Berre, L., Chapnik, B. and Poli, P. (2005), Diagnosis of observation, background and analysis-error statistics in observation space. Q.J.R. Meteorol. Soc., 131: 3385-3396. https://doi.org/10.1256/qj.05.108

Hersbach, H. et al, 2020: The ERA5 global reanalysis. https://doi.org/10.1002/qj.3803

Imfeld, N., Haimberger, L., Sterin, A., Brugnara, Y., and Brönnimann, S.: Intercomparisons, Error Assessments, and Technical Information on Historical Upper-Air Measurements, Earth Syst. Sci. Data, https://doi.org/10.5194/essd-2020-395, 2021.

Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H., Kennedy, J., Spencer, L. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T. A., Crouthamel, R., Domínguez⦣8364;Castro, F., Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan, A., Kubota, H., Le Blancq, F., Lee, T., Lorrey, A., Luterbacher, J., Maugeri, M., Mock, C. J., Moore, G. K., Przybylak, R., Pudmenzky, C., Reason, C., Slonosky, V. C., Smith, C., Tinz, B., Trewin, B., Valente, M. A., Wang, X. L., Wilkinson, C., Wood, K. and Wyszy;ski, P. (2019), Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q J R Meteorol Soc..   https://doi.org/10.1002/qj3598.

Soci, C., Hersbach, H., Simmons, A., Poli, P., Bell, B., Berrisford, P., et al. (2024) The ERA5 global reanalysis from 1940 to 2022. Quarterly Journal of the Royal Meteorological Society, 150(764), 4014–4048. Available from: https://doi.org/10.1002/qj.4803

Voggenberger, U., Haimberger, L., Ambrogi, F., Poli, P.,: Balloon drift estimation and improved position estimates for radiosondes, GMD, https://doi.org/10.5194/gmd-17-3783-2024,  2024

 Appendix

As noted above, the comprehensive in situ upper-air data set relies on a station inventory. It also contains a few special tables or variables, which are either extensions to the CDM, like the ERA5 observation feedback table, or where the CDM-OBS only gives table definitions and the data provider has to fill in the tables. Examples are the header_table or sensor_configuration tables.

1. Radiosonde type codes

Instrumentation metadata is essential for assessing the uncertainty of observations. Instrument type codes can be retrieved using the optional variable 'type', but in order to interpret the codes, a sensor_configuration table, which resolves the codes, is needed. The upper-air data set distinguishes between 3000 instrumentation codes. Most of them come from S. Schroeder's vapor data base (Schroeder, 2008). From 2013 onwards, WMO BUFR codes (as described in the WMO manual on codes https://library.wmo.int/records/item/35625-manual-on-codes-volume-i-2-international-codes) are used. The whole table can be downloaded not from the CDS API but from https://gitlab.phaidra.org/ulrichv94/CUON/-/blob/master/CUON/public/merge/sensor_configuration_all.csv, where the columns sensor_id and comments contain the relevant information. A few example rows are given below. 

...

" Z51 VIZ model 1463-510 US Army ML-659(V)1/TMQ-4A 403 MHz LoRAN-C {1983}^ <F=T403DL,P=Ac,T=TR,U=C,N=L>",
" Z52 VIZ model 1464-510 US Army ML-659(V)2/TMQ-4A 403 MHz Omega {1983}^ <F=T403DL,P=Ac,T=TR,U=C,N=O>",
" Z53 VIZ model 1460-510 US Army ML-659(V)3/TMQ-4A 1680 MHz RDF {1983}^ <F=T1680DL,P=Ac,T=TR,U=C,W=Ra>",
...

The sensor_id code given in the output from the CDS API matches to one row of this table. 

2. Station inventory

Table A. Links to comma-separated value station inventory files featuring station availability.

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