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 ESTIMATION AND ADAPTIVE ONLINE CORRECTION OF SYSTEMATIC ERRORS IN THE GLOBAL FORECAST SYSTEM (GFS) USING ANALYSIS INCREMENTS(2019) Bhargava, Kriti; Kalnay, Eugenia; Carton, James A; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Numerical Weather prediction models have improved drastically in the last few decades with advances in data assimilation, improved parameterization, and ensemble forecasting. Despite these developments, the performance of numerical weather prediction models like the Global Forecast System (GFS) is still limited by errors in the model forecasts. These errors arise from inaccuracies in the initial condition and model’s inability to accurately represent physics, dynamics, and chemical processes. Operation centers generally use an offline correction scheme that corrects the forecast error after the forecast is generated. Past research has shown that another class of correction schemes, the online correction schemes that correct for the forecast errors during the model integration have certain advantages over offline schemes. However, the online schemes tested so far are prohibitive for operation use. The goal of this work is to introduce and test an ``adaptive online correction scheme” based on the methodology developed by (Danforth et al., 2007) that is suitable for operational use is introduced and implemented. As a first step towards correcting the tendency equation, the model errors are estimated using the 6-hr Analysis Increments (AIs). Assuming initial linear error growth and absence of observation bias in the analysis, 6-hr AIs provide a measure of model errors that can later be used to estimate model tendency errors. Seasonal means of 6-hr AIs during the period from 2012-2016 indicate robust model biases despite the changes in the model and data assimilation during that period. Apart from the season means, GFS also has significant periodic errors that are dominated by errors in the diurnal and semi-diurnal cycle. An adaptive online correcting scheme that uses 6-hr AIs, averaged over a moving training period to compute the bias correction term to be added in the model integration equation is then implemented with GFS. The scheme is tested using training periods of different lengths ranging from past 7 to 28 days. This scheme is remarkably stable and reduces the forecasts errors significantly in forecasts all over the globe at lead times of 1 day and shorter and over the tropics at longer lead times. An offline correction scheme was also tested but found to be less effective than the online correction scheme especially at lead times longer than 1-day.Item Multivariate Correlations: Balance Operators and Variable Localization in Ensemble Data Assimilation(2017) Thomas, Catherine; Ide, Kayo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Localization is performed in ensemble data assimilation schemes to eliminate correlations that are contaminated by sampling error. This method is frequently necessary within numerical weather prediction (NWP) applications due to the computational constraints present, limiting the number of ensemble members to a size much smaller than the dimension of the system. The most common form of localization occurs in the spatial dimensions, reducing the correlations for points that are distant and likely dominated by sampling error. Spatial localization can introduce imbalance in the system due to the disruption of physical relationships that are dictated by gradients or column integrated quantities, which produce fast-moving gravity waves within NWP models and degrade the forecast. The first part of this dissertation explores the impact of including a balance operator within ensemble data assimilation schemes and how the type of spatial localization interacts with it. The inclusion of a balance operator allows the localization to be performed on the unbalanced portion of the correlation, preserving the balanced correlation. Two data assimilation schemes are explored: a hybrid 4D ensemble-variational (4DEnVar) scheme and a Local Ensemble Transform Kalman Filter (LETKF). Observing system simulation experiments are performed using an intermediate complexity model, SPEEDY. It is shown that localizing on the background error as in the hybrid 4DEnVar is more effective than localizing on the observation error as in the LETKF. Within the LETKF, the balance operator can only propagate information one way, for example, from streamfunction to temperature, but not vice versa as in the hybrid 4DEnVar. Many applications contain variables that are physically unrelated and should not be correlated, but contain nonzero correlations. The second part of this dissertation presents two forms of variable localization in a unified framework: observation space variable localization (VO) and model space variable localization (VM). VO restricts the impact that observations have to certain model variables. VM removes the cross-correlations during the computation of the background error covariance. VM is more computationally expensive, but it has the added advantages of not requiring knowledge of observation types and allowing a single observation to impact multiple model variables whose cross-correlations have been removed.Item ENSEMBLE KALMAN FILTER EXPERIMENTS WITH A PRIMITIVE-EQUATION GLOBAL MODEL(2005-06-30) Miyoshi, Takemasa; Kalnay, Eugenia; Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The ultimate goal is to develop a path towards an operational ensemble Kalman filtering (EnKF) system. Several approaches to EnKF for atmospheric systems have been proposed but not systematically compared. The sensitivity of EnKF to the imperfections of forecast models is unclear. This research explores two questions: 1. What are the relative advantages and disadvantages of two promising EnKF methods? 2. How large are the effects of model errors on data assimilation, and can they be reduced by model bias correction? Chapter 2 contains a theoretical review, followed by the FORTRAN development and testing of two EnKF methods: a serial ensemble square root filter (serial EnSRF, Whitaker and Hamill 2002) and a local EnKF (LEKF, Ott et al. 2002; 2004). We reproduced the results obtained by Whitaker and Hamill (2002) and Ott et al. (2004) on the Lorenz (1996) model. If we localize the LEKF error covariance, LEKF outperforms serial EnSRF. We also introduce a method to objectively estimate the optimal covariance inflation. In Chapter 3 we apply the two EnKF methods and the three-dimensional variational method (3DVAR) to the SPEEDY primitive-equation global model (Molteni 2003), a fast but relatively realistic model. Perfect model experiments show that EnKF greatly outperforms 3DVAR. The 2-day forecast "errors of the day" are very similar to the analysis errors, but they are not similar among different methods except in low ensemble dimensional regions. Overall, serial EnSRF outperforms LEKF, but their difference is substantially reduced if we localize the LEKF error covariance or increase the ensemble size. Since LEKF is much more efficient than serial EnSRF when using parallel computers and many observations, LEKF would be the only feasible choice in operations. In Chapter 4 we remove the perfect model assumption using the NCEP/NCAR reanalysis as the "nature" run. The advantage of EnKF to 3DVAR is reduced. When we apply the model bias estimation proposed by Dee and da Silva (1998), we find that the full dimensional model bias estimation fails. However, if instead we assume that the bias is low dimensional, we obtain a substantial improvement in the EnKF analysis.