Ensemble Sensitivity Analysis Research Articles

Understanding the initial conditions contributing to the rapid intensification of typhoons through ensemble sensitivity analysis

While steady improvements have been achieved for the track forecasts of typhoons, there has been a lack of improvement for intensity forecasts. One challenge for intensity forecasts is to capture the rapid intensification (RI), whose nonlinear characteristics impose great difficulties for numerical models. The ensemble sensitivity analysis (ESA) method is used here to analyze the initial conditions that contribute to typhoon intensity forecasts, especially with RI. Six RI processes from five typhoons (Chaba, Haima, Meranti, Sarika, and Songda) in 2016, are applied with ESA, which also gives a composite initial condition that favors subsequent RI. Results from individual cases have generally similar patterns of ESA, but with different magnitudes, when various cumulus parameterization schemes are applied. To draw the initial conditions with statistical significance, sample-mean azimuthal components of ESA are obtained. Results of the composite sensitivity show that typhoons that experience RI in 24 h favor enhanced primary circulation from low to high levels, intensified secondary circulation with increased radial inflow at lower levels and increased radial outflow at upper levels, a prominent warm core at around 300 hPa, and increased humidity at low levels. As the forecast lead time increases, the patterns of ESA are retained, while the sensitivity magnitudes decay. Given the general and quantitative composite sensitivity along with associated uncertainties for different cumulus parameterization schemes, appropriate sampling of the composite sensitivity in numerical models could be beneficial to capturing the RI and improving the forecasting of typhoon intensity.摘要针对台风强度特别是快速增强 (RI) 的预报难点, 本文对2016年5个台风的6次RI过程应用集合敏感性分析 (ESA), 以分析有利于RI的初始条件. 对于不同的个例和积云参数化方案, ESA获得的有利于台风RI的集合敏感性相似但量级存在差异. 复合敏感性揭示了经历RI的台风所需的初始条件, 其有更强的主环流, 更显著的暖心及增加的低层湿度. ESA估计的集合敏感性随预报时长增加而减弱. 通过采样复合敏感性可有望改进集合初始条件及台风强度预报.

Impact of Data Assimilation in Sensitive Features on the Predictability of the 2012 Great Arctic Cyclone

AbstractThe Great Arctic Cyclone 2012 (AC12) is used to understand the role of initial condition errors in the predictability at 2–3‐day forecast range of a high‐impact summer Arctic Cyclone (AC). Ensemble sensitivity analysis (ESA) is first performed to identify potentially sensitive regions of the cyclone evolution using an ensemble baseline forecast with conventional in situ observations assimilated. A pseudo‐observation method is then introduced to investigate impacts of hypothetical observations in these sensitive but unobserved regions. In the baseline experiments with in situ observations assimilated, the forecasted AC12 reaches its peak intensity 18 hr earlier than in the verifying Global Forecast System Analysis (GFS‐ANL) and the cyclone track is biased toward the southwest. Using ESA, the time of peak intensity and the cyclone track error are identified to be sensitive to the upstream trough, downstream ridge, and the tropopause polar vortex (TPV) to the northeast (NE TPV) of the AC12. These features were not observed by the in situ observation networks. To examine the impact of the observation gaps, pseudo‐observations drawn from GFS‐ANL are assimilated. Pseudo‐observations sample the three features separately to study the impact of the initial condition error on the predictability of AC12. The cyclone peak intensity timing error and track error are greatly reduced when the initial condition error is reduced near the NE TPV. A southward expansion of the NE TPV and the corresponding southward shifting low‐level front lead the forecasted AC12 to progress to the east, which better agrees with the verifying GFS‐ANL.

An ensemble-based data assimilation system for forecasting variability of the Northwestern Pacific ocean

An adjoint-free four-dimensional variational (a4dVar) data assimilation (DA) is implemented in an operational ocean forecast system based on an eddy-resolving ocean general circulation model for the Northwestern Pacific. Validation of the system against independent observations demonstrates that fitting the model to time-dependent satellite altimetry during a 10-day DA window leads to substantial skill improvements in the succeeding 60-day hindcast. The a4dVar corrects representation of the Kuroshio path variation south of Japan by adjusting the dynamical balance between amplitude/wavelength of the meander and flow advection. A larger ensemble spread tends to reduce the skill in representing the observed sea surface height anomaly, suggesting that it is possible to use the ensemble information for quantifying the forecast error. The ensemble information is also utilized for modification of the background error covariance (BEC), which improves the accuracy of temperature and salinity distributions. The modified BEC yields the skill decline of the Kuroshio path variation during the 60-day hindcast period, and the ensemble sensitivity analysis shows that changes in the dynamical balance caused by the ensemble BEC result in such skill deterioration.

Using Ensemble Sensitivity Analysis to Identify Storm Characteristics Associated with Tornadogenesis in High-Resolution Simulated Supercells

Abstract This study aims to objectively identify storm-scale characteristics associated with tornado-like vortex (TLV) formation in an ensemble of high-resolution supercell simulations. An ensemble of 51 supercells is created using Cloud Model version 1 (CM1). The first member is initialized using a base state populated by the Rapid Update Cycle (RUC) proximity sounding near El Reno, Oklahoma, on 24 May 2011. The other 50 ensemble members are created by randomly perturbing the base state after a supercell has formed. There is considerable spread between ensemble members, with some supercells producing strong, long-lived TLVs, while others do not produce a TLV at all. The ensemble is analyzed using the ensemble sensitivity analysis (ESA) technique, uncovering storm-scale characteristics that are dynamically relevant to TLV formation. In the rear flank, divergence at the surface southeast of the TLV helps converge and contract existing vertical vorticity, but there is no meaningful sensitivity to rear-flank outflow temperature. In the forward flank, warm temperatures within the cold pool are important to TLV production and magnitude. The longitudinal positioning of strong streamwise vorticity is also a clear indicator of TLV formation and strength, especially within 5 min of when the TLV is measured. Significance Statement Tornadoes that form in supercell thunderstorms (long-lived storms with a rotating updraft) are heavily influenced by the features created by the storm itself, such as the temperature of a downdraft. In this study, many different iterations of a strong supercell thunderstorm are simulated, in which tornado-like features are formed at different times with widely different strengths. A statistical method is used to identify what the storms had in common when they produced a tornado-like feature, and what they had in common when one failed to form. This study is important because it highlights which storm features are most influential to tornado formation using an objective method, with results that can be used when observing supercells in the field.

The Onset of a Blocking Event as a “Traffic Jam”: Characterization with Ensemble Sensitivity Analysis

Abstract Recently, Nakamura and Huang proposed a theory of blocking onset based on the budget of finite-amplitude local wave activity on the midlatitude waveguide. Blocks form in their idealized model due to a mechanism that also describes the emergence of traffic jams in traffic theory. The current work investigates the development of a winter European block in terms of finite-amplitude local wave activity to evaluate the possible relevance of the “traffic jam” mechanism for the flow transition. Two hundred members of a medium-range ensemble forecast of the blocking onset period are analyzed with correlation- and cluster-based sensitivity techniques. Diagnostic evidence points to a traffic jam onset on 17 December 2016. Block development is sensitive to upstream Rossby wave activity up to 1.5 days prior to its initiation and consistent with expectations from the idealized theory. Eastward transport of finite-amplitude local wave activity in the southern part of the block is suppressed by nonlinear flux modification from the large-amplitude blocking pattern, consistent with the expected obstruction in the traffic jam model. The relationship of finite-amplitude local wave activity and its zonal flux as mapped by the ensemble exhibits established characteristics of a traffic jam. This study suggests that the traffic jam mechanism may play an important role in some cases of blocking onset and more generally that applying finite-amplitude local wave activity diagnostics to ensemble data is a promising approach for the further examination of individual onset events in light of the Nakamura and Huang theory. Significance Statement Blocking is an occasional phenomenon in the mid- and high-latitude atmosphere characterized by the stalling of weather systems. Episodes of blocking are linked to extreme weather but their occurrence is not completely understood. A recent theory suggests that blocks may form in the jet stream like traffic jams on a highway. The onset mechanism contained in the theory could explain why forecasts of blocking are sometimes poor. In this work, we investigate the formation of a 2016 European winter block in the context of the traffic jam theory. Though questions remain regarding the implications for forecast uncertainty, our findings strongly support the notion of a traffic jam onset.

Evolution of squall line variability and error growth in an ensemble of large eddy simulations

Abstract. A chain of processes is identified that regulates much of the spread in an ensemble of squall lines in large eddy simulations with tight initial conditions. Patterns of gravity wave propagation de-correlate and restructure the initial condition spread until a second phase of convective initiation is taking place, i.e. after 30 min of simulation time. Subsequently, variability in this convective initiation and mass overturn is associated with differences in cold pool propagation within the ensemble (propagation at 2–4 m s−1. An ensemble sensitivity analysis reveals that anomalies in squall-line-relative flow with respect to the ensemble mean are also associated with the secondary convective initiation. Downdraughts are fed with extra air by a convergence zone on the rearward flank of the updraughts. An analysis of difference growth within the ensemble shows that a substantial proportion of variability is explained by cold pool propagation contrasts during this stage (30–80 min), which is partly removed when a feature-relative perspective is taken. The patterns of coherent variability exist on the timescale of an hour and dissipate subsequently (80–100 min).

Convection-permitting ensemble forecasts of a double-rainbelt event in South China during the pre-summer rainy season

Heavy rainfall occurred at the inland frontal zone and coastal warm sector in South China on 12–13 June 2019. Three convection-permitting ensemble forecast (CPEF) experiments with perturbed initial conditions (ICs) and lateral boundary conditions (BCs) as well as model physical schemes have been conducted to predict the double-rainbelt process. This study evaluates the predictability of different CPEF experiments and analyzes the causes of precipitation forecast errors by identifying the sensitive factors for frontal rainfall (FR) and warm-sector rainfall (WR). In this double-rainbelt event, the forecast skill of FR is significantly better than that of WR. The experiment with perturbed ICs/BCs combining suitable model physical schemes performs best. Based on the optimal ensemble experiment, the ensemble sensitivity analysis has been conducted for the two rainbelts. FR is sensitive to the synoptic forcing and its sensitive area mainly locates near the frontal system, while the coastal boundary layer jet and water vapor from the South China Sea play an important role in the WR process. The ICs and BCs greatly influence the location of FR through varying the movement direction and velocity of the fronts. The physical schemes have a great impact on convection triggering along the coasts, thus affecting the occurrence of the whole WR process, of which the choice of microphysical scheme is critical.

Estimating the benefit of Doppler wind lidars for short‐term low‐level wind ensemble forecasts

AbstractThis work focuses on the potential of a network of Doppler lidars for the improvement of short‐term forecasts of low‐level wind. For the impact assessment, we developed a new methodology that is based on ensemble sensitivity analysis (ESA). In contrast to preceding network design studies using ESA, we calculate the explicit sensitivity including the inverse of the background covariance matrix to account directly for the localization scale of the assimilation system. The new method is applied to a pre‐existing convective‐scale 1,000‐member ensemble simulation to mitigate effects of spurious correlations. We evaluate relative changes in the variance of a forecast metric, that is, the low‐level wind components averaged over the Rhein–Ruhr metropolitan area in Germany. This setup allows us to compare the relative variance change associated with the assimilation of hypothetical observations from a Doppler wind lidar with respect to the assimilation of surface‐wind observations only. Furthermore, we assess sensitivities of derived variance changes to a number of settings, namely observation errors, localization length scale, regularization factor, number of instruments in the network, and their location, as well as data availability of the lidar measurements. Our results demonstrate that a network of 20–30 Doppler lidars leads to a considerable variance reduction of the forecast metric chosen. On average, an additional network of 25 Doppler lidars can reduce the 1–3 hr forecast error by a factor of 1.6–3.3 with respect to 10‐m wind observations only. The results provide the basis for designing an operational network of Doppler lidars for the improvement of short‐term low‐level wind forecasts that could be especially valuable for the renewable energy sector.

Multivariate ensemble sensitivity analysis applied for an extreme rainfall over Indian subcontinent

Ensemble sensitivity analysis (ESA) uses sample statistics of ensemble forecasts to estimate relationships between forecast metrics and initial conditions. The ensemble sensitivity analysis is often considered as a simple univariate regression as it includes an approximation of analysis covariance matrix with corresponding diagonal elements. In this work, univariate ensemble sensitivity is extended to multivariate ensemble sensitivity that incorporates the contribution from the full covariance matrix in the sensitivity calculations. The performance of multivariate ensemble sensitivity over univariate is examined for meso- and convective scale ensemble forecasts of a heavy rainfall event that happened over the Chennai city in India in December 2015. The ensemble forecasts and analyses are generated using the Advanced Research - Weather Research and Forecasting (WRF) model Data Assimilation Research Testbed (DART) based Ensemble Kalman Filter (EnKF). Multivariate ensemble sensitivity shows organized sensitivity patterns, while the sensitivity values are found to be broadly distributed in univariate ensemble sensitivity. Both the methods are validated using a perturbed initial condition approach, and the results indicate that the multivariate ensemble sensitivity method is effective in predicting the forecast response closest to the actual model response compared to the univariate ensemble sensitivity. The impact of model error on sensitivity calculations is examined by generating a new set of ensembles that uses the Stochastic Kinetic Energy Backscatter Scheme (SKEBS). In the presence of added model error, the forecast response estimated by multivariate using SKEBS ensembles compares better with the actual response. It is found that the performance of the multivariate approach depends on the optimal choice of localization radius, and if insufficient localization is applied, the spurious long-distance correlation contaminates the performance of the multivariate ensemble sensitivity method. The impact of various forecast lead times on the univariate and multivariate ensemble sensitivity analysis indicates that responses using multivariate ensemble sensitivity are more accurate than univariate, especially at longer lead times when nonlinearity becomes significant. The performance of univariate and multivariate methods in convection-permitting scale is examined by using the high-resolution ensemble forecasts, and it is found that the multivariate sensitivity with localization substantially improves the estimates in finer scales.

Storm-Scale Predictability and Analysis of the 13 April 2020 Central Savannah River Area Tornado Outbreak

Abstract An early morning tornado outbreak occurred on 13 April 2020 in the Central Savannah River Area. Multiple significant tornadoes were reported, resulting in fatalities and injuries. While the operational tornado warnings had positive lead times, the convective mode (quasi-linear convective system) increased the warning decision complexity. The timing of the event [0500–0600 local time (LT)] also made NWS-to-public communication difficult. The experimental NSSL Warn-on-Forecast System (WoFS) was run retrospectively for this case. The WoFS consists of 3–6-h ensemble forecasts initialized every 30 min, and the goals of the system are to bridge the gap between severe weather watches and warnings and to increase warning lead times. Multiple WoFS forecasts were initialized leading up to the first tornado report; those initialized prior to tornado warning issuance have high ensemble probabilities of low-level rotation in the appropriate areas based on subsequent tornado reports. This case highlights another example of the usefulness of WoFS before its eventual transition to operations. Using the WoFS forecasts, kinematic and thermodynamic storm–environment relationships are analyzed using ensemble sensitivity analysis (ESA). The analyses suggest variations in the mesoscale environmental vertical wind profile are not as influential on mesovortex intensity as variations in the thermodynamic environment. Surface observations recorded prior to the tornado outbreak reveal subtle temperature and moisture gradients that may be the impetus for mesovortex intensification and tornadogenesis.

New Perspectives on Ensemble Sensitivity Analysis with Applications to a Climatology of Severe Convection

Abstract Ensemble sensitivity analysis (ESA) is a statistical technique applied within an ensemble to reveal the atmospheric flow features that relate to a chosen aspect of the flow. Given its ease of use (it is simply a linear regression between a chosen function of the forecast variables and the entire atmospheric state earlier or simultaneously in time), ensemble sensitivity has been the focus of several studies over roughly the last 10 years. Such studies have primarily tried to understand the relevant dynamics and/or key precursors of high-impact weather events. Other applications of ESA have been more operationally oriented, including observation targeting within data assimilation systems and real-time adjustment techniques that attempt to utilize both sensitivity information and observations to improve forecasts. While ESA has gained popularity, its fundamental properties remain a substantially underutilized basis for realizing the technique’s full scientific potential. For example, the relationship between ensemble sensitivity and the pure dynamics of the system can teach us how to appropriately apply various sensitivity-based applications, and combining sensitivity with other ensemble properties such as spread can distinguish between a fluid dynamics problem and a predictability one. This work aims to present new perspectives on ensemble sensitivity, and clarify its fundamentals, with the hopes of making it a more accessible, attractive, and useful tool in the atmospheric sciences. These new perspectives are applied in part to a short climatology of severe convection forecasts to demonstrate the unique knowledge that can gained through broadened use of ESA.

Characteristics of extratropical cyclones and precursors to windstorms in northern Europe

Abstract. Extratropical cyclones play a major role in the atmospheric circulation and weather variability and can cause widespread damage and destruction. Extratropical cyclones in northern Europe, which is located at the end of the North Atlantic storm track, have been less studied than extratropical cyclones elsewhere. Our study investigates extratropical cyclones and windstorms in northern Europe (which in this study covers Norway; Sweden; Finland; Estonia; and parts of the Baltic, Norwegian, and Barents seas) by analysing their characteristics, spatial and temporal evolution, and precursors. We examine cold and warm seasons separately to determine seasonal differences. We track all extratropical cyclones in northern Europe, create cyclone composites, and use an ensemble sensitivity method to analyse the precursors. The ensemble sensitivity analysis is a novel method in cyclone studies where linear regression is used to statistically identify what variables possibly influence the subsequent evolution of extratropical cyclones. We investigate windstorm precursors for both the minimum mean sea level pressure (MSLP) and for the maximum 10 m wind gusts. The annual number of extratropical cyclones and windstorms has a large inter-annual variability and no significant linear trends during 1980–2019. Windstorms originate and occur over the Barents and Norwegian seas, whereas weaker extratropical cyclones originate and occur over land areas in northern Europe. During the windstorm evolution, the maximum wind gusts move from the warm sector to behind the cold front following the strongest pressure gradient. Windstorms in both seasons are located on the poleward side of the jet stream. The maximum wind gusts occur nearly at the same time as the minimum MSLP occurs. The cold-season windstorms have higher sensitivities and thus are potentially better predictable than warm-season windstorms, and the minimum MSLP has higher sensitivities than the maximum wind gusts. Of the four examined precursors, both the minimum MSLP and the maximum wind gusts are the most sensitive to the 850 hPa potential temperature anomaly, i.e. the temperature gradient. Hence, this parameter is likely important when predicting windstorms in northern Europe.

The impact of weak environmental steering flow on tropical cyclone track predictability

AbstractTyphoonsHaiyan(2013) andHagupit(2014) are examples of two tropical cyclones (TCs) for which, despite similarities in the track and intensity, the predictabilities differed greatly. Both TCs made landfall over the Philippines having followed a similar track across the Pacific and both reached intensities in excess of 60 ms−1. Operational global ensemble forecasts showed large uncertainty in the track of , whereas the ensemble spread for was considerably less. Using the Met Office's Unified Model, 5‐day global ensemble forecasts were produced for both storms. Consistent with the operational forecasts produced at the time of the storms, the spread of tracks is greater in the forecasts produced for than . The latter was located on the southern periphery of the subtropical high and embedded in a strong easterly flow. In contrast, the position of between two anticyclones earlier in the forecast is key to the subsequent motion of the storm in determining whether would make landfall over the Philippines or turn to the north. Upper‐level winds contributed the most to the depth‐averaged steering flow. Statistically significant differences in the strength of the upper‐level anticyclone to the east of the storm, the strength and position of the upper‐level ridge downstream of the storm and the location of a detached potential vorticity (PV) streamer appear between two groups of ensemble members – those which turn to the north and those which make landfall. Positional differences of the TC in different ensemble members earlier in the forecasts, particularly in the east to west direction, are correlated to larger northeast to southwest position differences later in the forecast. Ensemble sensitivity analysis suggests that this initial east–west positional variance is linked to the upper‐level geopotential height directly south of the storm. Accurately representing both the steering flow and the position of is vital for an accurate forecast.

Observation Impact Study of an Arctic Cyclone Associated with a Tropopause Polar Vortex (TPV)-Induced Rossby Wave Initiation Event

AbstractA case study characterized by Arctic cyclogenesis following a tropopause polar vortex (TPV)-induced Rossby wave initiation event is used to better understand how well existing observations constrain analyses of processes influencing Arctic cyclone predictive skill. Complementary techniques of observation system experiments (OSE) and ensemble sensitivity analysis (ESA) are used to investigate the impacts of existing observation networks on predictions for this case. The ESA reveals that the large-scale Rossby wave structure is correlated with both Arctic cyclone track and amplitude errors. The ensemble analyses of midlevel moisture in the warm conveyor belt region were correlated with forecast cyclone amplitude, but this feature was poorly sampled in existing observations. There is also a sensitivity of Arctic cyclone forecast amplitude error to low-level temperature in the air mass of the cyclogenesis region at analysis time and a sensitivity of Arctic cyclone forecast track error to low-level temperature in the region of an Arctic cold front and a coastal front at the analysis time. The OSEs for this case reveal that Arctic cyclone track error is more sensitive to denial of existing observations than amplitude error. While lower-level (below 700 hPa) observations had the greatest impact on the surface cyclone during the early stages, upper-level (above 500 hPa) observations had the dominant impact during its later evolution. Denying temperature from just three well-placed sondes substantially increased track error by degrading analyses of the TPV amplitude and its interaction with the waveguide and developing Rossby wave packet. These results are encouraging for further Arctic cyclone forecast improvements through addition of even a small number of well-placed observations.

Influence of a portable near-surface observing network on experimental ensemble forecasts of deep convection hazards during VORTEX-SE

AbstractEnsemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification and Origins of Rotation in Tornadoes EXperiment – Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the southeast United States. StickNet observations are assimilated with an experimental version of the HighResolution RapidRefresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis.

Ensemble sensitivity analysis of an extreme rainfall event over the Himalayas in June 2013

Forecast Sensitivity of an extreme rainfall event over the Uttarakhand state located in the Western Himalayas is investigated through Ensemble-based Sensitivity Analysis (ESA). ESA enables the assessment of forecast errors and its relation to the flow fields through linear regression approach. The ensembles are initialized from an Ensemble Kalman Filter (EnKF) Data Assimilation in Weather Research and Forecast (WRF) model. ESA is then applied to evaluate the dynamics and predictability at two different days of the extreme precipitation episode. Results indicate that the precipitation forecast over Uttarakhand is sensitive to the mid-tropospheric trough and moisture fields for both the days, in general. The day 1 precipitation shows negative sensitivity to the trough over upstream regions of the storm location while in day 2, the sensitive region is found to be located over the southward intruded branch of the mid–tropospheric trough. Perturbations introduced in the initial conditions (IC) over the most sensitive region over the west of the storm location indicate significant variations in the forecast location of precipitation. IC perturbed experiments show that the perturbation amplitude is correlated linearly with predicted change in precipitation, which becomes nonlinear as the forecast length increases. ESA performed on convection-permitting ensembles show that precipitation over the Uttarakhand is mostly non-convective. However, when the location of the response function box is moved north-westward of the Uttarakhand, the sensitivity patterns show signs of convection.

Ensemble Sensitivity Analysis of Tropical Cyclone Intensification Rate during the Development Stage

AbstractEnsemble sensitivity analysis based on convective-permitting ensemble simulations is used to understand the processes associated with tropical cyclone (TC) intensification under idealized conditions. Partial correlations between different variables and the future TC intensification rate, with the effect of intensity removed, are used to identify the sensitive factors. It is found that the equivalent potential temperature (θe) in the region from the radius of maximum wind (RMW) to 3 times the RMW below 2 km (hereafter, the sensitive region) has the largest correlation (over 0.7) with 2.5-h intensity change. It is found that higherθein the sensitive region is associated with not only a stronger updraft but also an inward shift of vertical motion in the mid- to upper eyewall. This suggests that higherθejust outside the RMW is favorable to TC intensification not only because of the larger amount of the heating, but also due to the heating location that is closer to the center. Trajectory analysis shows that the parcels in the sensitive region are mainly from the boundary layer inflow and the midlevel inflow. It is found that when the outer rainband is active, the midlevel inflow becomes stronger and is able to bring more low-θeair into the boundary layer, and theθeradially inward to the rainband decreases. Verification experiments justify that higherθearound the RMW to 3 times the RMW is favorable to TC intensification, while higherθeaway from 5 times the RMW is shown to be unfavorable for TC intensification.

A convective‐scale 1,000‐member ensemble simulation and potential applications

AbstractThis study presents the first convective‐scale 1,000‐member ensemble simulation over central Europe, which provides a unique data set for various applications. A comparison with the operational regional 40‐member ensemble of Deutscher Wetterdienst shows that the 1,000‐member simulation exhibits realistic spread properties overall. Based on this, we discuss two potential applications. First, we quantify the sampling error of spatial covariances of smaller subsets compared with the 1,000‐member simulation. Knowledge about sampling errors and their dependence on ensemble size is crucial for ensemble and hybrid data assimilation and for developing better approaches for localization in this context. Secondly, we present an approach for estimating the relative potential impact of different observable quantities using ensemble sensitivity analysis. This will provide the basis for consecutive studies developing future observation and data assimilation strategies. Sensitivity studies on the ensemble size indicate that about 200 ensemble members are required to estimate the potential impact of observable quantities with respect to precipitation forecasts.

Understanding the Predictability within Convection-Allowing Ensemble Forecasts in East China: Meteorological Sensitivity, Forecast Error Growth and Associated Precipitation Uncertainties Across Spatial Scales

This study investigates the practical predictability of two simulated mesoscale convective systems (MCS1 and MCS2) within a state-of-the-art convection-allowing ensemble forecast system. The two MCSs are both controlled by the synoptic Meiyu-front but differ in mesoscale orographic forcing. An observation system simulation experiment (OSSE) setup is first built, which includes flow-dependent multiple-scale initial and lateral boundary perturbations and a 12 h 30-member ensemble forecast is thereby created. In combination with the difference total energy, the decorrelation scale and the ensemble sensitivity analysis, both forecast error evolution, precipitation uncertainties and meteorological sensitivity that describe the practical predictability are assessed. The results show large variabilities of precipitation forecasts among ensemble members, indicative of the practical predictability limit. The study of forecast error evolution shows that the error energy in the MCS1 region in which the convection is blocked by the Dabie Mountains exhibits a simultaneous peak pattern for all spatial scales at around 6 h due to strong moist convection. On the other hand, when large-scale flow plays a more important role, the forecast error energy in the MCS2 region exhibits a stepwise increase with increasing spatial scale. As a result of error energy growth, the precipitation uncertainties evolve from small scales and gradually transfer to larger scales, implying a strong relationship between error growth and precipitation across spatial scales, thus explaining the great precipitation variability within ensemble members. These results suggest the additional forcing brought by the Dabie Mountains could regulate the predictability of Meiyu-frontal convection, which calls for a targeted perturbation design in convection-allowing ensemble forecast systems with respect to different forcing mechanisms.

Ensemble Sensitivity Analysis of Heavy Rainfall Associated With Three MCSs Coexisting Over Southern China

AbstractUsing ensemble‐based sensitivity analysis, a heavy rainfall event over southern China was analyzed to investigate the key controlling factors of rainfall amount and relevant predictability. The heavy rainfall event was caused by three coexisting mesoscale convective systems (MCSs) over southwestern China (MCS‐A), the south coast of China (MCS‐B), and northern Taiwan (MCS‐C). The three MCSs exhibited differences and similarities with respect to their forecast accuracy and relevant key factors contributing to the rainfall centers, such as low‐level vortex/shear lines and low‐level jets. The strength and shape of the southwest vortex, a mesoscale vortex usually located over southwestern China with a horizontal scale of 200–600 km, exerted a significant effect on MCS‐A and MCS‐B, whereas the stronger shear line near the east coast of China was accompanied by increased precipitation in MCS‐C. In addition, the low‐level jets (LLJs) and their associated moisture transports had a strong influence on the three MCSs according to the LLJ location, strength, height, and direction. A synoptic LLJ located to the southeast of the southwest vortex with a stronger meridional (zonal) wind component and an axis more to the north (south) was related to increased precipitation in the corresponding region of MCS‐A (MCS‐B). In addition to the synoptic LLJ, the rainfall of MCS‐B was also influenced by a boundary layer jet over the ocean. Uncertainties in the aforementioned key factors might further influence the predictability of the three MCSs and thus lead to the differences in their forecast accuracy.