Observational study of cloud base height and its frequency over a tropical station, Thiruvananthapuram, using a ceilometer

Varikoden, Hamza; Harikumar, R.; Vishnu, R.; Kumar, Vinod; Sampath, S.; Das, Sampa; Kumar, Gajendra

doi:10.1080/01431161.2010.542199

Cited by 29 publications

(11 citation statements)

References 25 publications

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“…Precipitation type, frequency and duration of rain events, DSD and diurnal variability are not similar during the winter, premonsoon, summer monsoon and postmonsoon seasons, especially in the tropics (Varikoden et al, 2011). One of the integral parameters of DSD, rain intensity gives the combined information of the quantity and how quickly water accumulates on the Earth's surface (Pagano & Sorooshian, 2006).…”

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confidence: 99%

Microphysical characteristics of rainfall during different seasons over a coastal tropical station using disdrometer

Sreekanth

Varikoden

Sukumar

et al. 2017

Hydrological Processes

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Drop size distribution (DSD) over the tropical region exhibit pronounced variations during different monsoon seasons. Measurements from an impact type Joss–Waldovgel disdrometer is used for characterization of drop size distribution and its integral parameters over a tropical coastal station (Thiruvananthapuram, 8.31°N, 76.54°E, 20 m asl). Rain events were identified during the winter, premonsoon, summer monsoon and postmonsoon seasons from 8 years, computed rain duration (min) and accumulated rain water (mm). Rain intensity (mm h−1), mean drop diameter (Dm, mm) and total number concentration of raindrops (NT, m−3) were calculated on each sampling interval and classified in to different bins. The different range bins of rain intensity and their relative contributions towards total rainfall are different for different seasons. Maximum events were reported on the R2 (heavy drizzle/light rain) type, but the contribution of rainfall (mm) is mainly registered on R4 (heavy rain) type. Similarly, the NT and Dm are also showing different characteristics during different monsoon seasons. Frequency of occurrence of Dm is higher in Dm2 (1–2 mm) followed by Dm1 (Dm < 1 mm) and then Dm3 (2–3 mm) with difference in magnitudes for different seasons. On analysing relative rainfall contribution from different mean diameter bins, it can be observed that Dm2 and Dm3 (1–3 mm) are the major contributors to the total rainfall. In the case of NT, both frequency and accumulated water are almost same or comparable for the different bins during all the seasons. The Dm and NT are positively related with different intensity bins. The lower rainfall intensity bins show higher duration during the summer monsoon season and lower duration during the premonsoon season, the higher intensity range bins show lower duration for the premonsoon season and higher duration for the postmonsoon season.

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confidence: 99%

Microphysical characteristics of rainfall during different seasons over a coastal tropical station using disdrometer

Sreekanth

Varikoden

Sukumar

et al. 2017

Hydrological Processes

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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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“…The associated monitoring tools include ground-based lidar, radiosonde, ceilometer, all-sky imager, and digital camera. The monitoring results are widely used in cloud, weather, and climate research (e.g., Mahesh 2005;Feister et al 2010;Janeiro et al 2010;Varikoden et al 2011;Zhang 2020).…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Estimating Low Cloud-Base Height in Southwest China

Wang

Zhou

Yang

et al. 2022

Journal of Applied Meteorology and Climatology

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The prevalence of low clouds significantly affects flight safety in Southwest China. However, relevant cloud parameters, especially Low Cloud Base Height(LCBH), lack accurate forecasts. Based on the hourly atmospheric vertical profiles of ERA5 from 2008 to 2019, we developed a new algorithm (RHs-CCL) for estimating LCBH by combining relative humidity threshold methods with Convective Condensation Level(CCL). To evaluate the performance of RHs-CCL, we use it to estimate the hourly LCBH of airports in Southwest China and compare the results with those based on the ground-based observations and the ERA5 CBH data. Using the observations as a ground truth, we compare the RHs-CCL algorithm with several existing algorithms with the following findings: (1) The correlation coefficient between RHs-CCL and observations reaches 0.5 on average and the error of RHs-CCL is smaller than those of existing algorithms with the minimum mean absolute error and root mean square error at the four airports studies can reach 243m and 321m,(2) The bias score of RHs-CCL is 0.97 on average and low clouds classification utilizing RHs-CCL attains the highest accuracy up to 86%, (3) The errors of ERA5 CBH are the largest compared with the others, (4) By implementing convective cloud occurrence condition and CCL, RHs-CCL has better applicability in regions of enhanced convective activity. These results suggest the potential of RHs-CCL as an algorithm moving forward for improvement of the LCBH estimates based upon high-resolution reanalysis products and for better predictions of the LCBH utilizing outputs from numerical weather prediction models.

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Observational study of cloud base height and its frequency over a tropical station, Thiruvananthapuram, using a ceilometer

Cited by 29 publications

References 25 publications

Microphysical characteristics of rainfall during different seasons over a coastal tropical station using disdrometer

Microphysical characteristics of rainfall during different seasons over a coastal tropical station using disdrometer

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

A New Algorithm for Estimating Low Cloud-Base Height in Southwest China

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