A robust automated technique for operational calibration of ceilometers using the integrated backscatter from totally attenuating liquid clouds

Hopkin, Emma; Illingworth, Anthony J.; Charlton‐Perez, Cristina; Westbrook, C. D.; Ballard, Sue

doi:10.5194/amt-12-4131-2019

Cited by 14 publications

(16 citation statements)

References 27 publications

(40 reference statements)

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“…Hogan (2006) implemented a fast approximate multiple-scattering code. This code has recently been used by Hopkin et al (2019) in their ceilometer calibration method. They noted that η is usually between 0.7 and 0.85 for wavelengths between 905 and 1064 nm.…”

Section: Multiple Scatteringmentioning

confidence: 99%

“…In the following discussion, we leave out the units. Several methods of calibration have been previously described: calibration based on LR in fully attenuating liquid stratocumulus clouds (O'Connor et al, 2004;Hopkin et al, 2019), calibration based on molecular backscattering (Wiegner et al, 2014) and calibration based on a high-spectral-resolution lidar reference (Heese et al, 2010;Jin et al, 2015). In addition, calibration can be assisted by sun-photometer or radiosonde measurements (Wiegner et al, 2014).…”

Section: Backscatter Calibrationmentioning

confidence: 99%

“…In addition, calibration can be assisted by sun-photometer or radiosonde measurements (Wiegner et al, 2014). Relatively large variability in the calibration coefficient has been determined for instruments of the same model (Hopkin et al, 2019). However, past studies can be useful for determining an approximate value of the coefficient before applying one of the calibration methods.…”

Section: Backscatter Calibrationmentioning

confidence: 99%

“…For the CL51, Jin et al (2015) reported a value of 1.2 ± 0.1 based on a multi-wavelength lidar reference. Hopkin et al (2019) reported mean values of 1.4-1.5 for a number of CL31 instruments (software version 202). For CHM 15k, Hopkin et al (2019) reported mean values between 0.3 and 0.8 for a majority of the instruments examined.…”

Section: Backscatter Calibrationmentioning

confidence: 99%

“…Atmospheric model evaluation is an ongoing task and a critical part of the model improvement process (Eyring et al, 2019;Hourdin et al, 2017;Schmidt et al, 2017). Traditionally, various types of observational and model datasets have been utilised -weather and climate station data, upperair soundings, ground-based and satellite remote sensing datasets, and high-resolution model simulations, amongst others.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Abstract. Automatic lidars and ceilometers (ALCs) provide valuable information on cloud and aerosols but have not been systematically used in the evaluation of general circulation models (GCMs) and numerical weather prediction (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 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 extensively used for spaceborne lidar intercomparisons. The ALCF implements all steps needed to transform and calibrate raw ALC data and create simulated attenuated volume backscattering coefficient profiles for one-to-one comparison and complete statistical analysis of clouds. The framework supports multiple common commercial ALCs (Vaisala CL31, CL51, Lufft CHM 15k and Droplet Measurement Technologies MiniMPL), reanalyses (JRA-55, ERA5 and MERRA-2) and models (the Unified Model and AMPS – the Antarctic Mesoscale Prediction System). 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. 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. While the current focus of the framework is on clouds, support for aerosol in the lidar simulator is planned in the future.

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Section: Multiple Scatteringmentioning

confidence: 99%

Section: Backscatter Calibrationmentioning

confidence: 99%

Section: Backscatter Calibrationmentioning

confidence: 99%

Section: Backscatter Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Evaluating atmospheric icing forecasts with ground‐based ceilometer profiles

Hämäläinen

Hirsikko

Leskinen

et al. 2020

Meteorological Applications

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Atmospheric icing can cause severe damage and danger for many different sectors of society, and accurate forecasts of icing can help mitigate this. The aim of the study is to demonstrate how new ceilometer-based icing observations can be used in icing model verification. The study focuses on icing in the lowest 2 km of the atmosphere. The lowest 400 m of the atmosphere is essential for wind energy production and electricity power lines, while the higher elevations are essential from the aviation point of view. For aviation, the most risky situations are takeoffs and landings. The ceilometer observations provide a new way to investigate atmospheric icing. Ceilometers provide vertical profiles of clouds which can be further processed in order to identify in-cloud icing conditions. The new type of ceilometer-based icing observations enables more detailed verification of icing forecasts both in the vertical and in terms of geographical location. Overall, the model shows good skill in predicting clear-sky situations. The verification results show that the icing model performs better for inland areas than for coastal areas, which is attributed to the tendency of the model to forecast clouds, and in-cloud icing, at higher altitudes than observed.

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Properties of the mixing layer height retrieved from ceilometer measurements in Slovakia and its relationship to the air pollutant concentrations

Nguyen,

Štefánik,

Šedivá

et al. 2023

Environ Sci Pollut Res

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A robust automated technique for operational calibration of ceilometers using the integrated backscatter from totally attenuating liquid clouds

Cited by 14 publications

References 27 publications

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

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

Evaluating atmospheric icing forecasts with ground‐based ceilometer profiles

Properties of the mixing layer height retrieved from ceilometer measurements in Slovakia and its relationship to the air pollutant concentrations

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