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

Hu, Xiaobin; Ge, Jinming; Du, Jiajing; Li, Qinghao; Huang, Jianping; Fu, Qiang

doi:10.5194/amt-14-1743-2021

Cited by 7 publications

(4 citation statements)

References 85 publications

(85 reference statements)

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“…There are also uncertainties in the cloud property retrievals. In addition, the surface-based radar and lidar observations are usually reasonable only at heights greater than 100 -150 m above ground (Griesche et al 2021, Hu et al 2021). However, these profiles of cloud properties serve as a reasonable data set of possible Arctic cloud scenarios covering a whole year, which serve as a reasonable data set to conduct this study.…”

Section: Uncertainty In the Resultsmentioning

confidence: 99%

Impacts of Active Satellite Sensors' Low-level Cloud Detection Limitations on Cloud Radiative Forcing

Liu

2022

Preprint

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Abstract. Previous studies revealed that satellites sensors with the best detection capability identify 25–40 % and 0–25 % fewer clouds below 0.5 km and between 0.5–1.0 km, respectively, over the Arctic land. Quantifying the impacts of cloud detection limitations on the radiation flux are critical especially over the Arctic Ocean considering the dramatic changes in Arctic sea ice. In this study, the proxies of the space-based radar, CloudSat, and lidar, CALIPSO, cloud masks are derived based on simulated radar reflectivity and cloud optical thickness using retrieved cloud properties from surface-based radar and lidar during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment. Limitations in low-level cloud detection by the space-based active sensors, and the impact of these limitations on the radiation fluxes at the surface and the top of atmosphere (TOA), are estimated. The results show that the combined CloudSat and CALIPSO product generally detects all clouds above 1 km, while detecting 25 % (9 %) fewer in absolute values below 600 m (600 m to 1 km) than surface observations. These detection limitations lead to uncertainties in the monthly mean cloud radiative forcing (CRF), with maximum absolute monthly mean values of 2.7 Wm−2 and 4.0 Wm−2 at the surface and TOA, respectively. The uncertainties for individual cases are larger – up to 30 Wm−2. Cloud information from only the CALIPSO or the CloudSat leads to larger cloud detection differences compared to the surface observations, and larger CRF uncertainties with absolute monthly means larger than 10.0 Wm−2. These uncertainties need to be considered when radiation flux products from CloudSat and CALIPSO are used in climate and weather studies.

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Section: Uncertainty In the Resultsmentioning

confidence: 99%

Impacts of Active Satellite Sensors' Low-level Cloud Detection Limitations on Cloud Radiative Forcing

Liu

2022

Preprint

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“…In this study, we primarily utilize the cloud mask product collected from August 2013 to October 2019. It is derived from a novel signal detection algorithm with a bilateral filter scheme and a clutter discrimination method based on a multi-dimensional probability distribution function, which have been proven to have high accuracy in cloud identification [26][27][28].…”

Section: Ground-based Cloud Radar Observationsmentioning

confidence: 99%

Cloud Overlap Features from Multi-Year Cloud Radar Observations at the SACOL Site and Comparison with Satellites

Yang,

Li,

et al. 2024

Remote Sensing

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Cloud overlap, referring to distinct cloud layers occurring over the same location, is essential for accurately calculating the atmospheric radiation transfer in numerical models, which, in turn, enhances our ability to predict future climate change. In this study, we analyze multi-year cloud overlap properties observed from the Ka-band Zenith Radar (KAZR) at the Semi-Arid Climate and Environment Observatory of Lanzhou University’s (SACOL) site. We conduct a series of statistical analyses and determine the suitable temporal-spatial resolution of 1 h with a 360 m scale for data analysis. Our findings show that the cloud overlap parameter and total cloud fraction are maximized during winter-spring and minimized in summer-autumn, and the extreme value of decorrelation length usually lags one or two seasons. Additionally, we find the cloud overlap assumption has distinct effects on the cloud fraction bias for different cloud types. The random overlap leads to the minimum bias of the cloud fraction for Low-Middle-High (LMH), Low-Middle (LM), and Middle-High (MH) clouds, while the maximum overlap is for Low (L), Middle (M), and High (H) clouds. We also incorporate observations from satellite-based active sensors, including CloudSat, Cloud-Aerosol Lidar, and Infrared Pathfinder Satellite Observations (CALIPSO), to refine our study area and specific cases by considering the total cloud fraction and sample size from different datasets. Our analysis reveals that the representativeness of random overlap strengthens and then weakens with increasing layer separations. The decorrelation length varies with the KAZR, CloudSat-CALIPSO, CloudSat, and CALIPSO datasets, measuring 1.43 km, 2.18 km, 2.58 km, and 1.11 km, respectively. For H, MH, and LMH clouds, the average cloud overlap parameter from CloudSat-CALIPSO aligns closely with KAZR. For L, M, and LM clouds, when the level separation of cloud layer pairs are less than 1 km, the representative assumption obtained from different datasets are maximum overlap.

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“…Corrections may be made routinely (e.g., [46]) or during post analysis [49] to ensure product consistency and accuracy. Additional corrections take into account certain factors, such as the beam elevation (which leads to underestimating or missing shallow precipitation systems) [50], removal of ground clutter [51][52][53], bright band detection and removal [54] and calibration against surface gauge data [55].…”

Section: Ground-based Measurementsmentioning

confidence: 99%

The State of Precipitation Measurements at Mid-to-High Latitudes

Milani,

Kidd

2023

Atmosphere

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The measurement of global precipitation is important for quantifying and understanding the Earth’s systems. While gauges form the basis of conventional measurements, global measurements are only truly possible using satellite observations. Over the last 50–60 years, satellite systems have evolved to provide a comprehensive suite of observing systems, including many sensors that are capable of precipitation retrievals. While much progress has been made in developing and implementing precipitation retrieval schemes, many techniques have concentrated upon retrievals over regions with well-defined precipitation systems, such as the tropics. At higher latitudes, such retrieval schemes are less successful in providing accurate and consistent precipitation estimates, especially due to the large diversity of precipitation regimes. Furthermore, the increasing dominance of snowfall at higher latitudes imposes a number of challenges that require further, urgent work. This paper reviews the state of the current observations and retrieval schemes, highlighting the key factors that need to be addressed to improve the estimation and measurement of precipitation at mid-to-high latitudes.

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

Cited by 7 publications

References 85 publications

Impacts of Active Satellite Sensors' Low-level Cloud Detection Limitations on Cloud Radiative Forcing

Impacts of Active Satellite Sensors' Low-level Cloud Detection Limitations on Cloud Radiative Forcing

Cloud Overlap Features from Multi-Year Cloud Radar Observations at the SACOL Site and Comparison with Satellites

The State of Precipitation Measurements at Mid-to-High Latitudes

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