Theses and Dissertations from UMD
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Item A New Quantitative Framework for Application of Ensemble Forecast Sensitivity to Observations in NWP(2021) Groff, David Neil; Kalnay, Eugenia; Chen, Tse-Chun; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Current global operational Numerical Weather Prediction (NWP) systems (e.g.the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS)) generally assimilate on the order of 10 million observations every 6 hours. Furthermore, there is substantial diversity in the sampling characteristics and associated error characteristics of the observation types assimilated. In this context, it is not feasible to obtain sufficiently detailed information for determining which available observations or observation types should be assimilated or rejected in NWP systems using traditional Observing System Experiment (OSE) approaches. Forecast sensitivity to observation impact (FSOI) based estimation techniques (Langland and Baker 2004) enable efficient estimation of forecast impacts due to assimilation of individual observations, and as such, represent a solution to this problem. The ensemble forecast sensitivity to observations (EFSO) (Kalnay et al. 2012)impact estimation technique uses ensembles of forecasts to perform linear mapping of innovations to forecast error changes. This mapping involves application of Kalman gain matrices consistent with the complete sets of observations assimilated during data assimilation cycles. As with the other forecast-sensitivity based observation impact estimation techniques there are two prominent “contextual” limitations for application of EFSO in NWP systems: i) the observation impacts are estimated with respect to simultaneously assimilating all other observations that contributed to an analysis, ii) EFSO calculations are relative to a background that includes information from all previously assimilated observations. To mitigate these “contextual” limitations in application of forecast-sensitivity based observation impact information, a new quantitative framework we call “EFSO-components” is developed by decomposing EFSO employed forecast errors and innovations into random and systematic components. Lorenz ’96 simple model experiments indicate that application of ”EFSO-components” provides potentially significant advantages in detection of specific observation flaws, and in further advancing the utility of EFSO-based PQC (Ota et al. 2013, Hotta et al. 2017a, Chen and Kalnay 2019, Chen and Kalnay 2020). As such, we explore how “EFSO-components” fundamentally addresses the aforementioned contextual limitations of forecast-sensitivity based observation impact estimation in a manner that explains the potential application advantages according to Lorenz ’96 simple model experiments. Additionally, a new technique we call predicted EFSO (PEFSO), which is astraightforward extension to EFSO, is introduced in this study. PEFSO represents a potential capability for estimating the hypothetical forecast impacts of unassimilated observations. We explore the potential application of PEFSO as a convenient low computational cost approach for comparing the efficiencies of observing systems in reducing forecast error using Lorenz ’96 simple model experiments.Item IMPROVING U.S. EXTREME PRECIPITATION PREDICTION AND PROCESS UNDERSTANDING USING A MESOSCALE CLIMATE MODEL MULTI-PHYSICS ENSEMBLE APPROACH(2019) sun, chao; Liang, Xin-zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite many recent improvements, climate models continue to poorly simulate extreme precipitation. I attempted to improve prediction of extreme precipitation, focusing on daily 95th percentile (P95) events, and to better understand the source of model biases in three ways: 1) determine which physics processes P95 is most sensitive to and which parameterization schemes best represent these processes; 2) understand the underlying mechanisms through which these processes impact P95; and 3) maximize advantages from the ensemble of the best performing models. First, to determine the sensitive processes affecting P95, I tested a 25-member ensemble of different physics configurations in the regional Climate-Weather Research and Forecasting model (CWRF) for 36-yr historical U.S. simulations. Of these, P95 simulation was most sensitive to cumulus parameterization. Overall, the ensemble cumulus parameterization best represented P95 seasonal mean spatial patterns and interannual variations, while one traditional cumulus scheme generally overestimated P95 and the other three severely underestimated P95, especially over the Gulf States (GS) and the Central-Midwest States (CM) in convection-dominated seasons. Second, I built structural equation models (SEMs) to identify the underlying processes through which cumulus parameterization affects precipitation. I discovered five distinct physical mechanisms, each involving unique interplays among water and energy supplies and surface and cloud forcings. The relative importance of these factors varied significantly by season and region. For example, water supply is the dominant factor for P95 in CM, but its effect reversed from positive in summer to negative in winter due to changes in the prevailing precipitation system. In contrast, the predominant factors affecting P95 in GS were cloud forcing in summer, but surface forcing in winter. Since the choice of cumulus parameterization affected how water and energy supplies acted through surface and cloud forcings, it determined CWRF’s ability to simulate extreme precipitation. Third, I improved P95 prediction by developing an optimized multi-model ensemble based on the Bayesian Model Averaging (BMA) approach. BMA is a model-selection method that weights ensemble members to create an optimal composite. However, many BMA methods rely on maximum likelihood estimation and thus may be flawed when the true solution is not among the ensemble, as is the case in extreme precipitation. To resolve this issue, I adapted three BMA variations to fit the needs of extreme precipitation problems. These methods significantly improved performance compared to both the ensemble mean and the single best model and provided a more reliable confidence interval. My work shows that to improve extreme precipitation simulation, a better understanding of physics processes, especially cumulus processes, is critical. For this, I applied the SEM framework, for the first time in the climate community, to uncover the underlying physical mechanisms essential to regional extreme precipitation predictions. Furthermore, I adapted new BMA methods into extreme precipitation ensembles to maximize the benefits from the most physically advanced models. These advances may help improve the prediction of extreme precipitation occurrences and future changes, one of the most difficult modeling challenges and one with huge socioeconomic significance.Item Nonlinear and Multiresolution Error Covariance Estimation in Ensemble Data Assimilation(2012) Rainwater, Sabrina; Hunt, Brian R; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Ensemble Kalman Filters perform data assimilation by forming a background covariance matrix from an ensemble forecast. The spread of the ensemble is intended to represent the algorithm's uncertainty about the state of the physical system that produces the data. Usually the ensemble members are evolved with the same model. The first part of my dissertation presents and tests a modified Local Ensemble Transform Kalman Filter (LETKF) that takes its background covariance from a combination of a high resolution ensemble and a low resolution ensemble. The computational time and the accuracy of this mixed-resolution LETKF are explored and compared to the standard LETKF on a high resolution ensemble, using simulated observation experiments with the Lorenz Models II and III (more complex versions of the Lorenz 96 model). The results show that, for the same computation time, mixed resolution ensemble analysis achieves higher accuracy than standard ensemble analysis. The second part of my dissertation demonstrates that it can be fruitful to rescale the ensemble spread prior to the forecast and then reverse this rescaling after the forecast. This technique, denoted ``forecast spread adjustment'' provides a tunable parameter that is complementary to covariance inflation, which cumulatively increases the ensemble spread to compensate for underestimation of uncertainty. As the adjustable parameter approaches zero, the filter approaches the Extended Kalman Filter when the ensemble size is sufficiently large. The improvement provided by forecast spread adjustment depends on ensemble size, observation error, and model error. The results indicate that it is most effective for smaller ensembles, smaller observation errors, and larger model error, though the effectiveness depends significantly on the type of model error.Item Strategies for Coupling Global and Limited-Area Ensemble Kalman Filter Assimilation(2011) Merkova, Dagmar; Szunyogh, Istvan; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis compares the forecast performance of four strategies for coupling global and limited area data assimilation: three strategies propagate information from the global to the limited area process, while the fourth strategy feeds back information from the limited area to the global process. All four strategies are formulated in the Local Ensemble Transform Kalman Filter (LETKF) framework. Numerical experiments are carried out with the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and the NCEP Regional Spectral Model (RSM). The limited area domain is an extended North-America region that includes part of the north-east Pacific. The GFS is integrated at horizontal resolution T62 (about 150 km in the middle latitudes), while the RSM is integrated at horizontal resolution 48 km. Experiments are carried out both under the perfect model hypothesis and in a realistic setting. The coupling strategies are evaluated by comparing their deterministic forecast performance at 12-hr and 48-hr lead times. The results suggest that the limited area data assimilation system has the potential to enhance the forecasts at 12-hr lead time in the limited area domain at the synoptic and sub-synoptic scales (in the global wave number range of about 10 to 40). There is a clear indication that between the forecast performance of the different coupling strategies those that cycle the limited area assimilation process produce the most accurate forecasts. In the realistic setting, at 12-hr forecast time the limited area systems produce more modest improvements compared to the global system than under the perfect model hypothesis, and at 48-hr forecast time the global forecasts are more accurate than the limited area forecasts.