The sensitivity and predictability of a rapidly developing extratropical cyclone, Xynthia, that had a severe impact on Europe is explored using a high-resolution moist adjoint modeling system. The adjoint diagnostics indicate that the intensity of severe winds associated with the front just prior to landfall was particularly sensitive to perturbations in the moisture and temperature fields and to a lesser degree the wind fields. The sensitivity maxima are found in the low-and midlevels, oriented in a sloped region along the warm front, and maximized within the warm conveyor belt. The moisture sensitivity indicates that only a relatively small filament of moisture within an atmospheric river present at the initial time was critically important for the development of Xynthia. Adjoint-based optimal perturbations introduced into the tangent linear and nonlinear models exhibit rapid growth over 36 h, while initial perturbations of the opposite sign show substantial weakening of the low-level jet and a marked reduction in the spatial extent of the strong low-level winds. The sensitivity fields exhibit an upshear tilt along the sloping warm conveyor belt and front, and the perturbations extract energy from the mean flow as they are untilted by the shear, consistent with the PV unshielding mechanism. The results of this study underscore the need for accurate moisture observations and data assimilation systems that can adequately assimilate these observations in order to reduce the forecast uncertainties for these severe extratropical cyclones. However, given the nature of the sensitivities and the potential for rapid perturbation and error growth, the intrinsic predictability of severe cyclones such as Xynthia is likely limited.

The sensitivity of tropical cyclogenesis and subsequent intensification is explored by applying small perturbations to the initial state in the presence of organized mesoscale convection and synoptic-scale forcing using the adjoint and tangent linear models for the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS). The forward, adjoint, and tangent linear models are used to compare and contrast predictability characteristics for the disturbance that became Typhoon Nuri and a nondeveloping organized convective cluster in the western Pacific during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) and the Tropical Cyclone Structure-2008 (TCS-08) experiments.The adjoint diagnostics indicate that the intensity (e.g., maximum surface wind speed, minimum surface pressure) of a tropical disturbance is very sensitive to perturbations in the moisture and temperature fields and to a lesser degree the wind fields. The highest-resolution adjoint simulations (grid increment of 13 km) indicate that the most efficient intensification is through moistening in the lower and middle levels and heating in banded regions that are coincident with vorticity maxima in the initial state. Optimal adjoint perturbations exhibit rapid growth for the Nuri case and only modest growth for the nondeveloping system. The adjoint results suggest that Nuri was near the threshold for development, indicative of low predictability. The low-level sensitivity maximum and tendency for optimal perturbation growth to extend vertically through the troposphere are consistent with a "bottom up" development process of TC genesis, although a secondary midlevel sensitivity maximum is present as well. Growth originates at small scales and projects onto the scale of the vortex, a manifestation of perturbations that project onto organized convection embedded in regions of cyclonic vorticity.

An adjoint modeling system based upon the Naval Research Laboratory's Coupled Ocean-AtmosphereMesoscale Prediction System's atmospheric component has been developed. The system includes the adjoint model of the explicit moist physics parameterization, which allows for gradients with respect to the initial hydrometeor concentrations to be calculated. This work focuses on the ability of the system to calculate evolved perturbations and gradients for the hydrometeor variables. Tests of the tangent linear and adjoint models for an idealized convective case at high model resolution (4-km horizontal grid spacing) are presented in this study. The tangent linear approximation is shown to be acceptable for all model variables (including the hydrometeors) with sizable perturbations for forecasts of 1 h. The adjoint model was utilized with the same convective case to demonstrate its applicability in four-dimensional variational data assimilation experiments. Identical twin experiments were conducted where the adjoint model produced gradients for all model variables, leading to improved analyses and forecasts. The best agreement between model forecasts and simulated observations occurred when information on all model variables was assimilated. In the case where only conventional data were assimilated, the agreement was not as good in the early forecast period. However, the hydrometeor values spun up quickly, and at later times, the forecast performed almost as well as when all data were assimilated.

The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

The initial state sensitivity of high-impact extratropical cyclones over the North Atlantic and United Kingdom is investigated using an adjoint modeling system that includes moist processes. The adjoint analysis indicates that the 48-h forecast of precipitation and high winds associated with the extratropical cyclone “Desmond” was highly sensitive to mesoscale regions of moisture at the initial time. Mesoscale moisture and potential vorticity structures along the poleward edge of an atmospheric river at the initialization time had a large impact on the development of Desmond as demonstrated with precipitation, kinetic energy, and potential vorticity response functions. Adjoint-based optimal perturbations introduced into the initial state exhibit rapidly growing amplitudes through moist energetic processes over the 48-h forecast. The sensitivity manifests as an upshear-tilted structure positioned along the cold and warm fronts. Perturbations introduced into the nonlinear and tangent linear models quickly expand vertically and interact with potential vorticity anomalies in the mid- and upper levels. Analysis of adjoint sensitivity results for the winter 2013/14 show that the moisture sensitivity magnitude at the initial time is well correlated with the kinetic energy error at the 36-h forecast time, which supports the physical significance and importance of the mesoscale regions of high moisture sensitivities.