Precipitation Clouds Delineation Scheme in Tropical Cyclones and Its Validation Using Precipitation and Cloud Parameter Datasets from TRMM

Chen, FengJiao; Sheng, Shaoxue; Bao, ZhengQing; Hong, Wen; Hua, LianSheng; Paul, Ngarukiyimana Jean; Fu, Yunfei

doi:10.1175/jamc-d-17-0157.1

Cited by 9 publications

(7 citation statements)

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“…It varies from 0.4 mm h −1 to 12 mm h −1 , but a proportion of about 10% of the EL-WPC (RR < 0.65 mm h −1 ) and EH-WPC (RR > 4.25 mm h −1 ) makes up the total WPC in the tropical Pacific Ocean (20° S-20° N, 120° E-70° W). Our work reported here is mainly based on the merged data of cloud parameters retrieved from merged datasets [43,[54][55][56][57]. The cloud parameters (Re and Tau) of WPC are retrieved simultaneously using the bispectral reflectance (BSR) method [58,59], which has been adapted by numerous satellite missions.…”

Section: Methodsmentioning

confidence: 99%

Cloud-Precipitation Parameters and Radiative Forcing of Warm Precipitating Cloud over the Tropical Pacific Ocean Based on TRMM Datasets and Radiative Transfer Model

Fang

Xian

2018

Atmosphere

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An approach is proposed for combining observations from the Precipitation Radar (PR) and the Visible and Infrared Scanner (VIRS) onboard the TRMM (Topical Rainfall Measuring Mission) satellite to investigate the climatology of warm precipitating cloud (WPC) microphysical properties, such as cloud effective radius (Re), cloud optical depth (Tau), and liquid water path (LWP) in the tropical Pacific Ocean (20 • S-20 • N) from 1998 to 2012. The WPCs are captured by VIRS/PR and categorized into two extreme (light and heavy) rain rate types (EL-WPC, EH-WPC). Their radiative effects are also simulated by the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) radiative transfer model. The results indicate that total, EL-WPC and EH-WPC reach their highest occurrence frequencies of 22%, 1.6% and 2.0% in the Northwest Pacific, Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ), respectively. Most of the EL-WPC has higher ratio to total WPC in the Pacific warm pool with warmer sea-surface temperature (SST), while the higher ratio for EH-WPC is located in SPCZ associated with deep convection. WPC has an average Re of 15.6 µm, Tau of 20, and LWP of 200 g m −2. EL-WPC is a little larger average Re than EH-WPC, and larger Re is distributed with higher echo top height (H). Moreover, for EH-WPC, the increased Re by the collision-coalescence process in lower H (<3.5 km) generates a stronger rain rate. In addition, although the H of EH-WPC decreases along the increased brightness temperature at 10.8 µm (BT 4), this is not obvious in EL-WPC possibly due to a certain echo height to generate a light precipitation. With an increased rain rate of WPC, Re becomes larger in EL-WPC and smaller in EH-WPC. EL-WPC induces a cooling of approximately −0.5 W m −2 for radiative forcing, which is −3.0 W m −2 less than the EH-WPC.

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Section: Methodsmentioning

confidence: 99%

Cloud-Precipitation Parameters and Radiative Forcing of Warm Precipitating Cloud over the Tropical Pacific Ocean Based on TRMM Datasets and Radiative Transfer Model

Fang

Xian

2018

Atmosphere

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“…If a cloud is high enough in altitude, thick enough to carry a large volume of water vapor, and contains ice particles in its upper cloud layers, the chance of producing precipitation increases. As an effective measurement of the potential for precipitation to form in the clouds, the liquid water path (LWP) can be calculated as the measurement of the vertically integrated liquid water content in clouds [34][35][36][37]:…”

Section: Spectral Parametersmentioning

confidence: 99%

Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images

Liu

et al. 2019

Remote Sensing

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Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters.

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“…The main aim of the TRMM is the effective observation of precipitation and energy exchange in tropical and subtropical regions. The unique instruments on board the satellite provide an excellent opportunity to study the 3D structure of precipitation (Nesbitt et al, 1999;Schumacher and Houze, 2003;Durden et al, 2003;Li and Fu, 2005;Liu and Zipser, 2009), whereas the visible and infrared scanner (VIRS) provides characteristics of cloud parameters near the cloud top (Liu and Fu, 2010;Fu, 2014;Yang et al, 2014;Chen and Fu, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…It is impossible to obtain simultaneous and comprehensive information using a single detection method or dataset as a result of the different ways of obtaining the cloud, precipitation and atmospheric parameters. The optimum choice to overcome this problem is to combine multiple remote sensing measurements and multi-source datasets (Fu et al, 2013;Chen and Fu, 2017). Hawkins et al (2008) combined multiple-source datasets, including satellite cloud imagery captured by cloud profile radar and stationary satellites, observations from experimental planes, and model simulations.…”

Section: Introductionmentioning

confidence: 99%

A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner

Liang

2021

Earth Syst. Sci. Data

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Abstract. Clouds and precipitation have vital roles in the global hydrological cycle and the radiation budget of the atmosphere–Earth system and are closely related to both the regional and the global climate. Changes in the status of the atmosphere inside clouds and precipitation systems are also important, but the use of multi-source datasets is hampered by their different spatial and temporal resolutions. We merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the multi-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and atmospheric parameters in the ERA5 reanalysis dataset. The merging of pixels between the precipitation parameters and multi-channel cloud-top radiance was shown to be reasonable. The 1B01-2A25 dataset of pixel-merged data (1B01-2A25-PMD) contains cloud parameters for each PR pixel. The 1B01-2A25 gridded dataset (1B01-2A25-GD) was merged spatially with the ERA5 reanalysis data. The statistical results indicate that gridding has no unacceptable influence on the parameters in 1B01-2A25-PMD. In one orbit, the difference in the mean value of the near-surface rain rate and the signals measured by the VIRS was no more than 0.87 and the standard deviation was no more than 2.38. The 1B01-2A25-GD and ERA5 datasets were spatiotemporally collocated to establish the merged 1B01-2A25 gridded dataset (M-1B01-2A25-GD). Three case studies of typical cloud and precipitation events were analyzed to illustrate the practical use of M-1B01-2A25-GD. This new merged gridded dataset can be used to study clouds and precipitation systems and provides a perfect opportunity for multi-source data analysis and model simulations. The data which were used in this paper are freely available at https://doi.org/10.5281/zenodo.4458868 (Sun and Fu, 2021).

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Precipitation Clouds Delineation Scheme in Tropical Cyclones and Its Validation Using Precipitation and Cloud Parameter Datasets from TRMM

Cited by 9 publications

References 45 publications

Cloud-Precipitation Parameters and Radiative Forcing of Warm Precipitating Cloud over the Tropical Pacific Ocean Based on TRMM Datasets and Radiative Transfer Model

Cloud-Precipitation Parameters and Radiative Forcing of Warm Precipitating Cloud over the Tropical Pacific Ocean Based on TRMM Datasets and Radiative Transfer Model

Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images

A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner

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