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Abstract
Tropical cyclone numbers can vary from week to week within a hurricane season. Recent studies suggest that convectively coupled Kelvin waves can be partly responsible for such variability. However, the precise physical mechanisms responsible for that modulation remain uncertain partly due to the inability of previous studies to isolate the effects of Kelvin waves from other factors. This study uses an idealized modeling framework—called an aquaplanet—to uniquely isolate the effects of Kelvin waves on tropical cyclogenesis. The framework also captures the convective-scale dynamics of both tropical cyclones and Kelvin waves. Our results confirm an uptick in tropical cyclogenesis after the passage of a Kelvin wave—twice as many tropical cyclones form 2 days after a Kelvin wave peak than at any other time lag from the peak. A detailed composite analysis shows anomalously weak ventilation during and after (or to the west of) the Kelvin wave peak. The weak ventilation stems primarily from anomalously moist conditions, with weaker vertical wind shear playing a secondary role. In contrast to previous studies, our results demonstrate that Kelvin waves modulate both kinematic and thermodynamic synoptic-scale conditions that are necessary for tropical cyclone formation. These results suggest that numerical models must capture the three-dimensional structure of Kelvin waves to produce accurate subseasonal predictions of tropical cyclone activity.
Significance Statement
Anticipating active tropical cyclone periods several weeks in advance could help mitigate the loss of lives and property due to these phenomena. Recent studies suggest that a type of tropical cloud cluster—known as convectively coupled Kelvin waves—can promote tropical cyclone formation. Kelvin waves travel around the world and can be detected days to weeks in advance. We use a simplified numerical model to isolate the effects of Kelvin waves on tropical cyclone formation. Our unique approach confirms that tropical cyclones are more likely to form 2 days after a Kelvin wave than before the wave. We also demonstrate that—contrary to previous perception—the enhancement of tropical cyclogenesis is due to both more moisture and weaker wind currents following the waves.
Abstract
Tropical cyclone numbers can vary from week to week within a hurricane season. Recent studies suggest that convectively coupled Kelvin waves can be partly responsible for such variability. However, the precise physical mechanisms responsible for that modulation remain uncertain partly due to the inability of previous studies to isolate the effects of Kelvin waves from other factors. This study uses an idealized modeling framework—called an aquaplanet—to uniquely isolate the effects of Kelvin waves on tropical cyclogenesis. The framework also captures the convective-scale dynamics of both tropical cyclones and Kelvin waves. Our results confirm an uptick in tropical cyclogenesis after the passage of a Kelvin wave—twice as many tropical cyclones form 2 days after a Kelvin wave peak than at any other time lag from the peak. A detailed composite analysis shows anomalously weak ventilation during and after (or to the west of) the Kelvin wave peak. The weak ventilation stems primarily from anomalously moist conditions, with weaker vertical wind shear playing a secondary role. In contrast to previous studies, our results demonstrate that Kelvin waves modulate both kinematic and thermodynamic synoptic-scale conditions that are necessary for tropical cyclone formation. These results suggest that numerical models must capture the three-dimensional structure of Kelvin waves to produce accurate subseasonal predictions of tropical cyclone activity.
Significance Statement
Anticipating active tropical cyclone periods several weeks in advance could help mitigate the loss of lives and property due to these phenomena. Recent studies suggest that a type of tropical cloud cluster—known as convectively coupled Kelvin waves—can promote tropical cyclone formation. Kelvin waves travel around the world and can be detected days to weeks in advance. We use a simplified numerical model to isolate the effects of Kelvin waves on tropical cyclone formation. Our unique approach confirms that tropical cyclones are more likely to form 2 days after a Kelvin wave than before the wave. We also demonstrate that—contrary to previous perception—the enhancement of tropical cyclogenesis is due to both more moisture and weaker wind currents following the waves.
Abstract
The ERA5 reanalysis during cold months (November–March) of 1979–2020 was used for determining four cluster centroids through the k-means for classifying regional anomalies of the daily geopotential height at 500 hPa (H500) over northeastern China. Empirical orthogonal function (EOF) was used to reduce dimensionality. Four clusters were linked to the EOF patterns with clear meteorological meanings, which are associated with the evolution of ridges and troughs over northeastern China. Those systems relate to warm and cold advections at 850 hPa. In each H500 cluster, the advection is the major contributor leading to temperature changes at 850 hPa, which significantly relates to the changes and anomalies of daily minimum air temperature at 2 m (T2min). Furthermore, the jet activities over Asia relate to more or less occurrence of specific H500 clusters in jet phases. This is because anomalous westerlies are generally in favor of positive anomalies of the vorticity tendency at 500 hPa. For the reforecasts during 2004–19 in the Chinese Meteorology Administration (CMA) S2S model, the hit rates above 50% for all the H500 clusters are within 9.5 days, which are in between those for the first two and the last two clusters. The correct prediction of H500 anomalies improves the T2min prediction up to 12 days, compared with 8 days for the incorrect one. The good prediction of the jet activities leads to a more accurate prediction of H500 anomalies. Therefore, improvement of the model prediction of jet activities and H500 anomalies will lead to better prediction of winter weather near the ground over northeastern China.
Abstract
The ERA5 reanalysis during cold months (November–March) of 1979–2020 was used for determining four cluster centroids through the k-means for classifying regional anomalies of the daily geopotential height at 500 hPa (H500) over northeastern China. Empirical orthogonal function (EOF) was used to reduce dimensionality. Four clusters were linked to the EOF patterns with clear meteorological meanings, which are associated with the evolution of ridges and troughs over northeastern China. Those systems relate to warm and cold advections at 850 hPa. In each H500 cluster, the advection is the major contributor leading to temperature changes at 850 hPa, which significantly relates to the changes and anomalies of daily minimum air temperature at 2 m (T2min). Furthermore, the jet activities over Asia relate to more or less occurrence of specific H500 clusters in jet phases. This is because anomalous westerlies are generally in favor of positive anomalies of the vorticity tendency at 500 hPa. For the reforecasts during 2004–19 in the Chinese Meteorology Administration (CMA) S2S model, the hit rates above 50% for all the H500 clusters are within 9.5 days, which are in between those for the first two and the last two clusters. The correct prediction of H500 anomalies improves the T2min prediction up to 12 days, compared with 8 days for the incorrect one. The good prediction of the jet activities leads to a more accurate prediction of H500 anomalies. Therefore, improvement of the model prediction of jet activities and H500 anomalies will lead to better prediction of winter weather near the ground over northeastern China.
Abstract
Supercells in landfalling tropical cyclones (TCs) often produce tornadoes within 50 km of the coastline. The prevalence of TC tornadoes near the coast is not explained by the synoptic environments of the TC, suggesting a mesoscale influence is likely. Past case studies point to thermodynamic contrasts between ocean and land or convergence along the coast as a possible mechanism for enhancing supercell mesocyclones and storm intensity. This study augments past work by examining the changes in the hurricane boundary layer over land in the context of vertical wind shear. Using ground-based single- and dual-Doppler radar analyses, we show that the reduction in the boundary layer wind results in an increase in vertical wind shear/storm-relative helicity inland of the coast. We also show that convergence along the coast may be impactful to supercells as they cross the coastal boundary. Finally, we briefly document the changes in mesocyclone vertical vorticity to assess how the environmental changes may impact individual supercells.
Abstract
Supercells in landfalling tropical cyclones (TCs) often produce tornadoes within 50 km of the coastline. The prevalence of TC tornadoes near the coast is not explained by the synoptic environments of the TC, suggesting a mesoscale influence is likely. Past case studies point to thermodynamic contrasts between ocean and land or convergence along the coast as a possible mechanism for enhancing supercell mesocyclones and storm intensity. This study augments past work by examining the changes in the hurricane boundary layer over land in the context of vertical wind shear. Using ground-based single- and dual-Doppler radar analyses, we show that the reduction in the boundary layer wind results in an increase in vertical wind shear/storm-relative helicity inland of the coast. We also show that convergence along the coast may be impactful to supercells as they cross the coastal boundary. Finally, we briefly document the changes in mesocyclone vertical vorticity to assess how the environmental changes may impact individual supercells.
Abstract
The particle flow filter (PFF) shows promise for fully nonlinear data assimilation (DA) in high-dimensional systems. However, its application in atmospheric models has been relatively unexplored. In this study, we develop a new algorithm, PFF-DART, in order to conduct DA for high-dimensional atmospheric models. PFF-DART combines the PFF and the two-step ensemble filtering algorithm in the Data Assimilation Research Testbed (DART), exploiting the highly parallel structure of DART. To evaluate the performance of PFF-DART, we conduct an observing system simulation experiment (OSSE) in a simplified atmospheric general circulation model and compare the performance of PFF-DART with an existing linear and Gaussian DA method. Using the PFF-DART algorithm, we demonstrate, for the first time, the capability of the PFF to yield stable results in a yearlong cycling DA OSSE. Moreover, PFF-DART retains the important ability of the PFF to improve the assimilation of nonlinear and non-Gaussian observations. Finally, we emphasize that PFF-DART is a versatile algorithm that can be integrated with numerous other non-Gaussian DA techniques. This quality makes it a promising method for further investigation within a more sophisticated numerical weather prediction model in the future.
Significance Statement
Data assimilation is an important tool in weather forecasting. However, fundamental issues arising from linear and Gaussian approximations still exist, which can limit our utilization of observations. The particle flow filter is a promising method that avoids these approximations, but efficient algorithms for this method have yet to be developed. In this study, we develop an algorithm for the particle flow filter that can be implemented in high-dimensional geophysical models. We have demonstrated, for the first time, that the particle flow filter runs efficiently for a yearlong experiment in an atmospheric model. The new algorithm also improves the results over a commonly used data assimilation method. The results in this work demonstrate the potential usage of the particle flow filter in the weather forecasting and other high-dimensional forecasting problems.
Abstract
The particle flow filter (PFF) shows promise for fully nonlinear data assimilation (DA) in high-dimensional systems. However, its application in atmospheric models has been relatively unexplored. In this study, we develop a new algorithm, PFF-DART, in order to conduct DA for high-dimensional atmospheric models. PFF-DART combines the PFF and the two-step ensemble filtering algorithm in the Data Assimilation Research Testbed (DART), exploiting the highly parallel structure of DART. To evaluate the performance of PFF-DART, we conduct an observing system simulation experiment (OSSE) in a simplified atmospheric general circulation model and compare the performance of PFF-DART with an existing linear and Gaussian DA method. Using the PFF-DART algorithm, we demonstrate, for the first time, the capability of the PFF to yield stable results in a yearlong cycling DA OSSE. Moreover, PFF-DART retains the important ability of the PFF to improve the assimilation of nonlinear and non-Gaussian observations. Finally, we emphasize that PFF-DART is a versatile algorithm that can be integrated with numerous other non-Gaussian DA techniques. This quality makes it a promising method for further investigation within a more sophisticated numerical weather prediction model in the future.
Significance Statement
Data assimilation is an important tool in weather forecasting. However, fundamental issues arising from linear and Gaussian approximations still exist, which can limit our utilization of observations. The particle flow filter is a promising method that avoids these approximations, but efficient algorithms for this method have yet to be developed. In this study, we develop an algorithm for the particle flow filter that can be implemented in high-dimensional geophysical models. We have demonstrated, for the first time, that the particle flow filter runs efficiently for a yearlong experiment in an atmospheric model. The new algorithm also improves the results over a commonly used data assimilation method. The results in this work demonstrate the potential usage of the particle flow filter in the weather forecasting and other high-dimensional forecasting problems.
Abstract
This study evaluates a parameterization scheme for subgrid-scale (SGS) fluxes based on the scale-similarity assumption and employing a large-eddy simulation of an idealized back-building convective system. In this parameterization, the SGS fluxes are decomposed into the “Leonard term,” which depends only on the resolved scale components, the “Reynolds term,” which depends only on the SGS components, and the “cross term,” which corresponds to the interaction between the resolved scale and SGS components. Assuming a linear relationship between the Leonard term and the Reynolds and cross terms, SGS fluxes are expressed as the product of an empirical coefficient and the Leonard term. The Leonard term reasonably represents the SGS flux derived by a smooth filter operation, including the countergradient vertical SGS transport of potential temperature, which cannot be represented by a traditional eddy-diffusivity model. The dependence of the empirical coefficient on filter width is also evaluated. This dependence is related mainly to the Reynolds term, the magnitude of which varies widely with the filter width. The estimation based on the spectral decomposition of the Reynolds term explains the obtained dependence of the empirical coefficient for the vertical flux on filter width. In contrast, the variation of the empirical coefficient with filter width is not required to obtain the horizontal flux. For the parameterization of SGS fluxes in kilometer-scale models that use finite difference or volume methods, the Leonard term is expressed by the horizontal gradient of variables on a discrete grid. The Leonard term on a discrete grid also accurately represents the amplitude and spatial pattern of the SGS flux.
Significance Statement
Kilometer-scale numerical weather prediction models can reproduce deep convection explicitly. However, they cannot reproduce smaller inner structures within this deep convection. Therefore, a parameterization scheme that can express the nature of the transport associated with such structures is required. Using a large-eddy simulation with a horizontal resolution sufficient to express such inner structures, we evaluated a parameterization based on the scale similarity assumption, including an empirical coefficient. Based on spectral decomposition, we quantitatively estimated the relationship between the empirical coefficient and grid spacing. Our results provide guidance in selecting the value of the empirical coefficient in this parameterization.
Abstract
This study evaluates a parameterization scheme for subgrid-scale (SGS) fluxes based on the scale-similarity assumption and employing a large-eddy simulation of an idealized back-building convective system. In this parameterization, the SGS fluxes are decomposed into the “Leonard term,” which depends only on the resolved scale components, the “Reynolds term,” which depends only on the SGS components, and the “cross term,” which corresponds to the interaction between the resolved scale and SGS components. Assuming a linear relationship between the Leonard term and the Reynolds and cross terms, SGS fluxes are expressed as the product of an empirical coefficient and the Leonard term. The Leonard term reasonably represents the SGS flux derived by a smooth filter operation, including the countergradient vertical SGS transport of potential temperature, which cannot be represented by a traditional eddy-diffusivity model. The dependence of the empirical coefficient on filter width is also evaluated. This dependence is related mainly to the Reynolds term, the magnitude of which varies widely with the filter width. The estimation based on the spectral decomposition of the Reynolds term explains the obtained dependence of the empirical coefficient for the vertical flux on filter width. In contrast, the variation of the empirical coefficient with filter width is not required to obtain the horizontal flux. For the parameterization of SGS fluxes in kilometer-scale models that use finite difference or volume methods, the Leonard term is expressed by the horizontal gradient of variables on a discrete grid. The Leonard term on a discrete grid also accurately represents the amplitude and spatial pattern of the SGS flux.
Significance Statement
Kilometer-scale numerical weather prediction models can reproduce deep convection explicitly. However, they cannot reproduce smaller inner structures within this deep convection. Therefore, a parameterization scheme that can express the nature of the transport associated with such structures is required. Using a large-eddy simulation with a horizontal resolution sufficient to express such inner structures, we evaluated a parameterization based on the scale similarity assumption, including an empirical coefficient. Based on spectral decomposition, we quantitatively estimated the relationship between the empirical coefficient and grid spacing. Our results provide guidance in selecting the value of the empirical coefficient in this parameterization.
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.
