Cloud liquid water content retrieval from expedition ARTofMELT, Arctic Ocean, 2023

Heather Guy, Ian Brooks

This dataset contains retrievals of cloud liquid water content profiles during the expedition ARTofMELT (Atmospheric Rivers and the Onset of Arctic Melt) in the Fram Strait pack ice during the spring transition, 8 May to 14 June of 2023.

Cloud liquid water content is calculated by adiabatically distributing liquid water throughout liquid layers detected by the cloudnet algorithm which is based on remote sensing instrumentation (cloud radar, ceilometer, microwave radiometer) and 6-hourly radiosoundings deployed on the Swedish icebreaker Oden.

This retrieval performs better for low-level single layer clouds due to large uncertainties in the distribution of cloud liquid water and cloud boundaries in multi-layer cloud systems. A quality control flag is included that indicates when the integrated liquid water content is approximately equal to the liquid water path measured by the microwave radiometer. Measurements ran continuously during the whole expedition.

AtmosphereCloudsAtmosphereCloudsLiquid water contentLiquid water pathCloud liquid waterArcticArctic OceanMeteorologyAtmosphereWeather observationsARTofMELTARTofMELT 2023 expeditionIcebreaker OdenCloudnetCloudnetpy

Name

Version

Citation

References

Guy H, Brooks I, Murto S, Tjernström M, Karalis M, Prytherch J (2025) Cloudnet categorize and classification products from expedition ARTofMELT, Arctic Ocean, 2023. Dataset version 1. Bolin Centre Database. https://doi.org/10.17043/oden-artofmelt-2023-cloudnet-1

Murto S, Tjernström M, Karalis M, Prytherch J (2024) Cloud base heights and atmospheric backscatter observations from expedition ARTofMELT, Arctic Ocean, 2023. Dataset version 1. Bolin Centre Database. https://doi.org/10.17043/oden-artofmelt-2023-ceilometer-1

Tukiainen S, O’Connor E, Korpinen A (2020) CloudnetPy: A Python package for processing cloud remote sensing data. Journal of Open Source Software 5:2123. https://doi.org/10.21105/joss.02123

Illingworth AJ, Hogan RJ, O’Connor EJ, Bouniol D, Brooks ME, Delanoé J, Donovan DP, Eastment JD, Gaussiat N, Goddard JWF, Haeffelin M, Baltink HK, Krasnov OA, Pelon J, Piriou J-M, Protat A, Russchenberg HWJ, Seifert A, Tompkins AM, van Zadelhoff G-J, Vinit F, Willén U, Wilson DR, Wrench CL (2007) Cloudnet. Bulletin of the American Meteorological Society 88:883–898. https://doi.org/10.1175/bams-88-6-883

Schimmel W, Kalesse-Los H, Maahn M, Vogl T, Foth A, Garfias PS, Seifert P (2022) Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks. Atmospheric Measurement Techniques 15:5343–5366. https://doi.org/10.5194/amt-15-5343-2022

Data description

This dataset containing 35 NetCDF (nc) files, one file per day, 2023-05-09 to 2023-06-12 inclusive, with ~30 MB per daily file.
Total size: 1.0 GB

Each file contains 11 variables with dimensions of time, height. A list of all variable names, with short explanations and units is provided below.

Dimensions

• time Time (UTC)
• height Height above mean sea level (m)

Variables

• latitude Latitude of site (degree north)
• longitude Longitude of site (degree east)
• day_of_year Time as decimal day of year
• lwc Liquid water content (kg/m³). Calculated by identifying liquid pixels from the cloudnet classification mask, smoothing the liquid mask by applying a median filter with a window of 1 minute and 100 m, calculating the theoretical lwc by assuming an adiabatic lapse rate throughout each liquid layer and using the temperature and pressure at the base of each layer from the cloudnet categorisation product (interpolated 6-hourly radiosondes).
• smoothed_cloud_mask Smoothed cloud phase mask. Calculated by applying a median filter (1 minute, 100 m) to the cloudnet classification product
• nlayers Number of liquid layers
• lwc_scaled Values from lwc scaled so that the integral matches the measured lwp from the microwave radiometer (kg/m³)
• lwp Liquid water path from the microwave radiometer (g/m²)
• qc_flag Retrievals are flagged as bad if either the difference between the measured LWP and the integrated LWC following the adiabatic assumption is greater than 30 g m⁻² or if the maximum LWC at any height exceeds 1 g m⁻³. Retrievals flagged as bad are likely due to poor liquid layer identification in deep multi-layer clouds or during precipitation and should be used with caution.

Comments

Algorithm description

The following steps were followed to generate this liquid water content retrieval:

1. Liquid layers were identified using the cloudnet classifcation product by Guy et al. (2025)
2. The liquid cloud mask was smoothed by applying a median filter with a time period of 1 minute and a height resolution of 100 m.
3. For shallow low level clouds (< 500 m liquid layer top), the liquid layer was extended down to the ceilometer detected cloud base height. This improves the algorithm performance for these clouds based on a comparison with measured LWP.
4. The smoothed liquid layers were filled with a theoretical liquid water content assuming 100 % saturation and an adiabatic profile throughout the cloud. Cloud base temperature and pressure for this calculation were extracted from the cloudnet categorization product (which comes from interpolated 6-hourly radiosondes).
5. A qc-flag was applied indicating where the retrieved liquid water content is unrealistic based on the observed liquid water path.
6. A scaled version of the liquid water content profile is calculated such that the integrated liquid water content is equal to the measured liquid water path from the microwave radiometer.

Quality control

Retrievals are flagged as bad if either the difference between the measured liquid water path and the integrated liquid water content (before scaling) is greater than 30 g m⁻² or if the maximum liquid water content at any height exceeds 1 g m⁻³. Retrievals flagged as bad are likely due to poor liquid layer identification in deep multi-layer clouds or during precipitation and should be used with caution.

Associated raw data and code

The input data used in this algorithm are the cloudnet categorisation product from Guy et al. (2025) and the ceilometer cloud base heights from Murto et al. (2024).

The python code used to generate this dataset is available at GitHub, heatherguy / AOM-WP1-BL.

The expedition

Data were collected during the ARTofMELT 2023 expedition on board the Swedish icebreaker Oden, which was organized by the Swedish Polar Research Secretariat from 8 May through 14 June, 2023.

Data creators

• Heather Guy: dataset creator
• Ian Brooks: principal investigator

Project

ARTofMELT 2023 expedition. The generation of this dataset was supported by the UKRI National Environment Research Council grant reference NE/X000087/1. The project ARTofMELT was funded by the Swedish Research Council (VR: 2022-03052). This dataset was collected with instruments that are part of Oden as a National Infrastructure, funded by the Swedish Infrastructure Council (RFI: 2021-00153).

License

Open Data Commons Attribution License (ODC-By) v1.0

First name

Heather

Last name or organisation

Guy

Email address

heather.guy@ncas.ac.uk

Address

National Centre for Atmospheric Science (NCAS); Fairbairn House; 71-75 Clarendon Rd

Postal code

LS2 9PH

City

Leeds

Country

United Kingdom

GCMD science keyword

Earth science > Atmosphere

GCMD location

Ocean > Atlantic Ocean > North Atlantic Ocean > Svalbard And Jan Mayen

Begin date

2023-05-09

End date

2023-06-12

DOI

Time