Improved Hydrometeor Detection Method: An Application to CloudSat

Hu, Xiaobin; Ge, Jinming; Li, Yanrong; Marchand, Roger; Huang, Jianping; Fu, Qiang

doi:10.1029/2019ea000900

Cited by 7 publications

(5 citation statements)

References 65 publications

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“…More recently, R05 data has been launched, where there is an overall decrease in CF mostly in the cumulus region (Cesana et al, 2019). Since these reported changes are not very significant (Cesana et al, 2019), or has some trade-off with loss of weak signal (Hu et al, 2020), we decided not to update the version due to computational and temporal constraints.…”

Section: Discussionmentioning

confidence: 99%

Comparative assessment of a near‐global view of individual cloud types from space‐borne active and passive sensors and ground‐based observations

Sarkar

Dey

Ganguly

et al. 2022

Intl Journal of Climatology

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The World Meteorological Organizations' International Cloud Atlas recognizes 10 basic cloud genera for classifying clouds. Many of these have been used for over 200 years and are based on cloud appearance and base altitude as seen from surface. Over the satellite era, several missions and programs provide public products that classify clouds into these cloud genera. Here, we provide the first comparison of three such satellite climatologies of cloud genera with surface observations. Specifically, we analyse 10 years (2007–2016) of CloudSat, 4 years (2007–2010) of joint CloudSat‐CALIPSO, 13 years (2000–2012) of ISCCP‐H, and 11 years (1998–2008) of the EECRA data between 50°N and 50°S for eight cloud genera. Averaged over this latitude range, the total cloud amounts for these datasets range from 0.56 to 0.65, with Cumulus (Cu) ranging from 0.06 to 0.14; Stratus (St) from 0.14 to 0.38; Altostratus (As) from 0.05 to 0.13; Altocumulus (Ac) from 0.07 to 0.17; Nimbostratus (Ns) from 0.03 to 0.06; Cirrus (Ci) from 0.1 to 0.19; and Deep‐convective (Dc) from 0.01 to 0.04. The largest disagreement among the sensors is observed for Dc cloud with the coefficient of variation of 44%. On the other hand, the cloud datasets show the best agreement for Ci cloud with the coefficient of variation of 24.1%. Regionally, however, the level of agreement and disagreement can vary drastically. For example, in Indian summer monsoon region (ISM 60°–90°E, 10°–30°N) Ci cloud shows a variation of 28%, whereas the Dc cloud shows 16% variation, which is the opposite of their near‐global feature. The observed discrepancies in cloud genera are discussed in terms of observing characteristics, including instrument, methods, and sampling. Greater effort is still required to reduce discrepancies among these datasets, and the assessment provided here can act as a guide for their use in climate studies.

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Section: Discussionmentioning

confidence: 99%

Comparative assessment of a near‐global view of individual cloud types from space‐borne active and passive sensors and ground‐based observations

Sarkar

Dey

Ganguly

et al. 2022

Intl Journal of Climatology

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“…probability estimate, we use the neighboring range gates to provide information to help make the final decision. A spatial filter with five range bins in vertical and five range bins in the horizontal, which is centered at each undecided classification bin, is employed here (Marchand et al, 2008;Hu et al, 2020). Following Ge et al (2017), if the number of cloud range bins in the box is less than 13, this range bin is considered to be clutter, otherwise it will be marked as a cloud bin.…”

Section: Applying Low Pass Spatial Filter To Undecided Maskmentioning

confidence: 99%

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

Hu¹,

Ge²,

Du³

et al. 2020

Preprint

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Abstract. Low-level clouds play a key role in the energy budget and hydrological cycle of the climate system. The long-term and accurate observation of low-level clouds is essential for understanding their climate effect and model constraints. Both ground-based and spaceborne millimeter-wavelength cloud radars can penetrate clouds but the detected low-level clouds are always contaminated by clutters, which needs to be removed. In this study, we develop an algorithm to accurately separate low-level clouds from clutters for ground-based cloud radar using multi-dimensional probability distribution functions along with the Bayesian method. The radar reflectivity, linear depolarization ratio, spectral width and their dependences on the time of the day, height and season are used as the discriminants. A low pass spatial filter is applied to the Bayesian undecided classification mask, considering the spatial correlation difference between clouds and clutters. The resulting feature mask shows a good agreement with lidar detection, which has a high probability of detection rate (98.45 %) and a low false alarm rate (0.37 %). This algorithm will be used to reliably detect low-level clouds at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site, to study their climate effect and the interaction with local abundant dust aerosol in semi-arid region.

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“…A Kaband zenith radar (KAZR) has been continuously running at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) since 2013 (Ge et al, 2018(Ge et al, , 2019Huang et al, 2008b) to investigate cloud properties over the site. SACOL is located in the downwind dust transport path about 2000 km to the east of the Taklimakan Desert (i.e., one of the most important global sources of atmospheric dust) (Ge et al, 2014;Huang et al, 2007;Jing Su et al, 2008). Low-level clouds in this semi-arid region with abound dust aerosols acting as cloud condensation nuclei may contain a larger number of small droplets (Givati and Rosenfeld, 2004;Huang et al, 2006), which may reflect more shortwave radiation, merge more slowly to fall as precipitation (Huang et al, 2014;Xue et al, 2008) and thus affect the regional energy budget and water cycle in specific ways.…”

Section: Introductionmentioning

confidence: 99%

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

et al. 2021

Atmos. Meas. Tech.

Self Cite

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Abstract. Low-level clouds play a key role in the energy budget and hydrological cycle of the climate system. The accurate long-term observation of low-level clouds is essential for understanding their climate effect and model constraints. Both ground-based and spaceborne millimeter-wavelength cloud radars can penetrate clouds but the detected low-level clouds are always contaminated by clutter, which needs to be removed. In this study, we develop an algorithm to accurately separate low-level clouds from clutter for ground-based cloud radar using multi-dimensional probability distribution functions along with the Bayesian method. The radar reflectivity, linear depolarization ratio, spectral width, and their dependence on the time of the day, height, and season are used as the discriminants. A low-pass spatial filter is applied to the Bayesian undecided classification mask by considering the spatial correlation difference between clouds and clutter. The final feature mask result has a good agreement with lidar detection, showing a high probability of detection rate (98.45 %) and a low false alarm rate (0.37 %). This algorithm will be used to reliably detect low-level clouds at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site for the study of their climate effect and the interaction with local abundant dust aerosol in semi-arid regions.

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Improved Hydrometeor Detection Method: An Application to CloudSat

Cited by 7 publications

References 65 publications

Comparative assessment of a near‐global view of individual cloud types from space‐borne active and passive sensors and ground‐based observations

Comparative assessment of a near‐global view of individual cloud types from space‐borne active and passive sensors and ground‐based observations

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

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