Search NASA⌕ Search

DOE OSTI · 2005137

Assessing Arctic low-level clouds and precipitation from above – a radar perspective

Abstract

Most Arctic clouds occur below 2 km altitude as revealed by CloudSat satellite observations. However, recent studies suggest that the relatively coarse spatial resolution, low sensitivity, and blind zone of the radar installed on CloudSat may not enable it to comprehensively document low-level clouds. We investigate the impact of these limitations on the Arctic low- level cloud fraction, which is the amount of cloudy points with respect to all points as a function of height, derived from CloudSat radar observations. For this purpose, we leverage highly resolved vertical profiles of low-level cloud fraction derived from downlooking Microwave Radar/radiometer for Arctic Clouds (MiRAC) radar reflectivity measurements. MiRAC has been operated during four aircraft campaigns taking place in the vicinity of Svalbard during different times of the year and covering more than 25,000 km. This allows us to study the dependence of CloudSat limitations on different synoptic and surface conditions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Schirmacher, Imke, Kollias, Pavlos, Lamer, Katia, Mech, Mario, Pfitzenmaier, Lukas, Wendisch, Manfred, Crewell, Susanne. 2023-04-04. Assessing Arctic low-level clouds and precipitation from above – a radar perspective. https://doi.org/10.5194/egusphere-2023-636

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗