Abstract

Severe rainfall has become increasingly frequent and intense in the Taipei metropolitan area. A complex thunderstorm in the Taipei Basin on 14 June 2015 produced an extreme rain rate (>130 mm h⁻¹), leading to an urban flash flood. This paper presents storms’ microphysical and dynamic features during the organizing and heavy rain stages, mainly based on observed polarimetric variables in a Doppler radar network and ground-based raindrop size distribution. Shallower isolated cells in the early afternoon characterized by big raindrops produced a rain rate > 10 mm h⁻¹, but the rain showers persisted for a short time. The storm’s evolution highlighted the behavior of merged convective cells before the heaviest rainfall (exceeding 60 mm within 20 min). The columnar features of differential reflectivity (Z _DR) and specific differential phase (K _DP) became more evident in merged cells, which correlated with the broad distribution of upward motion and mixed-phase hydrometeors. The K _DP below the environmental 0°C level increased toward the ground associated with the melted graupel and resulted in subsequent intense rain rates, showing the contribution of the ice-phase process. Due to the collision–breakup process, the highest concentrations of almost all drop sizes and smaller mass-weighted mean diameter occurred during the maximum rainfall stage.

Abstract

The second part of this series presents results from verifying a precipitation forecast calibration method discussed in part 1, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12) reforecasts were used in this study. The method was validated with pre-operational GEFSv12 forecasts from the between December 2017 and November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models.

Part 1 described adaptations to the methodology to leverage the ~ 20-year GEFSv12 reforecast data. As shown in this part 2, when compared to probabilistic quantitative precipitation forecasts (PQPFs) from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., < 5 days) and for forecasts of events from light precipitation to > 10 mm 6 h⁻¹. Cool-season events in the western US were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.

Abstract

Data from a dual-polarized, solid-state X-band radar and an operational C-band weather radar are used for high-resolution analyses of two hailstorms in the Vienna, Austria, region. The combination of both radars provides rapid-update (1 min) polarimetric data paired with wind field data of a dual-Doppler analysis. This is the first time that such an advanced setup is used to examine severe storm dynamics at the eastern Alpine fringe, where the influence of local topography is particularly challenging for thunderstorm prediction. We investigate two storms transitioning from the pre-Alps into the Vienna basin with different characteristics: 1) A rapidly evolving multicell storm producing large hail (5 cm), with observations of an intense Z _DR column preceding hail formation and the rapid development of multiple pulses of hail; and 2) a cold pool–driven squall line with small hail, for which we find that the updraft location inhibited the formation of larger hailstones. For both cases, we analyzed the evolution of different Z _DR column metrics as well as updraft speed and size and found that (i) the 90th percentile of Z _DR within the Z _DR column was highest for the cell later producing large hail, (ii) the peak 90th percentile of Z _DR preceded large hailfall by 20 min and highest updraft size and speed by 10 min, and (iii) sudden drops of the 90th percentile of Z_H within the Z _DR column indicated imminent hailfall.

Abstract

Based on the principle “learn from past errors to correct current forecasts,” statistical postprocessing consists of optimizing forecasts generated by numerical weather prediction (NWP) models. In this context, machine learning (ML) offers state-of-the-art tools for training statistical models and making predictions based on large datasets. In our study, ML-based solutions are developed to reduce forecast errors of 2-m temperature and 10-m wind speed of the ECMWF’s operational medium-range, high-resolution forecasts produced with the Integrated Forecasting System (IFS). IFS forecasts and other spatiotemporal indicators are used as predictors after careful selection with the help of ML interpretability tools. Different ML approaches are tested: linear regression, random forest decision trees, and neural networks. Statistical models of systematic and random errors are derived sequentially where the random error is defined as the residual error after bias correction. In terms of output, bias correction and forecast uncertainty prediction are made available at any point from locations around the world. All three ML methods show a similar ability to capture situation-dependent biases leading to noteworthy performance improvements (between 10% and 15% improvement in terms of root-mean-square error for all lead times and variables), and a similar ability to provide reliable uncertainty predictions.

Abstract

As a part of the Tropical Cyclone Rapid Intensification Project (TCRI), observations were made of the rapid intensification of Hurricane Sally (2020) as it passed over the Gulf of Mexico. High-altitude dropsondes and radar observations from NOAA’s Gulfstream IV, radar observations from WP-3D aircraft, the WSR-88D ground radar network, satellite images, and satellite-detected lightning strikes are used to apply recently developed theoretical knowledge about tropical cyclone intensification. As observed in many other tropical cyclones, strong, bottom-heavy vertical mass flux profiles are correlated with low (but positive) values of low- to midlevel moist convective instability along with high column relative humidity. Such mass flux profiles produce rapid spinup at low levels and the environmental conditions giving rise to them are associated with an intense midlevel vortex. This low-level spinup underneath the midlevel vortex results in the vertical alignment of the vortex column, which is a key step in the rapid intensification process. In the case of Sally, the spinup of the low-level vortex resulted from vorticity stretching, while the spinup of the midlevel vortex at 6 km resulted from vorticity tilting produced by the interaction of convective ascent with moderate vertical shear.

Abstract

Equatorial waves are a major driver of widespread convection in South East Asia and the tropics more widely, a region in which accurate heavy rainfall forecasts are still a challenge. Conditioning rainfall over land on local equatorial wave phases finds that heavy rainfall can be between two and four times more likely to occur in Indonesia, Malaysia, Vietnam, and the Philippines. Equatorial waves are identified in a global numerical weather prediction ensemble forecast (MOGREPS-G). Skill in the ensemble forecast of wave activity is highly dependent on region and time of year, although generally forecasts of equatorial Rossby waves and westward-moving mixed Rossby-gravity waves are substantially more skilful than for the eastward moving Kelvin wave. The observed statistical relationship between wave phases and rainfall is combined with ensemble forecasts of dynamicalwave fields to construct hybrid dynamical-statistical forecasts of rainfall probability using a Bayesian approach. The Brier Skill Score is used to assess the skill of forecasts of rainfall probability. Skill in the hybrid forecasts can exceed that of probabilistic rainfall forecasts taken directly from MOGREPS-G and can be linked to both the skill in forecasts of wave activity and the relationship between equatorial waves and heavy rainfall in the relevant region. The results show that there is potential for improvements of forecasts of high impact weather using this method as forecasts of large-scale waves improve.

Abstract

Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions non-parametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 — 2020, comparing results from the PF with those from an Ensemble Kalman Filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within pre-existing data assimilation frameworks.

Abstract

Numerous low-level vortices are initiated downwind of the Hoggar Mountains and progress towards the Atlantic coast on the northern path of African Easterly Waves (AEWs). These vortices occur mostly in July and August and more specifically when the northern position of the Saharan heat low (SHL) generates stronger and vertically expanded easterly winds over Hoggar mountains. At synoptic time-scales, a composite analysis reveals that vortex initiation and westward motion are also statistically triggered by a reinforcement of these easterly winds by a wide and persistent high-pressure anomaly developing around the Strait of Gibraltar and by a weak wave trough approaching from the east. The vortices are generated in the lee of the Hoggar, about 1000 km west of this approaching trough, and intensify rapidly. The evolution of the vortex perturbation is afterward comparable with the known evolution of the AEWs of the northern path and suggest a growth due to dry barotropic and baroclinic processes induced in particular by the strong cyclonic shear between the reinforced easterly winds and the monsoon flow. These results show that vortex genesis promoted by changes in orographic forcing due to the strengthening of easterly winds over Hoggar mountains is a source of intensification of the northern path of AEWs in July and August. These results also provide a possible mechanism to explain the role of the SHL and of particular mid-latitude intraseasonal disturbances on the intensity of these waves.

Abstract

Recent research has demonstrated a relationship between convectively coupled Kelvin waves (CCKWs) and tropical cyclogenesis, likely due to the influence of CCKWs on the large-scale environment. However, it remains unclear which environmental factors are most important and how they connect to TC genesis processes. Using a 39-year database of African easterly waves (AEWs) to create composites of reanalysis and satellite data, it is shown that genesis may be facilitated by CCKW-driven modifications to convection and moisture. First, stand-alone composites of genesis demonstrate the significant role of environmental preconditioning and convective aggregation. A moist static energy variance budget indicates that convective aggregation during genesis is dominated by feedbacks between convection and longwave radiation. These processes begin over two days prior to genesis, supporting previous observational work. Shifting attention to CCKWs, up to 76% of developing AEWs encounter at least one CCKW in their lifetime. An increase in genesis events following convectively active CCKW phases is found, corroborating earlier studies. A decrease in genesis events following convectively suppressed phases is also identified. Using CCKW-centered composites, we show that the convectively active CCKW phases enhances convection and moisture content in the vicinity of AEWs prior to genesis. Furthermore, enhanced convective activity is the main discriminator between AEW-CCKW interactions that result in genesis versus those that do not. This analysis suggests that CCKWs may influence genesis through environmental preconditioning and radiative-convective feedbacks, among other factors. A secondary finding is that AEW attributes as far east as Central Africa may be predictive of downstream genesis.

Abstract

The observation error covariance partially controls the weight assigned to an observation during data assimilation (DA). True observation error statistics are rarely known and likely vary depending on the meteorological state. However, operational DA systems often apply static methods that assign constant observation errors across a dataset. Previous studies show that these methods can degrade forecast quality when assimilating ground-based remote sensing datasets. To improve the impact of assimilating such observations, we propose two novel methods for estimating the observation error variance for high-frequency thermodynamic profilers. These methods include an adaptive observation error inflation technique and the Desroziers method that directly estimates the observation error variances using paired innovation and analysis residuals. Each method is compared for a nocturnal mesoscale convective system (MCS) observed during the Plains Elevated Convection at Night (PECAN) experiment. In general, we find that these novel methods better represent the large variability of observation error statistics for high-frequency profiles collected by Atmospheric Emitted Radiance Interferometers (AERIs). When assimilating AERIs by statically inflating retrieval error variances, the trailing stratiform region of the MCS is degraded compared to a baseline simulation with no AERI data assimilated. Assimilating the AERIs using the adaptive inflation or Desroziers method results in better maintenance of the trailing stratiform region and additional suppression of spurious convection. The forecast improvements from these novel methods are primarily linked to increased error variances for some moisture retrievals. These results indicate the importance of accurately estimating observation error statistics for convective-scale DA and suggest that accounting for flow dependence can improve the impacts from assimilating remote sensing datasets.