Understanding the Variability of Z-R Relationships Caused by Natural Variations in Raindrop Size Distributions (DSD): Implication of Drop Size and Number

Atmospheric and Climate Sciences

ISSN Print: 2160-0414
ISSN Online: 2160-0422
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"Understanding the Variability of Z-R Relationships Caused by Natural Variations in Raindrop Size Distributions (DSD): Implication of Drop Size and Number"
written by Abé D Ochou, Eric-Pascal Zahiri, Bakary Bamba, Manlandon Koffi,
published by Atmospheric and Climate Sciences, Vol.1 No.3, 2011

has been cited by the following article(s):

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[7]	Improving Radar Rainfall Estimation by Accounting for Microphysical Processes Using a Micro Rain Radar in West Africa
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[8]	Calibration of the reflectivity-rainfall rate (ZR) relationship using long-term radar reflectivity factor over the entire South Korea region in a Bayesian perspective
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[9]	Characteristics of Raindrop Size Distribution in Typhoon Nida (2016) before and after Landfall in Southern China from 2D Video Disdrometer Data
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[10]	Microphysical Origin of Raindrop Size Distributions During the Indian Monsoon
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[14]	One, No One, and One Hundred Thousand: The Paradigm of the Z–R Relationship
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[15]	Seasonal variability of raindrop size distributions characteristics regarding to climatic parameters over coastal area of West Africa
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[16]	Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations
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[17]	Discernment of near-oceanic precipitating clouds into convective or stratiform based on Z–R model over an Asian monsoon tropical site
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[20]	Analysis of Rain Types and Their Z–R Relationships at Different Locations in the High Andes of Southern Ecuador
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[24]	On the seasonal variability of raindrop size distribution and associated variations in reflectivity–Rainrate relations at Tirupati, a tropical station
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[25]	Radar rainfall estimation: consideration of input and structural uncertainty
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[26]	Consistency in Z-R Relationship Variability Regardless Precipitating Systems, Climatic Zones Observed from Two Types of Disdrometer
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[27]	Correcting bias in radar Z– R relationships due to uncertainty in point rain gauge networks
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[28]	Consistency in ZR Relationship Variability Regardless Precipitating Systems, Climatic Zones Observed from Two Types of Disdrometer
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[29]	Correcting bias in radar Z–R relationships due to uncertainty in point rain gauge networks
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[30]	The Adjustment of Radar Precipitation Estimation Based on the Kriging Method
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[1]	Unraveling the influence of varied airflow patterns on atmospheric instabilities, rain microphysics, and cloud features over Delhi, the capital city of India Advances in Space Research, 2024 DOI:10.1016/j.asr.2024.01.026

[2]	Unraveling the influence of varied airflow patterns on atmospheric instabilities, rain microphysics, and cloud features over Delhi, the capital city of India Advances in Space Research, 2024 DOI:10.1016/j.asr.2024.01.026

[3]	Modeling of Rain Drop Size Distribution in Association With Convective and Cloud Parameter Over a Tropical Location IEEE Transactions on Geoscience and Remote Sensing, 2023 DOI:10.1109/TGRS.2023.3248664

[4]	Modeling of Rain Drop Size Distribution in Association With Convective and Cloud Parameter Over a Tropical Location IEEE Transactions on Geoscience and Remote Sensing, 2023 DOI:10.1109/TGRS.2023.3248664

[5]	Modeling the Interdependence Structure between Rain and Radar Variables Using Copulas: Applications to Heavy Rainfall Estimation by Weather Radar Atmosphere, 2022 DOI:10.3390/atmos13081298

[6]	Microphysical Origin of Raindrop Size Distributions During the Indian Monsoon Geophysical Research Letters, 2021 DOI:10.1029/2021GL093581

[7]	Microphysical Origin of Raindrop Size Distributions During the Indian Monsoon Geophysical Research Letters, 2021 DOI:10.1029/2021GL093581

[8]	Microphysical Origin of Raindrop Size Distributions During the Indian Monsoon Geophysical Research Letters, 2021 DOI:10.1029/2021GL093581

[9]	Characteristics of Raindrop Size Distribution in Typhoon Nida (2016) before and after Landfall in Southern China from 2D Video Disdrometer Data Advances in Meteorology, 2021 DOI:10.1155/2021/9349738

[10]	Microphysical Origin of Raindrop Size Distributions During the Indian Monsoon Geophysical Research Letters, 2021 DOI:10.1029/2021GL093581

[11]	One, No One, and One Hundred Thousand: The Paradigm of the Z–R Relationship Journal of Hydrometeorology, 2020 DOI:10.1175/JHM-D-19-0177.1

[12]	Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations Atmosphere, 2019 DOI:10.3390/atmos10050251

[13]	Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations Atmosphere, 2019 DOI:10.3390/atmos10050251

[14]	Analysis of Rain Types and Their Z–R Relationships at Different Locations in the High Andes of Southern Ecuador Journal of Applied Meteorology and Climatology, 2017 DOI:10.1175/JAMC-D-17-0009.1

[15]	Correcting bias in radar Z – R relationships due to uncertainty in point rain gauge networks Journal of Hydrology, 2014 DOI:10.1016/j.jhydrol.2014.09.060

[16]	Consistency in Z-R Relationship Variability Regardless Precipitating Systems, Climatic Zones Observed from Two Types of Disdrometer Atmospheric and Climate Sciences, 2014 DOI:10.4236/acs.2014.45083

[17]	The Adjustment of Radar Precipitation Estimation Based on the Kriging Method Journal of the Korean earth science society, 2013 DOI:10.5467/JKESS.2013.34.1.13

[18]	Development and testing of the Active Temperature, Ozone and Moisture Microwave Spectrometer (ATOMMS) cm and mm wavelength occultation instrument Atmospheric Measurement Techniques, 2012 DOI:10.5194/amt-5-439-2012

[19]	Development and testing of the Active Temperature, Ozone and Moisture Microwave Spectrometer (ATOMMS) cm and mm wavelength occultation instrument Atmospheric Measurement Techniques, 2012 DOI:10.5194/amt-5-439-2012

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