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Abstract
This study explores gulf-breeze circulations (GBCs) and bay-breeze circulations (BBCs) in Houston–Galveston, investigating their characteristics, large-scale weather influences, and impacts on surface properties, boundary layer updrafts, and convective clouds. The results are derived from a combination of datasets, including satellite observations, ground-based measurements, and reanalysis datasets, using machine learning, changepoint detection method, and Lagrangian cell tracking. We find that anticyclonic synoptic patterns during the summer months (June–September) favor GBC/BBC formation and the associated convective cloud development, representing 74% of cases. The main Tracking Aerosol Convection Interactions Experiment (TRACER) site located close to the Galveston Bay is influenced by both GBC and BBC, with nearly half of the cases showing evident BBC features. The site experiences early frontal passages ranging from 1040 to 1630 local time (LT), with 1300 LT being the most frequent. These fronts are stronger than those observed at the ancillary site which is located further inland from the Galveston Bay, including larger changes in surface temperature, moisture, and wind speed. Furthermore, these fronts trigger boundary layer updrafts, likely promoting isolated convective precipitating cores that are short lived (average convective lifetime of 63 min) and slow moving (average propagation speed of 5 m s−1), primarily within 20–40 km from the coast.
Abstract
This study explores gulf-breeze circulations (GBCs) and bay-breeze circulations (BBCs) in Houston–Galveston, investigating their characteristics, large-scale weather influences, and impacts on surface properties, boundary layer updrafts, and convective clouds. The results are derived from a combination of datasets, including satellite observations, ground-based measurements, and reanalysis datasets, using machine learning, changepoint detection method, and Lagrangian cell tracking. We find that anticyclonic synoptic patterns during the summer months (June–September) favor GBC/BBC formation and the associated convective cloud development, representing 74% of cases. The main Tracking Aerosol Convection Interactions Experiment (TRACER) site located close to the Galveston Bay is influenced by both GBC and BBC, with nearly half of the cases showing evident BBC features. The site experiences early frontal passages ranging from 1040 to 1630 local time (LT), with 1300 LT being the most frequent. These fronts are stronger than those observed at the ancillary site which is located further inland from the Galveston Bay, including larger changes in surface temperature, moisture, and wind speed. Furthermore, these fronts trigger boundary layer updrafts, likely promoting isolated convective precipitating cores that are short lived (average convective lifetime of 63 min) and slow moving (average propagation speed of 5 m s−1), primarily within 20–40 km from the coast.
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
Episodic cold surges in the East Asian winter monsoon can penetrate deep into the South China Sea (SCS), enhance consequent tropical rainfall, and further strengthen the East Asia meridional overturning circulation. These cold surges can promote strong surface fluxes and lead to a deeper marine boundary layer (MBL). However, there is a lack of boundary layer studies over the SCS, unlike many other well-studied regions such as the North Atlantic Ocean and the central-eastern Pacific Ocean. In this study, we use high-resolution radiosonde data of temperature and humidity profiles over Dongsha Island (20.70°N, 116.69°E) to identify the inversion layer, mixed layer, cloud base, cloud top, and factors controlling low cloud cover for the period of December–February from 2010 to 2020. We perform an energy budget analysis with ERA5 meteorological variables and surface fluxes. Here, we show a strong turbulent flux convergence of both heat and moisture within the SCS MBL during cold surges, which leads to a lifting of the mixed layer to ∼1.0 km and the inversion layer to ∼2.0 km and associated cloud development over Dongsha Island. The cold and dry horizontal advection is balanced by this vertical turbulent flux convergence in the energy budget. Overall, cold surges over the SCS enhance a lower branch of winter monsoon meridional overturning circulation with stronger inversion and higher low cloud covers.
Significance Statement
Cold surges in the East Asian winter monsoon bring cold and dry air from Eurasian continent to the South China Sea where strong air–sea fluxes and pronounced shallow clouds are unique climatological features. The convective boundary layer (CBL) over the SCS and upstream northwest Pacific (NWP) is important in maintaining the East Asia (EA) meridional overturning circulation. However, the CBL over the SCS–NWP is poorly understood and the lack of understanding can lead to unrealistic boundary layer turbulence and energy transport such that the tropical convection and the overturning circulation are incorrectly represented. In this study, we use high-quality radiosonde data at Dongsha, reanalysis, and satellite cloud data to show the CBL structure and their evolution during the passage of cold surges in northern SCS. We anticipate that our study will motivate more atmosphere–ocean joint observation and PBL-related studies over the SCS–NWP.
Abstract
Episodic cold surges in the East Asian winter monsoon can penetrate deep into the South China Sea (SCS), enhance consequent tropical rainfall, and further strengthen the East Asia meridional overturning circulation. These cold surges can promote strong surface fluxes and lead to a deeper marine boundary layer (MBL). However, there is a lack of boundary layer studies over the SCS, unlike many other well-studied regions such as the North Atlantic Ocean and the central-eastern Pacific Ocean. In this study, we use high-resolution radiosonde data of temperature and humidity profiles over Dongsha Island (20.70°N, 116.69°E) to identify the inversion layer, mixed layer, cloud base, cloud top, and factors controlling low cloud cover for the period of December–February from 2010 to 2020. We perform an energy budget analysis with ERA5 meteorological variables and surface fluxes. Here, we show a strong turbulent flux convergence of both heat and moisture within the SCS MBL during cold surges, which leads to a lifting of the mixed layer to ∼1.0 km and the inversion layer to ∼2.0 km and associated cloud development over Dongsha Island. The cold and dry horizontal advection is balanced by this vertical turbulent flux convergence in the energy budget. Overall, cold surges over the SCS enhance a lower branch of winter monsoon meridional overturning circulation with stronger inversion and higher low cloud covers.
Significance Statement
Cold surges in the East Asian winter monsoon bring cold and dry air from Eurasian continent to the South China Sea where strong air–sea fluxes and pronounced shallow clouds are unique climatological features. The convective boundary layer (CBL) over the SCS and upstream northwest Pacific (NWP) is important in maintaining the East Asia (EA) meridional overturning circulation. However, the CBL over the SCS–NWP is poorly understood and the lack of understanding can lead to unrealistic boundary layer turbulence and energy transport such that the tropical convection and the overturning circulation are incorrectly represented. In this study, we use high-quality radiosonde data at Dongsha, reanalysis, and satellite cloud data to show the CBL structure and their evolution during the passage of cold surges in northern SCS. We anticipate that our study will motivate more atmosphere–ocean joint observation and PBL-related studies over the SCS–NWP.
Abstract
The upslope flow processes affecting the vertical extent of orographic cumulus convection are examined using observations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. Specifically, clear air returns from the U.S. Department of Energy (DOE) second-generation C-band scanning Atmospheric Radiation Measurement (ARM) precipitation radar (CSAPR2) are used to characterize the structure and variability of the ridge-normal (i.e., up/downslope) flow components, which transport mass to the crest of Argentina’s Sierras de Córdoba and contribute to convective initiation. Data are compiled for the entire CACTI period (October–April), including days with clear skies, shallow cumuli, cumulus congestus, and deep convection. To examine shared variability among >70 000 radar scans, we use (i) a principal component analysis (PCA) to isolate modes of variability in the upslope flow and (ii) composite analysis based on convective outcomes, determined from GOES-16 satellite observations. These data are contextualized with observed surface sensible heat fluxes, thermodynamic profiles, and synoptic-scale analysis. Results indicate distinct thermally and mechanically forced upslope flow modes, modulated by diurnal heating and synoptic-scale variations, respectively. In some instances, there is a superposition of thermal and mechanical forcing, yielding either deeper or shallower upslope flow. The composite analyses based on satellite data show that successively deeper convective outcomes are associated with successively deeper upslope flow layers that more readily transport mass to the ridge crest in conjunction with lower lifting condensation levels, facilitating convective initiation. These results help to isolate the forcing mechanisms for orographic convection and thus provide a foundation for parameterizing orographic convective processes in coarse resolution models.
Abstract
The upslope flow processes affecting the vertical extent of orographic cumulus convection are examined using observations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. Specifically, clear air returns from the U.S. Department of Energy (DOE) second-generation C-band scanning Atmospheric Radiation Measurement (ARM) precipitation radar (CSAPR2) are used to characterize the structure and variability of the ridge-normal (i.e., up/downslope) flow components, which transport mass to the crest of Argentina’s Sierras de Córdoba and contribute to convective initiation. Data are compiled for the entire CACTI period (October–April), including days with clear skies, shallow cumuli, cumulus congestus, and deep convection. To examine shared variability among >70 000 radar scans, we use (i) a principal component analysis (PCA) to isolate modes of variability in the upslope flow and (ii) composite analysis based on convective outcomes, determined from GOES-16 satellite observations. These data are contextualized with observed surface sensible heat fluxes, thermodynamic profiles, and synoptic-scale analysis. Results indicate distinct thermally and mechanically forced upslope flow modes, modulated by diurnal heating and synoptic-scale variations, respectively. In some instances, there is a superposition of thermal and mechanical forcing, yielding either deeper or shallower upslope flow. The composite analyses based on satellite data show that successively deeper convective outcomes are associated with successively deeper upslope flow layers that more readily transport mass to the ridge crest in conjunction with lower lifting condensation levels, facilitating convective initiation. These results help to isolate the forcing mechanisms for orographic convection and thus provide a foundation for parameterizing orographic convective processes in coarse resolution models.
Abstract
The uncertainty associated with many observed and modeled quantities of interest in Earth system prediction can be represented by mixed probability distributions that are neither discrete nor continuous. For instance, a forecast probability of precipitation can have a finite probability of zero precipitation, consistent with a discrete distribution. However, nonzero values are not discrete and are represented by a continuous distribution; the same is true for rainfall rate. Other examples include snow depth, sea ice concentration, the amount of a tracer, or the source rate of a tracer. Some Earth system model parameters may also have discrete or mixed distributions. Most ensemble data assimilation methods do not explicitly consider the possibility of mixed distributions. The quantile-conserving ensemble filter framework is extended to explicitly deal with discrete or mixed distributions. An example is given using bounded normal rank histogram probability distributions applied to observing system simulation experiments in a low-order tracer advection model. Analyses of tracer concentration and tracer source are shown to be improved when using the extended methods. A key feature of the resulting ensembles is that there can be ensemble members with duplicate values. An extension of the rank histogram diagnostic method to deal with potential duplicates shows that the ensemble distributions from the extended assimilation methods are more consistent with the truth.
Significance Statement
Data assimilation is a statistical method that is used to combine information from computer forecasts with measurements of the Earth system. The result is a better estimate of what is occurring in the physical system. As an example, data assimilation is used for making weather predictions. Some Earth system quantities, like precipitation, have special values that can occur very frequently. For instance, zero rainfall is quite common, while any other specific amount of rainfall, say, 0.42 in., is unusual. New data assimilation tools that work well for quantities like this are introduced and should lead to better estimates and predictions of the Earth system.
Abstract
The uncertainty associated with many observed and modeled quantities of interest in Earth system prediction can be represented by mixed probability distributions that are neither discrete nor continuous. For instance, a forecast probability of precipitation can have a finite probability of zero precipitation, consistent with a discrete distribution. However, nonzero values are not discrete and are represented by a continuous distribution; the same is true for rainfall rate. Other examples include snow depth, sea ice concentration, the amount of a tracer, or the source rate of a tracer. Some Earth system model parameters may also have discrete or mixed distributions. Most ensemble data assimilation methods do not explicitly consider the possibility of mixed distributions. The quantile-conserving ensemble filter framework is extended to explicitly deal with discrete or mixed distributions. An example is given using bounded normal rank histogram probability distributions applied to observing system simulation experiments in a low-order tracer advection model. Analyses of tracer concentration and tracer source are shown to be improved when using the extended methods. A key feature of the resulting ensembles is that there can be ensemble members with duplicate values. An extension of the rank histogram diagnostic method to deal with potential duplicates shows that the ensemble distributions from the extended assimilation methods are more consistent with the truth.
Significance Statement
Data assimilation is a statistical method that is used to combine information from computer forecasts with measurements of the Earth system. The result is a better estimate of what is occurring in the physical system. As an example, data assimilation is used for making weather predictions. Some Earth system quantities, like precipitation, have special values that can occur very frequently. For instance, zero rainfall is quite common, while any other specific amount of rainfall, say, 0.42 in., is unusual. New data assimilation tools that work well for quantities like this are introduced and should lead to better estimates and predictions of the Earth system.
Abstract
The Model for Prediction Across Scales (MPAS) with variable resolution (60–15–1 km) is used to investigate the track deflection of Typhoon Chanthu (2021) near Taiwan. Chanthu exhibited a rightward track deflection as it approached southeast Taiwan and underwent a leftward deflection when moving northward offshore of northeast Taiwan. Numerical experiments are conducted to identify the physical processes for the track deflection. The rightward deflection of the northbound typhoon is induced by the recirculating flow resulting from the effect of Taiwan’s topography. A wavenumber-1 potential vorticity (PV) budget analysis indicates that horizontal PV advection dominates the earlier rightward deflection, while the later leftward deflection is mainly in response to stronger asymmetric cloud heating at low levels at the offshore quadrant of the typhoon. A pair of cyclonic and anticyclonic gyres in the wavenumber-1 flow difference is induced by Taiwan’s topography. These rotate counterclockwise to drive the track deflection, most often in westbound typhoons. Idealized WRF simulations are also conducted to explore the track deflection under different northbound conditions. The simulations confirm the track deflection mechanism with similar PV dynamics to the MPAS simulations for Chanthu and illustrate the variabilities of the track deflection for different steering conditions and vortex origins. The rightward deflection of northbound typhoons is essentially determined by a reduced ratio of R/LE , where R is the vortex size and LE is the effective length of the mountain range.
Abstract
The Model for Prediction Across Scales (MPAS) with variable resolution (60–15–1 km) is used to investigate the track deflection of Typhoon Chanthu (2021) near Taiwan. Chanthu exhibited a rightward track deflection as it approached southeast Taiwan and underwent a leftward deflection when moving northward offshore of northeast Taiwan. Numerical experiments are conducted to identify the physical processes for the track deflection. The rightward deflection of the northbound typhoon is induced by the recirculating flow resulting from the effect of Taiwan’s topography. A wavenumber-1 potential vorticity (PV) budget analysis indicates that horizontal PV advection dominates the earlier rightward deflection, while the later leftward deflection is mainly in response to stronger asymmetric cloud heating at low levels at the offshore quadrant of the typhoon. A pair of cyclonic and anticyclonic gyres in the wavenumber-1 flow difference is induced by Taiwan’s topography. These rotate counterclockwise to drive the track deflection, most often in westbound typhoons. Idealized WRF simulations are also conducted to explore the track deflection under different northbound conditions. The simulations confirm the track deflection mechanism with similar PV dynamics to the MPAS simulations for Chanthu and illustrate the variabilities of the track deflection for different steering conditions and vortex origins. The rightward deflection of northbound typhoons is essentially determined by a reduced ratio of R/LE , where R is the vortex size and LE is the effective length of the mountain range.
Abstract
The analyses produced by a data assimilation system may be unbalanced, which is dynamically inconsistent with the forecasting model, leading to noisy forecasts and reduced skill. While there are effective procedures to reduce synoptic-scale imbalance, the situation on the convective scale is less clear because the flow on this scale is strongly divergent and nonhydrostatic. In this study, we compare three measures of imbalance relevant to convective-scale data assimilation: (i) surface pressure tendencies, (ii) vertical velocity variance in the vicinity of convective clouds, and (iii) departures from the vertical velocity prescribed by the weak temperature gradient (WTG) approximation. These are applied in a numerical weather prediction system, with three different data assimilation algorithms: 1) latent heat nudging (LHN), 2) local ensemble transform Kalman filter (LETKF), and 3) LETKF in combination with incremental analysis updates (IAUs). Results indicate that surface pressure tendency diagnoses a different type of imbalance than the vertical velocity variance and the WTG departure. The LETKF induces a spike in surface pressure tendencies, with a large-scale spatial pattern that is not clearly related to the precipitation pattern. This anomaly is notably reduced by the IAU. LHN does not generate a pronounced signal in the surface pressure but produces the most imbalance in the other two measures. The imbalances measured by the partitioned vertical velocity variance and WTG departures are similar and closely coupled to the convective precipitation. Between these two measures, the WTG departure has the advantage of being simpler and more economical to compute.
Abstract
The analyses produced by a data assimilation system may be unbalanced, which is dynamically inconsistent with the forecasting model, leading to noisy forecasts and reduced skill. While there are effective procedures to reduce synoptic-scale imbalance, the situation on the convective scale is less clear because the flow on this scale is strongly divergent and nonhydrostatic. In this study, we compare three measures of imbalance relevant to convective-scale data assimilation: (i) surface pressure tendencies, (ii) vertical velocity variance in the vicinity of convective clouds, and (iii) departures from the vertical velocity prescribed by the weak temperature gradient (WTG) approximation. These are applied in a numerical weather prediction system, with three different data assimilation algorithms: 1) latent heat nudging (LHN), 2) local ensemble transform Kalman filter (LETKF), and 3) LETKF in combination with incremental analysis updates (IAUs). Results indicate that surface pressure tendency diagnoses a different type of imbalance than the vertical velocity variance and the WTG departure. The LETKF induces a spike in surface pressure tendencies, with a large-scale spatial pattern that is not clearly related to the precipitation pattern. This anomaly is notably reduced by the IAU. LHN does not generate a pronounced signal in the surface pressure but produces the most imbalance in the other two measures. The imbalances measured by the partitioned vertical velocity variance and WTG departures are similar and closely coupled to the convective precipitation. Between these two measures, the WTG departure has the advantage of being simpler and more economical to compute.
Abstract
Observational and modeling efforts have explored the formation and maintenance of mesovortices, which contribute to severe hazards in quasi-linear convective systems (QLCSs). There exists an important interplay between environmental shear and cold-pool-induced circulations which, when balanced, allow for upright QLCS updrafts with maximized lift along storm outflow boundaries. Numerical simulations have primarily tested the sensitivity of squall lines to zonally varying low-level (LL) shear profiles (i.e., purely line-normal, assuming a north–south-oriented system), but observed near-storm environments of mesovortex-producing QLCSs exhibit substantial LL hodograph curvature (i.e., line-parallel shear). Therefore, previous QLCS simulations may fail to capture the full impacts of LL shear variability on mesovortex characteristics. To this end, this study employs an ensemble of idealized QLCS simulations with systematic variations in the orientation and magnitude of the ambient LL shear vector, all while holding 0–3-km line-normal shear constant. This allows for a nuanced examination of how line-parallel shear modulates system structure, as well as mesovortex strength, size, and longevity. Results indicate that hodographs with LL curvature support squall lines with prominent bowing segments and wider, more intense rotating updrafts. Shear orientation also impacts mesovortex characteristics, with curved hodographs favoring cyclonic vortices that are stronger, wider, deeper, and longer-lived than those produced with straight-line wind profiles. These results provide a more complete physical understanding of how LL shear variability influences the generation of rotation in squall lines.
Significance Statement
Research related to linear storms has largely focused on vertical changes in winds (i.e., shear) oriented perpendicular to squall lines given its ability to balance storm cold pools and keep updrafts upright, thus promoting long-lived storms that presumably can go on to produce rotation. However, squall lines that produce a great deal of rotation often have a component of low-level shear oriented parallel to storms. This study gauges the sensitivity of simulated squall lines to changes in the direction and strength of shear close to the surface. We find that shear oriented parallel to linear storms creates stronger and larger updrafts that in turn support the development of intense and persistent rotation with characteristics supportive of tornadoes. These insights have impacts on both our physical understanding and prediction of the rotation and associated hazards of linear storms.
Abstract
Observational and modeling efforts have explored the formation and maintenance of mesovortices, which contribute to severe hazards in quasi-linear convective systems (QLCSs). There exists an important interplay between environmental shear and cold-pool-induced circulations which, when balanced, allow for upright QLCS updrafts with maximized lift along storm outflow boundaries. Numerical simulations have primarily tested the sensitivity of squall lines to zonally varying low-level (LL) shear profiles (i.e., purely line-normal, assuming a north–south-oriented system), but observed near-storm environments of mesovortex-producing QLCSs exhibit substantial LL hodograph curvature (i.e., line-parallel shear). Therefore, previous QLCS simulations may fail to capture the full impacts of LL shear variability on mesovortex characteristics. To this end, this study employs an ensemble of idealized QLCS simulations with systematic variations in the orientation and magnitude of the ambient LL shear vector, all while holding 0–3-km line-normal shear constant. This allows for a nuanced examination of how line-parallel shear modulates system structure, as well as mesovortex strength, size, and longevity. Results indicate that hodographs with LL curvature support squall lines with prominent bowing segments and wider, more intense rotating updrafts. Shear orientation also impacts mesovortex characteristics, with curved hodographs favoring cyclonic vortices that are stronger, wider, deeper, and longer-lived than those produced with straight-line wind profiles. These results provide a more complete physical understanding of how LL shear variability influences the generation of rotation in squall lines.
Significance Statement
Research related to linear storms has largely focused on vertical changes in winds (i.e., shear) oriented perpendicular to squall lines given its ability to balance storm cold pools and keep updrafts upright, thus promoting long-lived storms that presumably can go on to produce rotation. However, squall lines that produce a great deal of rotation often have a component of low-level shear oriented parallel to storms. This study gauges the sensitivity of simulated squall lines to changes in the direction and strength of shear close to the surface. We find that shear oriented parallel to linear storms creates stronger and larger updrafts that in turn support the development of intense and persistent rotation with characteristics supportive of tornadoes. These insights have impacts on both our physical understanding and prediction of the rotation and associated hazards of linear storms.
Abstract
We present an extended case study analysis based on observed extreme weather events (EWEs) and the planetary- and synoptic-scale variability of a persistent flow regime spanning the month of February 2019 across the North Pacific (NPAC) basin and western North America. The EWEs are clustered into two periods during February 2019: record cold and kona low conditions over Hawaii, lower elevation snow across Washington, and heavy AR-related rainfall in Southern California from 9 to 15 February; and heavy snow in Arizona and Oregon and heavy rainfall in Northern California from 21 to 28 February. From a weather regime perspective, the NPAC flow was dominated by a persistent ridge around 150°W, a retracted NPAC jet stream, repeated western NPAC (WPAC) cyclogenesis events, and frequent positively tilted troughs in the eastern NPAC and over western North America. Dynamically relevant features on the subseasonal-to-seasonal (S2S) time scale include a slowly propagating MJO signal in phases 6 and 7, a rapid NPAC jet retraction around 9 February, and subsequent eastward extension toward a climatological jet position around 21 February. On synoptic time scales, Rossby wave breaking on the southern flank of the NPAC jet and within the aforementioned persistent ridge led to the kona low formation and many of the positively tilted troughs responsible for the extreme precipitation events. In addition, frequent cyclogenesis west of the date line helped to maintain the persistent ridge strength and location through favorable heat and vorticity fluxes. The chronology and complex linkages between these aforementioned features and mechanisms are explained in depth in this paper.
Significance Statement
This study identified several extreme cold, rain, and snow events across the western contiguous United States and Hawaii, showed how these events are all connected to a persistent weather pattern upstream over the North Pacific basin, and identified key mechanisms for why the weather pattern was so persistent. Some of the physical mechanisms for keeping the weather pattern stagnant include anomalous convection within the tropics off the east coast of Asia, breaking waves in the atmosphere like waves breaking on a beach, and repeated cyclone development off the coast of Russia and Alaska. We hope that the results of this paper encourage others to look for similar mechanisms for similar stagnant weather patterns in a more holistic manner.
Abstract
We present an extended case study analysis based on observed extreme weather events (EWEs) and the planetary- and synoptic-scale variability of a persistent flow regime spanning the month of February 2019 across the North Pacific (NPAC) basin and western North America. The EWEs are clustered into two periods during February 2019: record cold and kona low conditions over Hawaii, lower elevation snow across Washington, and heavy AR-related rainfall in Southern California from 9 to 15 February; and heavy snow in Arizona and Oregon and heavy rainfall in Northern California from 21 to 28 February. From a weather regime perspective, the NPAC flow was dominated by a persistent ridge around 150°W, a retracted NPAC jet stream, repeated western NPAC (WPAC) cyclogenesis events, and frequent positively tilted troughs in the eastern NPAC and over western North America. Dynamically relevant features on the subseasonal-to-seasonal (S2S) time scale include a slowly propagating MJO signal in phases 6 and 7, a rapid NPAC jet retraction around 9 February, and subsequent eastward extension toward a climatological jet position around 21 February. On synoptic time scales, Rossby wave breaking on the southern flank of the NPAC jet and within the aforementioned persistent ridge led to the kona low formation and many of the positively tilted troughs responsible for the extreme precipitation events. In addition, frequent cyclogenesis west of the date line helped to maintain the persistent ridge strength and location through favorable heat and vorticity fluxes. The chronology and complex linkages between these aforementioned features and mechanisms are explained in depth in this paper.
Significance Statement
This study identified several extreme cold, rain, and snow events across the western contiguous United States and Hawaii, showed how these events are all connected to a persistent weather pattern upstream over the North Pacific basin, and identified key mechanisms for why the weather pattern was so persistent. Some of the physical mechanisms for keeping the weather pattern stagnant include anomalous convection within the tropics off the east coast of Asia, breaking waves in the atmosphere like waves breaking on a beach, and repeated cyclone development off the coast of Russia and Alaska. We hope that the results of this paper encourage others to look for similar mechanisms for similar stagnant weather patterns in a more holistic manner.
Abstract
Numerical weather prediction (NWP) models struggle to skillfully predict tropical precipitation occurrence and amount, calling for alternative approaches. For instance, it has been shown that fairly simple, purely data-driven logistic regression models for 24-h precipitation occurrence outperform both climatological and NWP forecasts for the West African summer monsoon. More complex neural network–based approaches, however, remain underdeveloped due to the non-Gaussian character of precipitation. In this study, we develop, apply, and evaluate a novel two-stage approach, where we train a U-Net convolutional neural network (CNN) model on gridded rainfall data to obtain a deterministic forecast and then apply the recently developed, nonparametric Easy Uncertainty Quantification (EasyUQ) approach to convert it into a probabilistic forecast. We evaluate CNN+EasyUQ for 1-day-ahead 24-h accumulated precipitation forecasts over northern tropical Africa for 2011–19, with the Integrated Multi-satellitE Retrievals for GPM (IMERG) data serving as ground truth. In the most comprehensive assessment to date, we compare CNN+EasyUQ to state-of-the-art physics-based and data-driven approaches such as monthly probabilistic climatology, raw and postprocessed ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), and traditional statistical approaches that use up to 25 predictor variables from IMERG and the ERA5 reanalysis. Generally, statistical approaches perform about on par with postprocessed ECMWF ensemble forecasts. The CNN+EasyUQ approach, however, clearly outperforms all competitors in terms of both occurrence and amount. Hybrid methods that merge CNN+EasyUQ and physics-based forecasts show slight further improvement. Thus, the CNN+EasyUQ approach can likely improve operational probabilistic forecasts of rainfall in the tropics and potentially even beyond.
Significance Statement
Precipitation forecasts in the tropics remain a great challenge despite their enormous potential to create socioeconomic benefits in sectors such as food and energy production. Here, we develop a purely data-driven, machine learning–based prediction model that outperforms traditional, physics-based approaches to 1-day-ahead forecasts of rainfall occurrence and rainfall amount over northern tropical Africa in terms of both forecast skill and computational costs. A combined data-driven and physics-based (hybrid) approach yields further (slight) improvement in terms of forecast skill. These results suggest new avenues to more accurate and more resource-efficient operational precipitation forecasts in the Global South.
Abstract
Numerical weather prediction (NWP) models struggle to skillfully predict tropical precipitation occurrence and amount, calling for alternative approaches. For instance, it has been shown that fairly simple, purely data-driven logistic regression models for 24-h precipitation occurrence outperform both climatological and NWP forecasts for the West African summer monsoon. More complex neural network–based approaches, however, remain underdeveloped due to the non-Gaussian character of precipitation. In this study, we develop, apply, and evaluate a novel two-stage approach, where we train a U-Net convolutional neural network (CNN) model on gridded rainfall data to obtain a deterministic forecast and then apply the recently developed, nonparametric Easy Uncertainty Quantification (EasyUQ) approach to convert it into a probabilistic forecast. We evaluate CNN+EasyUQ for 1-day-ahead 24-h accumulated precipitation forecasts over northern tropical Africa for 2011–19, with the Integrated Multi-satellitE Retrievals for GPM (IMERG) data serving as ground truth. In the most comprehensive assessment to date, we compare CNN+EasyUQ to state-of-the-art physics-based and data-driven approaches such as monthly probabilistic climatology, raw and postprocessed ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), and traditional statistical approaches that use up to 25 predictor variables from IMERG and the ERA5 reanalysis. Generally, statistical approaches perform about on par with postprocessed ECMWF ensemble forecasts. The CNN+EasyUQ approach, however, clearly outperforms all competitors in terms of both occurrence and amount. Hybrid methods that merge CNN+EasyUQ and physics-based forecasts show slight further improvement. Thus, the CNN+EasyUQ approach can likely improve operational probabilistic forecasts of rainfall in the tropics and potentially even beyond.
Significance Statement
Precipitation forecasts in the tropics remain a great challenge despite their enormous potential to create socioeconomic benefits in sectors such as food and energy production. Here, we develop a purely data-driven, machine learning–based prediction model that outperforms traditional, physics-based approaches to 1-day-ahead forecasts of rainfall occurrence and rainfall amount over northern tropical Africa in terms of both forecast skill and computational costs. A combined data-driven and physics-based (hybrid) approach yields further (slight) improvement in terms of forecast skill. These results suggest new avenues to more accurate and more resource-efficient operational precipitation forecasts in the Global South.
