Atmospheric boundary‐layer characteristics from ceilometer measurements. Part 2: Application to London's urban boundary layer

Kotthaus, Simone; Grimmond, Sue

doi:10.1002/qj.3298

Cited by 36 publications

(47 citation statements)

References 47 publications

(74 reference statements)

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“…Considering the uncertainty introduced by the spatial representation of the SYNOP data to describe the central London area, the CABAM classification scheme for ABL conditions compares generally well to the observed cloud classes. Especially the significant agreement during daytime suggests the ALC‐derived classes are sufficiently accurate to enhance analysis of the mixed layer height climatology (Kotthaus and Grimmond, ).…”

Section: Resultsmentioning

confidence: 99%

“…Further, application of CABAM to other regions where cloud types are different to southeast England could help to improve the classification scheme. The current algorithm is considered to greatly benefit the analysis of mixed layer height statistics (Kotthaus and Grimmond, ), and provides the first ABL classification scheme that distinguishes cloud types solely based on ALC observations.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…Mixed layer height results are evaluated against temperature inversions derived from AMDAR profiles and the ABL classification scheme is compared to SYNOP reports (section 4). The summary (section 5) suggests this new, automatic tool is suitable to characterise the ABL based on long‐term ALC measurements (Kotthaus and Grimmond, ).…”

Section: Introductionmentioning

confidence: 99%

“…From comparison with SYNOP reports, the ABL classification scheme generally correctly distinguishes between convective and stratiform boundary‐layer clouds, with slightly better performance during daytime. Applied to 6 years of ALC observations in central London, Kotthaus and Grimmond (), a companion paper, demonstrate CABAM results are valuable to characterise the urban boundary layer over London, United Kingdom, where clouds of various types are frequent.…”

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confidence: 99%

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Atmospheric boundary‐layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds

Kotthaus

Grimmond

2018

Quart J Royal Meteoro Soc

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119

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The use of Automatic Lidars and Ceilometers (ALC) is increasingly extended beyond monitoring cloud base height to the study of atmospheric boundary layer (ABL) dynamics. Therefore, long-term sensor network observations require robust algorithms to automatically detect the mixed layer height (Z ML ). Here, a novel automatic algorithm CABAM (Characterising the Atmospheric Boundary layer based on ALC Measurements) is presented. CABAM is the first non-proprietary mixed layer height algorithm specifically designed for the commonly deployed Vaisala CL31 ceilometer. The method tracks Z ML , takes into account precipitation, classifies the ABL based on cloud cover and cloud type, and determines the relation between Z ML and cloud base height. CABAM relies solely on ALC measurements. Results perform well against independent reference (AMDAR: Aircraft Meteorological Data Relay) measurements and supervised Z ML detection. AMDAR-derived temperature inversion heights allow Z ML evaluation throughout the day. Very good agreement is found in the afternoon when the mixed layer height extends over the full ABL. However, during night or the morning transition the temperature inversion is more likely associated with the top of the residual layer. From comparison with SYNOP reports, the ABL classification scheme generally correctly distinguishes between convective and stratiform boundary-layer clouds, with slightly better performance during daytime. Applied to 6 years of ALC observations in central London, Kotthaus and Grimmond (2018), a companion paper, demonstrate CABAM results are valuable to characterise the urban boundary layer over London, United Kingdom, where clouds of various types are frequent. KEYWORDSABL, ALC, AMDAR, boundary-layer clouds, CABAM, ceilometer, mixed layer height Abbreviations: ABL, atmospheric boundary layer; agl, above ground level; ALC, automatic lidars and ceilometers; AMDAR, Aircraft Meteorological Data Relay; AN, afternoon (mid-point between solar noon and sunset); B, end time of layer; CABAM, Characterising the Atmospheric Boundary layer based on ALC Measurements; CBH, cloud base height; CC, cloud cover; Cu, cumulus cloud, also ABL class of days dominated by Cu; d, prefix, indicating a difference; D, second derivative of attenuated backscatter; day SR , 24 hr periods centred on sunrise; ET, evening transition; g, range gate index; G, vertical gradient of attenuated backscatter; H day , duration of day; H night , duration of night; i, index of iteration; IQR, inter-quartile range; K, layer detection density; L, layer duration; MBE, mean bias error; MH± 1 , mixed layer height at end of previous day/beginning of next day; ML, mixed layer; MN, midnight; MT, morning transition; N, number of layers; n, number of layers fulfilling certain criteria; NT, nocturnal layer after sunset; P, number of points forming a layer; p, percentile; q, index of layer; r, range (distance from instrument); R, size of range window; RL, residual layer; RLH, height of residual layer; RMSE, root-mean-square error; SC, stratocumul...

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Section: Resultsmentioning

confidence: 99%

Section: Summary and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Atmospheric boundary‐layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds

Kotthaus

Grimmond

2018

Quart J Royal Meteoro Soc

Self Cite

119

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“…Recently ceilometer data were used for the validation of transport models, e.g. to improve forecasts of the dispersion of aerosol layers (e.g., Emeis et al, 2011;Cazorla et al, 2017;Chan et al, 2018), and to support air quality studies (e.g., Schäfer et al, 2011;Geiß et al, 2017;Kotthaus and Grimmond, 2018b), whereas data assimilation in numerical weather forecast models is still limited to case studies (e.g., Geisinger, 2017;Warren et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Aerosol backscatter profiles from ceilometers: validation of water vapor correction in the framework of CeiLinEx2015

Wiegner¹,

Mattis²,

Pattantyús-Ábrahám³

et al. 2018

Preprint

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Abstract. With the rapidly growing number of automated single-wavelength backscatter lidars (ceilometers) their potential benefit for aerosol remote sensing received considerable scientific attention. When studying the accuracy of retrieved particle backscatter coefficients it must be considered that most of the ceilometers are influenced by water vapor absorption in the spectral range around 910 nm. In the literature methodologies to correct for this effect have been proposed, however, a validation was not yet performed. In the framework of the ceilometer intercomparison campaign CeiLinEx2015 in Lindenberg, Germany, hosted by the German Weather Service, it was possible to tackle this open issue. Ceilometers from Lufft (CHM15k and CHM15kx, operating at 1064 nm), from Vaisala (CL51 and CL31) and from Campbell Scientific (CS135), all operating at a wavelength of approximately 910 nm, were deployed together with a multi-wavelength research lidar (RALPH) that served as reference. In this paper the validation of the water vapor correction is performed by comparing ceilometer backscatter signals with measurements of the reference system extrapolated to the water vapor regime. One inherent problem of the validation is the spectral extrapolation of particle optical properties. For this purpose AERONET measurements and inversions of RALPH signals were used. Another issue is that the vertical range where validation is possible is limited to the upper part of the mixing layer due incomplete overlap, and the in general low signal to noise ratio and signal artefacts above that layer. Our intercomparisons show that the water vapor correction leads to a quite good agreement between the extrapolated reference signals and the measurements in the case of CL51 ceilometers at one or more wavelengths in the specified range of the laser diode's emission. This equivocation is due to the similar effective water vapor transmission at several wavelengths. In the case of CL31 and CS135 ceilometers the validation was not always successful. That suggests that error sources beyond the water vapor absorption might be dominant. For future applications we recommend to monitor the emitted wavelength and to provide dark measurements on a regular basis.

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Spatial variability of forward modelled attenuated backscatter in clear‐sky conditions over a megacity: Implications for observation network design

Warren

Charlton‐Perez

Lean

et al. 2022

Quart J Royal Meteoro Soc

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Sensors that measure the attenuated backscatter coefficient (e.g., automatic lidars and ceilometers [ALCs]) provide information on aerosols that can impact urban climate and human health. To design an observational network of ALC sensors for supporting data assimilation and to improve prediction of urban weather and air quality, a methodology is needed. In this study, spatio-temporal patterns of aerosol-attenuated backscatter coefficient are modelled using Met Office numerical weather prediction (NWP) models at two resolutions, 1.5 km (UKV) and 300 m (London Model [LM]), for 28 clear-sky days and nights.Initially, attenuated backscatter coefficient data are analysed using S-mode principal component analysis (PCA) with varimax rotation. Four to seven empirical orthogonal functions (EOFs) are produced for each model level, with common EOFs found across different heights (day and night) for both NWP models.EOFs relate strongly to orography, wind, and emissions source location, highlighting these as critical controls of attenuated backscatter coefficient spatial variability across the megacity. Urban-rural differences are largest when wind speeds are low and vertical boundary-layer dynamics can more effectively distribute near-surface aerosol emissions vertically. In several night-time EOFs, gravity-wave features are found for both NWP models. Increasing the horizontal resolution of native ancillaries (model input parameters) and improving the urban surface scheme in the LM may enhance the urban signal in the EOFs. PCA output, with agglomerative Ward cluster analysis (CA), minimises intra-group variance. The UKV and LM CA shape and size results are similar and strongly related to orography. PCA-CA is a simple, but adaptable methodology, allowing close alignment with observation network design goals. Here, CA is used with wind roses to suggest the optimised ALC deployment is one in the city to observeThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Atmospheric boundary‐layer characteristics from ceilometer measurements. Part 2: Application to London's urban boundary layer

Cited by 36 publications

References 47 publications

Atmospheric boundary‐layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds

Atmospheric boundary‐layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds

Aerosol backscatter profiles from ceilometers: validation of water vapor correction in the framework of CeiLinEx2015

Spatial variability of forward modelled attenuated backscatter in clear‐sky conditions over a megacity: Implications for observation network design

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