Abstract
The long-term characteristics of four hydrometeor species (cloud water, cloud ice, rain, and snow) in precipitating clouds over eastern China (divided into South China, Jianghuai, and North China) and their relationships with surface rainfall are first investigated using the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) hourly dataset from May to August during 1979–2020. The results show that the cloud water path decreases significantly from south to north as a result of the large-scale circulation and water vapor distribution, with the maximum value of 180 g m−2 in South China and only one-half of that value in North China. The slope in linear relationship between rainwater path and precipitation intensity is at the maximum (5.68 h−1) in South China, implying the highest conversion rate from rainwater to precipitation in this region. When the precipitation rate exceeds 15 mm h−1, the ice-phase hydrometeor contents in South China become the largest among the three regions, indicating that the cold-rain process is crucial to heavy rainfall. The moisture-related processes play a dominant role in the precipitation intensity. Although the contribution of hydrometeor advection to precipitation is generally between −5% and 5%, we found that it can jointly modulate the location of heavy rainfall. In addition, the peaks of cloud water path commonly appear 2–3 h ahead of precipitation, whereas the peaks of ice-phase particles occur 2 and 1 h behind the afternoon precipitation onset in South China and Jianghuai, respectively, which is mainly attributed to the different upward velocity and water vapor convergence in the mid–upper troposphere.
Significance Statement
Reanalysis data and satellite retrievals have been widely used in investigating cloud water and cloud ice in nonprecipitating clouds. However, studies on long-term characteristics of precipitating hydrometeors in precipitating clouds, which are directly connected and crucial to surface rainfall, are still very limited to date because of limitations in observations of precipitating clouds. In this study, the latest ERA5 reanalysis hourly dataset is first used to quantitatively explore the climatological characteristics of four hydrometeors (cloud water, cloud ice, rain, and snow) in precipitating clouds as well as their relationships with precipitation intensity over eastern China from 1979 to 2020. The results advance our understanding of precipitation mechanisms from the perspective of hydrometeor climatology.
Abstract
The long-term characteristics of four hydrometeor species (cloud water, cloud ice, rain, and snow) in precipitating clouds over eastern China (divided into South China, Jianghuai, and North China) and their relationships with surface rainfall are first investigated using the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) hourly dataset from May to August during 1979–2020. The results show that the cloud water path decreases significantly from south to north as a result of the large-scale circulation and water vapor distribution, with the maximum value of 180 g m−2 in South China and only one-half of that value in North China. The slope in linear relationship between rainwater path and precipitation intensity is at the maximum (5.68 h−1) in South China, implying the highest conversion rate from rainwater to precipitation in this region. When the precipitation rate exceeds 15 mm h−1, the ice-phase hydrometeor contents in South China become the largest among the three regions, indicating that the cold-rain process is crucial to heavy rainfall. The moisture-related processes play a dominant role in the precipitation intensity. Although the contribution of hydrometeor advection to precipitation is generally between −5% and 5%, we found that it can jointly modulate the location of heavy rainfall. In addition, the peaks of cloud water path commonly appear 2–3 h ahead of precipitation, whereas the peaks of ice-phase particles occur 2 and 1 h behind the afternoon precipitation onset in South China and Jianghuai, respectively, which is mainly attributed to the different upward velocity and water vapor convergence in the mid–upper troposphere.
Significance Statement
Reanalysis data and satellite retrievals have been widely used in investigating cloud water and cloud ice in nonprecipitating clouds. However, studies on long-term characteristics of precipitating hydrometeors in precipitating clouds, which are directly connected and crucial to surface rainfall, are still very limited to date because of limitations in observations of precipitating clouds. In this study, the latest ERA5 reanalysis hourly dataset is first used to quantitatively explore the climatological characteristics of four hydrometeors (cloud water, cloud ice, rain, and snow) in precipitating clouds as well as their relationships with precipitation intensity over eastern China from 1979 to 2020. The results advance our understanding of precipitation mechanisms from the perspective of hydrometeor climatology.
Abstract
Globally available environmental observations (EOs), specifically from satellites and coupled Earth system models, represent some of the largest datasets of the digital age. As the volume of global EOs continues to grow, so does the potential of these data to help Earth scientists discover trends and patterns in Earth systems at large spatial scales. To leverage global EOs for scientific insight, Earth scientists need targeted and accessible exposure to skills in reproducible scientific computing and spatiotemporal data science, and to be empowered to apply their domain understanding to interpret data-driven models for knowledge discovery. The Generalizable, Reproducible, Robust, and Interpreted Environmental (GRRIEn) analysis framework was developed to prepare Earth scientists with an introductory statistics background and limited/no understanding of programming and computational methods to use global EOs to successfully generalize insights from local/regional field measurements across unsampled times and locations. GRRIEn analysis is generalizable, meaning results from a sample are translated to landscape scales by combining direct environmental measurements with global EOs using supervised machine learning; robust, meaning that the model shows good performance on data with scale-dependent feature and observation dependence; reproducible, based on a standard repository structure so that other scientists can quickly and easily replicate the analysis with a few computational tools; and interpreted, meaning that Earth scientists apply domain expertise to ensure that model parameters reflect a physically plausible diagnosis of the environmental system. This tutorial presents standard steps for achieving GRRIEn analysis by combining conventions of rigor in traditional experimental design with the open-science movement.
Significance Statement
Earth science researchers in the digital age are often tasked with pioneering big data analyses, yet have limited formal training in statistics and computational methods such as databasing or computer programming. Earth science researchers often spend tremendous amounts of time learning core computational skills, and making core analytical mistakes, in the process of bridging this training gap, at risk to the reputability of observational geostatistical research. The GRRIEn analytical framework is a practical guide introducing community standards for each phase of the computational research pipeline (dataset engineering, model training, and model diagnostics) to promote rigorous, accessible use of global EOs in Earth systems research.
Abstract
Globally available environmental observations (EOs), specifically from satellites and coupled Earth system models, represent some of the largest datasets of the digital age. As the volume of global EOs continues to grow, so does the potential of these data to help Earth scientists discover trends and patterns in Earth systems at large spatial scales. To leverage global EOs for scientific insight, Earth scientists need targeted and accessible exposure to skills in reproducible scientific computing and spatiotemporal data science, and to be empowered to apply their domain understanding to interpret data-driven models for knowledge discovery. The Generalizable, Reproducible, Robust, and Interpreted Environmental (GRRIEn) analysis framework was developed to prepare Earth scientists with an introductory statistics background and limited/no understanding of programming and computational methods to use global EOs to successfully generalize insights from local/regional field measurements across unsampled times and locations. GRRIEn analysis is generalizable, meaning results from a sample are translated to landscape scales by combining direct environmental measurements with global EOs using supervised machine learning; robust, meaning that the model shows good performance on data with scale-dependent feature and observation dependence; reproducible, based on a standard repository structure so that other scientists can quickly and easily replicate the analysis with a few computational tools; and interpreted, meaning that Earth scientists apply domain expertise to ensure that model parameters reflect a physically plausible diagnosis of the environmental system. This tutorial presents standard steps for achieving GRRIEn analysis by combining conventions of rigor in traditional experimental design with the open-science movement.
Significance Statement
Earth science researchers in the digital age are often tasked with pioneering big data analyses, yet have limited formal training in statistics and computational methods such as databasing or computer programming. Earth science researchers often spend tremendous amounts of time learning core computational skills, and making core analytical mistakes, in the process of bridging this training gap, at risk to the reputability of observational geostatistical research. The GRRIEn analytical framework is a practical guide introducing community standards for each phase of the computational research pipeline (dataset engineering, model training, and model diagnostics) to promote rigorous, accessible use of global EOs in Earth systems research.
