Abstract
The total poleward energy transport (PET) is set by the top of atmosphere radiation flux and is therefore sensitive to any process which can alter those fluxes, particularly in the shortwave. One example is the direct and indirect effects of anthropogenic aerosols, which increase the local reflection of solar radiation back into space. The historic emission of sulfur dioxide, which peaked in the northern midlatitudes during the 1980s, has been proposed as a primary contributor to historic anomalies in cross-equatorial energy transport, as well as related processes such as a shift in the tropical rainband. In this study, we analyze simulations from the Community Earth System Model, version 2 (CESM2), large ensemble and single-forcing projects to better understand the forced response of PET to historical forcings. First, analysis of the single-forcing project reveals that the position of the intertropical convergence zone (ITCZ) responds in a nonlinear manner to greenhouse gas forcing in CESM2. This type of nonlinearity has been found previously in the context of the aerosol-only simulations in the CESM2 single-forcing project but may be the first identification of a similar effect in the greenhouse gas–only simulations. Second, through analysis of the full CESM2 large ensemble simulations, we find that anomalous heat transport occurred in both the atmosphere (through the mean meridional circulation and atmospheric eddies) and the oceans (through the Atlantic and Indo-Pacific sectors) due to a variety of related processes including the Hadley cells, the midlatitude storm tracks, the Atlantic meridional overturning circulation (AMOC), and the Pacific wind-driven subtropical gyre.
Significance Statement
In this study, we investigate how the Earth system changed the transport of energy from the tropics to the poles in response to historic pollution. We analyze a large number of climate model simulations of the recent past (1850 to present) and find that historic emissions of sulfur dioxide caused the model to transport more energy in the form of stronger ocean currents, stronger storms, and stronger prevailing winds. This is because the modeled currents in the Atlantic were too sensitive to historic pollution and transported too much warm water northward.
Abstract
The total poleward energy transport (PET) is set by the top of atmosphere radiation flux and is therefore sensitive to any process which can alter those fluxes, particularly in the shortwave. One example is the direct and indirect effects of anthropogenic aerosols, which increase the local reflection of solar radiation back into space. The historic emission of sulfur dioxide, which peaked in the northern midlatitudes during the 1980s, has been proposed as a primary contributor to historic anomalies in cross-equatorial energy transport, as well as related processes such as a shift in the tropical rainband. In this study, we analyze simulations from the Community Earth System Model, version 2 (CESM2), large ensemble and single-forcing projects to better understand the forced response of PET to historical forcings. First, analysis of the single-forcing project reveals that the position of the intertropical convergence zone (ITCZ) responds in a nonlinear manner to greenhouse gas forcing in CESM2. This type of nonlinearity has been found previously in the context of the aerosol-only simulations in the CESM2 single-forcing project but may be the first identification of a similar effect in the greenhouse gas–only simulations. Second, through analysis of the full CESM2 large ensemble simulations, we find that anomalous heat transport occurred in both the atmosphere (through the mean meridional circulation and atmospheric eddies) and the oceans (through the Atlantic and Indo-Pacific sectors) due to a variety of related processes including the Hadley cells, the midlatitude storm tracks, the Atlantic meridional overturning circulation (AMOC), and the Pacific wind-driven subtropical gyre.
Significance Statement
In this study, we investigate how the Earth system changed the transport of energy from the tropics to the poles in response to historic pollution. We analyze a large number of climate model simulations of the recent past (1850 to present) and find that historic emissions of sulfur dioxide caused the model to transport more energy in the form of stronger ocean currents, stronger storms, and stronger prevailing winds. This is because the modeled currents in the Atlantic were too sensitive to historic pollution and transported too much warm water northward.
Abstract
This study investigates how entrainment’s diluting effect on cumulonimbus updraft buoyancy is affected by the temperature of the troposphere, which is expected to increase by the end of the century. A parcel model framework is constructed that allows for independent variations in the temperature (T), the entrainment rate ε, the free-tropospheric relative humidity (RH), and the convective available potential energy (CAPE). Using this framework, dilution of buoyancy is evaluated with T and RH independently varied and with CAPE either held constant or increased with temperature. When CAPE is held constant, buoyancy decreases as T increases, with parcels in warmer environments realizing substantially smaller fractions of their CAPE as kinetic energy (KE). This occurs because the increased moisture difference between an updraft and its surroundings at warmer temperatures drives greater updraft dilution. Similar results are found in midlatitude and tropical conditions when CAPE is increased with temperature. With the expected 6%–7% increase in CAPE per kelvin of warming, KE only increases at 2%–4% K−1 in narrow updrafts but tracks more closely with CAPE at 4%–6% in wider updrafts. Interestingly, the rate of increase in the KE with T becomes larger than that of CAPE when the later quantity increases at more than 10% K−1. These findings emphasize the importance of considering entrainment in studies of moist convection’s response to climate change, as the entrainment-driven dilution of buoyancy may partially counteract the influence of increases in CAPE on updraft intensity.
Significance Statement
Cumulonimbus clouds mix air with their surrounding environment through a process called entrainment, which controls how efficiently environmental energy is converted into upward speed in thunderstorm updrafts. Our research shows that warmer temperatures will exacerbate the moisture difference between cumulonimbus updrafts and their surroundings, leading to greater mixing and less efficient conversion of environmental energy into updraft speeds. This effect should be considered in future research that investigates how climate change will affect cumulonimbus clouds.
Abstract
This study investigates how entrainment’s diluting effect on cumulonimbus updraft buoyancy is affected by the temperature of the troposphere, which is expected to increase by the end of the century. A parcel model framework is constructed that allows for independent variations in the temperature (T), the entrainment rate ε, the free-tropospheric relative humidity (RH), and the convective available potential energy (CAPE). Using this framework, dilution of buoyancy is evaluated with T and RH independently varied and with CAPE either held constant or increased with temperature. When CAPE is held constant, buoyancy decreases as T increases, with parcels in warmer environments realizing substantially smaller fractions of their CAPE as kinetic energy (KE). This occurs because the increased moisture difference between an updraft and its surroundings at warmer temperatures drives greater updraft dilution. Similar results are found in midlatitude and tropical conditions when CAPE is increased with temperature. With the expected 6%–7% increase in CAPE per kelvin of warming, KE only increases at 2%–4% K−1 in narrow updrafts but tracks more closely with CAPE at 4%–6% in wider updrafts. Interestingly, the rate of increase in the KE with T becomes larger than that of CAPE when the later quantity increases at more than 10% K−1. These findings emphasize the importance of considering entrainment in studies of moist convection’s response to climate change, as the entrainment-driven dilution of buoyancy may partially counteract the influence of increases in CAPE on updraft intensity.
Significance Statement
Cumulonimbus clouds mix air with their surrounding environment through a process called entrainment, which controls how efficiently environmental energy is converted into upward speed in thunderstorm updrafts. Our research shows that warmer temperatures will exacerbate the moisture difference between cumulonimbus updrafts and their surroundings, leading to greater mixing and less efficient conversion of environmental energy into updraft speeds. This effect should be considered in future research that investigates how climate change will affect cumulonimbus clouds.
Abstract
Diurnal off-mountain propagation is a distinctive feature of rainfall over terrestrial areas, whereas the causes of this phenomenon are not well understood. Focused on the rainfall downstream of the Yungui Plateau (YGP), this study aims to examine whether the gravity waves stimulated by an assumed terrain-related thermal forcing could explain the feature. The results show that the rainfall diurnal phase propagates eastward at a speed of approximately 13 m s−1 over the lee side of YGP during warm seasons. The diurnal amplitude reaches its zonally maximum over the slope of the YGP and drops sharply downstream the terrain. The low-level vertical velocity exhibits similar diurnal characteristics. A linear model forced by a hollow heating is proposed to mimic the thermal forcing related to a mountain. Experiments with the model show that there are mainly two branches of waves around the terrain. One is over the upstream with upwind-tilting phase lines that move toward −z and −x directions. The other branch exists over the lee side of terrain with downwind-tilting phase lines that move toward −z and +x directions, which is considered to be relevant to the off-mountain propagation feature. The wave behavior over the YGP is then reproduced using the model. It is shown that the main features of the diurnal phase lag and the zonal amplitude distribution pattern of the low-level updraft could be captured by the model, suggesting an important role of the gravity wave in driving the diurnal propagation of vertical velocity and rainfall downstream large terrains.
Significance Statement
The purpose of this study is to better understand why diurnal off-mountain rainfall propagation exists over the downstream of Yungui Plateau (YGP). This is important because the off-mountain rainfall propagation influences the rainfall events over the lee side of mountains and can further influence people’s lives therein. Inspired by previous studies on coastal rainfall, we proposed a simplified linear gravity wave model forced by a topography-related heating function. The results showed that the thermally forced gravity waves could reproduce the main features of the propagating phase and amplitude pattern of upward motion disturbance over the lee side of the YGP. Our results highlight the importance of gravity waves stimulated by topography-related heating on the diurnal propagation features over the downstream of mountains.
Abstract
Diurnal off-mountain propagation is a distinctive feature of rainfall over terrestrial areas, whereas the causes of this phenomenon are not well understood. Focused on the rainfall downstream of the Yungui Plateau (YGP), this study aims to examine whether the gravity waves stimulated by an assumed terrain-related thermal forcing could explain the feature. The results show that the rainfall diurnal phase propagates eastward at a speed of approximately 13 m s−1 over the lee side of YGP during warm seasons. The diurnal amplitude reaches its zonally maximum over the slope of the YGP and drops sharply downstream the terrain. The low-level vertical velocity exhibits similar diurnal characteristics. A linear model forced by a hollow heating is proposed to mimic the thermal forcing related to a mountain. Experiments with the model show that there are mainly two branches of waves around the terrain. One is over the upstream with upwind-tilting phase lines that move toward −z and −x directions. The other branch exists over the lee side of terrain with downwind-tilting phase lines that move toward −z and +x directions, which is considered to be relevant to the off-mountain propagation feature. The wave behavior over the YGP is then reproduced using the model. It is shown that the main features of the diurnal phase lag and the zonal amplitude distribution pattern of the low-level updraft could be captured by the model, suggesting an important role of the gravity wave in driving the diurnal propagation of vertical velocity and rainfall downstream large terrains.
Significance Statement
The purpose of this study is to better understand why diurnal off-mountain rainfall propagation exists over the downstream of Yungui Plateau (YGP). This is important because the off-mountain rainfall propagation influences the rainfall events over the lee side of mountains and can further influence people’s lives therein. Inspired by previous studies on coastal rainfall, we proposed a simplified linear gravity wave model forced by a topography-related heating function. The results showed that the thermally forced gravity waves could reproduce the main features of the propagating phase and amplitude pattern of upward motion disturbance over the lee side of the YGP. Our results highlight the importance of gravity waves stimulated by topography-related heating on the diurnal propagation features over the downstream of mountains.
Abstract
The NASA Convective Processes Experiment-Cabo Verde (CPEX-CV) field campaign took place in September 2022 out of Sal Island, Cabo Verde. A unique payload aboard the NASA DC-8 aircraft equipped with advanced remote sensing and in situ instrumentation, in conjunction with radiosonde launches and satellite observations, allowed CPEX-CV to target the coupling between atmospheric dynamics, marine boundary layer properties, convection, and the dust-laden Saharan air layer in the data-sparse tropical East Atlantic region. CPEX-CV provided measurements of African easterly wave environments, diurnal cycle impacts on convective life cycle, and several Saharan dust outbreaks, including the highest dust optical depth observed by the DC-8 interacting with what would become Tropical Storm Hermine. Preliminary results from CPEX-CV underscore the positive impact of dedicated tropical East Atlantic observations on downstream forecast skill, including sampling environmental forcings impacting the development of several nondeveloping and developing convective systems such as Hurricanes Fiona and Ian. Combined airborne radar, lidar, and radiometer measurements uniquely provide near-storm environments associated with convection on various spatiotemporal scales and, with in situ observations, insights into controls on Saharan dust properties with transport. The DC-8 also collaborated with the European Space Agency to perform coordinated validation flights under the Aeolus spaceborne wind lidar and over the Mindelo ground site, highlighting the enhanced sampling potential through partnership opportunities. CPEX-CV engaged in professional development through dedicated team-building exercises that equipped the team with a cohesive approach for targeting CPEX-CV science objectives and promoted active participation of scientists across all career stages.
Abstract
The NASA Convective Processes Experiment-Cabo Verde (CPEX-CV) field campaign took place in September 2022 out of Sal Island, Cabo Verde. A unique payload aboard the NASA DC-8 aircraft equipped with advanced remote sensing and in situ instrumentation, in conjunction with radiosonde launches and satellite observations, allowed CPEX-CV to target the coupling between atmospheric dynamics, marine boundary layer properties, convection, and the dust-laden Saharan air layer in the data-sparse tropical East Atlantic region. CPEX-CV provided measurements of African easterly wave environments, diurnal cycle impacts on convective life cycle, and several Saharan dust outbreaks, including the highest dust optical depth observed by the DC-8 interacting with what would become Tropical Storm Hermine. Preliminary results from CPEX-CV underscore the positive impact of dedicated tropical East Atlantic observations on downstream forecast skill, including sampling environmental forcings impacting the development of several nondeveloping and developing convective systems such as Hurricanes Fiona and Ian. Combined airborne radar, lidar, and radiometer measurements uniquely provide near-storm environments associated with convection on various spatiotemporal scales and, with in situ observations, insights into controls on Saharan dust properties with transport. The DC-8 also collaborated with the European Space Agency to perform coordinated validation flights under the Aeolus spaceborne wind lidar and over the Mindelo ground site, highlighting the enhanced sampling potential through partnership opportunities. CPEX-CV engaged in professional development through dedicated team-building exercises that equipped the team with a cohesive approach for targeting CPEX-CV science objectives and promoted active participation of scientists across all career stages.
Abstract
We have quantified the impacts of varying thermodynamic environments on tropical congestus and cumulonimbus clouds (CCCs) within maritime tropical regions. To elucidate this relationship, we employed the Regional Atmospheric Modeling System (RAMS) to conduct high-resolution (1 km) simulations of convection over the Philippine Archipelago for a month-long period in 2019. We subsequently performed a cloud-object-based analysis, identifying and tracking hundreds of thousands of individual CCCs using the Tracking and Object-Based Analysis of Clouds (tobac) tracking library. Using this object-oriented dataset of tracked cells, we examined differences in individual storm strength, organization, and morphology due to the storm’s initial environment. We found that storm strength, defined here as maximum midlevel updraft velocity, was controlled primarily by convective available potential energy (CAPE) and precipitable water (PW); high CAPE (>2500 J kg−1) and high (approximately 63 mm) PW were both required for midlevel CCC updraft velocities to reach at least 10 m s−1. Of the CCCs with the most vigorous updrafts, 80.9% were also in the upper tercile of precipitation rates, with the strongest precipitation rates requiring even higher PW. Further, we found that vertical wind shear was the primary differentiator between organized and isolated convective storms. Within the set of organized storms, linearly oriented CCC systems have significantly weaker vertical wind shear than nonlinear CCCs in low- (0–1, 0–3 km) and midlevels (0–5, 2–7 km). Overall, these results provide new insights into the environmental conditions determining the CCC properties in maritime tropical regions.
Abstract
We have quantified the impacts of varying thermodynamic environments on tropical congestus and cumulonimbus clouds (CCCs) within maritime tropical regions. To elucidate this relationship, we employed the Regional Atmospheric Modeling System (RAMS) to conduct high-resolution (1 km) simulations of convection over the Philippine Archipelago for a month-long period in 2019. We subsequently performed a cloud-object-based analysis, identifying and tracking hundreds of thousands of individual CCCs using the Tracking and Object-Based Analysis of Clouds (tobac) tracking library. Using this object-oriented dataset of tracked cells, we examined differences in individual storm strength, organization, and morphology due to the storm’s initial environment. We found that storm strength, defined here as maximum midlevel updraft velocity, was controlled primarily by convective available potential energy (CAPE) and precipitable water (PW); high CAPE (>2500 J kg−1) and high (approximately 63 mm) PW were both required for midlevel CCC updraft velocities to reach at least 10 m s−1. Of the CCCs with the most vigorous updrafts, 80.9% were also in the upper tercile of precipitation rates, with the strongest precipitation rates requiring even higher PW. Further, we found that vertical wind shear was the primary differentiator between organized and isolated convective storms. Within the set of organized storms, linearly oriented CCC systems have significantly weaker vertical wind shear than nonlinear CCCs in low- (0–1, 0–3 km) and midlevels (0–5, 2–7 km). Overall, these results provide new insights into the environmental conditions determining the CCC properties in maritime tropical regions.
Abstract
Hailstorms cause billions of dollars in damage across the United States each year. Part of this cost could be reduced by increasing warning lead times. To contribute to this effort, we developed a nowcasting machine learning model that uses a 3D U-Net to produce gridded severe hail nowcasts for up to 40 minutes in advance. The three U-Net dimensions uniquely incorporate one temporal and two spatial dimensions. Our predictors consist of a combination of output from the National Severe Storms Laboratory Warn-on-Forecast System (WoFS) numerical weather prediction ensemble and remote sensing observations from Vaisala’s National Lightning Detection Network (NLDN). Ground truth for prediction was derived from the Maximum Expected Size of Hail calculated from the gridded NEXRAD WSR-88D radar (GridRad) dataset. Our U-Net was evaluated by comparing its test set performance against rigorous hail nowcasting baselines. These baselines included WoFS ensemble HAILCAST and a logistic regression model trained on WoFS 2-5 km updraft helicity. The 3D U-Net outperformed both these baselines for all forecast period timesteps. Its predictions yielded a neighborhooded maximum critical success index (max CSI) of ~0.48 and ~0.30 at forecast minutes 20 and 40, respectively. These max CSIs exceeded the ensemble HAILCAST max CSIs by as much as ~0.35. The NLDN observations were found to increase the U-Net performance by more than a factor of 4 at some timesteps. This system has shown success when nowcasting hail during complex severe weather events, and if used in an operational environment, may prove valuable.
Abstract
Hailstorms cause billions of dollars in damage across the United States each year. Part of this cost could be reduced by increasing warning lead times. To contribute to this effort, we developed a nowcasting machine learning model that uses a 3D U-Net to produce gridded severe hail nowcasts for up to 40 minutes in advance. The three U-Net dimensions uniquely incorporate one temporal and two spatial dimensions. Our predictors consist of a combination of output from the National Severe Storms Laboratory Warn-on-Forecast System (WoFS) numerical weather prediction ensemble and remote sensing observations from Vaisala’s National Lightning Detection Network (NLDN). Ground truth for prediction was derived from the Maximum Expected Size of Hail calculated from the gridded NEXRAD WSR-88D radar (GridRad) dataset. Our U-Net was evaluated by comparing its test set performance against rigorous hail nowcasting baselines. These baselines included WoFS ensemble HAILCAST and a logistic regression model trained on WoFS 2-5 km updraft helicity. The 3D U-Net outperformed both these baselines for all forecast period timesteps. Its predictions yielded a neighborhooded maximum critical success index (max CSI) of ~0.48 and ~0.30 at forecast minutes 20 and 40, respectively. These max CSIs exceeded the ensemble HAILCAST max CSIs by as much as ~0.35. The NLDN observations were found to increase the U-Net performance by more than a factor of 4 at some timesteps. This system has shown success when nowcasting hail during complex severe weather events, and if used in an operational environment, may prove valuable.
Abstract
The impact of assimilating targeted uncrewed aircraft system (UAS) observations on the prediction of radiation and river valley fog is assessed using observing system experiments (OSEs). Two multirotor UASs were deployed during Frequent in situ Observations above Ground for Modeling and Advanced Prediction of fog (FOGMAP) which took place during the summer of 2022 in northern Kentucky. Targeted UAS missions were flown to sample the spatiotemporal variability of temperature and moisture in the vicinity of the Cincinnati/Northern Kentucky International Airport. During each mission, the UAS performed near-continuous profiling at two locations between the surface to 120 m AGL throughout the night. Data denial experiments were performed using the ensemble adjustment Kalman filter available in NSF NCAR’s Data Assimilation Research Testbed (DART) to determine the impact of assimilating UAS observations on the skill of analyses and forecasts issued during potential fog events. Simulations that only assimilated conventional observations tended to have a dry bias in the analyses and forecasts. The dry bias in the analyses was reduced in experiments that assimilated UAS observations leading to improved probabilistic predictions of fog. Sensitivity tests revealed that the ensemble mean analyses were improved when assimilating UAS observations of specific humidity rather than relative humidity (RH) due to the existence of a cold bias near the surface and the negative covariance between RH and temperature. It was also found that either the assumed observation error variance of (1 g kg−1)2 or the ensemble spread of the background specific humidity was too large since their sum tended to overestimate the root-mean-square error (RMSE) of the predicted ensemble mean values.
Abstract
The impact of assimilating targeted uncrewed aircraft system (UAS) observations on the prediction of radiation and river valley fog is assessed using observing system experiments (OSEs). Two multirotor UASs were deployed during Frequent in situ Observations above Ground for Modeling and Advanced Prediction of fog (FOGMAP) which took place during the summer of 2022 in northern Kentucky. Targeted UAS missions were flown to sample the spatiotemporal variability of temperature and moisture in the vicinity of the Cincinnati/Northern Kentucky International Airport. During each mission, the UAS performed near-continuous profiling at two locations between the surface to 120 m AGL throughout the night. Data denial experiments were performed using the ensemble adjustment Kalman filter available in NSF NCAR’s Data Assimilation Research Testbed (DART) to determine the impact of assimilating UAS observations on the skill of analyses and forecasts issued during potential fog events. Simulations that only assimilated conventional observations tended to have a dry bias in the analyses and forecasts. The dry bias in the analyses was reduced in experiments that assimilated UAS observations leading to improved probabilistic predictions of fog. Sensitivity tests revealed that the ensemble mean analyses were improved when assimilating UAS observations of specific humidity rather than relative humidity (RH) due to the existence of a cold bias near the surface and the negative covariance between RH and temperature. It was also found that either the assumed observation error variance of (1 g kg−1)2 or the ensemble spread of the background specific humidity was too large since their sum tended to overestimate the root-mean-square error (RMSE) of the predicted ensemble mean values.
Abstract
Beginning with the TOPEX/Poseidon altimetry mission in 1992, along-track satellite sea surface height (SSH) estimates have been taken every 6–7 km at 10-day intervals at a cluster of satellite tracks across the Bering Strait near 66°N where the satellite tracks turn. Year-to-year geostrophic SSH estimates of the Bering Strait flow show that the flow in the warmer months is almost entirely confined to a 50-m-depth channel rather than the whole cross-sectional area and that the peak-to-peak transport change is comparable to the approximate 0.8 Sverdrup mean (Sv; 1 Sv ≡ 106 m3 s−1). Energy flux calculations using the high-resolution SSH across the Bering Strait, as well as an analysis of historical coastal Russian sea levels and wind stress along the Russian Arctic and Bering Sea shelves, suggest that the interannual Bering Strait transport in the warmer water months can be described using wind-forced arrested topographic waves (ATWs) driven by along-shelf wind stress on both the Russian Arctic and Bering Sea shelves. Most of the Bering Strait transport is barotropic and in the 50-m-depth channel, but analysis of SSH and in situ Alaskan coastal sea level shows that interannual transport variations in the narrow baroclinic Alaskan Coastal Current are not negligible. In disagreement with previous transport estimates, the satellite SSH analysis and almost all of the in situ current measurements do not indicate a significant long-term trend in the barotropic Bering Strait northward transport in the warmer water late summer/fall months.
Significance Statement
The shallow, narrow Bering Strait between eastern Siberia and Alaska is a key climate pathway linking the Pacific Ocean and the Bering Sea to the Arctic Ocean. By utilizing nearly three decades of satellite sea surface height, we found that warmer water month volume transport through the Bering Strait in this remote, harsh environment can be monitored by satellite. Analysis suggests that the average warmer month interannual flows are remotely driven by interannual along-shelf winds on the Russian Arctic and Bering Sea shelves and that the warmer month flow has not increased over the last three decades. As the Arctic continues to warm, more ice-free data will only enhance the utility of this remote Bering Strait transport monitoring.
Abstract
Beginning with the TOPEX/Poseidon altimetry mission in 1992, along-track satellite sea surface height (SSH) estimates have been taken every 6–7 km at 10-day intervals at a cluster of satellite tracks across the Bering Strait near 66°N where the satellite tracks turn. Year-to-year geostrophic SSH estimates of the Bering Strait flow show that the flow in the warmer months is almost entirely confined to a 50-m-depth channel rather than the whole cross-sectional area and that the peak-to-peak transport change is comparable to the approximate 0.8 Sverdrup mean (Sv; 1 Sv ≡ 106 m3 s−1). Energy flux calculations using the high-resolution SSH across the Bering Strait, as well as an analysis of historical coastal Russian sea levels and wind stress along the Russian Arctic and Bering Sea shelves, suggest that the interannual Bering Strait transport in the warmer water months can be described using wind-forced arrested topographic waves (ATWs) driven by along-shelf wind stress on both the Russian Arctic and Bering Sea shelves. Most of the Bering Strait transport is barotropic and in the 50-m-depth channel, but analysis of SSH and in situ Alaskan coastal sea level shows that interannual transport variations in the narrow baroclinic Alaskan Coastal Current are not negligible. In disagreement with previous transport estimates, the satellite SSH analysis and almost all of the in situ current measurements do not indicate a significant long-term trend in the barotropic Bering Strait northward transport in the warmer water late summer/fall months.
Significance Statement
The shallow, narrow Bering Strait between eastern Siberia and Alaska is a key climate pathway linking the Pacific Ocean and the Bering Sea to the Arctic Ocean. By utilizing nearly three decades of satellite sea surface height, we found that warmer water month volume transport through the Bering Strait in this remote, harsh environment can be monitored by satellite. Analysis suggests that the average warmer month interannual flows are remotely driven by interannual along-shelf winds on the Russian Arctic and Bering Sea shelves and that the warmer month flow has not increased over the last three decades. As the Arctic continues to warm, more ice-free data will only enhance the utility of this remote Bering Strait transport monitoring.
Abstract
In this study, a machine-learning based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Mission Combined Radar-Radiometer Algorithm. Ground clutter can corrupt and obscure precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflectivity observations above the clutter are included in a fixed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A Neural Network Model (NN) is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN’s limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.
Abstract
In this study, a machine-learning based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Mission Combined Radar-Radiometer Algorithm. Ground clutter can corrupt and obscure precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflectivity observations above the clutter are included in a fixed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A Neural Network Model (NN) is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN’s limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.
Abstract
The implications of definitions of excessive rainfall observations on machine learning model forecast skill are assessed using the Colorado State University Machine Learning Probabilities (CSU-MLP) forecast system. The CSU-MLP uses historical observations along with reforecasts from a global ensemble to train random forests to probabilistically predict excessive rainfall events. Here, random forest models are trained using two distinct rainfall datasets, one that is composed of fixed-frequency (FF) average recurrence intervals exceedances and flash flood reports and the other a compilation of flooding and rainfall proxies [Unified Flood Verification System (UFVS)]. Both models generate 1–3-day forecasts and are evaluated against a climatological baseline to characterize their overall skill as a function of lead time, season, and region. Model comparisons suggest that regional frequencies in excessive rainfall observations contribute to when and where the ML models issue forecasts and subsequently their skill and reliability. Additionally, the spatiotemporal distribution of observations has implications for ML model training requirements, notably, how long of an observational record is needed to obtain skillful forecasts. Experiments reveal that shorter-trained UFVS-based models can be as skillful as longer-trained FF-based models. In essence, the UFVS dataset exhibits a more robust characterization of excessive rainfall and impacts, and machine learning models trained on more representative datasets of meteorological hazards may not require as extensive training to generate skillful forecasts.
Significance Statement
Machine learning (ML) models have shown significant promise in recent years when used to predict high-impact weather hazards. Here, we explore two similarly trained ML models tasked with predicting excessive rainfall, but they use datasets that define excessive rainfall differently. We explore how definitions of excessive rainfall, for example, an amount of rainfall that would be expected to fall once per year, contribute to forecast skill through where these observations are reported. Generally, we find that the two models have substantial skill relative to climatology out to 3 days, but skill varies by geographical region and season in part because of the distribution of observations geographically. These results suggest that careful attention should be paid to how ML models are trained to predict meteorological hazards like excessive rainfall.
Abstract
The implications of definitions of excessive rainfall observations on machine learning model forecast skill are assessed using the Colorado State University Machine Learning Probabilities (CSU-MLP) forecast system. The CSU-MLP uses historical observations along with reforecasts from a global ensemble to train random forests to probabilistically predict excessive rainfall events. Here, random forest models are trained using two distinct rainfall datasets, one that is composed of fixed-frequency (FF) average recurrence intervals exceedances and flash flood reports and the other a compilation of flooding and rainfall proxies [Unified Flood Verification System (UFVS)]. Both models generate 1–3-day forecasts and are evaluated against a climatological baseline to characterize their overall skill as a function of lead time, season, and region. Model comparisons suggest that regional frequencies in excessive rainfall observations contribute to when and where the ML models issue forecasts and subsequently their skill and reliability. Additionally, the spatiotemporal distribution of observations has implications for ML model training requirements, notably, how long of an observational record is needed to obtain skillful forecasts. Experiments reveal that shorter-trained UFVS-based models can be as skillful as longer-trained FF-based models. In essence, the UFVS dataset exhibits a more robust characterization of excessive rainfall and impacts, and machine learning models trained on more representative datasets of meteorological hazards may not require as extensive training to generate skillful forecasts.
Significance Statement
Machine learning (ML) models have shown significant promise in recent years when used to predict high-impact weather hazards. Here, we explore two similarly trained ML models tasked with predicting excessive rainfall, but they use datasets that define excessive rainfall differently. We explore how definitions of excessive rainfall, for example, an amount of rainfall that would be expected to fall once per year, contribute to forecast skill through where these observations are reported. Generally, we find that the two models have substantial skill relative to climatology out to 3 days, but skill varies by geographical region and season in part because of the distribution of observations geographically. These results suggest that careful attention should be paid to how ML models are trained to predict meteorological hazards like excessive rainfall.
