Abstract

A direct piece-by-piece data assimilation targeting strategy through observing system simulation experiments was used to examine the quality of the target area for forecast metrics with different nonlinearities in a mesoscale convective vortex–associated rainfall event from both a deterministic and probabilistic perspective.

The target area was determined based on the impact of assimilating synthetic wind-profiler observations, piece by piece, on the forecast error of strongly nonlinear rainfall and weakly nonlinear total energy around the initial vortex center. The quality of the target area in terms of its effectiveness and variability was examined for members of a reasonable ensemble. Apparently different target areas were found for different members, even for those with very small differences for both forecast metrics, with a larger variability observed for rainfall than for total energy. This result indicated that target areas estimated in deterministic scenarios are likely unreliable.

Probabilistic target areas were created by averaging data-impact index values over the ensemble. Significant differences were also observed in the ensemble-based target areas for rainfall and total energy. For total energy, assimilating data in an inaccurate target area could decrease the forecast error at a similar magnitude as that in the target area. For rainfall, however, much less error reduction was obtained, the magnitude of which was almost comparable to the no-data-assimilation experiment. Overall, the results of this study suggest that designing a particular observation plan based on an estimated target area could be unnecessary for total energy and useless for rainfall, given the difficulty involved in accurately determining a target area in an operational setting.

Abstract

Warm-sector heavy rainfall in southern China refers to the heavy rainfall that occurs within the warm sector hundreds of kilometers south of a front or without a front during April–June, characterized by poor predictability and a close relationship with low-level jets (LLJs). Based on 45 warm-sector heavy rainfall episodes in 2013 and 2014 in southern China, this study examines their general characteristics and evaluates the performance of convection-permitting WRF Model simulations from an LLJ perspective. The results show that 64% of the warm-sector heavy rainfall episodes are associated with an LLJ (LLJ type) and 36% are not (no-LLJ type). The LLJ type is distinct from the no-LLJ type, with large rainfall accumulation along the coastal area. It is more common for LLJs to occur at both 800 and 925 hPa in the LLJ type, where there is a wide 800-hPa LLJ west of Guangdong Province and two 925-hPa LLJs over Beibu Gulf and the South China Sea (SCS). The coastal convergence associated with the terminus of the LLJ on 925 hPa is conducive to the coastal rainfall. WRF generally presents lower QPF skill in the LLJ type than in the no-LLJ type, due to the severe underestimation of coastal rainfall. The QPF skill of the LLJ type is significantly correlated with the forecast accuracy of LLJs, especially at 925 hPa. The north bias of the simulated LLJ on 925 hPa over the SCS and the associated overestimation of wind speed below ~900 hPa over the inland region weaken the coastal convergence and eventually lead to the underestimation in coastal precipitation.

Abstract

Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forecasts to estimate flow-dependent background error covariance and is best known by varying forms of ensemble Kalman filters (EnKFs). The EnKF has recently emerged as one of the primary alternatives to the variational data assimilation methods widely used in both global and limited-area numerical weather prediction models. In addition to comparing the EnKF with variational methods, this article reviews recent advances and challenges in the development and applications of the EnKF, including its hybrid with variational methods, in limited-area models that resolve weather systems from convective to meso- and regional scales.

Abstract

On 21 July 2012, severe wind damage occurred in Beijing, China, during a heavy rainfall event. Through a damage survey that had the most detailed information in all of the published tornado damage surveys so far in China, this work showed significant evidence that the wind damage was caused by a mesocyclonic tornado rated as a category 3 storm on the enhanced Fujita scale (EF3) that was observed by people but of which not a single picture was taken. This was the first tornado ever reported or documented in Beijing. The most influential evidence indicating a tornado included a narrow damage swath 30–400 m wide and ~10 km long and convergent surface winds at multiple places along the swath. The radar analyses examined here show that the tornado was embedded in a strong mesocyclone. The initial linear and later sinusoidal tornado track was likely due to the intensification and expansion of the mesocyclone. The location, timing, and intensity variation of the wind damage were precisely collocated with those of a tornadic vortex signature. Descending reflectivity cores as well as their associated jetlets and counterrotating vortices were detected both before tornadogenesis and prior to the reintensification of the tornado damage. A tornadic debris signature was also detected in the later stages of the tornado. Compared to the U.S. climatology of forecast parameters for different storm categories, this storm developed in an environment that was favorable for the formation of supercells or weakly tornadic supercells rather than significantly tornadic supercells.

Abstract

In Part I of this two-part work, the feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation through various observing system simulation experiments was demonstrated assuming a perfect forecast model for a winter snowstorm event that occurred on 24–26 January 2000. The current study seeks to explore the performance of the EnKF for the same event in the presence of significant model errors due to physical parameterizations by assimilating synthetic sounding and surface observations with typical temporal and spatial resolutions. The EnKF performance with imperfect models is also examined for a warm-season mesoscale convective vortex (MCV) event that occurred on 10–13 June 2003. The significance of model error in both warm- and cold-season events is demonstrated when the use of different cumulus parameterization schemes within different ensembles results in significantly different forecasts in terms of both ensemble mean and spread. Nevertheless, the EnKF performed reasonably well in most experiments with the imperfect model assumption (though its performance can sometimes be significantly degraded). As in Part I, where the perfect model assumption was utilized, most analysis error reduction comes from larger scales. Results show that using a combination of different physical parameterization schemes in the ensemble forecast can significantly improve filter performance. A multischeme ensemble has the potential to provide better background error covariance estimation and a smaller ensemble bias. There are noticeable differences in the performance of the EnKF for different flow regimes. In the imperfect scenarios considered, the improvement over the reference ensembles (pure ensemble forecasts without data assimilation) after 24 h of assimilation for the winter snowstorm event ranges from 36% to 67%. This is higher than the 26%–45% improvement noted after 36 h of assimilation for the warm-season MCV event. Scale- and flow-dependent error growth dynamics and predictability are possible causes for the differences in improvement. Compared to the power spectrum analyses for the snowstorm, it is found that forecast errors and ensemble spreads in the warm-season MCV event have relatively smaller power at larger scales and an overall smaller growth rate.

Abstract

The feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation has been demonstrated in the authors’ recent studies via observing system simulation experiments (OSSEs) both under a perfect-model assumption and in the presence of significant model error. The current study extends the EnKF to assimilate real-data observations for a warm-season mesoscale convective vortex (MCV) event on 10–12 June 2003. Direct comparison between the EnKF and a three-dimensional variational data assimilation (3DVAR) system, both implemented in the Weather Research and Forecasting model (WRF), is carried out. It is found that the EnKF consistently performs better than the 3DVAR method by assimilating either individual or multiple data sources (i.e., sounding, surface, and wind profiler) for this MCV event. Background error covariance plays an important role in the performance of both the EnKF and the 3DVAR system. Proper covariance inflation and the use of different combinations of physical parameterization schemes in different ensemble members (the so-called multischeme ensemble) can significantly improve the EnKF performance. The 3DVAR system can benefit substantially from using short-term ensembles to improve the prior estimate (with the ensemble mean). Noticeable improvement is also achieved by including some flow dependence in the background error covariance of 3DVAR.

Abstract

Based on a 3-yr (2007–09) mosaic of radar reflectivity and conventional surface and synoptic radiosonde observations, the general features of squall lines preceding landfalling tropical cyclones (TCs) (pre-TC) in China are examined and compared with their midlatitude and subtropical counterparts. The results show that about 40% of landfalling TCs are associated with pre-TC squall lines with high-occurring frequency in August and from late afternoon to midnight. Most pre-TC squall lines form in a broken-line mode with a trailing-stratiform organization. On average, they occur about 600 km from the TC center in the front-right quadrant with a maximum length of 220 km, a maximum radar reflectivity of 57–62 dBZ, a life span of 4 h, and a moving speed of 12.5 m s⁻¹. Pre-TC squall lines are generally shorter in lifetime and length than typical midlatitude squall lines.

Pre-TC squall lines tend to form in the transition area between the parent TC and subtropical high in a moist environment and with a weaker cold pool than their midlatitude counterparts. The environmental 0–3-km vertical shear is around 10 m s⁻¹ and generally normal to the orientation of the squall lines. This weak shear makes pre-TC squall lines in a suboptimal condition according to the Rottuno–Klemp–Weisman (RKW) theory. Convection is likely initiated by low-level mesoscale frontogenesis, convergence, and/or confluence instead of synoptic-scale forcing. The parent TC may contribute to (i) the development of convection by enhancing conditional instability and low-level moisture supply, and (ii) the linear organization of discrete convection through the interaction between the TC and the neighboring environmental system.

Abstract

In previous works in this series study, an ensemble Kalman filter (EnKF) was demonstrated to be promising for mesoscale and regional-scale data assimilation in increasingly realistic environments. Parts I and II examined the performance of the EnKF by assimilating simulated observations under both perfect- and imperfect-model assumptions. Part III explored the application of the EnKF to a real-data case study in comparison to a three-dimensional variational data assimilation (3DVAR) method in the Weather Research and Forecasting (WRF) model. The current study extends the single-case real-data experiments over a period of 1 month to examine the long-term performance and comparison of both methods at the regional scales. It is found that the EnKF systematically outperforms 3DVAR for the 1-month period of interest in which both methods assimilate the same standard rawinsonde observations every 12 h over the central United States. Consistent with results from the real-data case study of Part III, the EnKF can benefit from using a multischeme ensemble that partially accounts for model errors in physical parameterizations. The benefit of using a multischeme ensemble (over a single-scheme ensemble) is more pronounced in the thermodynamic variables (including temperature and moisture) than in the wind fields. On average, the EnKF analyses lead to more accurate forecasts than the 3DVAR analyses when they are used to initialize 60 consecutive, deterministic 60-h forecast experiments for the month. Results also show that deterministic forecasts of up to 60 h initiated from the EnKF analyses consistently outperform the WRF forecasts initiated from the National Centers for Environmental Prediction final analysis field of the Global Forecast System.

Abstract

Through observing system simulation experiments, this two-part study exploits the potential of using the ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation. Part I focuses on the performance of the EnKF under the perfect model assumption in which the truth simulation is produced with the same model and same initial uncertainties as those of the ensemble, while Part II explores the impacts of model error and ensemble initiation on the filter performance. In this first part, the EnKF is implemented in a nonhydrostatic mesoscale model [the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)] to assimilate simulated sounding and surface observations derived from simulations of the “surprise” snowstorm of January 2000. This is an explosive East Coast cyclogenesis event with strong error growth at all scales as a result of interactions between convective-, meso-, and subsynoptic-scale dynamics.

It is found that the EnKF is very effective in keeping the analysis close to the truth simulation under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the control experiment, in which the truth simulation was produced with the same model and same initial uncertainties as those of the ensemble, a 24-h continuous EnKF assimilation of sounding and surface observations of typical temporal and spatial resolutions is found to reduce the error by as much as 80% (compared to a 24-h forecast without data assimilation) for both observed and unobserved variables including zonal and meridional winds, temperature, and pressure. However, it is observed to be relatively less efficient in correcting errors in the vertical velocity and moisture fields, which have stronger smaller-scale components. The analysis domain-averaged root-mean-square error after 24-h assimilation is ∼1.0–1.5 m s⁻¹ for winds and ∼1.0 K for temperature, which is comparable to or less than typical observational errors. Various sensitivity experiments demonstrated that the EnKF is quite successful in all realistic observational scenarios tested. However, as will be presented in Part II, the EnKF performance may be significantly degraded if an imperfect forecast model is used, as is likely the case when real observations are assimilated.

Abstract

Based on mosaics of composite radar reflectivity patterns during the 2-yr period of 2008–09, a total of 96 squall lines were identified in east China with a maximum frequency of occurrence in north China near the boundaries between Shandong, Henan, Anhui, and Jiangsu Provinces. The squall lines form from March to October with a peak in July. Their diurnal variation shows a major peak in the early evening and two minor peaks in the early morning and early afternoon. The time between squall-line formation and the first echo is about 4.8 h. The squall lines have a dominant southwest–northeast orientation, an eastward motion at a speed of 14.4 m s⁻¹, a maximum length of 243 km, a maximum intensity of 58–63 dBZ, and a duration of 4.7 h on average. The squall lines commonly form in a broken-line mode, display a trailing-stratiform pattern, and dissipate in a reversed broken-line mode. Composite rawinsonde analyses show that squall lines in midlatitude east China tend to form in a moister environment with comparable background instability, and weaker vertical shear relative to their U.S. counterparts. The rawinsondes were also composited with respect to different formation and organizational modes. The environmental flows of the squall lines in the area with high frequency of formation were classified into six synoptic weather patterns: pre–short trough, pre–long trough, cold vortex, subtropical high, tropical cyclone (TC), and posttrough. About one-third of the squall lines form in the dominant pre-short-trough pattern. Favorable conditions of various patterns were examined in terms of moisture supply, instability, vertical wind shear, low-level jet, etc.