Statistical investigation of cloud base height time evolution

Kitova, N.; Mikhalev, M. A.; Ausloos, Marcel

doi:10.1117/12.519500

Cited by 2 publications

(2 citation statements)

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“…In other words, the error bars should become negligible or irrelevant for the discussion purpose. As in other papers on multifractals [60][61][62] or critical exponent search [63,64] by the authors and co-workers, great care has thus been taken such that the here below presented data is reliable both from physics and statistics criteria. No need to say that it takes much time to do so and all steps are not recorded.…”

Section: A Multifractal Analysismentioning

confidence: 93%

Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series

Ausloos¹

2012

Phys. Rev. E

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A nonlinear dynamics approach can be used in order to quantify complexity in written texts. As a first step, a one-dimensional system is examined : two written texts by one author (Lewis Carroll) are considered, together with one translation, into an artificial language, i.e. Esperanto are mapped into time series. Their corresponding shuffled versions are used for obtaining a "base line". Two different one-dimensional time series are used here: (i) one based on word lengths (LTS), (ii) the other on word frequencies (FTS). It is shown that the generalized Hurst exponent h(q) and the derived f (α) curves of the original and translated texts show marked differences. The original "texts" are far from giving a parabolic f (α) function, -in contrast to the shuffled texts. Moreover, the Esperanto text has more extreme values. This suggests cascade model-like, with multiscale time asymmetric features as finally written texts. A discussion of the difference and complementarity of mapping into a LTS or FTS is presented. The FTS f (α) curves are more opened than the LTS ones

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Section: A Multifractal Analysismentioning

confidence: 93%

Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series

Ausloos¹

2012

Phys. Rev. E

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“…The cloud boundary in this study mainly refers to the cloud bottom and top boundaries. Multilayer clouds also include boundary information of intermediate discontinuous clouds (Zhou et al, 2019;Varikoden et al, 2011;Li et al, 2013;Ward and Merceret 2004;Zhang et al, 2018;Kuji, 2013;Kitova et al, 2003;Cao et al, 2021). With the development of remote sensing detection technology, Ka-band millimetre-wave cloud radar (MMCR) (Görsdorf et al, 2015;Kollias et al, 2007a, b) and lidar (Apituley et al, 2000;Protat at et al, 2011;Motty et al, 2018;Cordoba-Jabonero et al, 2017) have become effective instruments for cloud boundary detection.…”

Section: Introductionmentioning

confidence: 99%

Detection and analysis of cloud boundary in Xi'an, China, employing 35 GHz cloud radar aided by 1064 nm lidar

Yuan

Liu

et al. 2022

Atmos. Meas. Tech.

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Abstract. Lidar at 1064 nm and Ka-band millimetre-wave cloud radar (MMCR) are powerful tools for detecting the height distribution of cloud boundaries and can monitor the entire life cycle of cloud layers. In this study, lidar and MMCR are employed to jointly detect cloud boundaries under different conditions. By enhancing the echo signal of lidar at 1064 nm and combining its signal-to-noise ratio (SNR), the cloud signal can be accurately extracted from the aerosol signals and background noise. The interference signal is eliminated from Doppler spectra of the MMCR by using the noise ratio of the smallest measurable cloud signal (SNRmin⁡) and the spectral point continuous threshold (Nts). Moreover, the quality control of the reflectivity factor of MMCR obtained by the inversion is conducted, which improves the detection accuracy of the cloud signal. We analysed three typical cases studies; case one presents two interesting phenomena: (a) at 19:00–20:00 CST (China standard time), the ice crystal particles at the cloud top boundary are too small to be detected by MMCR, but they are well detected by lidar. (b) At 19:00–00:00 CST, the cirrus cloud changes to altostratus where the cloud particles eventually grow into large sizes, producing precipitation. Further, MMCR has more advantages than lidar in detection of the cloud top boundary within this period. Considering the advantages of the two devices, the change characteristics of the cloud boundary in Xi'an from December 2020 to November 2021 were analysed, with MMCR detection data as the main data and lidar data as the assistant data. The seasonal variation characteristics of clouds show that, in most cases, high clouds often occur in summer and autumn, and the low clouds are usually in winter. The normalized cloud cover shows that the maximum and minimum cloud cover occur in summer and winter, respectively. Furthermore, the cloud boundary frequency distribution results for the whole of the observation period show that the cloud bottom boundary below 1.5 km is more than 1 %, the frequency within the height range of 3.06–3.6 km is approximately 0.38 %, and the frequency above 8 km is less than 0.2 %. The cloud top boundary frequency distribution exhibits the characteristics of a bimodal distribution. The first narrow peak lies at approximately 1.0–3.1 km, and the second peak appears at 6.4–9.8 km.

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Statistical investigation of cloud base height time evolution

Cited by 2 publications

References 0 publications

Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series

Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series

Detection and analysis of cloud boundary in Xi'an, China, employing 35 GHz cloud radar aided by 1064 nm lidar

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