Monthly Weather Review

Rohith, B; Gasparin, Florent; Ruggiero, Giovanni; Remy, Elisabeth; Cravatte, Sophie

2025 Monthly Weather Review

doi: 10.1175/mwr-d-24-0027.1

AbstractThis study investigates the ability of a global ocean reanalysis at 1/12° horizontal resolution, GLORYS12, to represent oceanic processes at intraseasonal and higher-frequency scales. GLORYS12, which includes data assimilation of satellite and multi-instrument in situ observations, is compared to a twin-free simulation (with no assimilation) in the Tropical Pacific Ocean. Spectral analyses show that data assimilation improves the realism of sea surface height intraseasonal variability in the entire Tropical Pacific Ocean, both in amplitude and phase, with an increase in amplitude of more than 50% for the 20-90 days band, and up to 15% for the 2-20 days band. The improvement is largest along the 5°N/S latitudes, where the magnitude of tropical instability waves is maximum, but is limited along the equator where steric height variability is dominated by intraseasonal oceanic Kelvin waves, already well-represented in the free simulation. Wavenumber-frequency spectra show that data assimilation constraint improves both the spatial and temporal scales of intraseasonal waves, and their timing. Data assimilation impacts the realism of oceanic simulations in two ways. By modifying the background oceanic stratification, it corrects the westward propagating waves phase speed. It is also shown that the intraseasonal component of analysis increments (data assimilation corrections applied) is dynamically consistent and exhibits clear intraseasonal propagation. By demonstrating the benefits of data assimilation for intraseasonal processes in the Tropical Pacific Ocean, this study highlights the high value of both in situ and satellite observations to constrain ocean models in a wide range of timescales.

Munsell, Erin B.; Braun, Scott A.; Greenwald, Tom; Bennartz, Ralf; Blackwell, William J.

2025 Monthly Weather Review

doi: 10.1175/mwr-d-23-0191.1

AbstractThis 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; Nolan et al. 2013) 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 theHNR1 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 – 200 km from the surface center) in the 350–550 mb 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.

Wu, Pin-Ying; Kawabata, Takuya; Duc, Le

2025 Monthly Weather Review

doi: 10.1175/mwr-d-24-0067.1

AbstractEnsemble simulations involve perturbations of error sources in numerical models to represent uncertainties. The rank of the perturbation matrix is expected to be equal to the ensemble size; thus, each ensemble member may have an independent perturbation. However, ensembles without independent perturbations for each member have been commonly adopted, and their impacts remain unknown. This study explores how a lack of perturbation rank affects the quality of an ensemble and its estimates. Focusing on mesoscale weather forecasts, lateral boundary conditions (LBCs) were considered since they are a primary source of uncertainty in regional models. Two sets of 1000-member ensemble simulations of Typhoon Hagibis (2019) were performed with different ranks of LBC perturbations (LBPs). A lack of perturbation rank caused members to cluster based on the given LBC because the LBCs strongly constrained the simulated states. This clustering is a sign of poor orthogonality, that is, a deficiency in the effective ensemble size. Clustering also implies distortion in the ensemble error space. A resulting spurious probability distribution can occur even when the ensemble mean and spread are valid. From the perspective of physical space, spurious probability distributions are caused by the clustering of typhoon locations and synoptic-scale environmental conditions owing to the lack of an LBP rank. Additionally, using positive and negative perturbation pairs was one cause of the perturbation rank defect. Our findings highlight the importance of the perturbation rank, particularly for high-order estimates of probability distributions with large ensembles.

Stone, Jack; Gervais, Melissa; Bowley, Kevin A.; Zarzycki, Colin

2025 Monthly Weather Review

doi: 10.1175/mwr-d-23-0265.1

AbstractNorth American Cold Air Outbreaks (CAOs) are large-scale temperature extremes that typically originate in the high latitudes and impact the midlatitudes in winter. As they transit southward, they can have significant socioeconomic consequences. CAOs from winter (DJF) 1979 to 2020 were identified in the European Center for Medium-Range Weather Forecasts reanalysis dataset (ERA5) using an automated feature tracking approach (TempestExtremesV2.1). This allowed for the systematic identification of a large number of cases without using pre-determined, Eulerian regions. Another important advantage of this approach was the ability to compute a feature tracked thermodynamic energy budget in a non-fixed domain for every identified CAO event. As an example, the thermodynamic energy budget analysis was used to quantify important processes for the 18th–23rd January, 1985 CAO. The dominant mechanisms of cooling and warming as well as lysis locations (i.e. eastern or western) were then used to generalize detected CAO events into subcategories. The associated statistics, spatial footprints, and composites of 500-hPa height, sea level pressure, and temperature and winds at 850-hPa were analyzed for three subcategories that contained the majority of events. This analysis revealed that CAO events that form and dissipate through different mechanisms occur in different regions, have different intensities, and are associated with different large-scale circulation patterns. Finally, analysis of associated North Atlantic Oscillation (NAO) and Pacific-North American Pattern (PNA) teleconnections revealed that the PNA is typically in a positive phase for eastern CAO events and negative phase for western events resulting primarily from horizontal advection, whereas the NAO did not have any significant relationship.

Lopez, Andres; Kirshbaum, Daniel J.; Lareau, Neil

2025 Monthly Weather Review

doi: 10.1175/mwr-d-24-0125.1

AbstractThe north-south oriented Sierras de Córdoba (SDC) ridge in central Argentina is noted for initiating thunderstorms that may grow into intense mesoscale convective systems (MCSs). It also initiates more isolated, shorter-lived cells under weaker synoptic forcing. These cells are less impactful than MCSs but may be difficult to predict in convective-scale NWP due to their strong sensitivities to subgrid and partially resolved processes. To study the mechanisms and predictability of such cells, convection-permitting ensemble simulations were conducted of an isolated, diurnally forced SDC thunderstorm during CACTI/RELAMPAGO. The rich observational data facilitated detailed ensemble verification, where dry biases in the surface energy balance and soil moisture were identified. These biases promoted rapid removal of convective inhibition and an early onset of precipitating cells over the SDC that were shallower and weaker than the observed cell. Correction, and then over-correction, of the soil-moisture bias in two successive ensembles was required to rectify the surface energy balance and improve the representation of the SDC cell. Nevertheless, substantial ensemble variability in convective precipitation was found, with some members producing more widespread convection than observed and others producing no deep convection at all. This variability was largely explained by a combination of thermodynamic and dynamic mechanisms, dominated by a positive sensitivity of convective precipitation to preconvective moist instability over the ridge. Secondary sensitivities were found to low-level upward mass flux and midlevel cross-barrier winds, the latter of which caused gravity waves with elevated downdrafts that tended to suppress incipient clouds.