Atmospheric & Oceanic Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2747
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Item Exploring the Observation Impacts and Enhancing the Predictability for Ensemble-Based Coupled Data Assimilation(2023) Chang, Chu-Chun; Kalnay, Eugenia EK; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research aims to explore the observation impacts in coupled data assimilation (CDA) and improve the predictability of coupled systems by advanced DA approaches. Three topics are discussed in this dissertation: (1) An enhanced application of the correlation cutoff method (Yoshida and Kalnay, 2018) as a spatial localization is introduced. We investigated the feasibility and characteristics of the traditional distance-dependent (Gaspari and Cohn, 1999) and the correlation-dependent localizations preliminary on the Lorenz (1996) model with the local ensemble transform Kalman filter (LETKF). We further discussed the potential of integrative localization strategies and the application of the correlation cutoff method on Mars DA. (2) We found that the surface sea temperature (SST) relaxation operationally used in the Climate Forecast System version 2 (CFSv2) is not effective in reducing existing SST biases. To address this issue, we replaced the SST relaxation with the weakly coupled data assimilation (WCDA) of satellite-retrieved SST products. A series of experiments with real observations were conducted on the CFSv2-LETKF (Sluka et al., 2018) to investigate the impacts of SST WCDA on the CFSv2 analysis and the forecasts. (3) The Ensemble Forecast Sensitivity to Observations (EFSO, Kalnay et al., 2012) is a powerful tool to identify the beneficial or detrimental impact of every observation and has been widely used in atmospheric ensemble-based DA. However, EFSO has not yet been applied to any ocean or coupled DA due to the lack of a proper error norm for oceanic variables. This study first introduces a novel density-based error norm that simultaneously includes sea temperature and salinity forecast errors, by which EFSO becomes available to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2-LETKF for quantifying the individual impact of ocean observations and explored the great potential of EFSO to be extended as a data selection criterion to improve the CFSv2 forecasts.Item Investigations in Earth System Predictability(2021) Bach, Eviatar; Kalnay, Eugenia; Mote, Safa; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis covers several projects in predictability, prediction, and data assimilation of the Earth system: 1. The first is an analysis of the predictability of the atmosphere–ocean system. Due to the physical coupling between atmosphere and ocean, information about the ocean helps to better predict the future of the atmosphere, and in turn, information about the atmosphere helps to better predict the ocean. Here, we investigate the spatial and temporal nature of this predictability: where, for how long, and at what frequencies does the ocean significantly improve prediction of the atmosphere, and vice-versa? We apply Granger causality, a statistical test to measure whether a variable improves prediction of another, to local time-series of sea-surface temperature (SST) and low-level atmospheric variables. We calculate the detailed spatial structure of the atmosphere-to-ocean and ocean-to-atmosphere predictability. We find that the atmosphere improves prediction of the ocean most in the extratropics, especially in regions of large SST gradients. This atmosphere-to-ocean predictability is weaker but longer-lived in the tropics, where it can last for several months in some regions. On the other hand, the ocean improves prediction of the atmosphere most significantly in the tropics, where this predictability lasts for months to over a year. However, we find a robust signature of the ocean on the atmosphere almost everywhere in the extratropics, an influence that has been difficult to demonstrate with model studies. We find that both the atmosphere-to-ocean and ocean-to-atmosphere predictability are maximal at low frequencies, and both are larger in the summer hemisphere. The patterns we observe generally agree with dynamical understanding and the results of the Kalnay dynamical rule, which diagnoses the direction of forcing between the atmosphere and ocean by considering the local phase relationship between simultaneous sea-surface temperature and vorticity anomaly signals. We discuss applications to coupled data assimilation. 2. The second presents the analysis of the Lyapunov spectrum of a coupled quasi-geostrophic atmosphere–ocean model, the Modular Arbitrary-Order Ocean-Atmosphere Model. We show that the Lyapunov spectrum of the forced ocean has a stepwise structure, unlike the smooth spectrum of the coupled ocean. The spectrum of the forced atmosphere, however, is nearly identical to that of the coupled atmosphere. By computing the conditional Lyapunov exponent (CLE) spectrum, we find that the uncoupled atmosphere and ocean, when forced with output from the coupled system, do not synchronize with the coupled system. However, assimilating observations from the coupled atmosphere to the forced atmosphere makes the CLE spectrum negative, a necessary condition for synchronization with the coupled model. However, assimilating observations from the coupled ocean to the forced ocean does not make the CLE spectrum negative. 3. The third presents a novel forecasting method for leveraging the predictability of oscillatory modes. Oscillatory modes of the climate system are one of its most predictable features, especially at intraseasonal time scales. These oscillations can be predicted well with data-driven methods, often with better skill than dynamical models. However, since the oscillations only represent a portion of the total variance, a method for beneficially combining oscillation forecasts with dynamical forecasts of the full system was not previously known. We introduce Ensemble Oscillation Correction (EnOC), a general method to correct oscillatory modes in ensemble forecasts from dynamical models. We compute the ensemble mean with only the best ensemble members, as determined by their discrepancy from a data-driven forecast of the oscillatory modes. We also present an alternate method which uses ensemble data assimilation to combine the oscillation forecasts with an ensemble of dynamical forecasts of the system (EnOC-DA). The oscillatory modes are extracted with a time-series analysis method called singular spectrum analysis (SSA), and forecast using an analog method. We test the method using chaotic toy models with significant oscillatory components, and show that it robustly reduces error compared to the uncorrected ensemble. 4. The fourth applies EnOC to the South Asian monsoon. Prediction of the monsoon on intraseasonal timescales is critical for agriculture and flood planning. The summer monsoon season displays northward-propagating modes of convection known as the monsoon intraseasonal oscillations (MISOs). These modes, which have periods of about 45–50 days and 20–30 days, characterize the active and break phases of the monsoon, as well as much of the regional rainfall patterns. It has been recognized that MISO is a source of predictability for monsoon rainfall beyond the synoptic weather timescale, and several studies have shown it can be predicted well by data-driven methods. We apply EnOC to the ECMWF ensemble seasonal hindcasts from 1998 to 2016, initialized in May, June, July, August, and September. We use analog-based forecasts of the leading MISO mode, as represented by a trajectory in a two-dimensional principal component (PC) space. Ensemble members are projected into the PC space using a lasso regression technique. We see increases in forecast skill over the regular ensemble mean, as measured by the anomaly correlation and mean-square error, at lead times between 14 and 40 days, especially for forecasts starting in May and June. The largest error reductions are seen in the West and South regions of India, which contain the Western Ghats.Item APPLICATIONS OF ENSEMBLE FORECAST SENSITIVITY TO OBSERVATIONS FOR IMPROVING NUMERICAL WEATHER PREDICTION(2018) Chen, Tse-Chun; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Massive amounts of observations are assimilated every day into modern Numerical Weather Prediction (NWP) systems, and more are being deployed. The large volume of data prevents thorough monitoring and screening (QC) the impact of each assimilated observation using standard observing system experiments (OSEs). The presence of so many observations also makes very difficult to estimate the impact of a new observing system using OSEs. Forecast Sensitivity to Observation using adjoint formulation (AFSO, Langland and Baker, 2004) provides an efficient impact evaluation of each observation on forecasts. We propose 3 applications using the simpler ensemble formulation of FSO (EFSO, Kalnay et al., 2012) to improve NWP, namely (1) online monitoring tool, (2) data selection, and (3) proactive quality control (PQC). We first demonstrate PQC on a simple Lorenz (1996) model, showing that EFSO is able to identify artificially '`flawed" observations. We then show that PQC improves the quality of analysis and forecast of the system, even if the observations are flawless, and the improvement is robust against common sub-optimal of DA configurations in operation. A PQC update method reusing the original Kalman gain is found to be both accurate and computationally efficient. EFSO and PQC are then explored with realistic GFS systems. A close-to-operation GFS-GSI Hybrid En-Var system is used to examine the data monitoring and selection applications. The benefit of the online observation monitoring and data rejection based on EFSO is very apparent. Identifying and deleting detrimental radiance channels results in a forecast improvement. Results obtained on a lower resolution GFS system show that PQC significantly improves the quality of analysis and 5-day forecasts for all variables over the globe. Most of the improvement comes from "cycling" PQC, which accumulates improvements brought by deleting detrimental observations over many cycles, rather than from deleting detrimental observations in the current cycle. Thus we avoid using "future data" in PQC and its implementation is shown to be computationally feasible in NCEP operations.