Theses and Dissertations from UMD
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Item A Data Assimilation System for Lake Erie Based on the Local Ensemble Transform Kalman Filter(2024) Russell, David Scott; Ide, Kayo; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Data assimilation (DA) is the process by which a model forecast is adjusted to account for recent observations, taking into account both forecast and observation uncertainties. Although DA is common in numerical weather prediction (NWP) and other applications at global and regional scales, DA for large lakes such as North America's Great Lakes is still at an early stage of research and is not yet used operationally. In particular, the use of an ensemble-based approach to DA has scarcely been explored for large lakes, despite its growing popularity in operational NWP centers worldwide due to its dynamic estimation of the forecast covariance. Using Lake Erie as a test case, this study investigates the potential of ensemble DA to i) propagate forecast improvements throughout the lake and across forecast variables, and ii) inform the design of in-situ observing systems. The local ensemble transform Kalman filter (LETKF) is an efficient, localized, flexible variant of the ensemble Kalman filter (EnKF) that is used in multiple operational NWP centers. This work presents the development of a DA system for Lake Erie, which uses the LETKF to adjust forecasts of temperatures, currents, and water levels throughout the lake, using only lake surface temperature (LST) and temperature profile (TP) observations. The impact of both types of observations on all three forecast variables is evaluated within the framework of observing system simulation experiments (OSSEs), in which a DA system attempts to reconstruct a nature run (NR) by assimilating simulated observations of the NR. Observing system design questions are explored by comparing three different TP configurations. Assimilation of LST observations alone produces strong improvements to temperatures throughout the epilimnion (upper layer), while assimilation of TP observations extends these improvements to the hypolimnion (lower layer) near each profile. TP assimilation also shows improved representation of strong gyre currents and associated changes to thermocline depth and surface height, particularly when profiles sample from locations and depths where the thermal stratification in the forecast has been strongly affected by erroneous gyre currents. This work shows that the LETKF can be an efficient and effective tool for improving both forecasts and observing systems for large lakes, two essential ingredients in predicting the onset and development of economically and ecologically important phenomena such as harmful algal blooms (HABs) and hypoxia.Item Bridging Gaussian and non-Gaussian Data Assimilation for High-Dimensional Geophysical Models(2023) Kurosawa, Kenta; Poterjoy, Jonathan; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Strongly nonlinear model dynamics and observation operators can induce bias in Gaussian-based data assimilation methods commonly used for numerical weather prediction, such as ensemble Kalman filters (EnKFs) and the 4D variational method (4DVar). This limitation is apparent for multiscale weather prediction systems that exhibit large uncertainty in smaller scales, or when observations are sensitive to cloud processes. Several methods have been proposed for improving data assimilation performance in nonlinear regimes. Examples include the adoption of an "outer loop" in variational methods, which helps reduce bias caused by linear assumptions. Likewise, numerous "iterative ensemble methods" exist, which periodically re-linearize model and measurement operators in the same manner. While the convergence properties of the latter methods are not completely known, numerical experiments performed by several previous studies suggest they can provide accurate solutions for mildly nonlinear problems. Another strategy that has gained momentum in recent years is to apply dimension-reduction procedures (namely, localization) to particle filters (PFs). PFs avoid the parametric estimation of Bayesian posterior densities, thus providing great flexibility for solving non-Gaussian data assimilation problems. However, these methods are more easily affected by sampling error than Gaussian-based methods—even when using localization. My research introduces new approaches that bridge Gaussian and non-Gaussian data assimilation for geophysical models. To begin, the first part of this study investigates intrinsic limitations in data assimilation methods that are currently used for nonlinear applications in geoscience. We then propose novel data assimilation strategies for combining PFs with Gaussian-based methods that are more robust to sampling error. We demonstrate that the approaches have significant value within modern high-resolution regional atmospheric modeling systems, which are designed specifically for predicting tropical cyclones and severe convective storms. We further emphasize that this research has general implications for data assimilation within Earth-system models.Item IMPROVEMENTS IN THE ASSIMILATION OF DOPPLER RADIAL WINDS AT NCEP IN REGIONAL AND GLOBAL FORECAST SYSTEMS(2022) Lippi, Donald; Kleist, Daryl T; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Doppler radial winds have been an underutilized observation in U.S. operational forecast systems. This has typically been owing to limitations in formulation of the observation operator, the amount of data thinning via super-obbing, or simple exclusion from assimilation in global modeling systems. In this work we explore some of the more feasible aspects of radial wind assimilation that could more readily be applied to the operational systems with the main goal to improve the use of radial winds in the operational forecast systems used by NOAA. It will be shown that improvements could be made to systems not only operating at the convective scale, but also that global systems could benefit from the assimilation of radial winds. Experiments featuring results from the regional NAM version 4 forecast system along with the GFS version 15 and 16 will be shown. The GFS experiments explore the potential radial wind impact via hypothetical observing networks tested with observing system simulation experiments. We further extend this study to a real-data case with a land falling tropical cyclone event and novel, rapidly-updated version of the GFS.This work is a first step toward improving the use of radial wind observations and tests their use for the very first time in the GFS. This effort demonstrates potential for radial wind assimilation in the GFS, a potentially important observation type as we increase update cadence and spatial resolution.Item Characterizing a Multi-Sensor System for Terrestrial Freshwater Remote Sensing via an Observing System Simulation Experiment (OSSE)(2022) Wang, Lizhao; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Terrestrial freshwater storage (TWS) is the vertically-integrated sum of snow, ice, soilmoisture, vegetation water content, surface water impoundments, and groundwater. Among these components, snow, soil moisture, and vegetation are the most dynamic (i.e., shortest residence time) as well as the most variable across space. However, accurately retrieving estimates of snow, soil moisture, or vegetation using space-borne sensors often requires simultaneous knowledge of one or more of the other components. In other words, reasonably characterizing terrestrial freshwater requires careful consideration of the coupled snow-soil moisture-vegetation response that is implicit in both TWS and the hydrologic cycle. One challenge is to optimally determine the multi-variate, multi-sensor remote sensing observations needed to best characterize the coupled snow-soil moisture-vegetation system. Different types of sensors each have their own unique strengths and limitations. Meanwhile, remote sensing data is inherently discontinuous across time and space, and that the revisit cycle of remote sensing observations will dictate much of the efficacy in capturing the dynamics of the coupled snow-soil moisture-vegetation response. This study investigates different snow sensors and simulates the sensor coverage as a function of different orbital configurations and sensor properties in order to quantify the discontinuous nature of remotely-sensed observations across space and time. The information gleaned from this analysis, coupled with a time-varying snow binary map, is used to evaluate the efficacy of a single sensor (or constellation of sensors) to estimate terrestrial snow on a global scale. A suite of different combinations, and permutations, of different sensors, including different orbital characteristics, is explored with respect to 1-day, 3-day, and 30-day repeat intervals. The results show what can, and what cannot, be observed by different sensors. The results suggest that no single sensor can accurately measure all types of snow, but that a constellation composed of different types of sensors could better compensate for the limitations of a single type of sensor. Even though only snow is studied here, a similar procedure could be conducted for soil moisture or vegetation. To better investigate the coupled snow-soil moisture-vegetation system, an observing system simulation experiment (OSSE) is designed in order to explore the value of coordinated observations of these three separate, yet mutually dependent, state variables. In the experiment, a “synthetic truth” of snow water equivalent, surface soil moisture, and/or vegetation biomass is generated using the NoahMP-4.0.1 land surface model within the NASA Land Information System (LIS). Afterwards, a series of hypothetical sensors with different orbital configurations is prescribed in order to retrieve snow, soil moisture, and vegetation. The ground track and footprint of each sensor is approximated using the Trade-space Analysis Tool for Constellations (TAT-C) simulator. A space-time subsampler predicated on the output from TAT-C is then applied to the synthetic truth. Furthermore, a hypothesized amount of observation error is injected into the synthetic truth in order to yield a realistic synthetic retrieval for each of the hypothetical sensor configurations considered as part of this dissertation. The synthetic retrievals are then assimilated into the NoahMP-4.0.1 land surface model using different boundary conditions from those used to generate the synthetic truth such that the differences between the two sets of boundary conditions serve as a realistic proxy for real-world boundary condition errors. A baseline Open Loop simulation where no retrievals are assimilated is conducted in order to evaluate the added utility associated with assimilation of one (or more) of the synthetic retrievals. The impact of the assimilation of a given suite of one or more retrievals on land surface model estimates of snow, soil moisture, vegetation, and runoff serve as a numeric laboratory in order to assess which sensor(s), either separate or in a coordinated fashion, yield the most utility in terms of improved model performance. The results from this OSSE show that the assimilation of a single type of retrieval (i.e., snow or soil moisture or vegetation) may only improve the estimation of a small part of the snow-soil moisture-vegetation system, but may also degrade of other parts of that same system. Alternatively, the assimilation of more than one type of retrieval may yield greater benefits to all the components of the snow-soil moisture-vegetation system, because it yields a more complete, holistic view of the coupled system. This OSSE framework could potentially serve as an aid to mission planners in determining how to get the most observational “bang for the buck” based on the myriad of different sensor types, orbital configurations, and error characteristics available in the selection of a future terrestrial freshwater mission.Item Developments in Lagrangian Data Assimilation and Coupled Data Assimilation to Support Earth System Model Initialization(2019) Sun, Luyu; Carton, James A.; Penny, Stephen G.; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The air-sea interface is one of the most physically active interfaces of the Earth's environments and significantly impacts the dynamics in both the atmosphere and ocean. In this doctoral dissertation, developments are made to two types of Data Assimilation (DA) applied to this interface: Lagrangian Data Assimilation (LaDA) and Coupled Data Assimilation (CDA). LaDA is a DA method that specifically assimilates position information measured from Lagrangian instruments such as Argo floats and surface drifters. To make a better use of this Lagrangian information, an augmented-state LaDA method is proposed using Local Ensemble Transform Kalman Filter (LETKF), which is intended to update the ocean state (T/S/U/V) at both the surface and at depth by directly assimilating the drifter locations. The algorithm is first tested using "identical twin" Observing System Simulation Experiments (OSSEs) in a simple double gyre configuration with the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 4.1 (MOM4p1). Results from these experiments show that with a proper choice of localization radius, the estimation of the state is improved not only at the surface, but throughout the upper 1000m. The impact of localization radius and model error in estimating accuracy of both fluid and drifter states are investigated. Next, the algorithm is applied to a realistic eddy-resolving model of the Gulf of Mexico (GoM) using Modular Ocean Model version 6 (MOM6) numerics, which is related to the 1/4-degree resolution configuration in transition to operational use at NOAA/NCEP. Atmospheric forcing is first used to produce the nature run simulation with forcing ensembles created using the spread provided by the 20 Century Reanalysis version 3 (20CRv3). In order to assist the examination on the proposed LaDA algorithm, an updated online drifter module adapted to MOM6 is developed, which resolves software issues present in the older MOM4p1 and MOM5 versions of MOM. In addition, new attributions are added, such as: the output of the intermediate trajectories and the interpolated variables: temperature, salinity, and velocity. The twin experiments with the GoM also show that the proposed algorithm provides positive impacts on estimating the ocean state variables when assimilating the drifter position together with surface temperature and salinity. Lastly, an investigation of CDA explores the influence of different couplings on improving the simultaneous estimation of atmosphere and ocean state variables. Synchronization theory of the drive-response system is applied together with the determination of Lyapunov Exponents (LEs) as an indication of the error convergence within the system. A demonstration is presented using the Ensemble Transform Kalman Filter on the simplified Modular Arbitrary-Order Ocean-Atmosphere Model, a three-layer truncated quasi-geostrophic model. Results show that strongly coupled data assimilation is robust in producing more accurate state estimates and forecasts than traditional approaches of data assimilation.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.Item The role of the Indian Ocean sector and sea surface salinity for prediction of the coupled Indo-Pacific system(2016) Hackert, Eric C.; Busalacchi, Antonio J.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The purpose of this dissertation is to evaluate the potential downstream influence of the Indian Ocean (IO) on El Niño/Southern Oscillation (ENSO) forecasts through the oceanic pathway of the Indonesian Throughflow (ITF), atmospheric teleconnections between the IO and Pacific, and assimilation of IO observations. Also the impact of sea surface salinity (SSS) in the Indo-Pacific region is assessed to try to address known problems with operational coupled model precipitation forecasts. The ITF normally drains warm fresh water from the Pacific reducing the mixed layer depths (MLD). A shallower MLD amplifies large-scale oceanic Kelvin/Rossby waves thus giving ~10% larger response and more realistic ENSO sea surface temperature (SST) variability compared to observed when the ITF is open. In order to isolate the impact of the IO sector atmospheric teleconnections to ENSO, experiments are contrasted that selectively couple/decouple the interannual forcing in the IO. The interannual variability of IO SST forcing is responsible for 3 month lagged widespread downwelling in the Pacific, assisted by off-equatorial curl, leading to warmer NINO3 SST anomaly and improved ENSO validation (significant from 3-9 months). Isolating the impact of observations in the IO sector using regional assimilation identifies large-scale warming in the IO that acts to intensify the easterlies of the Walker circulation and increases pervasive upwelling across the Pacific, cooling the eastern Pacific, and improving ENSO validation (r ~ 0.05, RMS~0.08C). Lastly, the positive impact of more accurate fresh water forcing is demonstrated to address inadequate precipitation forecasts in operational coupled models. Aquarius SSS assimilation improves the mixed layer density and enhances mixing, setting off upwelling that eventually cools the eastern Pacific after 6 months, counteracting the pervasive warming of most coupled models and significantly improving ENSO validation from 5-11 months. In summary, the ITF oceanic pathway, the atmospheric teleconnection, the impact of observations in the IO, and improved Indo-Pacific SSS are all responsible for ENSO forecast improvements, and so each aspect of this study contributes to a better overall understanding of ENSO. Therefore, the upstream influence of the IO should be thought of as integral to the functioning of ENSO phenomenon.Item Information Synthesis Across Scales in Atmospheric State Estimation: Theory and Numerical Experiments(2015) Kretschmer, Matthew; Ott, Edward; Hunt, Brian R; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis studies the benefits of simultaneously considering system information from different sources when performing ensemble data assimilation. In particular, in Chapter 2 we consider ensemble data assimilation using both a global dynamical model and climatological forecast error information, and, in Chapters 3 and 4, using both a global dynamical model and at least one higher-resolution limited-area dynamical model. Focus is given to applying data assimilation for atmospheric state estimation. Introductory material on ensemble forecasting is given in Chapter 1. In Chapter 2, I first investigate how the forecast background-error climatology can be used to help improve state estimates, and subsequent forecasts initialized from those state estimates. ``Climatological perturbations'' derived from an estimate of the background-error covariance matrix are added to the dynamic ensemble that has been forecasted from the previous analysis time, enlarging the space of possible analysis increments. Numerical experiments on a one-dimensional toy model test this method and illustrate that climatologically augmenting the dynamical forecast ensemble during the analysis has a positive impact on state estimation and forecast accuracy. Chapter 3 studies data assimilation that considers state information from various spatial scales. In practice, it is common for regional-scale weather forecasts to be created using limited-area atmospheric models which have relatively high spatial resolution. Limited-area model forecasts require lateral boundary conditions, which often come from a lower resolution forecast model (with different model physics) defined over a larger, often global, domain. Here I describe how data assimilation may be performed on a composite forecast state containing information from all available forecast models, and show results from numerical experiments that detail the benefits of this approach. Chapter 4 of this thesis explores forecast model bias, which is the result of uncertain, unknown or incorrect model physics. I adapt a strategy for correcting forecast model bias to use when performing data assimilation using the composite state method described in Chapter 3. In numerical experiments, I test this bias correction strategy for differently biased global and limited-area models, and observe that analysis and forecast accuracy is dramatically improved when compared to forecasts made without bias correction.Item Proactive Quality Control based on Ensemble Forecast Sensitivity to Observations(2014) Hotta, Daisuke; Kalnay, Eugenia; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite recent major improvements in numerical weather prediction (NWP) systems, operational NWP forecasts occasionally suffer from an abrupt drop in forecast skill, a phenomenon called "forecast skill dropout." Recent studies have shown that the "dropouts" occur not because of the model's deficiencies but by the use of flawed observations that the operational quality control (QC) system failed to filter out. Thus, to minimize the occurrences of forecast skill dropouts, we need to detect and remove such flawed observations. A diagnostic technique called Ensemble Forecast Sensitivity to Observations (EFSO) enables us to quantify how much each observation has improved or degraded the forecast. A recent study (Ota et al., 2013) has shown that it is possible to detect flawed observations that caused regional forecast skill dropouts by using EFSO with 24-hour lead-time and that the forecast can be improved by not assimilating the detected observations. Inspired by their success, in the first part of this study, we propose a new QC method, which we call Proactive QC (PQC), in which flawed observations are detected 6 hours after the analysis by EFSO and then the analysis and forecast are repeated without using the detected observations. This new QC technique is implemented and tested on a lower-resolution version of NCEP's operational global NWP system. The results we obtained are extremely promising; we have found that we can detect regional forecast skill dropouts and the flawed observations after only 6 hours from the analysis and that the rejection of the identified flawed observations indeed improves 24-hour forecasts. In the second part, we show that the same approximation used in the derivation of EFSO can be used to formulate the forecast sensitivity to observation error covariance matrix R, which we call EFSR. We implement the EFSR diagnostics in both an idealized system and the quasi-operational NWP system and show that it can be used to tune the R matrix so that the utility of observations is improved. We also point out that EFSO and EFSR can be used for the optimal assimilation of new observing systems.Item AN EVALUATION OF HYBRID VARIATIONAL-ENSEMBLE DATA ASSIMILATION FOR THE NCEP GFS(2012) Kleist, Daryl Timothy; Ide, Kayo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Several variants of hybrid data assimilation algorithms have been developed and tested within recent years, particularly for numerical weather prediction (NWP). The hybrid algorithms are designed to combine the strengths of variational and ensemble-based techniques while at the same time attempting to mitigate their weaknesses. One such variational-based algorithm is under development for use with the National Centers for Environmental Prediction's (NCEP) global forecast system (GFS) model. In this work, we attempt to better understand the impact of utilizing a hybrid scheme on the quality of analyses and subsequent forecasts, as well as explore alternative extensions to make better use of the ensemble information within the variational solver. A series of Observing System Simulation Experiments (OSSEs) are carried out. It is demonstrated that analysis and subsequent forecast errors are generally reduced in a 3D-hybrid scheme relative to 3DVAR. Several variational-based 4D extensions are proposed and tested, including the use of a variety of dynamic constraints. A simple approach for hybridizing the 4D-ensemble with a time-invariant contribution is proposed and tested. The 4D variants are shown to be superior to the 3D-hybrid, with positive contributions from static B as well as the dynamic constraint formulations. It is clear from both the 3D and 4D experiments that more sophisticated methods for dealing with inflation and localization in the ensemble update are needed even within the hybrid paradigm. Lastly, a method for applying piecewise scale-dependent weights is proposed and successfully tested. The 3D OSSE-based results are also compared with results from an experiment using real observations to corroborate the findings. It is found that in general, most of the results are comparable, though the positive impact in the real system is more consistent and impressive.