Precipitation structure during various phases the life cycle of precipitating cloud systems using geostationary satellite and space-based precipitation radar over Peru

Kumar, Shailendra; Castillo-Velarde, Carlos Del; Flores, José Isabel Cortés; Moya-Álvarez, Aldo S.; Castro, Daniel Martínez; Srivastava, Shweta; Silva, Yamina

doi:10.1080/15481603.2020.1843846

Cited by 16 publications

(9 citation statements)

References 99 publications

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“…The innovation in the form of an integrated approach using the strengths of both satellites and radar, based on proper intercalibration over the common regions of convective storm occurrence, is therefore highly desirable. Efforts have been made to merge observations from radars and satellites for the monitoring of convective storms (Mishra, 2018;Kumar et al, 2020). Recent studies have focused on the reconstruction of radar reflectivity for the identification of weather events by merging radar observations with satellite signatures for weather events (Duan et al, 2021;Yang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Weather, Vol. 78, no. 8, August 2023, pp. 210‐236

2023

Weather

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Section: Introductionmentioning

confidence: 99%

Weather, Vol. 78, no. 8, August 2023, pp. 210‐236

2023

Weather

View full text Add to dashboard Cite

show abstract

“…The innovation in the form of an integrated approach using the strengths of both satellites and radar, based on proper intercalibration over the common regions of convective storm occurrence, is therefore highly desirable. Efforts have been made to merge observations from radars and satellites for the monitoring of convective storms (Mishra, 2018; Kumar et al ., 2020). Recent studies have focused on the reconstruction of radar reflectivity for the identification of weather events by merging radar observations with satellite signatures for weather events (Duan et al ., 2021; Yang et al ., 2023).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing radar reflectivity by integrating GPM and INSAT‐3D observations for near real‐time monitoring of convective storms over India

Mishra

Tomar

Kumar

et al. 2023

Weather

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This study focuses on integrating dual‐frequency precipitation radar (DPR) observations on‐board the Global Precipitation Mission with the storm index from INSAT‐3D for the development of reconstructed radar reflectivity. Performance of the derived product was tested against the independent DPR observations. A correlation coefficient of 0.81 is reported between the reconstructed radar reflectivity and the DPR observations. Derived reconstructed reflectivity is shown to be an excellent product for the weather monitoring over the regions without radar coverage.

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“…Our previous study combined the droplet size distribution (DSD) from the GPM DPR and from the Himawari-8 geostationary satellite dataset for April-August 2016 and constructed a microphysical conceptual model of precipitating clouds at different life stages over eastern China . Kumar et al (2020) showed the vertical structure of the precipitating system at different cloud life stages in the mountains of the Andes. Research is limited by the large amount of satellite data and complex processing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Resilient dataset of rain clusters with life cycle evolution during April to June 2016–2020 over eastern Asia based on observations from the GPM DPR and Himawari-8 AHI

Zhang

Chen

et al. 2022

Earth Syst. Sci. Data

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Abstract. Our knowledge of the properties of precipitation and clouds over their life cycles has progressed with the rapid development of satellite observations. However, previous studies have focused on the life cycle evolution of the macroscale features of precipitation and clouds, whereas the evolution of the microphysical properties of precipitation and clouds over their life cycles is yet to be determined. One of the reasons for this lack of knowledge is the fact that there is no single dataset providing both the three-dimensional structure of precipitation and the relevant life cycle properties. We identified initial rain clusters (RCs) from the Global Precipitation Measurement (GPM) 2ADPR dataset and mesoscale convective systems (MCSs) from the Himawari-8 Advanced Himawari Image (AHI) gridded product. Based on the contours of the initial RCs and MCSs, we then carried out a series of resilient processes, including filtration, segmentation, and consolidation, to obtain the final RCs. The final RCs had a one-to-one correspondence with the relevant MCS. We extracted the RC area, central location, average radar reflectivity profile, average droplet size distribution profile, and other precipitation information from the contours of the final RCs and GPM 2ADPR dataset. The life cycle evolution of the MCS area, location, and cloud-top brightness temperature were retrieved from the corresponding MCSs and their tracks from Himawari-8 observations. The final dataset provides both three-dimensional precipitation information and life cycle information of precipitating clouds during April to June 2016–2020 over eastern Asia. This dataset facilitates studies of the life cycle evolution of precipitation and provides a good foundation for convection parameterizations in precipitation simulations. The dataset used in this paper is freely available at https://doi.org/10.5281/zenodo.6198716 (Zhang et al., 2022).

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Precipitation structure during various phases the life cycle of precipitating cloud systems using geostationary satellite and space-based precipitation radar over Peru

Cited by 16 publications

References 99 publications

Weather, Vol. 78, no. 8, August 2023, pp. 210‐236

Weather, Vol. 78, no. 8, August 2023, pp. 210‐236

Reconstructing radar reflectivity by integrating GPM and INSAT‐3D observations for near real‐time monitoring of convective storms over India

Resilient dataset of rain clusters with life cycle evolution during April to June 2016–2020 over eastern Asia based on observations from the GPM DPR and Himawari-8 AHI

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