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Abstract Based on 20-day control forecasts by the 9-km Integrated Forecasting System (IFS) at the European Centre for Medium-Range Weather Forecasts (ECMWF) for selected periods of summer and winter events, this study investigates global distributions of gravity wave momentum fluxes resolved by the highest-resolution-ever global operational numerical weather prediction model. Two supplementary datasets, including 18-km ECMWF IFS experiments and the 30-km ERA5, are included for comparison. In the stratosphere, there is a clear dominance of westward momentum fluxes over the winter extratropics with strong baroclinic instability, while eastward momentum fluxes are found in the summer tropics. However, meridional momentum fluxes, locally as important as the above zonal counterpart, show different behaviors of global distribution characteristics, with northward and southward momentum fluxes alternating with each other especially at lower altitudes. Both events illustrate conclusive evidence that stronger stratospheric fluxes are found in the ECMWF forecast with finer resolution, and that ERA5 datasets have the weakest signals in general, regardless of whether regridding is applied. In the troposphere, probability distributions of vertical motion perturbations are highly asymmetric with more strong positive signals especially over latitudes covering heavy rainfall, likely caused by convective forcing. With the aid of precipitation accumulation, a simple filtering method is proposed in an attempt to eliminate those tropospheric asymmetries by convective forcing, before calculating tropospheric wave-induced fluxes. Furthermore, this research demonstrates promising findings that the proposed filtering method could help in reducing the potential uncertainties with respect to estimating tropospheric wave-induced fluxes. Finally, absolute momentum flux distributions with proposed approaches are presented, for further assessment in the future. 

Abstract As a follow-on to a previous study on secondary eyewall formation (SEF) in a simulation of Hurricane Matthew (2016), this study investigates the emergence and maintenance of an asymmetric rainband updraft region that leads to an SEF event. Under moderate deep-layer environmental wind shear, the storm develops a quasi-stationary rainband complex with intense, persistent updrafts in its left-of-shear, downwind end. Using a budget of equivalent potential temperature θ E , it is demonstrated that the maintenance of the left-of-shear updraft is aided by a mesoscale cold pool induced by rainband stratiform cooling which interacts with the storm’s moist envelope of high- θ E air. An extended period of destabilization occurs through differential horizontal advection of θ E in the boundary layer, which continuously replenishes the moist instability that would otherwise be depleted by the updrafts. The initial lifting of the updraft is found to be the result of buoyancy advection resulting from the density contrast between the surface cold pool and the inner-core high- θ E air. A potential vorticity (PV) budget analysis shows that these left-of-shear updrafts generate low- to midlevel PV through diabatic heating and boundary layer processes, which shapes the local PV enhancement and propagates cyclonically downwind. Meanwhile, in the mid- to upper levels, eddy PV flux convergence and PV generation continue to occur in the stratiform precipitation extending downwind into the upshear quadrants, which substantially increases the azimuthal mean PV at the radius of the developing secondary eyewall and marks the occurrence of the axisymmetrization process. 

Abstract Ensembles of predictions are critical to modern weather forecasting. However, visualizing ensembles and their means in a useful way remains challenging. Existing methods of creating ensemble means do not recognize the physical structures that humans could identify within the ensemble members; therefore, visualizations for variables such as reflectivity lose important information and are difficult for human forecasters to interpret. In response, the authors create an improved ensemble mean that retains more structural information. The authors examine and expand upon the object-based Geometry-Sensitive Ensemble Mean (GEM) defined by Li and Zhang from a meteorological perspective. The authors apply low-intensity thresholding to WRF-simulated radar reflectivity images of lake-effect snowbands, tropical cyclones, and severe thunderstorms and then process them with the GEM system. Gaussian mixture model–based signatures retain the geometric structure of these phenomena and are used to compute a Wasserstein barycenter as the centroid for the ensemble; D2 clustering is employed to examine different scenarios among the ensemble members. Three types of ensemble mean image are created from the centroid of the ensemble or cluster, which each improve upon the traditional pixel-wise average in different ways, successfully capture aspects of the ensemble members’ structure, and have potential applications for future forecasting efforts. The adjusted best member is a better representative member, the Bayesian posterior mean is an improved structure-based weighted average, and the mixture density mean is an outline of the key structures in the ensemble. Each is shown to improve upon a simple arithmetic mean via quantitative comparison with observations. 

Here we present a new theoretical framework that connects the error growth behavior in numerical weather prediction (NWP) with the atmospheric kinetic energy spectrum. Building on previous studies, our newly proposed framework applies to the canonical observed atmospheric spectrum that has a -3 slope at synoptic scales and a -5/3 slope at smaller scales. Based on this realistic hybrid energy spectrum, our new experiment using hybrid numerical models provides reasonable estimations for the finite predictable ranges at different scales. We further derive an analytical equation that helps understand the error growth behavior. Despite its simplicity, this new analytical error growth equation is capable of capturing the results of previous comprehensive theoretical and observational studies of atmospheric predictability. The success of this new theoretical framework highlights the combined effects of quasi-two-dimensional dynamics at synoptic-scales (-3 slope) and three-dimensional turbulence-like small-scale chaotic flows (-5/3 slope) in dictating the error growth. It is proposed that this new framework could serve as a guide for understanding and estimating the predictability limit in the real world. 

Abstract The dynamics of an asymmetric rainband complex leading into secondary eyewall formation (SEF) are examined in a simulation of Hurricane Matthew (2016), with particular focus on the tangential wind field evolution. Prior to SEF, the storm experiences an axisymmetric broadening of the tangential wind field as a stationary rainband complex in the downshear quadrants intensifies. The axisymmetric acceleration pattern that causes this broadening is an inward-descending structure of positive acceleration nearly 100 km wide in radial extent and maximizes in the low levels near 50 km radius. Vertical advection from convective updrafts in the downshear-right quadrant largely contributes to the low-level acceleration maximum, while the broader inward-descending pattern is due to horizontal advection within stratiform precipitation in the downshear-left quadrant. This broad slantwise pattern of positive acceleration is due to a mesoscale descending inflow (MDI) that is driven by midlevel cooling within the stratiform regions and draws absolute angular momentum inward. The MDI is further revealed by examining the irrotational component of the radial velocity, which shows the MDI extending downwind into the upshear-left quadrant. Here, the MDI connects with the boundary layer, where new convective updrafts are triggered along its inner edge; these new upshear-left updrafts are found to be important to the subsequent axisymmetrization of the low-level tangential wind maximum within the incipient secondary eyewall. 

Warm sector rainfall (WSR) occurs, by definition, in a warm air region that is isolated from any forcing related to synoptic frontal boundaries at the surface. This study explores the use of an object-oriented technique to objectively and automatically identify various WSR events over North China from June to September in 2012-2017. A total of 768 substantive events are identified over the 6 years. They have a mean maximum rainfall accumulation of 35 mm/hr. Most such events occur over the plains; with two frequency maxima, one to the south of the Yanshan Mountain Ranges, and the other near the junction of Henan, Shandong and Jiangsu provinces. WSR-related rainstorms can form in all warm-season months but are most commonly seen between mid-July and mid-August (40% of all events occurred then). Geographically, the region at greatest risk moves gradually northward from mid-June to mid-August, consistent with the progression of the East Asian summer monsoon. There are two diurnal peaks in WSR activity, one from late afternoon to early evening and the other from late evening to early morning. Three classes of upper-level synoptic pattern seem to be conducive to WSR: i) a “Mongolia front pattern”, ii) “northern China front pattern”, iii) a “southern front pattern”. All of these patterns are accompanied by warm and moist southwesterly flow at low levels. Prior to WSR events, there is usually an upper level trough. According to other studies, such a feature is not usually seen for WSR events in South China. 

More than 6000 independent radiosonde observations from three major Tibetan Plateau experiments during the warm seasons (May–August) of 1998, 2008, and 2015–16 are used to assess the quality of four leading modern atmospheric reanalysis products (CFSR/CFSv2, ERA-Interim, JRA-55, and MERRA-2), and the potential impact of satellite data changes on the quality of these reanalyses in the troposphere over this data-sparse region. Although these reanalyses can reproduce reasonably well the overall mean temperature, specific humidity, and horizontal wind profiles against the benchmark independent sounding observations, they have nonnegligible biases that can be potentially bigger than the analysis-simulated mean regional climate trends over this region. The mean biases and mean root-mean-square errors of winds, temperature, and specific humidity from almost all reanalyses are reduced from 1998 to the two later experiment periods. There are also considerable differences in almost all variables across different reanalysis products, though these differences also become smaller during the 2008 and 2015–16 experiments, in particular for the temperature fields. The enormous increase in the volume and quality of satellite observations assimilated into reanalysis systems is likely the primary reason for the improved quality of the reanalyses during the later field experiment periods. Besides differences in the forecast models and data assimilation methodology, the differences in performance between different reanalyses during different field experiment periods may also be contributed by differences in assimilated information (e.g., observation input sources, selected channels for a given satellite sensor, quality-control methods). 

Long-lived, zonally propagating diurnal rainfall disturbances are a highly pronounced and common feature in the Maritime Continent (MC). A recent study argues that these disturbances can be explained as diurnally phase-locked gravity waves. Here we explore the origins of these waves through regional cloud-permitting numerical model experiments. The gravity waves are reproduced and isolated in the model framework through the combined use of realistic geography and diurnally cyclic lateral boundary conditions representative of both characteristic easterly and westerly background zonal flow regimes. These flow regimes are characteristic of the Madden–Julian oscillation (MJO) suppressed and active phase in the MC, respectively. Tests are conducted wherein Borneo, Sumatra, or both islands and/or their orography are removed. These tests imply that the diurnal gravity waves are excited and maintained directly by latent heating from the vigorous mesoscale convective systems (MCSs) that form nocturnally in both Borneo and Sumatra. Removing orography has only a secondary impact on both the MCSs and the gravity waves, implying that it is not critical to these waves. We therefore hypothesize that diurnal gravity waves are fundamentally driven by mesoscale organized deep convection, and are only sensitive to orography to the measure that the convection is affected by the orography and its mesoscale flows. Factor separation further reveals that the nonlinear interaction of synchronized diurnal cycles in Sumatra and Borneo slightly amplifies this gravity wave mode compared to if either island existed in isolation. This nonlinear feedback appears most prominently at longitudes directly between the two islands. 

Diurnal variations of gravity waves over the Tibetan Plateau (TP) in summer 2015 were investigated based on high-resolution downscaled simulations from WRF-EnKF (Weather Research and Forecasting model and an ensemble Kalman filter) regional reanalysis data with particular emphasis on wave source, wave momentum fluxes and wave energies. Strong diurnal precipitations, which mainly happen along the south slope of the TP, tend to excite upward-propagating gravity waves. The spatial and temporal distributions of the momentum fluxes of small-scale (10–200 km) and meso-scale (200–500 km) gravity waves agree well with the diurnal precipitation distributions. The power spectra of momentum fluxes also show that the small- and meso-scale atmospheric processes become important during the period of the strongest rainfall. Eastward momentum fluxes and northward momentum fluxes are dominant. Wave energies are described in terms of kinetic energy (KE), potential energy (PE) and vertical fluctuation energy (VE). The diurnal variation and spatial distribution of VE in the lower stratosphere correspond to the diurnal rainfall in the troposphere.