Preprints
https://doi.org/10.5194/gmd-2020-25
https://doi.org/10.5194/gmd-2020-25
Submitted as: methods for assessment of models
12 May 2020
Submitted as: methods for assessment of models | 12 May 2020

Ground-based lidar processing and simulator framework for comparing models and observations (ALCF 1.0)

Peter Kuma1, Adrian J. McDonald1, Olaf Morgenstern2, Richard Querel3, Israel Silber4, and Connor J. Flynn5 Peter Kuma et al.
  • 1University of Canterbury, Christchurch, New Zealand
  • 2National Institute of Water & Atmospheric Research (NIWA), Wellington, New Zealand
  • 3National Institute of Water & Atmospheric Research (NIWA), Lauder, New Zealand
  • 4Department of Meteorology and Atmospheric Science, Pennsylvania State University, PA, USA
  • 5School of Meteorology, University of Oklahoma, Norman, OK, USA

Abstract. Automatic lidars and ceilometers provide valuable information on cloud and aerosols, but have not been used systematically in the evaluation of GCMs and NWP models. Obstacles associated with the diversity of instruments, a lack of standardisation of data products and open processing tools mean that the value of the large ALC networks worldwide is not being realised. We discuss a tool, called the Automatic Lidar and Ceilometer Framework (ALCF), that overcomes these problems and also includes a ground-based lidar simulator, which calculates the radiative transfer of laser radiation, and allows one-to-one comparison with models. Our ground-based lidar simulator is based on the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) which has been used extensively for spaceborne lidar intercomparisons. The ALCF implements all steps needed to transform and calibrate raw ALC data and create simulated backscatter profiles for one-to-one comparison and complete statistical analysis of cloud. The framework supports multiple common commercial ALCs (Vaisala CL31, CL51, Lufft CHM 15k and Sigma Space MiniMPL), reanalyses (JRA-55, ERA5 and MERRA-2) and models (AMPS and the Unified Model). To demonstrate its capabilities, we present case studies evaluating cloud in the supported reanalyses and models using CL31, CL51, CHM 15k and MiniMPL observations at three sites in New Zealand. We show that the reanalyses and models generally underestimate cloud fraction and overestimate cloud albedo, the common too few too bright problem. If sufficiently high temporal resolution model output is available (better than 6 hourly), a direct comparison of individual clouds is also possible. We demonstrate that the ALCF can be used as a generic evaluation tool to examine cloud occurrence and cloud properties in reanalyses, NWP models and GCMs, potentially utilising the large amounts of ALC data already available. This tool is likely to be particularly useful for the analysis and improvement of low-level cloud simulations which are not well monitored from space. This has previously been identified as a critical deficiency in contemporary models, limiting the accuracy of weather forecasts and future climate projections.

Journal article(s) based on this preprint

Peter Kuma et al.

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Peter Kuma on behalf of the Authors (15 Oct 2020)  Author's response    Manuscript
ED: Publish as is (10 Nov 2020) by Volker Grewe

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Peter Kuma on behalf of the Authors (15 Oct 2020)  Author's response    Manuscript
ED: Publish as is (10 Nov 2020) by Volker Grewe

Journal article(s) based on this preprint

Peter Kuma et al.

Model code and software

Automatic Lidar and Ceilometer Framework (ALCF) P. Kuma, A. McDonald, O. Morgenstern, R. Querel, I. Silber, and C. Flynn https://doi.org/10.5281/zenodo.3779518

Peter Kuma et al.

Viewed

Total article views: 751 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
395 310 46 751 26 37 35
  • HTML: 395
  • PDF: 310
  • XML: 46
  • Total: 751
  • Supplement: 26
  • BibTeX: 37
  • EndNote: 35
Views and downloads (calculated since 12 May 2020)
Cumulative views and downloads (calculated since 12 May 2020)

Viewed (geographical distribution)

Total article views: 656 (including HTML, PDF, and XML) Thereof 654 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Jan 2023
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.