Clark Amerault

Tests of an Adjoint Mesoscale Model with Explicit Moist Physics on the Cloud Scale

Abstract An adjoint modeling system based upon the Naval Research Laboratory’s Coupled Ocean–Atmosphere Mesoscale 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 ...

Initial Condition Sensitivity and Predictability of a Severe Extratropical Cyclone Using a Moist Adjoint

AbstractThe 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...

Comparison of Model-Produced and Observed Microwave Radiances and Estimation of Background Error Covariances for Hydrometeor Variables within Hurricanes

Abstract A radiative transfer model was updated to better simulate Special Sensor Microwave Imager (SSM/I)–observed brightness temperatures in areas of high ice concentration. The difference between the lowest observed and model-produced brightness temperatures at 85 GHz has been reduced from over 100 K to roughly 20 K. Probability distribution functions of model-produced and SSM/I-observed brightness temperatures show that the model overestimates the areas of precipitation, but overall matches the SSM/I observations quite well. Estimates of vertical background error covariance matrices and their inverses were calculated for all hydrometeor variables (both liquid and frozen). For cloud and rainwater, the largest values in the matrices are located in the lower levels of the atmosphere, while the largest values in the cloud ice, snow, and graupel matrices are in the upper levels of the atmosphere. The inverse background matrices can be used as weightings for hydrometeor variables in assimilation experiments...

Adjoint Sensitivity and Predictability of Tropical Cyclogenesis

AbstractThe 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...

Assimilation of rain-affected radiances with an adjoint model

The ability to assimilate microwave radiance observations of the Earths atmosphere affected by precipitation is investigated with an emphasis on channels that are sensitive to frozen hydrometeors. Mesoscale numerical weather prediction and radiative transfer models, as well as their corresponding adjoint models are utilized in sensitivity and data assimilation experiments. Special Sensor Microwave / Imager observations of Hurricane Bonnie (1998) are compared with model results that are transferred to radiance space with the radiative transfer model. Sensitivity results indicate that the model error in radiance space in areas of precipitation at the initial time is most dependent on the initial hydrometeor fields. At later forecast times, the model error is more sensitive to initial conventional model variables such as water vapor and temperature. When radiance data is assimilated, the model fields have better agreement with the observations compared to a control experiment for all observed channels at the initial time. However, at the next observation time 12 h later, the quantitative error measurements for the control and post assimilation forecasts are approximately the same value. Although this study demonstrates the ability to assimilate observations sensitive to atmospheric ice (as well as liquid) concentrations in a variational framework, important aspects such a background error correlation and bias have been ignored for simplification. More observations in a more complex data assimilation system will be needed in order to fully maximize the forecast impact of these observations.

Diagnosing Tropical Cyclone Sensitivity

Using sensitivity calculations, its possible to better understand complex influences on typhoon evolution from organized convection to larger-scale weather systems. With Typhoon Lupit, for example, the rapid growth of small perturbations led to multiple errors that limited the accuracy of path and intensity forecasts.

Sensitivity analysis of mountain waves using an adjoint model

An adjoint modeling system developed for the COAMPS nonhydrostatic model is used to explore the sensitivity of lee-side winds to the upstream atmospheric conditions for flow over a two-dimensional obstacle. For relatively small hills in the hydrostatic wave regime, the sensitivity patterns exhibit a dual lobed structure that is a manifestation of a superposition of internal gravity waves with downward-directed energy propagation. Nonhydrostatic waves generated by small terrain have corresponding sensitivities that are tilted vertically against the shear, which when introduced into the flow as perturbations, evolve into structures that resemble vertically decaying evanescent waves. Flow over higher obstacles near the gravity wave breaking threshold exhibits complex sensitivity patterns characterized by a wave-like packet of maxima and minima upstream of the middle- and upper-tropospheric region of wave breaking. In general, as the mountain height is increased, the tangent linear approximation becomes less accurate. However, the strongest nonlinearity occurs for flows very near the wave breaking threshold, rather than fully within the wave breaking regime forced by higher terrain.

Tropical Cyclone Track and Intensity Predictability

Atmospheric ensemble and ad joint systems can provide valuable insight into the practical limitations of our ability to predict the path of tropical cyclones and their strength. An ensemble forecast system can be used to address tropical cyclone forecast uncertainty. An ad joint model can be used for the efficient and rigorous computation of numerical weather forecast sensitivity to changes in the initial state. The sensitivity calculations illustrate complex influences on tropical cyclone evolution that occur over a wide range of scales from convective clusters to larger scale weather systems. Rapid growth of small perturbations can lead to errors on multiple scales that conspire to limit the forecast accuracy of the path and intensity of tropical cyclones. Inherent uncertainties in tropical cyclone forecasts motivate the need for development of ensemble prediction systems that can provide probabilistic forecast guidance for tropical cyclones. New capabilities have been developed for the Navys global weather ensemble forecast system including a high-resolution system and new methods for perturbing the initial state.

Assimilation of small scale observations with a nested adjoint model

An adjoint limited area numerical weather prediction model with multiple nests has been developed. The adjoint modeling system has the capability to pass gradient information from the finer spaced nest to the coarser spaced domain. Therefore, gradients of scalar functions calculated from small scale features can be computed with respect to the large scale model state. Sensitivity experiments were performed to show that the nested adjoint model produces physically meaningful gradients. Results from data assimilation experiments will be presented at the conference. An adjoint model such as the one presented here, could be an important tool for variational assimilation schemes that intend to make use of high resolution remotely sensed data.

Mesoscale assimilation of rain-affected observations

Important issues involving the assimilation of rain-affected observations using an adjoint mesoscale modeling system are addressed in this study. The adjoint model of the explicit moist physics parameterization is included in the modeling system, which allows for the calculation of gradients with respect to the initial hydrometeor concentrations (cloud water/ice, rain, snow, and graupel). Cloud-scale idealized four dimensional variational data assimilation experiments demonstrate the benefit of assimilating precipitation information and the ability of the adjoint model to produce useful gradients with respect to the hydrometeor fields. The agreement between model fields and observations is greater (especially for the early forecast hydrometeor fields) when rainy observations are incorporated into the assimilation process versus only assimilating conventional model data (windspeeds, temperature, pressure). Additional data assimilation experiments are conducted with microwave radiances. These data improve the initial precipitation structure of a tropical cyclone. These experiments are promising steps for the incorporation of rain-affected observations in operational data assimilation systems.