Abstract
Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting WRF model, namely WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times defined as the age of tagged water since its source. We apply a three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times and to derive atmospheric water transit times defined as the age of tagged water since its source originating from a particular physical or dynamical process. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the MPI-ESM Representative Concentration Pathway 8.5 scenario exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged water source area), for historical (1980-1989) and future (2040-2049) times. In the warmer (+1.9 °C for temperature and +2% for evaporation) and drier (−21% for precipitation) future, the residence time for the tagged water vapor will regionally decrease by 1.8 hours (from 14.3 hours) due to enhanced local evaporation contributions, but the transit time for the tagged precipitation will increase by 1.8 hours (from 12.9 hours) partly due to slower fallout of precipitating moisture components.
Abstract
Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting WRF model, namely WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times defined as the age of tagged water since its source. We apply a three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times and to derive atmospheric water transit times defined as the age of tagged water since its source originating from a particular physical or dynamical process. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the MPI-ESM Representative Concentration Pathway 8.5 scenario exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged water source area), for historical (1980-1989) and future (2040-2049) times. In the warmer (+1.9 °C for temperature and +2% for evaporation) and drier (−21% for precipitation) future, the residence time for the tagged water vapor will regionally decrease by 1.8 hours (from 14.3 hours) due to enhanced local evaporation contributions, but the transit time for the tagged precipitation will increase by 1.8 hours (from 12.9 hours) partly due to slower fallout of precipitating moisture components.
Abstract
In this study, we introduce an ensemble approach to provide a probabilistic seasonal outlook of the length and seasonal rainfall anomaly of the wet season over Florida using the observed variations of the onset date of the season at the granularity of ∼10km grid resolution (which is the spatial resolution of the observed rainfall data used for this work). The timeseries of daily precipitation at the grid resolution of NASA’s Global Precipitation Mission is randomly perturbed 1000 times to account for the uncertainty of synoptic to mesoscale variations on the diagnosis of the onset and demise date of the wet season. The strong co-variability of the onset date with the seasonal length and seasonal rainfall anomaly of the wet season is then leveraged to provide the seasonal outlooks by monitoring the onset date of the wet season. This simple seasonal outlook is effective in predicting extreme tercile and even extreme pentile anomalies across Florida. We suggest that the proposed approach to the seasonal outlook of the wet season of Florida provides a viable alternative in the absence of strong external forcing like ENSO or tropical Atlantic variability that potentially limits the predictability of numerical climate models used for seasonal prediction.
Abstract
In this study, we introduce an ensemble approach to provide a probabilistic seasonal outlook of the length and seasonal rainfall anomaly of the wet season over Florida using the observed variations of the onset date of the season at the granularity of ∼10km grid resolution (which is the spatial resolution of the observed rainfall data used for this work). The timeseries of daily precipitation at the grid resolution of NASA’s Global Precipitation Mission is randomly perturbed 1000 times to account for the uncertainty of synoptic to mesoscale variations on the diagnosis of the onset and demise date of the wet season. The strong co-variability of the onset date with the seasonal length and seasonal rainfall anomaly of the wet season is then leveraged to provide the seasonal outlooks by monitoring the onset date of the wet season. This simple seasonal outlook is effective in predicting extreme tercile and even extreme pentile anomalies across Florida. We suggest that the proposed approach to the seasonal outlook of the wet season of Florida provides a viable alternative in the absence of strong external forcing like ENSO or tropical Atlantic variability that potentially limits the predictability of numerical climate models used for seasonal prediction.
Abstract
The three-body scatter spike (TBSS), an echo artifact in radar imagery, manifests as a weak, linear echo extending radially from a core of high reflectivity. Adequately, though not indispensably, indicating the presence of large hail in convective storms, the automatic identification of the TBSS proves advantageous in significantly improving the effectiveness of hailstorm detection. This study introduces an algorithm that synergizes Jensen–Shannon divergence (JSD) and support vector machine (SVM) for rapid TBSS detection in two decades’ worth of single-polarization radar data across China. The algorithm, tested on data from 50 S-band China Next Generation Weather Radar (CINRAD) in central and eastern China, utilized reflectivity factor images for sample extraction. An application in Chenzhou, China, demonstrates the algorithm’s efficacy in improving hailstorm detection resolution.
Significance Statement
In recent years, China’s hail recordkeeping, primarily based on manual observations at national surface meteorological stations, has suffered from limited spatial and temporal detail. However, the advent of the China Next Generation Weather Radar (CINRAD) network offers a new avenue for hailstorm detection. TBSS, a secondary but significant indicator for large hail in S-band radar, presents an opportunity for enhanced hail warning capabilities. By automating TBSS detection in radar archives spanning two decades, this research significantly enhances the resolution of hailstorm climatology, contributing to more effective hail disaster mitigation and management.
Abstract
The three-body scatter spike (TBSS), an echo artifact in radar imagery, manifests as a weak, linear echo extending radially from a core of high reflectivity. Adequately, though not indispensably, indicating the presence of large hail in convective storms, the automatic identification of the TBSS proves advantageous in significantly improving the effectiveness of hailstorm detection. This study introduces an algorithm that synergizes Jensen–Shannon divergence (JSD) and support vector machine (SVM) for rapid TBSS detection in two decades’ worth of single-polarization radar data across China. The algorithm, tested on data from 50 S-band China Next Generation Weather Radar (CINRAD) in central and eastern China, utilized reflectivity factor images for sample extraction. An application in Chenzhou, China, demonstrates the algorithm’s efficacy in improving hailstorm detection resolution.
Significance Statement
In recent years, China’s hail recordkeeping, primarily based on manual observations at national surface meteorological stations, has suffered from limited spatial and temporal detail. However, the advent of the China Next Generation Weather Radar (CINRAD) network offers a new avenue for hailstorm detection. TBSS, a secondary but significant indicator for large hail in S-band radar, presents an opportunity for enhanced hail warning capabilities. By automating TBSS detection in radar archives spanning two decades, this research significantly enhances the resolution of hailstorm climatology, contributing to more effective hail disaster mitigation and management.
Abstract
The forest microclimate shapes many aspects of forest functioning, including plant regeneration and wildfire occurrence. In complex landscapes with varying terrain, the forest microclimate varies at fine spatial scales (10–100 m2). However, accurately mapping this variation remains challenging. Vapor pressure deficit (VPD) is an important microclimatic variable for plant growth and fire activity, yet few studies have specifically focused on downscaling VPD. The aim of this study was to examine the drivers of in-forest VPD in temperate eucalypt forests and develop a model to predict in-forest VPD. We use microclimate data from 37 in-forest weather stations, installed across an aridity gradient in southeastern Australia. We used linear mixed models within an information theoretic approach to develop a predictive model for daily maximum in-forest VPD using open VPD and topographic variables. The highest-ranked model included fundamental topographic drivers of vegetation structure and microclimate in forested landscapes: aspect, elevation, and slope, in addition to open VPD. The model had high accuracy when tested against independent data. We used this model to map fine-scale (30 m2, daily) maximum in-forest VPD across a topographically complex case study landscape. Predicted in-forest VPD showed considerable spatial and temporal variations not captured by coarse-scale open VPD. This represents a significant advancement in our ability to model microclimatic conditions in temperate eucalypt forests and has the potential to advance our understanding of how ecosystem processes vary at fine spatial scales.
Abstract
The forest microclimate shapes many aspects of forest functioning, including plant regeneration and wildfire occurrence. In complex landscapes with varying terrain, the forest microclimate varies at fine spatial scales (10–100 m2). However, accurately mapping this variation remains challenging. Vapor pressure deficit (VPD) is an important microclimatic variable for plant growth and fire activity, yet few studies have specifically focused on downscaling VPD. The aim of this study was to examine the drivers of in-forest VPD in temperate eucalypt forests and develop a model to predict in-forest VPD. We use microclimate data from 37 in-forest weather stations, installed across an aridity gradient in southeastern Australia. We used linear mixed models within an information theoretic approach to develop a predictive model for daily maximum in-forest VPD using open VPD and topographic variables. The highest-ranked model included fundamental topographic drivers of vegetation structure and microclimate in forested landscapes: aspect, elevation, and slope, in addition to open VPD. The model had high accuracy when tested against independent data. We used this model to map fine-scale (30 m2, daily) maximum in-forest VPD across a topographically complex case study landscape. Predicted in-forest VPD showed considerable spatial and temporal variations not captured by coarse-scale open VPD. This represents a significant advancement in our ability to model microclimatic conditions in temperate eucalypt forests and has the potential to advance our understanding of how ecosystem processes vary at fine spatial scales.
Abstract
Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved to be a Bureau operational verification system in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and “Evidence Targeted Automation” dashboards for exploring the performance of competing forecast systems. Additionally, Jive includes a Jupyter Notebook server with the Jive Python library which supports research experiments, case studies, and the development of new verification metrics and tools.
This paper describes the Jive verification system and how it helped bring verification to the forefront at the Bureau of Meteorology, leading to more accurate, streamlined forecasts. Jive has provided evidence to support forecast automation decisions and has helped to understand the evolving role of meteorologists in the forecast process. It has given operational meteorologists tools for evaluating forecast processes, including identifying when and how manual interventions lead to superior predictions. Work on Jive led to new verification science, including novel metrics that are decision-focused, including diagnostics for extreme conditions. Jive also provided the Bureau with an enterprise-wide data analysis environment and has prompted a clarification of forecast definitions.
These collective impacts have resulted in more accurate forecasts, ultimately benefiting society, and building trust with forecast users. These positive outcomes highlight the importance of meteorological organizations investing in verification science and technology.
Abstract
Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved to be a Bureau operational verification system in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and “Evidence Targeted Automation” dashboards for exploring the performance of competing forecast systems. Additionally, Jive includes a Jupyter Notebook server with the Jive Python library which supports research experiments, case studies, and the development of new verification metrics and tools.
This paper describes the Jive verification system and how it helped bring verification to the forefront at the Bureau of Meteorology, leading to more accurate, streamlined forecasts. Jive has provided evidence to support forecast automation decisions and has helped to understand the evolving role of meteorologists in the forecast process. It has given operational meteorologists tools for evaluating forecast processes, including identifying when and how manual interventions lead to superior predictions. Work on Jive led to new verification science, including novel metrics that are decision-focused, including diagnostics for extreme conditions. Jive also provided the Bureau with an enterprise-wide data analysis environment and has prompted a clarification of forecast definitions.
These collective impacts have resulted in more accurate forecasts, ultimately benefiting society, and building trust with forecast users. These positive outcomes highlight the importance of meteorological organizations investing in verification science and technology.
Abstract
Here, we examine the relation between U.S. tornado activity and a new year-round classification of North American weather regimes. The regime classification is based on 500-hPa geopotential height anomalies and classifies each day as Pacific Trough, Pacific Ridge, Alaskan Ridge, Greenland High, or No regime. During the period 1979–2022, we find statistically significant relations between average tornado report numbers and weather regimes in all months except June–August. Tornado activity is enhanced on Pacific Ridge days during late winter and spring, reduced on Pacific Trough days in spring, and reduced on Alaskan Ridge and Greenland High days during fall and early winter. During active regimes, the probability of many tornadoes occurring also increases, and there is greater variability in the number of tornadoes reported each day. A reanalysis-based tornado index reproduces the regional features of the modulation of tornado activity by the weather regimes and attributes them to changes in storm relative helicity, convective available potential energy, and convective precipitation. The phase of El Niño–Southern Oscillation (ENSO) also plays a role. In winter and spring, Pacific Ridge days occur more often and average more reports per day during cool ENSO conditions. During warm ENSO conditions, Pacific Trough days occur more often and are associated with widespread reduced tornado activity.
Significance Statement
Daily weather patterns over North America can be classified into five categories. The purpose of this study was to examine whether the number of U.S. tornado reports on a given day depends on the weather category of that day. We found robust relations between the average number of tornado reports and the weather pattern category in all months except June–August, with some weather patterns associated with increased tornado numbers and others with decreased tornado numbers. The El Niño–Southern Oscillation (ENSO) phenomenon plays a role, with weather patterns that are favorable for tornadoes being more frequent and having more tornadoes per day during cool ENSO conditions.
Abstract
Here, we examine the relation between U.S. tornado activity and a new year-round classification of North American weather regimes. The regime classification is based on 500-hPa geopotential height anomalies and classifies each day as Pacific Trough, Pacific Ridge, Alaskan Ridge, Greenland High, or No regime. During the period 1979–2022, we find statistically significant relations between average tornado report numbers and weather regimes in all months except June–August. Tornado activity is enhanced on Pacific Ridge days during late winter and spring, reduced on Pacific Trough days in spring, and reduced on Alaskan Ridge and Greenland High days during fall and early winter. During active regimes, the probability of many tornadoes occurring also increases, and there is greater variability in the number of tornadoes reported each day. A reanalysis-based tornado index reproduces the regional features of the modulation of tornado activity by the weather regimes and attributes them to changes in storm relative helicity, convective available potential energy, and convective precipitation. The phase of El Niño–Southern Oscillation (ENSO) also plays a role. In winter and spring, Pacific Ridge days occur more often and average more reports per day during cool ENSO conditions. During warm ENSO conditions, Pacific Trough days occur more often and are associated with widespread reduced tornado activity.
Significance Statement
Daily weather patterns over North America can be classified into five categories. The purpose of this study was to examine whether the number of U.S. tornado reports on a given day depends on the weather category of that day. We found robust relations between the average number of tornado reports and the weather pattern category in all months except June–August, with some weather patterns associated with increased tornado numbers and others with decreased tornado numbers. The El Niño–Southern Oscillation (ENSO) phenomenon plays a role, with weather patterns that are favorable for tornadoes being more frequent and having more tornadoes per day during cool ENSO conditions.
Abstract
The North Atlantic Ocean forcings are considered an important origin of the North Atlantic atmospheric multidecadal variability. Here, we reveal the energetics mechanisms of the phenomenon using the perturbation potential energy (PPE) theory. Supporting the previous model studies, a cyclic pattern involving the Atlantic multidecadal oscillation (AMO) and the North Atlantic tripole (NAT) is observed: positive AMO phase (AMO+) → NAT− → AMO− → NAT+, with a phase lag of approximately 15–20 years. An atmospheric mode characterized by basinscale sea level pressure anomaly in the North Atlantic is associated with the AMO, which is termed the North Atlantic uniformity (NAU). The AMO+ induces positive uniform PPE anomalies over the ocean through precipitation heating, leading to decreased energy conversion to perturbation kinetic energy (PKE) and a large-scale anomalous cyclone. For the NAT+, tripolar precipitation anomalies result in tripolar PPE anomalies. Anomalous energy conversions occur where the PPE anomaly gradient is large, explained by an energy balance derived from thermal wind relationship. The PKE around 15° and 50°N (25° and 75°N) increases (decreases), forming the anomalous anticyclone and cyclone at subtropical and subpolar regions, respectively, known as the North Atlantic Oscillation (NAO). The reverse holds for the NAT− and AMO−. As the phases of the ocean modes alternate, the energetics induce the NAU−, NAO−, NAU+, and NAO+ sequentially. In the multidecadal cycle, the accumulated energetics process is related to delayed effect, and the difference in variance explanation between the NAU and NAO is attributed to the feedback mechanisms.
Significance Statement
The North Atlantic Ocean’s multidecadal changes affect the atmosphere above it. Our study explores the energy processes behind this phenomenon. The North Atlantic Ocean’s temperature distribution goes through a shift every 15–20 years, persistently affecting the air’s potential energy through the heat release related to vapor condensation. The changed potential energy converts into kinetic energy, causing the atmospheric circulation to alternate between different states. Our study provides a comprehensive explanation of how the ocean affects the region’s climate. This insight may contribute to making more accurate models and predictions of climate changes in the North Atlantic.
Abstract
The North Atlantic Ocean forcings are considered an important origin of the North Atlantic atmospheric multidecadal variability. Here, we reveal the energetics mechanisms of the phenomenon using the perturbation potential energy (PPE) theory. Supporting the previous model studies, a cyclic pattern involving the Atlantic multidecadal oscillation (AMO) and the North Atlantic tripole (NAT) is observed: positive AMO phase (AMO+) → NAT− → AMO− → NAT+, with a phase lag of approximately 15–20 years. An atmospheric mode characterized by basinscale sea level pressure anomaly in the North Atlantic is associated with the AMO, which is termed the North Atlantic uniformity (NAU). The AMO+ induces positive uniform PPE anomalies over the ocean through precipitation heating, leading to decreased energy conversion to perturbation kinetic energy (PKE) and a large-scale anomalous cyclone. For the NAT+, tripolar precipitation anomalies result in tripolar PPE anomalies. Anomalous energy conversions occur where the PPE anomaly gradient is large, explained by an energy balance derived from thermal wind relationship. The PKE around 15° and 50°N (25° and 75°N) increases (decreases), forming the anomalous anticyclone and cyclone at subtropical and subpolar regions, respectively, known as the North Atlantic Oscillation (NAO). The reverse holds for the NAT− and AMO−. As the phases of the ocean modes alternate, the energetics induce the NAU−, NAO−, NAU+, and NAO+ sequentially. In the multidecadal cycle, the accumulated energetics process is related to delayed effect, and the difference in variance explanation between the NAU and NAO is attributed to the feedback mechanisms.
Significance Statement
The North Atlantic Ocean’s multidecadal changes affect the atmosphere above it. Our study explores the energy processes behind this phenomenon. The North Atlantic Ocean’s temperature distribution goes through a shift every 15–20 years, persistently affecting the air’s potential energy through the heat release related to vapor condensation. The changed potential energy converts into kinetic energy, causing the atmospheric circulation to alternate between different states. Our study provides a comprehensive explanation of how the ocean affects the region’s climate. This insight may contribute to making more accurate models and predictions of climate changes in the North Atlantic.
Abstract
An analytical method for diagnosing the interaction between the primary and secondary circulations of a tropical cyclone (TC) and vortex intensification is developed. It includes a diagnostic equation describing the mean secondary circulation of a TC in an unbalanced framework by including the radial eddy forcing in the analytical system. It is an extension of the Sawyer-Eliassen equation (SEE) developed from the strict gradient-wind balance. This generalized SEE (GSEE) remediates some of the limitations of SEE and can be used to diagnose both balanced and unbalanced dynamical processes during the TC evolution. Using GSEE, this study investigates how the tangential and radial eddy forcing affects the TC intensification simulated by the Hurricane Research and Forecasting (HWRF) model with differently parameterized turbulent mixing. The diagnostic results show that the super-gradient component of radial eddy forcing contributes positively to the acceleration of the peak tangential wind, whereas the sub-gradient component of the radial eddy forcing tends to lower the height of peak tangential wind. The relative importance of negative and positive effects of tangential eddy forcing on TC intensification varies depending on the details of turbulence parameterization. For a turbulent kinetic energy (TKE) scheme used in this study, a large sloping curvature of mixing length in the low troposphere causes the tangential eddy forcing to produce a net positive tangential wind tendency near the location of the peak tangential wind. In contrast, a small sloping curvature of mixing length generates a net negative tangential wind tendency at the peak tangential wind.
Abstract
An analytical method for diagnosing the interaction between the primary and secondary circulations of a tropical cyclone (TC) and vortex intensification is developed. It includes a diagnostic equation describing the mean secondary circulation of a TC in an unbalanced framework by including the radial eddy forcing in the analytical system. It is an extension of the Sawyer-Eliassen equation (SEE) developed from the strict gradient-wind balance. This generalized SEE (GSEE) remediates some of the limitations of SEE and can be used to diagnose both balanced and unbalanced dynamical processes during the TC evolution. Using GSEE, this study investigates how the tangential and radial eddy forcing affects the TC intensification simulated by the Hurricane Research and Forecasting (HWRF) model with differently parameterized turbulent mixing. The diagnostic results show that the super-gradient component of radial eddy forcing contributes positively to the acceleration of the peak tangential wind, whereas the sub-gradient component of the radial eddy forcing tends to lower the height of peak tangential wind. The relative importance of negative and positive effects of tangential eddy forcing on TC intensification varies depending on the details of turbulence parameterization. For a turbulent kinetic energy (TKE) scheme used in this study, a large sloping curvature of mixing length in the low troposphere causes the tangential eddy forcing to produce a net positive tangential wind tendency near the location of the peak tangential wind. In contrast, a small sloping curvature of mixing length generates a net negative tangential wind tendency at the peak tangential wind.
