Abstract The tangent linear model (TLM) is an essential component of four-dimensional variational (4DVar) data assimilation systems. Recent efforts have been developed to improve TLM skill using ensemble-based or machine learning corrections for missing or imperfectly-modeled processes within the TLM. The extent to which these improvements may benefit forecasting skill is still an open question. Here we examine this question using a global shallow water model framework including a hybrid ensemble 4DVar data assimilation system with TLMs of varying skill. Our data assimilation system is an observation-space formulation based on the “2-step representer” or “dual” approach. We examine the sensitivity of 4DVar analyses to TLM and adjoint errors in both the conjugate gradient “solver” step and in the “post-multiplier” step. The errors include unavoidable errors due to the neglecting of nonlinear processes in the shallow water model as well as additional imposed errors that represent imperfectly-modeled physical processes. We find less sensitivity to TLM and adjoint imposed errors in the solver step than in the post-multiplier step. We show that the TLM used for the final “forward sweep” can be replaced with forecasts using the nonlinear model, thereby reducing analysis errors. We also show that analysis errors can be reduced using a “Hybrid TLM” approach in which an ensemble-based linear regression model is employed to attempt to correct for TLM errors. 10-day forecast errors using the Hybrid TLM are also shown to be smaller than when using the standard TLM with imposed errors.

Abstract Past studies have shown baroclinic zones between warm ambient inflow and cooled downdraft air to be key in the generation of horizontal vorticity and in potential tornadogenesis if tilted and stretched in the vertical by updrafts. While previous work has focused on Great Plains’ supercells, a much narrower body of work has considered similar vorticity sources in high-shear, low-CAPE supercells and Quasi-Linear Convective Systems (QLCSs). In this study, 166 Texas Tech University StickNet cold pool observations from the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign, including 76 mesovortex (23 tornadic and 53 nontornadic) and 35 high-shear, low-CAPE supercell intercepts, are statistically and spatiotemporally analyzed. Larger virtual potential temperature gradients were observed near rotating segments of the QLCS. No statistically significant differences in cold pool characteristics are evident globally between nontornadic and tornadic mesovortices, though significant differences do exist when considering specific periods of the mesovortex lifecycle and mesovortex-relative position. Three fine-scale array (~1 km spacing) mesovortex intercepts during the nontornadic, time-of-tornado, and post-tornadic phase support the statistical analysis, possibly implicating baroclinic vorticity generation as a viable source of vorticity to achieve tornadogenesis within QLCS mesovortices. Further, this study found supercells in high-shear, low-CAPE regimes uniformly have weaker cold pools than supercells in larger CAPE environments. After QLCS-supercell mergers, the resultant QLCS cold pool tended to weaken immediately downstream of the merger but with no disruption in the tornado potential of the QLCS segment.

Abstract We examine the environmental conditions that lead to well- and poorly predicted MJO events in the Navy Earth System Prediction Capability (ESPC) global coupled forecast system. Individual MJO events are tracked using an MJO tracking algorithm following Chikira (2014). Good and poor forecasts are determined by how well the forecasted MJO object matches with the observed MJO objects. The primary difference between good and poor MJO forecasts is location and timing of forecasted MJO events. Good MJO forecasts capture the evolution of observed environmental moisture and low-level wind anomalies, while poor MJO forecasts do not build sufficient moisture anomalies to support the MJO’s amplifications and propagation. The poor forecasts struggle to simulate the horizontal advection and evaporation. The errors in the evaporation are likely driven by errors in the environmental and MJO-scale zonal wind anomalies. The errors in the evolution of the horizontal advection are largely driven by the evolution of the environmental wind and moisture gradient, which induces errors in the evolution of the zonal moisture advection. The effect of the good and poor MJO forecasts on the extended range MJO teleconnections is examined. It is found that MJO teleconnections are best simulated following the MJO’s enhanced convection over the Maritime Continent. There are systematic biases in the MJO-induced Rossby wave trains in the Navy ESPC, in some cases despite a good representation of the MJO. A northeastward tilt in the subtropical jet exit region is identified in the Navy ESPC and is another possible cause of these biases in the Rossby wave trains.

Abstract The fractions skill score (FSS) is a widely used metric for assessing forecast skill, with applications ranging from precipitation to volcanic ash forecasts. By evaluating the fraction of grid squares exceeding a threshold in a neighborhood, the intuition is that it can avoid the pitfalls of pixelwise comparisons and identify length scales at which a forecast has skill. The FSS is typically interpreted relative to a “useful” criterion, where a forecast is considered skillful if its score exceeds a simple reference score. However, the typical reference score used is problematic, since it is not derived in a way that provides obvious meaning, does not scale with neighborhood size, and may not be exceeded by forecasts that have skill. We, therefore, provide a new method to determine forecast skill from the FSS, by deriving an expression for the FSS achieved by a random forecast, which provides a more robust and meaningful reference score to compare with. Through illustrative examples, we show that this new method considerably changes the length scales at which a forecast would be regarded as skillful and reveals subtleties in how the FSS should be interpreted. Significance Statement Forecast verification metrics are crucial to assess accuracy and identify where forecasts can be improved. In this work, we investigate a popular verification metric, the fractions skill score, and derive a more robust method to decide if a forecast has sufficiently high skill. This new method significantly improves the quality of insights that can be drawn from this score.

Abstract This study aims at improving the assimilation of Fengyun-4A (FY-4A)/Advanced Geostationary Radiation Imager (AGRI) clear-sky surface-sensitive brightness temperature over land using the Weather Research and Forecasting and the Gridpoint Statistical Interpolation three-dimensional variational data assimilation for summer convective cases over eastern China. We investigated the influences of the planetary boundary layer on the assimilation of FY-4A/AGRI surface-sensitive channels, focusing on model low-level vertical resolution and diurnal variation of background error covariance. The vertical levels are increased from 48 to 91 by adding more near-surface levels. First, stronger low-level vertical background error correlations of humidity and temperature are found during daytime than during nighttime. A total of five numerical experiments are then conducted to examine the impacts of FY-4A/AGRI assimilation with two vertical resolutions. The experiments WV48 and WV91 assimilate only the water vapor channels, and ALL48, ALL91, and ALL91M assimilate the water vapor and surface-sensitive channels. The diurnally varying background error covariance, which is estimated at 3-h intervals, is incorporated into ALL91M. The 6-h cycling data assimilation at 1-h interval prior to convection initiation is conducted for each of the seven cases. Overall, a comparison between WV48/WV91 and ALL48/ALL91M revealed a consistent positive impact of AGRI brightness temperature assimilation over land on convective precipitation forecasts. The added values of assimilating AGRI surface-sensitive channels are more pronounced by using the finer vertical resolution due to low-level humidity enhancements that are related to the rise of the planetary boundary layer. With the diurnally varying background error covariance, ALL91M outperformed ALL91 because of improved low-level analyzed structures that are associated with proper low-level vertical error correlations of humidity and thermal variables.

Abstract This study compares the impacts of two stochastic parameterization schemes in a convective-scale model. The implementation of the physically based stochastic perturbation (PSP) scheme in the Weather Research and Forecasting (WRF) Model represents uncertainties arising from unresolved boundary layer processes due to the finite grid size. Uncertainty in the microphysical processes is represented by the stochastic parameter perturbation applied to the microphysics parameterization (SPPMP) scheme. We examine the regime dependence of the impacts with 48-h forecasts of days with weakly and strongly forced convection, as well as winter storms. Early in the forecasts, the two stochastic parameterizations have different effects. The PSP scheme responds to boundary layer turbulence and produces strong perturbations that grow in phase with the diurnal cycle of convection. In regions with existing precipitation, SPPMP produces perturbations that grow more slowly with lead time, independent of the time of day. Perturbation growth in the PSP experiments is stronger for convective weather and can increase the total precipitation by triggering new convection, but SPPMP can dominate when precipitation occurs under stable conditions at night or in winter storms. The differences between the two schemes are relatively short-lived, and within a day of simulation, the amplitude and structure of differences introduced by both schemes are similar. This is found to be associated with saturation of perturbation growth on small scales (up to about 50 km). The locations and amplitudes of upscale perturbation growth appear to be determined by the larger-scale dynamics, independent of the details of the stochastic physics. Significance Statement This study investigates how a physically based stochastic perturbation (PSP) scheme and a stochastic parameter perturbation to the microphysics parameterization (SPPMP) scheme impact forecast perturbation growth under different weather situations. The findings suggest that while the two stochastic parameterizations yield different impacts in the short term, they demonstrate overlapping effects in longer lead times despite their distinct theoretical foundations. This is due to the fact that once introduced, perturbations naturally grow in regions where the atmospheric flow exhibits moist instabilities. Consequently, sampling the two sources of uncertainty does not result in a proportional spread. This finding elucidates the notable success of ad hoc stochastic perturbation methods since forecast perturbation growth is primarily dominated by the underlying flow rather than the details of the stochastic schemes. The insights gained from this study are valuable for the community, particularly in the realm of developing ensemble systems. Moving forward, it is crucial to evaluate their impact within comprehensive ensemble systems that account for both initial and boundary condition uncertainties.

Abstract Mechanisms of near-cloud turbulence (NCT) occurring at two different altitudes within the upper-level outflow of tropical cyclone (TC) “Hagibis” in the northwestern Pacific Ocean on 11 October 2019 are investigated using observations and high-resolution numerical simulations. Two NCT regions located several hundred kilometers from the TC center are the focus of this study. These include the 1) top half of cirrus anvil cloud near z = 14 km at 0330 UTC and 2) the anticyclonic outflow at a lower altitude within the TC anvil at z = 11 km later at 0850 UTC. In region 1, the simulated NCT occurred within banded cirrus, which was observed by a geostationary satellite, was organized by horizontal convective rolls resulting from differential thermal advection and enhanced vertical shear within the radial outflow of the TC. In region 2, simulated NCT collocated with observed light-to-moderate turbulence occurred in the radial outflow in the northwestern quadrant of the northward-moving TC that was located along the anticyclonic shear side of the midlatitude jet. Here, strong radial accelerations within the TC outflow toward areas of inertial instability generated the Kelvin–Helmholtz billows, which resulted in localized convective instability with 1-km deep convective overturning in the half bottom of anvil cloud. Simulated turbulence intensities represented by the eddy dissipation rate (EDR) derived from both subgrid-scale turbulent kinetic energy (TKE) and resolved TKE from model domains with subkilometer horizontal grid spacing were in agreement with observed EDR from the aircraft data. Significance Statement We investigated a case of upper-level turbulence encounter that was reported in hundreds of kilometers away from the center of super Typhoon [tropical cyclone (TC)] Hagibis in the northwestern Pacific Ocean. We conducted a high-resolution numerical simulation to understand how small-scale turbulence can be generated in the northwestern quadrant of the anticyclonic upper-level outflow of the TC. The main objective of this paper is to examine processes that induce turbulence near the base of accelerated radial outflows of the TC emerged by the interaction between the TC and upper-level jet stream in midlatitude. This is very important for the aviation industry to safely avoid possible areas of turbulence encounters along the flight routes between East Asia and North America across the northwestern Pacific Ocean.

Abstract This study examines the three-dimensional thermodynamic structure of a rapidly intensifying tropical cyclone (TC) through the utilization of synthetic retrievals based off of the specifications of NASA’s Time-Resolved Observations of Precipitation Structure and Storm Intensity with a Constellation of Smallsats (TROPICS) mission. Proxy TROPICS vertical profiles of temperature and water vapor mixing ratio generated from the Hurricane Nature Run (HNR1) are utilized to analyze the TC structure over a 10-day period that includes the HNR1 TC’s rapid intensification (RI) from a tropical storm to a major hurricane. Analyses are performed to assess how accurately TROPICS may be able to determine thermodynamic profiles both within the storm and in the environment by validating against the HNR1 model data. It is found that the TROPICS retrievals compare favorably with the HNR1 data at most heights and times with errors consistently less than the proposed mission requirements (2 K for temperature; 25% for humidity). In addition, the retrievals show the ability to qualitatively track extensive dry air that is present in the vicinity of the TC. Although a substantial dry bias is present within the storm region of the TC (between 0 and 200 km from the surface center) in the 350–550-hPa layer in the TROPICS retrievals, this bias is reduced when the retrievals associated with precipitating grid points are removed from the analyses. However, despite this filtering, a significant bias remains, which suggests that the TROPICS retrievals will likely lose accuracy in regions of stronger scattering.