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Abstract
While GCM horizontal resolution has received the majority of scale improvements in recent years, ample evidence suggests that a model’s vertical resolution exerts a strong control on its ability to accurately simulate the physics of the marine boundary layer. Here we show that, regardless of parameter tuning, the ability of a single-column model (SCM) to simulate the subtropical marine boundary layer improves when its vertical resolution is improved. We introduce a novel objective tuning technique to optimize the parameters of an SCM against profiles of temperature and moisture and their turbulent fluxes, horizontal winds, cloud water, and rainwater from large-eddy simulations (LES). We use this method to identify optimal parameters for simulating marine stratocumulus and shallow cumulus. The novel tuning method utilizes an objective performance metric that accounts for the uncertainty in the LES output, including the covariability between model variables. Optimization is performed independently for different vertical grid spacings and value of time step, ranging from coarse scales often used in current global models (120 m, 180 s) to fine scales often used in parameterization development and large-eddy simulations (10 m, 15 s). Uncertainty-weighted disagreement between the SCM and LES decreases by a factor of ∼5 when vertical grid spacing is improved from 120 to 10 m, with time step reductions being of secondary importance. Model performance is shown to converge at a vertical grid spacing of 20 m, with further refinements to 10 m leading to little further improvement.
Significance Statement
In successive generations of computer models that simulate Earth’s atmosphere, improvements have been mainly accomplished by reducing the horizontal sizes of discretized grid boxes, while the vertical grid spacing has seen comparatively lesser refinements. Here we advocate for additional attention to be paid to the number of vertical layers in these models, especially in the model layers closest to Earth’s surface where climatologically important marine stratocumulus and shallow cumulus clouds reside. Our experiments show that the ability of a one-dimensional model to represent physical processes important to these clouds is strongly dependent on the model’s vertical grid spacing.
Abstract
While GCM horizontal resolution has received the majority of scale improvements in recent years, ample evidence suggests that a model’s vertical resolution exerts a strong control on its ability to accurately simulate the physics of the marine boundary layer. Here we show that, regardless of parameter tuning, the ability of a single-column model (SCM) to simulate the subtropical marine boundary layer improves when its vertical resolution is improved. We introduce a novel objective tuning technique to optimize the parameters of an SCM against profiles of temperature and moisture and their turbulent fluxes, horizontal winds, cloud water, and rainwater from large-eddy simulations (LES). We use this method to identify optimal parameters for simulating marine stratocumulus and shallow cumulus. The novel tuning method utilizes an objective performance metric that accounts for the uncertainty in the LES output, including the covariability between model variables. Optimization is performed independently for different vertical grid spacings and value of time step, ranging from coarse scales often used in current global models (120 m, 180 s) to fine scales often used in parameterization development and large-eddy simulations (10 m, 15 s). Uncertainty-weighted disagreement between the SCM and LES decreases by a factor of ∼5 when vertical grid spacing is improved from 120 to 10 m, with time step reductions being of secondary importance. Model performance is shown to converge at a vertical grid spacing of 20 m, with further refinements to 10 m leading to little further improvement.
Significance Statement
In successive generations of computer models that simulate Earth’s atmosphere, improvements have been mainly accomplished by reducing the horizontal sizes of discretized grid boxes, while the vertical grid spacing has seen comparatively lesser refinements. Here we advocate for additional attention to be paid to the number of vertical layers in these models, especially in the model layers closest to Earth’s surface where climatologically important marine stratocumulus and shallow cumulus clouds reside. Our experiments show that the ability of a one-dimensional model to represent physical processes important to these clouds is strongly dependent on the model’s vertical grid spacing.
Abstract
Convective initiation (CI) nowcasting is crucial for reducing losses of human life and property caused by severe convective weather. A novel deep learning method based on the U-net model (named as CIUnet) was developed for forecasting CI during the warm season with eight interest fields of Himawari-8 Advanced Himawari Imager (AHI) and terrain height. The results showed that the CIUnet model produced probability forecasts of CI occurrence location and time with POD (probability of detection) at 93.3±0.3% and FAR (false alarm ratio) at 18.3±0.4% at a lead time of 30-min.
Sensitivity and permutation importance experiments on the input fields of the CIUnet model revealed that the differences in brightness temperature for spectral channels were more critical for CI nowcasts than the original infrared channel brightness temperatures. The brightness temperature difference between Band10 (7.3 μm) and Band13 (10.4 μm), which represents the cloud-top height relative to the lower-troposphere, is identified as the most important input fields for CI nowcasting. The tri-spectral brightness temperature difference (TTD), which represents cloud-top glaciation, is ranked the second and it significantly reduced the FAR of the CI forecast. Using terrain heights as an extra input feature improved the POD, but slightly overestimated CI over complex terrain. In addition, a layer-wise relevance propagation (LRP) analyses was performed, and confirmed that the CIUnet model can effectively identify the crucial regions and features of the input fields for accurate CI prediction. Therefore, both permutation importance experiments and LPR analyses are useful for improving the CIUnet model and advancing the understanding of CI mechanisms.
Abstract
Convective initiation (CI) nowcasting is crucial for reducing losses of human life and property caused by severe convective weather. A novel deep learning method based on the U-net model (named as CIUnet) was developed for forecasting CI during the warm season with eight interest fields of Himawari-8 Advanced Himawari Imager (AHI) and terrain height. The results showed that the CIUnet model produced probability forecasts of CI occurrence location and time with POD (probability of detection) at 93.3±0.3% and FAR (false alarm ratio) at 18.3±0.4% at a lead time of 30-min.
Sensitivity and permutation importance experiments on the input fields of the CIUnet model revealed that the differences in brightness temperature for spectral channels were more critical for CI nowcasts than the original infrared channel brightness temperatures. The brightness temperature difference between Band10 (7.3 μm) and Band13 (10.4 μm), which represents the cloud-top height relative to the lower-troposphere, is identified as the most important input fields for CI nowcasting. The tri-spectral brightness temperature difference (TTD), which represents cloud-top glaciation, is ranked the second and it significantly reduced the FAR of the CI forecast. Using terrain heights as an extra input feature improved the POD, but slightly overestimated CI over complex terrain. In addition, a layer-wise relevance propagation (LRP) analyses was performed, and confirmed that the CIUnet model can effectively identify the crucial regions and features of the input fields for accurate CI prediction. Therefore, both permutation importance experiments and LPR analyses are useful for improving the CIUnet model and advancing the understanding of CI mechanisms.
Abstract
A multiscale analysis of the environment supporting tornadoes in Southeast South America (SESA) was conducted based on a self-constructed database of 74 reports. Composites of environmental and convective parameters from ERA5 were generated relative to tornado events. The distribution of the reported tornadoes maximizes over the Argentine plains, while events are rare close to the Andes and south of Sierras de Córdoba. Events are relatively common in all seasons except in winter.
Proximity environment evolution shows enhanced instability, deep-layer vertical wind shear, storm-relative helicity, reduced convective inhibition, and a lowered lifting condensation level before or during the development of tornadic storms in SESA. No consistent signal in low-level wind shear is seen during tornado occurrence. However, a curved hodograph with counterclockwise rotation is present. The Significant Tornado Parameter (STP) is also maximized prior to tornadogenesis, most strongly associated with enhanced CAPE. Differences in the convective environment between tornadoes in SESA and the U.S. Great Plains are discussed.
On the synoptic scale, tornado events are associated with a strong anomalous trough crossing the southern Andes that triggers lee cyclogenesis, subsequently enhancing the South American Low-Level Jet (SALLJ) that increases moisture advection to support deep convection. This synoptic trough also enhances vertical shear that, along with enhanced instability, sustains organized convection capable of producing tornadic storms. At planetary scales, the tornadic environment is modulated by Rossby wave trains that appear to be forced by convection near northern Australia. Madden-Julian oscillation phase 3 preferentially occurs one to two weeks ahead of tornado occurrence.
Abstract
A multiscale analysis of the environment supporting tornadoes in Southeast South America (SESA) was conducted based on a self-constructed database of 74 reports. Composites of environmental and convective parameters from ERA5 were generated relative to tornado events. The distribution of the reported tornadoes maximizes over the Argentine plains, while events are rare close to the Andes and south of Sierras de Córdoba. Events are relatively common in all seasons except in winter.
Proximity environment evolution shows enhanced instability, deep-layer vertical wind shear, storm-relative helicity, reduced convective inhibition, and a lowered lifting condensation level before or during the development of tornadic storms in SESA. No consistent signal in low-level wind shear is seen during tornado occurrence. However, a curved hodograph with counterclockwise rotation is present. The Significant Tornado Parameter (STP) is also maximized prior to tornadogenesis, most strongly associated with enhanced CAPE. Differences in the convective environment between tornadoes in SESA and the U.S. Great Plains are discussed.
On the synoptic scale, tornado events are associated with a strong anomalous trough crossing the southern Andes that triggers lee cyclogenesis, subsequently enhancing the South American Low-Level Jet (SALLJ) that increases moisture advection to support deep convection. This synoptic trough also enhances vertical shear that, along with enhanced instability, sustains organized convection capable of producing tornadic storms. At planetary scales, the tornadic environment is modulated by Rossby wave trains that appear to be forced by convection near northern Australia. Madden-Julian oscillation phase 3 preferentially occurs one to two weeks ahead of tornado occurrence.
Abstract
On 28 May 2019, a tornadic supercell, observed as part of TORUS (Targeted Observation by UAS and Radars of Supercells) produced an EF-2 tornado near Tipton, KS. The supercell was observed to interact with multiple preexisting airmass boundaries. These boundaries and attendant air masses were examined using unoccupied aircraft system (UAS), mobile mesonets, radiosondes, and dual-Doppler analyses derived from TORUS mobile radars. The cool side air mass of one of these boundaries was found to have higher equivalent potential temperature and backed winds relative to the warm side air mass; features associated with MAHTEs (mesoscale air masses with high theta-E). It is hypothesized that these characteristics may have facilitated tornadogenesis. The two additional boundaries were produced by a nearby supercell and appeared to weaken the tornadic supercell. This work represents the first time that UAS have been used to examine the impact of preexisting airmass boundaries on a supercell, and it provides insights into the influence environmental heterogeneities can have on the evolution of a supercell.
Abstract
On 28 May 2019, a tornadic supercell, observed as part of TORUS (Targeted Observation by UAS and Radars of Supercells) produced an EF-2 tornado near Tipton, KS. The supercell was observed to interact with multiple preexisting airmass boundaries. These boundaries and attendant air masses were examined using unoccupied aircraft system (UAS), mobile mesonets, radiosondes, and dual-Doppler analyses derived from TORUS mobile radars. The cool side air mass of one of these boundaries was found to have higher equivalent potential temperature and backed winds relative to the warm side air mass; features associated with MAHTEs (mesoscale air masses with high theta-E). It is hypothesized that these characteristics may have facilitated tornadogenesis. The two additional boundaries were produced by a nearby supercell and appeared to weaken the tornadic supercell. This work represents the first time that UAS have been used to examine the impact of preexisting airmass boundaries on a supercell, and it provides insights into the influence environmental heterogeneities can have on the evolution of a supercell.
Abstract
In this study, downscaling, ensemble of data assimilation, time-lagging, and their combination were used to generate initial condition (IC) perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–2020. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. This study investigated the impacts of various IC perturbation methods on multiscale characteristics of perturbations and the forecast performance for both nonprecipitation and precipitation variables. These perturbation methods represented different-source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. Combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. Combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively.
Abstract
In this study, downscaling, ensemble of data assimilation, time-lagging, and their combination were used to generate initial condition (IC) perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–2020. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. This study investigated the impacts of various IC perturbation methods on multiscale characteristics of perturbations and the forecast performance for both nonprecipitation and precipitation variables. These perturbation methods represented different-source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. Combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. Combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively.
Abstract
Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden-Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous U.S. (CONUS) using environmental variables (convective available potential energy and precipitation) andNational Lightning Detection Network data for 2003–2016. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO-lightning relationship at daily resolution and the winter-spring ENSO-lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the Coastal Southeast U.S. during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River Valley during winter.
Abstract
Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden-Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous U.S. (CONUS) using environmental variables (convective available potential energy and precipitation) andNational Lightning Detection Network data for 2003–2016. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO-lightning relationship at daily resolution and the winter-spring ENSO-lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the Coastal Southeast U.S. during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River Valley during winter.
Abstract
Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson-Eidhammer microphysical parameterization scheme.
RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are (1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson-Eidhammer parameterization, and (2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015-16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson-Eidhammer configuration. For the multi-month OLYMPEX period, we show that rain rates, rain water mixing ratios, and rain drop number concentrations were increased relative to the Thompson-Eidhammer microphysical parameterization, while concurrently decreasing rain drop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest.
Abstract
Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson-Eidhammer microphysical parameterization scheme.
RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are (1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson-Eidhammer parameterization, and (2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015-16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson-Eidhammer configuration. For the multi-month OLYMPEX period, we show that rain rates, rain water mixing ratios, and rain drop number concentrations were increased relative to the Thompson-Eidhammer microphysical parameterization, while concurrently decreasing rain drop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest.
Abstract
This case study analyzes a tornadic supercell observed in northeast Louisiana as part of the Verification of the Origins of Rotation in Tornadoes Experiment Southeast (VORTEX-SE) on 6–7 April 2018. One mobile research radar (SR1-P), one WSR-88D equivalent (KULM), and two airborne radars (TAFT and TFOR) have sampled the storm at close proximity for ∼70 min through its mature phase, tornadogenesis at 2340 UTC, and dissipation and subsequent ingestion into a developing MCS segment. The 4D wind field and reflectivity from up to four Doppler analyses, combined with 4D diabatic Lagrangian analysis (DLA) retrievals, has enabled kinematic and thermodynamic analysis of storm-scale boundaries leading up to, during, and after the dissipation of the NWS-surveyed EF0 tornado. The kinematic and thermodynamic analyses reveal a transient current of low-level streamwise vorticity leading into the low-level supercell updraft, appearing similar to the streamwise vorticity current (SVC) that has been identified in supercell simulations and previously observed only kinematically. Vorticity dynamical calculations demonstrate that both baroclinity and horizontal stretching play significant roles in the generation and amplification of streamwise vorticity associated with this SVC. While the SVC does not directly feed streamwise vorticity to the tornado–cyclone, its development coincides with tornadogenesis and an intensification of the supercell’s main low-level updraft, although a causal relationship is unclear. Although the mesoscale environment is not high-shear/low-CAPE (HSLC), the updraft of the analyzed supercell shares some similarities to past observations and simulations of HSLC storms in the Southeast United States, most notably a pulse-like updraft that is maximized in the low- to midlevels of the storm.
Significance Statement
The purpose of this study is to analyze the airflow and thermodynamics of a highly observed tornado-producing supercell. While computer simulations can provide us with highly detailed looks at the complicated evolution of supercells, it is rare, due to the difficulty of data collection, to collect enough data to perform a highly detailed analysis on a particular supercell, especially in the Southeast United States. We identified a “current” of vorticity—rotating wind—that develops at the intersection of the supercell’s rain-cooled outflow and warm inflow, similar to previous simulations. This vorticity current develops and feeds the storm’s updraft as its tornado develops and the storm intensifies, although it does not directly enter the tornado.
Abstract
This case study analyzes a tornadic supercell observed in northeast Louisiana as part of the Verification of the Origins of Rotation in Tornadoes Experiment Southeast (VORTEX-SE) on 6–7 April 2018. One mobile research radar (SR1-P), one WSR-88D equivalent (KULM), and two airborne radars (TAFT and TFOR) have sampled the storm at close proximity for ∼70 min through its mature phase, tornadogenesis at 2340 UTC, and dissipation and subsequent ingestion into a developing MCS segment. The 4D wind field and reflectivity from up to four Doppler analyses, combined with 4D diabatic Lagrangian analysis (DLA) retrievals, has enabled kinematic and thermodynamic analysis of storm-scale boundaries leading up to, during, and after the dissipation of the NWS-surveyed EF0 tornado. The kinematic and thermodynamic analyses reveal a transient current of low-level streamwise vorticity leading into the low-level supercell updraft, appearing similar to the streamwise vorticity current (SVC) that has been identified in supercell simulations and previously observed only kinematically. Vorticity dynamical calculations demonstrate that both baroclinity and horizontal stretching play significant roles in the generation and amplification of streamwise vorticity associated with this SVC. While the SVC does not directly feed streamwise vorticity to the tornado–cyclone, its development coincides with tornadogenesis and an intensification of the supercell’s main low-level updraft, although a causal relationship is unclear. Although the mesoscale environment is not high-shear/low-CAPE (HSLC), the updraft of the analyzed supercell shares some similarities to past observations and simulations of HSLC storms in the Southeast United States, most notably a pulse-like updraft that is maximized in the low- to midlevels of the storm.
Significance Statement
The purpose of this study is to analyze the airflow and thermodynamics of a highly observed tornado-producing supercell. While computer simulations can provide us with highly detailed looks at the complicated evolution of supercells, it is rare, due to the difficulty of data collection, to collect enough data to perform a highly detailed analysis on a particular supercell, especially in the Southeast United States. We identified a “current” of vorticity—rotating wind—that develops at the intersection of the supercell’s rain-cooled outflow and warm inflow, similar to previous simulations. This vorticity current develops and feeds the storm’s updraft as its tornado develops and the storm intensifies, although it does not directly enter the tornado.
Abstract
In this study, a new way to assimilate clear-sky Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) observations over land is investigated for improving quantitative precipitation forecasts (QPFs) in eastern China. To alleviate problems arising from inaccurate surface temperature in radiance simulations, surface station observations of land surface skin temperature (LSST) together with conventional and AMSU-A observations are assimilated to improve AHI surface-sensitive TB simulations of the Community Radiative Transfer Model (CRTM) before AHI data assimilation. First, the Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVar) system is updated with the additional control variable of surface temperature and its background error covariances. Second, surface temperature and emissivity sensitivity checks are designed for the quality control of the surface-sensitive AHI channels. Finally, the impacts of a two-time data assimilation strategy are assessed for a local convection rainfall case and a synoptic-scale precipitation case. The experiment in which AHI data are assimilated after assimilating LSST data (ExpL2) outperforms the traditional experiment in which the LSST is not updated (ExpL) in terms of its 24-h QPF skill score. A better description of atmospheric instability and moisture convergence forcing is obtained in ExpL2 than in ExpL. Both experiments show additional low-level temperature and humidity adjustments compared to the experiment that does not assimilate AHI surface-sensitive channels (ExpNL). Lower AHI TB simulation biases are found in the ExpL2 experiment, which improve the analyzed field and subsequent QPFs. The results in this study suggest the importance of proper utilization of LSST observations for AHI surface-sensitive TB assimilations over land.
Abstract
In this study, a new way to assimilate clear-sky Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) observations over land is investigated for improving quantitative precipitation forecasts (QPFs) in eastern China. To alleviate problems arising from inaccurate surface temperature in radiance simulations, surface station observations of land surface skin temperature (LSST) together with conventional and AMSU-A observations are assimilated to improve AHI surface-sensitive TB simulations of the Community Radiative Transfer Model (CRTM) before AHI data assimilation. First, the Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVar) system is updated with the additional control variable of surface temperature and its background error covariances. Second, surface temperature and emissivity sensitivity checks are designed for the quality control of the surface-sensitive AHI channels. Finally, the impacts of a two-time data assimilation strategy are assessed for a local convection rainfall case and a synoptic-scale precipitation case. The experiment in which AHI data are assimilated after assimilating LSST data (ExpL2) outperforms the traditional experiment in which the LSST is not updated (ExpL) in terms of its 24-h QPF skill score. A better description of atmospheric instability and moisture convergence forcing is obtained in ExpL2 than in ExpL. Both experiments show additional low-level temperature and humidity adjustments compared to the experiment that does not assimilate AHI surface-sensitive channels (ExpNL). Lower AHI TB simulation biases are found in the ExpL2 experiment, which improve the analyzed field and subsequent QPFs. The results in this study suggest the importance of proper utilization of LSST observations for AHI surface-sensitive TB assimilations over land.
Abstract
Tropical cyclones (TCs) accompanied by an upper tropospheric cold low (CL) can experience unusual tracks. Idealized simulations resembling observed scenarios are designed in this study to investigate the impacts of a CL on TC tracks. The sensitivity of the TC motion to its location relative to the CL is examined. The results show that a TC follows a counterclockwise semicircle track if initially located east of a CL while a TC experiences a small southward looping track, followed by a sudden northward turn if initially located west of a CL. A TC on the west side experiences opposing CL and β steering, while they act in the same direction when a TC is on the east side of CL.
The steering flow analyses show that the steering vector is dominated by upper-level flow induced by the CL at early stage. The influence of CL extends downward and contributes to the lower-tropospheric asymmetric flow pattern of TC. As these two systems approach, the TC divergent outflow erodes the CL. The CL circulation is deformed and eventually merged with the TC when they are close. Since the erosion of CL, the TC motion is primarily related to β gyres at later stage.
The sensitivity of TC motion to the CL depth is also examined. TCs located west of a CL experience a westward track if the CL is shallow. In contrast, TCs initially located east of a CL all take a smooth track irrespective of the CL depth, and the CL depth mainly influences the track curvature and the TC translation speed.
Abstract
Tropical cyclones (TCs) accompanied by an upper tropospheric cold low (CL) can experience unusual tracks. Idealized simulations resembling observed scenarios are designed in this study to investigate the impacts of a CL on TC tracks. The sensitivity of the TC motion to its location relative to the CL is examined. The results show that a TC follows a counterclockwise semicircle track if initially located east of a CL while a TC experiences a small southward looping track, followed by a sudden northward turn if initially located west of a CL. A TC on the west side experiences opposing CL and β steering, while they act in the same direction when a TC is on the east side of CL.
The steering flow analyses show that the steering vector is dominated by upper-level flow induced by the CL at early stage. The influence of CL extends downward and contributes to the lower-tropospheric asymmetric flow pattern of TC. As these two systems approach, the TC divergent outflow erodes the CL. The CL circulation is deformed and eventually merged with the TC when they are close. Since the erosion of CL, the TC motion is primarily related to β gyres at later stage.
The sensitivity of TC motion to the CL depth is also examined. TCs located west of a CL experience a westward track if the CL is shallow. In contrast, TCs initially located east of a CL all take a smooth track irrespective of the CL depth, and the CL depth mainly influences the track curvature and the TC translation speed.
