Atmospheric & Oceanic Science
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Formerly known as the Department of Meteorology.
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Item NON-GAUSSIAN ENSEMBLE FILTERING AND ADAPTIVE INFLATION FOR SOIL MOISTURE DATA ASSIMILATION(2024) Dibia, Emmanuel; Liang, Xin-Zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The forecast error distribution in modern day land data assimilation systems is typically modeled as a Gaussian. The explicit tracking of only the first two moments can be problematic when trying to assimilate bounded quantities like soil moisture that are more accurately described using more general parameterizations. Given this issue, it is worthwhile to test how performance of land models is affected when the accompanying data assimilation system abides by a relatively more relaxed set of underlying assumptions. To study this problem, we perform experiments using the ensemble Kalman filter (EnKF) and rank histogram filter (RHF) to assimilate surface soil moisture content observations into the NASA Catchment land surface model. The EnKF acts as the traditional (Gaussian) standard of comparison whereas the RHF represents the novel and more general data assimilation method. An additional parameter of our tests is the usage of an adaptive inflation scheme that is only applied to the ensemble prior. This is done in an attempt to mitigate the negative effects of systematic deficiencies not accounted for by either filter. The examinations were carried out at a number of globally-distributed test locations, deliberately coinciding with sites used to validate NASA SMAP soil moisture retrieval products. Initial comparisons of the two filtering approaches in a perfect model context show both filters to provide significant benefits to the soil moisture modeling problem, with the RHF edging out the EnKF as the more performant filter. The relative performance gain of the RHF was most noticeable with respect to bias mitigation metrics and to the surface-level anomaly correlation scores, an interesting result given that neither filter is formulated to explicitly accommodate a systematic bias. When additionally applying adaptive inflation, both filters showed improvement in skill but such improvements were not significant. The use of synthetic observations and lack of a bias correction implementation may have led to exaggerated results. To address this concern, the experiments were performed again but using real observations from SMAP soil moisture retrievals, with in situ validation data proxying as truth. A robust bias correction scheme was used as well to more closely approximate practices used in operational settings. The RHF continues to show better metrics than the EnKF, but no longer in a statistically significant sense. A similar result was noted with respect to inflation usage. The most likely reason for this outcome is the low observation count. The findings obtained from the data assimilation experiments in this dissertation offer insight on how best to focus development efforts in soil moisture modeling and land data assimilation.Item A Neural-Network Based MPAS—Shallow Water Model and Its 4D-Var Data Assimilation System(MDPI, 2023-01-10) Tian, Xiaoxu; Conibear, Luke; Steward, JeffreyThe technique of machine learning has been increasingly applied in numerical weather predictions. The aim of this study is to explore the application of a neural network in data assimilation by making use of the convenience in obtaining the tangent linear and adjoint (TL/AD) of a neural network (NN) and formulating a NN-based four-dimensional variational (4D-Var) DA system. A NN-based shallow water (SW) model is developed in this study. The NN model consists of three layers. The weights and biases in the NN-based SW model are trained with 60 years of hourly ERA5 geopotentials and wind field at 500 hPa as initial conditions and the corresponding 12-h forecasts by Model for Prediction Across Scales (MPAS)-SW, in total of 534,697 sets of samples. The 12-h forecasts from independent dates made by NN-based SW prove to closely emulate the simulations by the actual MPAS-SW model. This study further shows that the TL/AD of an NN model can be easily developed and validated. The ease of obtaining the TL/AD makes NN conveniently applicable in various aspects within a data assimilation (DA) system. To demonstrate such, a continuous 4D-Var DA system is also developed with the forward NN and its adjoint. To demonstrate the functionality of the NN-based 4D-Var DA system, the results from a higher resolution simulation will be treated as observations and assimilated to analyze the low resolution initial conditions. The forecasts starting from the analyzed initial conditions will be compared with those without assimilation to demonstrate improvements.Item Implementation of a Discrete Dipole Approximation Scattering Database Into Community Radiative Transfer Model(Wiley, 2022-12-07) Moradi, Isaac; Stegmann, Patrick; Johnson, Benjamin; Barlakas, Vasileios; Eriksson, Patrick; Geer, Alan; Gelaro, Ronald; Kalluri, Satya; Kleist, Daryl; Liu, Quanhua; Mccarty, WillThe Community Radiative Transfer Model (CRTM) is a fast model that requires bulk optical properties of hydrometeors in the form of lookup tables to simulate all-sky satellite radiances. Current cloud scattering lookup tables of CRTM were generated using the Mie-Lorenz theory thus assuming spherical shapes for all frozen habits, while actual clouds contain frozen hydrometeors with different shapes. The Discrete Dipole Approximation (DDA) technique is an effective technique for simulating the optical properties of non-spherical hydrometeors in the microwave region. This paper discusses the implementation and validation of a comprehensive DDA cloud scattering database into CRTM for the microwave frequencies. The original DDA database assumes total random orientation in the calculation of single scattering properties. The mass scattering parameters required by CRTM were then computed from single scattering properties and water content dependent particle size distributions. The new lookup tables eliminate the requirement for providing the effective radius as input to CRTM by using the cloud water content for the mass dimension. A collocated dataset of short-term forecasts from Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data was used for the evaluation of results. The results overall showed that the DDA lookup tables, in comparison with the Mie tables, greatly reduce the differences among simulated and observed values. The Mie lookup tables especially introduce excessive scattering for the channels operating below 90 GHz and low scattering for the channels above 90 GHz.Item Regularization and tempering for a moment-matching localized particle filter(Wiley, 2022-05-31) Poterjoy, JonathanIterative ensemble filters and smoothers are now commonly used for geophysical models. Some of these methods rely on a factorization of the observation likelihood function to sample from a posterior density through a set of “tempered” transitions to ensemble members. For Gaussian-based data assimilation methods, tangent linear versions of nonlinear operators can be relinearized between iterations, thus leading to a solution that is less biased than a single-step approach. This study adopts similar iterative strategies for a localized particle filter (PF) that relies on the estimation of moments to adjust unobserved variables based on importance weights. This approach builds off a “regularization” of the local PF, which forces weights to be more uniform through heuristic means. The regularization then leads to an adaptive tempering, which can also be combined with filter updates from parametric methods, such as ensemble Kalman filters. The role of iterations is analyzed by deriving the localized posterior probability density assumed by current local PF formulations and then examining how single-step and tempered PFs sample from this density. From experiments performed with a low-dimensional nonlinear system, the iterative and hybrid strategies show the largest benefits in observation-sparse regimes, where only a few particles contain high likelihoods and prior errors are non-Gaussian. This regime mimics specific applications in numerical weather prediction, where small ensemble sizes, unresolved model error, and highly nonlinear dynamics lead to prior uncertainty that is larger than measurement uncertainty.Item Strongly Coupled Ocean-Atmosphere Data Assimilation with the Local Ensemble Transform Kalman Filter(2018) Sluka, Travis Cole; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Current state-of-the-art coupled data assimilation systems handle the ocean and atmosphere separately when generating an analysis, even though ocean atmosphere models are subsequently run as a coupled system for forecasting. Previous research using simple 1-dimensional coupled models has shown that strongly coupled data assimilation (SCDA), whereby a coupled system is treated as a single entity when creating the analysis, reduces errors for both domains when using an ensemble Kalman filter. A prototype method for SCDA is developed with the local ensemble transform Kalman filter (LETKF). This system is able to use the cross-domain background error covariance from the coupled model ensemble to enable assimilation of atmospheric observations directly into the ocean. This system is tested first with the intermediate complexity SPEEDYNEMO model in an observing system simulation experiment (OSSE), and then with real observations and an operational coupled model, the Climate Forecasting System v2 (CFSv2). Finally, the development of a major upgrade to ocean data assimilation used at NCEP (the Hybrid-GODAS) is presented, and shown how this new system could help present a path forward to operational strongly coupled DA.Item Breeding Analysis of Growth and Decay in Nonlinear Waves and Data Assimilation and Predictability in the Martian Atmosphere(2014) Zhao, Yongjing; Kalnay, Eugenia; Nigam, Sumant; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The effectiveness of the breeding method in determining growth and decay characteristics of certain solutions to the Kortweg-de Vries (KdV) equation and the Nonlinear Schrödinger equation is investigated. Bred vectors are a finite amplitude, finite time generalization of Leading Lyapunov Vectors (LLV), and breeding has been used to predict large impending fluctuations in many systems, including chaotic systems. Here, the focus is on predicting fluctuations associated with extreme waves. The bred vector analysis is applied to the KdV equation with two types of initial conditions: soliton collisions, and a Gaussian distribution which decays into a group of solitons. The soliton solutions are stable, and the breeding analysis enables tracking of the growth and decay during the interactions. Furthermore, this study with a known stable system helps validate the use of breeding method for waves. This analysis is also applied to characterize rogue wave type solutions of the NLSE, which have been used to describe extreme ocean waves. In the results obtained, the growth rate maxima and the peaks of the bred vector always precede the rogue wave peaks. This suggests that the growth rate and bred vectors may serve as precursors for predicting energy localization due to rogue waves. Finally, the results reveal that the breeding method can be used to identify numerical instabilities. Effective simulation of diurnal variability is an important aspect of many geophysical data assimilation systems. For the Martian atmosphere, thermal tides are particularly prominent and contribute much to the Martian atmospheric circulation, dynamics and dust transport. To study the Mars diurnal variability (or thermal tides), the GFDL Mars Global Climate Model (MGCM) with the 4D-Local Ensemble Transform Kalman Filter (4D-LETKF) is used to perform a reanalysis of spacecraft temperature retrievals. We find that the use of a "traditional" 6-hr assimilation cycle induces spurious forcing of a resonantly-enhanced semi-diurnal Kelvin waves represented in both surface pressure and mid-level temperature by forming a wave 4 pattern in the diurnal averaged analysis increment that acts as a "topographic" stationary forcing. Different assimilation window lengths in the 4D-LETKF are introduced to remove the artificially induced resonance. It is found that short assimilation window lengths not only remove the spurious resonance, but also push the migrating semi-diurnal temperature variation at 50 Pa closer to the estimated "true" tides even in the absence of a radiatively active water ice cloud parameterization. In order to compare the performance of different assimilation window lengths, short-term to long-term forecasts based on the hour 00 and 12 assimilation are evaluated and compared. Results show that during NH summer, it is not the assimilation window length, but the radiatively active water ice cloud that influences the model prediction. A "diurnal bias correction" that includes bias correction fields dependent on the local time is shown to effectively reduce the forecast root mean square differences (RMSD) between forecasts and observations, compensate for the absence of water ice cloud parameterization, and enhance Martian atmosphere prediction. The implications of these results for data assimilation in the Earth's atmosphere are also discussed.Item Ensemble Assimilation of Global Large-scale Precipitation(2014) Lien, Guo-Yuan; Kalnay, Eugenia; Miyoshi, Takemasa; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many attempts to assimilate precipitation observations in numerical models have been made, but they have resulted in little or no forecast improvement at the end of the precipitation assimilation. This is due to the nonlinearity of the model precipitation parameterization, the non-Gaussianity of precipitation variables, and the large and unknown model and observation errors. In this study, we investigate the assimilation of global large-scale satellite precipitation using the local ensemble transform Kalman filter (LETKF). The LETKF does not require linearization of the model, and it can improve all model variables by giving higher weights in the analysis to ensemble members with better precipitation, so that the model will "remember" the assimilation changes during the forecasts. Gaussian transformations of precipitation are applied to both model background precipitation and observed precipitation, which not only makes the error distributions more Gaussian, but also removes the amplitude-dependent biases between the model and the observations. In addition, several quality control criteria are designed to reject precipitation observations that are not useful for the assimilation. Our ideas are tested in both an idealized system and a realistic system. In the former, observing system simulation experiments (OSSEs) are conducted with a simplified general circulation model; in the latter, the TRMM Multisatellite Precipitation Analysis (TMPA) data are assimilated into a low-resolution version of the NCEP Global Forecasting System (GFS). Positive results are obtained in both systems, showing that both the analyses and the 5-day forecasts are improved by the effective assimilation of precipitation. We also demonstrate how to use the ensemble forecast sensitivity to observations (EFSO) to analyze the effectiveness of precipitation assimilation and provide guidance for determining appropriate quality control. These results are very promising for the direct assimilation of satellite precipitation data in numerical weather prediction models, especially with the forthcoming Global Precipitation Measurement (GPM) sensors.Item ADVANCES IN SEQUENTIAL DATA ASSIMILATION AND NUMERICAL WEATHER FORECASTING: AN ENSEMBLE TRANSFORM KALMAN-BUCY FILTER, A STUDY ON CLUSTERING IN DETERMINISTIC ENSEMBLE SQUARE ROOT FILTERS, AND A TEST OF A NEW TIME STEPPING SCHEME IN AN ATMOSPHERIC MODEL(2012) Amezcua, Javier; Kalnay, Eugenia; Ide, Kayo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn't represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.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.Item Local ensemble transform Kalman filter with realistic observations(2007-08-03) Li, Hong; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The main goal of my research is to improve the performance of the EnKF in assimilating real observations in order to accelerate the development of EnKF systems towards operational applications. A Local Ensemble Transform Kalman Filter (LETKF, Hunt et al. 2007) is used as an efficient representative of other EnKF systems. This dissertation has addressed several issues relating to the EnKF for assimilating real data. The first issue is model errors. We assimilated observations generated from the NCEP/NCAR reanalysis fields into the SPEEDY model. The performance of the LETKF without accounting for model errors is seriously degraded compared with that in the perfect model scenario. We then investigated several methods to handle model errors including model bias and system-noise. Our results suggest that the pure bias removal methods (DdSM and LDM) are not able to beat the multiplicative or additive inflation schemes that account for the effects of total model errors. By contrast, when the bias removal methods (DdSM+ and LDM+) are supplemented by additive noise for representing the system-noise, they outperform the inflation schemes. Of these augmented methods, the LDM+, where the constant bias, diurnal bias and state-dependent errors are estimated from a large sample of 6-hour forecast errors, gives the best results. The other two issues addressed are the estimation of the inflation factor and of observation error variance. Without the accurate observation error statistics, a scheme for adaptively estimating inflation alone does not work, and vice versa. We propose to estimate simultaneously both the adaptive inflation and observation error variance. Our results for the Lorenz-96 model examples suggest that the simultaneous approach works perfectly in the perfect model scenario and in the presence of random model errors. For the case of systematic model bias, although it underestimates the observation error variance, our algorithm produces analyses that are comparable with the best tuned inflation value. SPEEDY model experiments indicate that our method is able to retrieve the true error variance for different types of instrument separately when applied to a more realistic high-dimension model. Our research in this dissertation suggests the need to develop a more advanced LETKF with both bias correction and adaptive estimation of inflation within the system.