Abstract
Understanding convective aggregation is very important for understanding tropical climate and climate sensitivity. However, we still lack a full understanding of how aggregation evolves in the real world or what phenomena and scales are analogous to the self-aggregation observed in idealized models. In this study, we apply the moist static energy (MSE) variance budget framework to ERA5 reanalysis data to study the evolution of large-scale aggregation over tropical oceans at basin wide scales. Our novel phase space diagnostics focus on the variability of observed aggregation compared to most previous self-aggregation studies, which focus more on the aggregated mean state. We visualize observed aggregation to evolve anomalously around a mean state in a cyclical fashion forming aggregation - disaggregation cycles. We find horizontal advection of MSE to play the primary role in determining when the domain aggregates or disaggregates. In contrast, all advective, radiative and surface flux feedbacks are found important for determining the magnitude of the aggregation anomalies. Surface fluxes and horizontal advection tend to dampen aggregation anomalies, while radiative fluxes and vertical advection tend to amplify aggregation anomalies. Looking deeper into the advection terms, we find that changes in vertical advection are dominated by an enhanced low level subsidence over the dry regions during the more aggregated states. This creates an anomalous drying tendency over the dry regions, which maintains aggregation anomalies. In contrast, horizontal advection changes are found to be dominated by increased moisture advection out of the moist columns with stronger aggregation.
Abstract
Understanding convective aggregation is very important for understanding tropical climate and climate sensitivity. However, we still lack a full understanding of how aggregation evolves in the real world or what phenomena and scales are analogous to the self-aggregation observed in idealized models. In this study, we apply the moist static energy (MSE) variance budget framework to ERA5 reanalysis data to study the evolution of large-scale aggregation over tropical oceans at basin wide scales. Our novel phase space diagnostics focus on the variability of observed aggregation compared to most previous self-aggregation studies, which focus more on the aggregated mean state. We visualize observed aggregation to evolve anomalously around a mean state in a cyclical fashion forming aggregation - disaggregation cycles. We find horizontal advection of MSE to play the primary role in determining when the domain aggregates or disaggregates. In contrast, all advective, radiative and surface flux feedbacks are found important for determining the magnitude of the aggregation anomalies. Surface fluxes and horizontal advection tend to dampen aggregation anomalies, while radiative fluxes and vertical advection tend to amplify aggregation anomalies. Looking deeper into the advection terms, we find that changes in vertical advection are dominated by an enhanced low level subsidence over the dry regions during the more aggregated states. This creates an anomalous drying tendency over the dry regions, which maintains aggregation anomalies. In contrast, horizontal advection changes are found to be dominated by increased moisture advection out of the moist columns with stronger aggregation.
Abstract
High-latitudes, including the Bering Sea, are experiencing unprecedented rates of change. Long-term Bering Sea warming trends have been identified, and marine heatwaves (MHWs), event-scale elevated sea surface temperature (SST) extremes, have also increased in frequency and longevity in recent years. Recent work has shown that variability in air-sea coupling plays a dominant role in driving Bering Sea upper ocean thermal variability, and that surface forcing has driven an increase in the occurrence of positive ocean temperature anomalies since 2010. In this work, we characterize the drivers of the anomalous surface air-sea heat fluxes in the Bering Sea over the period 2010-2022 using ERA5 fields. We show that the surface turbulent heat flux dominates the net surface heat flux variability from September-April, and is primarily a result of near-surface air temperature and specific humidity anomalies. The air-mass anomalies that account for the majority of the turbulent heat flux variability are a function of wind direction, with southerly (northerly) wind advecting anomalously warm (cool), moist (dry) air over the Bering Sea, resulting in positive (negative) surface turbulent flux anomalies. During the remaining months of the year, anomalies in the surface radiative fluxes account for the majority of the net surface heat flux variability, and are a result of anomalous cloud coverage, anomalous lower tropospheric virtual temperature, and sea ice coverage variability. Our results indicate that atmospheric variability drives much of the Bering Sea upper ocean temperature variability through mediation of the surface heat fluxes during the analysis period.
Abstract
High-latitudes, including the Bering Sea, are experiencing unprecedented rates of change. Long-term Bering Sea warming trends have been identified, and marine heatwaves (MHWs), event-scale elevated sea surface temperature (SST) extremes, have also increased in frequency and longevity in recent years. Recent work has shown that variability in air-sea coupling plays a dominant role in driving Bering Sea upper ocean thermal variability, and that surface forcing has driven an increase in the occurrence of positive ocean temperature anomalies since 2010. In this work, we characterize the drivers of the anomalous surface air-sea heat fluxes in the Bering Sea over the period 2010-2022 using ERA5 fields. We show that the surface turbulent heat flux dominates the net surface heat flux variability from September-April, and is primarily a result of near-surface air temperature and specific humidity anomalies. The air-mass anomalies that account for the majority of the turbulent heat flux variability are a function of wind direction, with southerly (northerly) wind advecting anomalously warm (cool), moist (dry) air over the Bering Sea, resulting in positive (negative) surface turbulent flux anomalies. During the remaining months of the year, anomalies in the surface radiative fluxes account for the majority of the net surface heat flux variability, and are a result of anomalous cloud coverage, anomalous lower tropospheric virtual temperature, and sea ice coverage variability. Our results indicate that atmospheric variability drives much of the Bering Sea upper ocean temperature variability through mediation of the surface heat fluxes during the analysis period.
Abstract
Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.
Abstract
Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.
Abstract
The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C.
Significance Statement
Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.
Abstract
The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C.
Significance Statement
Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.
Abstract
Eastward-moving moist deep convection and atmospheric circulation signals associated with the tropical Madden Julian Oscillation (MJO) sometimes break down as they cross the Maritime Continent region, but other times the signal propagates across the region maintaining amplitude or regaining it over the West Pacific Basin. This paper assesses the hypothesis that upper tropospheric zonal diffluence of the background wind over the Maritime Continent causes much of this Maritime Continent barrier effect and its variation over time, through two mechanisms. 1. By slowing down the MJO as stronger than average background upper tropospheric zonal wind over the Indian Ocean advects the MJO circulation signal westward, slowing its eastward advance, and 2. through zonal advection of background wind by subseasonal zonal wind across a region of zonal diffluence of the background wind, which advects background wind of the opposite sign to the MJO wind. Advection of the opposite-signed background wind counteracts the MJO wind and reduces its associated upper tropospheric mass divergence, weakening the mechanisms of the upper tropospheric Kelvin wave component of the MJO circulation. Composites of MJO-associated zonal wind and outgoing longwave radiation signals diminish as they cross the Maritime Continent region when the region’s background zonal winds are diffluent, and composites of data reconstructing the relevant advection terms reveal the direct action of the advection mechanisms.
Abstract
Eastward-moving moist deep convection and atmospheric circulation signals associated with the tropical Madden Julian Oscillation (MJO) sometimes break down as they cross the Maritime Continent region, but other times the signal propagates across the region maintaining amplitude or regaining it over the West Pacific Basin. This paper assesses the hypothesis that upper tropospheric zonal diffluence of the background wind over the Maritime Continent causes much of this Maritime Continent barrier effect and its variation over time, through two mechanisms. 1. By slowing down the MJO as stronger than average background upper tropospheric zonal wind over the Indian Ocean advects the MJO circulation signal westward, slowing its eastward advance, and 2. through zonal advection of background wind by subseasonal zonal wind across a region of zonal diffluence of the background wind, which advects background wind of the opposite sign to the MJO wind. Advection of the opposite-signed background wind counteracts the MJO wind and reduces its associated upper tropospheric mass divergence, weakening the mechanisms of the upper tropospheric Kelvin wave component of the MJO circulation. Composites of MJO-associated zonal wind and outgoing longwave radiation signals diminish as they cross the Maritime Continent region when the region’s background zonal winds are diffluent, and composites of data reconstructing the relevant advection terms reveal the direct action of the advection mechanisms.
Abstract
We consider the combined and individual influences of Arctic sea-ice loss, sea surface temperature (SST) warming, and the direct radiative effect of increased CO2 on the Northern Hemispheric climate. The surface climate (e.g., temperature, precipitation) and atmospheric circulation responses (e.g., sea level pressure, wind) to these drivers are quantified using simulations from the Polar Amplification Model Intercomparison Project (for sea-ice loss and SST warming) and the Cloud Feedback Model Intercomparison Project (for increased CO2). We verify the linear additivity of the PAMIP-derived winter responses to sea-ice loss and SST change. The responses to SST change are of greater magnitude than that due to sea-ice loss or due to CO2 direct radiative forcing in most seasons and regions, excluding the Arctic. Notably however, sea-ice loss is at least as important as SST change for the winter atmospheric circulation response over the North Atlantic and Siberia. The dynamical responses to sea-ice loss and SST change oppose each other in many regions in winter, while the responses to SST and CO2 direct radiative forcing are often opposing in summer. Such opposing responses are less evident for the thermodynamical response. The sum of all three responses reproduces well the spatial patterns of change at 2 °C global warming in winter and autumn in the Coupled Model Intercomparison Project phase 6 projections, but overestimates their magnitude.
Abstract
We consider the combined and individual influences of Arctic sea-ice loss, sea surface temperature (SST) warming, and the direct radiative effect of increased CO2 on the Northern Hemispheric climate. The surface climate (e.g., temperature, precipitation) and atmospheric circulation responses (e.g., sea level pressure, wind) to these drivers are quantified using simulations from the Polar Amplification Model Intercomparison Project (for sea-ice loss and SST warming) and the Cloud Feedback Model Intercomparison Project (for increased CO2). We verify the linear additivity of the PAMIP-derived winter responses to sea-ice loss and SST change. The responses to SST change are of greater magnitude than that due to sea-ice loss or due to CO2 direct radiative forcing in most seasons and regions, excluding the Arctic. Notably however, sea-ice loss is at least as important as SST change for the winter atmospheric circulation response over the North Atlantic and Siberia. The dynamical responses to sea-ice loss and SST change oppose each other in many regions in winter, while the responses to SST and CO2 direct radiative forcing are often opposing in summer. Such opposing responses are less evident for the thermodynamical response. The sum of all three responses reproduces well the spatial patterns of change at 2 °C global warming in winter and autumn in the Coupled Model Intercomparison Project phase 6 projections, but overestimates their magnitude.
Abstract
The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook forums and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Stratosphere-troposphere Processes And their Role in Climate (SPARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022-2026 from their original initialization dates, and in most cases also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this SPARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.
Abstract
The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook forums and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Stratosphere-troposphere Processes And their Role in Climate (SPARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022-2026 from their original initialization dates, and in most cases also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this SPARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.
Abstract
We investigate the role of Southern Ocean topography and wind stress in the deep and abyssal ocean overturning and water mass composition using a suite of idealized global ocean circulation models. Specifically, we address how the presence of a meridional ridge in the vicinity of Drake Passage and the formation of an associated Southern Ocean gyre influences the water mass composition of the abyssal cell. Our experiments are carried out using a numerical representation of the global ocean circulation in an idealized two-basin geometry under varying wind-stress and Drake Passage ridge height. In the presence of a low Drake Passage ridge the overall strength of the meridional overturning circulation is primarily influenced by wind stress, with a topographically-induced weakening of the mid-depth cell and concurrent strengthening of the abyssal cell occurring only after ridge height passes 2500m. Passive tracer experiments show that a strengthening mid-depth cell leads to increased abyssal ventilation by North Atlantic water masses, as more North Atlantic Deep Water (NADW) enters the Southern Ocean and then spreads into the Indo-Pacific. We repeat our tracer experiments without restoring in the high-latitude Southern Ocean in order to identify the origin of water masses that circulate through the Southern Ocean before sinking into the abyss as Antarctic Bottom Water. Our results from these “exchange” tracer experiments show that an increasing ridge height in Drake Passage and the concurrent gyre spin-up lead to substantially decreased NADW-origin waters in the abyssal ocean, as more surface waters from north of the ACC are transferred into the Antarctic Bottom Water formation region.
Abstract
We investigate the role of Southern Ocean topography and wind stress in the deep and abyssal ocean overturning and water mass composition using a suite of idealized global ocean circulation models. Specifically, we address how the presence of a meridional ridge in the vicinity of Drake Passage and the formation of an associated Southern Ocean gyre influences the water mass composition of the abyssal cell. Our experiments are carried out using a numerical representation of the global ocean circulation in an idealized two-basin geometry under varying wind-stress and Drake Passage ridge height. In the presence of a low Drake Passage ridge the overall strength of the meridional overturning circulation is primarily influenced by wind stress, with a topographically-induced weakening of the mid-depth cell and concurrent strengthening of the abyssal cell occurring only after ridge height passes 2500m. Passive tracer experiments show that a strengthening mid-depth cell leads to increased abyssal ventilation by North Atlantic water masses, as more North Atlantic Deep Water (NADW) enters the Southern Ocean and then spreads into the Indo-Pacific. We repeat our tracer experiments without restoring in the high-latitude Southern Ocean in order to identify the origin of water masses that circulate through the Southern Ocean before sinking into the abyss as Antarctic Bottom Water. Our results from these “exchange” tracer experiments show that an increasing ridge height in Drake Passage and the concurrent gyre spin-up lead to substantially decreased NADW-origin waters in the abyssal ocean, as more surface waters from north of the ACC are transferred into the Antarctic Bottom Water formation region.
Abstract
Atmospheric blocking is quantified by a variety of different blocking indices. Here, the index by Davini et al. (DAV12) is modified to a hybrid index by additionally considering the extent of the blocking anticyclone. Applying both indices, the DAV12 and the hybrid index, to the 20th century reanalyses ERA-20C and the 20CRv3 (hereafter 20CR) ensemble reveals large differences between the reanalyses. The annual blocking frequency increases widespread and significantly in ERA-20C, but only regionally and non-significantly in the 20CR ensemble-mean. Trend analysis reveals a higher reliability of the 20CR ensemble-mean than ERA-20C blocking results. Seasonally, the changes in summer blocking are most pronounced and significantly positive around Greenland. The CMIP6-mean historical climate simulation agrees well in magnitude with 20CR during the period 1900–2014, with some underestimation of blocking frequency in the last decades related to inconsistent trends. For a future climate, the average of the CMIP6 models projects a decrease in blocking frequency in major parts of the Northern Hemisphere in summer and in winter. A CMIP6 sub-set of models which agree best to reanalyses shows a less negative future trend. Because of the mismatch of historical trends, especially in summer, the future projections are uncertain.
Abstract
Atmospheric blocking is quantified by a variety of different blocking indices. Here, the index by Davini et al. (DAV12) is modified to a hybrid index by additionally considering the extent of the blocking anticyclone. Applying both indices, the DAV12 and the hybrid index, to the 20th century reanalyses ERA-20C and the 20CRv3 (hereafter 20CR) ensemble reveals large differences between the reanalyses. The annual blocking frequency increases widespread and significantly in ERA-20C, but only regionally and non-significantly in the 20CR ensemble-mean. Trend analysis reveals a higher reliability of the 20CR ensemble-mean than ERA-20C blocking results. Seasonally, the changes in summer blocking are most pronounced and significantly positive around Greenland. The CMIP6-mean historical climate simulation agrees well in magnitude with 20CR during the period 1900–2014, with some underestimation of blocking frequency in the last decades related to inconsistent trends. For a future climate, the average of the CMIP6 models projects a decrease in blocking frequency in major parts of the Northern Hemisphere in summer and in winter. A CMIP6 sub-set of models which agree best to reanalyses shows a less negative future trend. Because of the mismatch of historical trends, especially in summer, the future projections are uncertain.
Quality Control of Geostationary Lightning Mapper Observations for Tropical Cyclone ApplicationsAbstract
The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, “bar” effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC was not applied beforehand.
Significance Statement
The Geostationary Lightning Mapper (GLM) provides total lightning observations in tropical cyclones that can benefit forecasts of intensity change. However, nonlightning artifacts in GLM observations make interpreting lightning observations challenging for automated techniques to predict intensity change. Quality control procedures have been developed to aid the TC community in using GLM observations for statistical and pattern-matching techniques.
Abstract
The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, “bar” effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC was not applied beforehand.
Significance Statement
The Geostationary Lightning Mapper (GLM) provides total lightning observations in tropical cyclones that can benefit forecasts of intensity change. However, nonlightning artifacts in GLM observations make interpreting lightning observations challenging for automated techniques to predict intensity change. Quality control procedures have been developed to aid the TC community in using GLM observations for statistical and pattern-matching techniques.
