Ice cloud detection from AMSU-A, MHS, and HIRS satellite instruments inferred by cloud profiling radar

Islam, Tanvir; Srivastava, Prashant K.; Dai, Qiang; Gupta, Manika

doi:10.1080/2150704x.2014.990643

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…A similar reasoning may be applied to ice clouds: IR radiation is greatly sensitive to low cloud ice water path (CIWP) while MW radiation is more sensitive to thicker ice clouds with higher CIWP (Sun and Weng, 2012). Therefore, a proper combination of IR and MW measurements would be able to provide useful information over the whole range of cloud water paths and also help in determining the cloud vertical structure, particularly in case of overlapping cloud layers, as suggested by several investigators (e.g., Chevallier et al, 2002;Huang et al 2006;Romano et al 2007;Islam et al 2014).…”

Section: Rationalementioning

confidence: 99%

“…However, such a synergy is still under-explored for the retrieval and investigation of cloud microphysics. Despite some previous investigations have demonstrated the feasibility and benefits of MW-IR synergy (Chevallier et al 2002;Huang et al 2006;Romano et al 2007;Islam et al 2014;Holl et al 2014;Maddy and Boukabara 2021), several key parameters for cloud physical processes are not part of the operational level 2 products available from EPS sensors, such as the Advanced Microwave Sounding (AMSU), the Microwave Humidity Sounder (MHS), and the Infrared Atmospheric Sounding Interferometer (IASI). Among the key parameters are the cloud particle effective radius (Re), the profiles of cloud liquid water content (CLWC) and cloud ice water content (CIWC), and their path-integrated cloud liquid water path (CLWP) and cloud ice water path (CIWP).…”

Section: Current Statusmentioning

confidence: 99%

“…Previous studies have demonstrated MW-IR synergy for cloud properties retrievals using a variety of methods, including variational (Chevallier et al 2002), physical (Huang et al 2006;Romano et al 2007), and machine learning (Islam et al 2014;Holl et al 2014;Marke et al 2016;Maddy and Boukabara 2021) approaches. Machine learning methods are increasingly applied to data inversion because of their ability to find non-linear statistical relationships between target variables and input variables, such those arising in satellite remote sensing of geophysical parameters through spectral radiances (Holloway and Mengersen 2018;Belgiu and Drăguţ 2016;Izquierdo-Verdiguier and Zurita-Milla 2020).…”

Section: Algorithm Developmentmentioning

confidence: 99%

See 2 more Smart Citations

Spectrum Synergy for Investigating Cloud Microphysics

Cimini

Serio

Masiello

et al. 2023

Bulletin of the American Meteorological Society

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Observations from spaceborne microwave (MW) and infrared (IR) passive sensors are the backbone of current satellite meteorology, essential for data assimilation into modern numerical weather prediction and for climate benchmarking. While MW and IR observations from space offer complementary features with respect to cloud properties, their synergy for cloud investigation is currently under-explored, despite the presence of both MW and IR sensors on operational meteorological satellites such as the European Polar Satellite (EPS) Metop series. As such, several key cloud microphysical properties are not part of the operational products available from EPS Metop sensors. In addition, the EPS second generation (EPS-SG) series, scheduled for launch starting from 2024 onwards, will carry sensors such as the Microwave Sounder (MWS) and IASI next generation (IASI-NG), enhancing spatial and spectral resolutions and thus capacity to retrieve cloud properties. This article presents the Combined MWS and IASI-NG Soundings for Cloud Properties (ComboCloud) project, funded by EUMETSAT with the overall objective to specify, prototype and validate algorithms for the retrieval of cloud microphysical properties (e.g., water content and drop effective radius) from the synergy of passive MW and IR observations. The article presents the synergy rationale, the algorithm design, and the results obtained exploiting simulated observations from EPS and EPS-SG sensors, quantifying the benefits to be expected from the MW-IR synergy and the new generation sensors.

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Section: Rationalementioning

confidence: 99%

Section: Current Statusmentioning

confidence: 99%

Section: Algorithm Developmentmentioning

confidence: 99%

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Spectrum Synergy for Investigating Cloud Microphysics

Cimini

Serio

Masiello

et al. 2023

Bulletin of the American Meteorological Society

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“…Gradient Boosting Decision Tree (GBDT) algorithm achieves the state-of-the-art performance on tabular data (data size less than 1,000, more than 10,000, or even more than 1 million) instead of image data, and the ensemble properties of GBDT may avoid the overfitting. Radar measurements are widely recognized as relatively accurate labels in ML-based cloud classifier models [17]. Compared with the cloud radar, ground-based weather radar has wider temporal and spatial coverage, thus having obvious superiority in collocating with polar-orbiting satellite measurements.…”

Section: Cdl: a Cloud Detection Algorithm Over Land For Mwhs-2 Based mentioning

confidence: 99%

CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree

Liu

Yin

Chu

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper presents a stand-alone cloud detection algorithm over land (CDL) for Microwave Humidity Sounder-2 (MWHS-2), which is characterized by the first operational satellite sensor measuring 118.75 GHz. The CDL is based on the advanced machine learning (ML) algorithm Gradient Boosting Decision Tree (GBDT), which achieves the state-of-the-art performance on tabular data, with high accuracy, fast training speed, great generalization ability, and weight factor ranking of predictors (or features). Given that the new generation weather radar of China (CINRAD) provides improved cloud information with extensive temporal-spatial coverage, the observations from CINRAD are used to train the algorithm in this study. There are four groups of radiometric information employed to evaluate the CDL: all frequency ranges from MWHS-2 (all-algorithm), the humidity channels near 183.31 GHz (hum-algorithm), the temperature channels near 118.75 GHz (tem-algorithm), and the window channels at 89 and 150 GHz (win-algorithm). It is revealed that the tem-algorithm (around 118.75 GHz) has a superior performance for CDL along with the optimal values of most evaluation metrics. Although the all-algorithm uses all available frequencies, it shows inferior ability for CDL. Followed are the win-algorithm and humalgorithm, and the win-algorithm performs better. The analysis also indicates that the latitude, zenith angle, and the azimuth are the top ranking features for all four algorithms. The presented algorithm CDL can be applied in the quality control processes of assimilating microwave (MW) radiances or in the retrieval of atmospheric and surface parameters for cloud filtering.

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“…MODIS-ST which uses multi-spectral IR/NIR/VIS and time-space variances for cloud detection [13,14], and IR sounders e.g. HIRS-NOAA a high resolution infrared radiation sounder onboard the NOAA-19 satellite [15].…”

Section: Introductionmentioning

confidence: 99%

22 Years of Near-zero Cloud Cover Variability across Varying Geographical Landscapes of Southern Africa: A Surprising Anomaly

Nkolola

2021

IJECC

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In physical geography, clouds are known to dictate global energy budgets and to have crucial ripple effects on other climatic variables such as diurnal range of temperature (DTR), a key indicator of climate change. Here, a 115-year state-of-the-art station based gridded dataset from the Climatic Research Unit (CRU) is interrogated to understand the evolution of cloud cover across southern Africa for the period 1901 - 2016. Results show near-constant variability from 1901 – 1922. It was therefore hypothesised that the observed near-constant variability would result in a similar pattern for some climatic variables such as DTR as the opposite would bring into question our current knowledge of geographical mechanisms underlying DTR control across the region. Further analyses showed little to no association between cloud cover and other climatological variables (including DTR) for the period 1901 – 1922 but strong and significant association from 1923 – 2016. This is the first observational evidence of near-constant cloud cover variability; it is surprising, and counterintuitive. This constant variation can be attributed to limited ground-based observations that went into the construction of the CRU gridded dataset during the 1901 – 1922 period and therefore, caution needs to be exercised by studies that need to use the data for the said period. This is a crucial area of scientific enquiry, and a lack of caution can lead to misleading conclusions on cloud cover evolution and how that relates to climate change.

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Ice cloud detection from AMSU-A, MHS, and HIRS satellite instruments inferred by cloud profiling radar

Cited by 5 publications

References 29 publications

Spectrum Synergy for Investigating Cloud Microphysics

Spectrum Synergy for Investigating Cloud Microphysics

CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree

22 Years of Near-zero Cloud Cover Variability across Varying Geographical Landscapes of Southern Africa: A Surprising Anomaly

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