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Browsing by Author "van Leeuwen, Peter Jan, advisor"

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    Formation of rain layers in the Indian Ocean and their feedbacks to atmospheric convection
    (Colorado State University. Libraries, 2023) Shackelford, Kyle T., author; van Leeuwen, Peter Jan, advisor; DeMott, Charlotte, advisor; Maloney, Eric, committee member; Venayagamoorthy, Karan, committee member
    Rainfall over the tropical warm pool spanning the Indian and West Pacific Oceans is relatively colder, fresher, and less dense than the near-surface ocean. Thus, under low-to-moderate winds, rainfall can act to stably stratify the upper ocean, forming a rain layer (RL). RLs cool and freshen the ocean surface and shoal ocean mixed layer depth, confining air-sea interaction to a thin, near-surface ocean layer. The shallow, transient nature of RLs has limited their observation, and RL impact on air-sea interaction is not well understood. This two-part thesis aims to address knowledge gaps surrounding 1) RL formation and characteristic traits, and 2) RL feedbacks to the atmosphere. In the first part of this thesis, we examine Indian Ocean RLs and their potential feedbacks to the atmosphere using a 1D ocean model. Initial experiments focus on model validation, and demonstrate that the model is able to effectively replicate upper ocean response to precipitation as revealed by in situ measurements. Following model validation, Indian Ocean RL characteristics are studied by forcing a 2D array of 1D model columns with atmospheric output from an existing convection-permitting simulation. Results from this experiment demonstrate that SST reduction within RLs persists on time scales longer than those of the parent rain event. To evaluate RL feedbacks to the atmosphere, a second 2D array experiment is conducted over the same domain with identical atmospheric forcing except rainfall is set to zero at every time step. Comparison between simulations with and without rain forcing demonstrate that RLs reduce SST through cold rain input to the ocean surface, and maintain and enhance SST reductions through a stable salinity stratification. Through prolonged SST reduction, RLs also enhance spatial SST gradients that have previously been shown to excite atmospheric convection. In the second part of this thesis, RL feedbacks to the Madden-Julian Oscillation (MJO) are studied by conducting regional ocean-atmosphere coupled simulations. Output from two convection-permitting coupled simulations of the November 2011 MJO event, one with rain coupling to the ocean surface and a second without rain coupling, is used to evaluate two potential RL feedback mechanisms. The first feedback is the ''SST gradient effect,'' which refers to RL-enhanced SST gradients imposing low-level patterns of convergence/divergence in the atmospheric boundary layer. The second is the ''SST effect,'' which refers to RL-induced SST perturbations altering turbulent heat fluxes. During the MJO transition from suppressed to enhanced convection, the SST gradient effect and SST effect have opposing feedbacks to convection, as RL-enhanced SST gradients favor convective initiation, while RL-induced SST reduction hinders convection. Comparison of coupled simulations with and without rain coupling to the ocean demonstrates that RL-induced SST reduction has a more substantial impact than enhanced SST gradients during this transitory phase. A delayed pathway in which RLs feedback to the MJO through the SST effect arises from frequent RL presence during the disturbed phase, which isolates subsurface ocean heat from the atmosphere. At the onset of the MJO active phase, westerly wind bursts erode near-surface RLs and release previously trapped subsurface ocean heat to the atmosphere, amplifying the intensity of MJO convection. Between the direct and delayed SST effect, RLs are shown to modify intraseasonal tropical variability.
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    High-dimensional nonlinear data assimilation with non-Gaussian observation errors for the geosciences
    (Colorado State University. Libraries, 2023) Hu, Chih-Chi, author; van Leeuwen, Peter Jan, advisor; Kummerow, Christian, committee member; Anderson, Jeffrey, committee member; Bell, Michael, committee member; Kirby, Michael, committee member
    Data assimilation (DA) plays an indispensable role in modern weather forecasting. DA aims to provide better initial conditions for the model by combining the model forecast and the observations. However, modern DA methods for weather forecasting rely on linear and Gaussian assumptions to seek efficient solutions. These assumptions can be invalid, e.g., for problems associated with clouds, or for the assimilation of remotely-sensed observations. Some of these observations are either discarded, or not used properly due to these inappropriate assumptions in DA. Therefore, the goal of this dissertation is to seek solutions to tackle the issues arising from the linear and Gaussian assumptions in DA. This dissertation can be divided into two parts. In the first part, we explore the potential of the particle flow filter (PFF) in high dimensional systems. First, we tested the PFF in the 1000- dimensional Lorenz 96 model. The key innovation is we find that using a matrix kernel in the PFF can prevent the collapse of particles along the observed directions, for a sparsely observed and high-dimensional system with only a small number of particles. We also demonstrate that the PFF is able to represent a multi-modal posterior distribution in a high-dimensional space. Next, in order to apply the PFF for the atmospheric problem, we devise a parallel algorithm for PFF in the Data Assimilation Research Testbed (DART), called PFF-DART. A two-step PFF was developed that closely resembles the original PFF algorithm. A year-long cycling data assimilation experiment with a simplified atmospheric general circulation model shows PFF-DART is able to produce stable and comparable results to the Ensemble Adjustment Kalman Filter (EAKF) for linear and Gaussian observations. Moreover, PFF-DART can better assimilate the non-linear observations and reduce the errors of the ensemble, compared to the EAKF. In the second part, we shift our focus to the observation error in data assimilation. Traditionally, observation errors have been assumed to follow a Gaussian distribution mainly for two reasons: it is difficult to estimate observation error statistics beyond its second moment, and most of the DA methods assume a Gaussian observation error by construction. We developed the so-called Deconvolution-based Observation Error Estimation (DOEE), that can estimate the full distribution of the observation error. We apply DOEE to the all-sky microwave radiances and show that they indeed have non-Gaussian observation errors, especially in a cloudy and humid environment. Next, in order to incorporate the non-Gaussian observation errors into variational methods, we explore an evolving-Gaussian approach, that essentially uses a state dependent Gaussian observation error in each outer loop of the minimization. We demonstrate the merits of this method in an idealized experiment, and implemented it in the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts. Preliminary results show improvement for the short-term forecast of lower-tropospheric humidity, cloud, and precipitation when the observation error models of a small set of microwave channels are replaced by the non-Gaussian error models. In all, this dissertation provides possible solutions for outstanding non-linear and non-Gaussian data assimilation problems in high-dimension systems. While there are still important remaining issues, we hope this dissertation lays a foundation for the future non-linear and non-Gaussian data assimilation research and practice.
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    On the certainty framework for causal network discovery with application to tropical cyclone rapid intensification
    (Colorado State University. Libraries, 2022) DeCaria, Michael, author; van Leeuwen, Peter Jan, advisor; Chiu, Christine, committee member; Barnes, Elizabeth, committee member; Ebert-Uphoff, Imme, committee member
    Causal network discovery using information theoretic measures is a powerful tool for studying new physics in the earth sciences. To make this tool even more powerful, the certainty framework introduced by van Leeuwen et al. (2021) adds two features to the existing information theoretic literature. The first feature is a novel measure of relative strength of driving processes created specifically for continuous variables. The second feature consists of three decompositions of mutual information between a process and its drivers. These decompositions are 1) coupled influences from combinations of drivers, 2) information coming from a single driver coupled with a specific number of other drivers (mlinks), and 3) total influence of each driver. To represent all the coupled influences, directed acyclic hypergraphs replace the standard directed acyclic graphs (DAGs). The present work furthers the interpretation of the certainty framework. Measuring relative strength is described thermodynamically. Two-driver coupled influence is interpreted using DAGs, introducing the concept of separability of drivers' effects. Coupled influences are proved to be a type of interaction information. Also, total influence is proved to be nonnegative, meaning the total influences constitute a nonnegative decomposition of mutual information. Furthermore, a new reference distribution for calculating self-certainty is introduced. Finally, the framework is generalized for variables that are continuous with one discrete mode, for which partial Shannon entropy is introduced. The framework was then applied to the rapid intensification of Hurricane Patricia (2015). The hourly change in maximum tangential windspeed was used as the target. The four drivers were outflow layer (OL) maximum radial windspeed (uu), boundary layer (BL) radial windspeed at radius of maximum wind (RMW) (ul), equivalent potential temperature at BL RMW (θe), and the temperature difference between the OL and BL (ΔT). All variables were azimuthally averaged. The drivers explained 45.5% of the certainty. The certainty gain was 35.8% from θe, 24.5% from ΔT, 24.0% from uu, and 15.7% from ul. The total influence of θe came mostly from inseparable effects, while the total influence of uu came mostly from separable effects. Physical mechanisms, both accepted in current literature and suggested from this application, are discussed.