Atmospheric & Oceanic Science
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Formerly known as the Department of Meteorology.
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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 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 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.Item Mars Weather and Predictability: Modeling and Ensemble Data Assimilation of Spacecraft Observations(2011) Greybush, Steven J.; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Combining the perspectives of spacecraft observations and the GFDL Mars General Circulation Model (MGCM) in the framework of ensemble data assimilation leads to an improved understanding of the weather and climate of Mars and its atmospheric predictability. The bred vector (BV) technique elucidates regions and seasons of instability in the MGCM, and a kinetic energy budget reveals their physical origins. Instabilities prominent in the late autumn through early spring seasons of each hemisphere along the polar temperature front result from baroclinic conversions from BV potential to BV kinetic energy, whereas barotropic conversions dominate along the westerly jets aloft. Low level tropics and the northern hemisphere summer are relatively stable. The bred vectors are linked to forecast ensemble spread in data assimilation and help explain the growth of forecast errors. Thermal Emission Spectrometer (TES) temperature profiles are assimilated into the MGCM using the Local Ensemble Transform Kalman Filter (LETKF) for a 30-sol evaluation period during the northern hemisphere autumn. Short term (0.25 sol) forecasts compared to independent observations show reduced error (3-4 K global RMSE) and bias compared to a free running model. Several enhanced techniques result in further performance gains. Spatially-varying adaptive inflation and varying the dust distribution among ensemble members improve estimates of analysis uncertainty through the ensemble spread, and empirical bias correction using time mean analysis increments help account for model biases. With bias correction, we estimate a predictability horizon of about 5 sols during which temperature, wind, and surface pressure forecasts initialized from an assimilation analysis are superior to a free running model forecast. LETKF analyses, when compared with the UK reanalysis, show a superior correspondence to independent radio science temperature profiles. Traveling waves in both hemispheres share a correspondence in phase, and temperature differences between the analyses are generally less than 5 K. Assimilation of Mars Climate Sounder (MCS) temperature profiles reveals the importance of vertical distributions of dust and water ice aerosol in reducing model bias. A strategy for assimilation of TES and MCS aerosol products is outlined for future work.Item Assimilation of trace gas retrievals with the Local Ensemble Transform Kalman Filter(2009) Kuhl, David Derieg; Kalnay, Eugenia; Szunyogh, Istvan; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Over the 50 year history of Numerical Weather Prediction (NWP), the focus has been on the modeling and prediction of meteorological parameters such as surface pressure, temperature, wind, and precipitation. However, due to concerns over pollution and to recent advancements in satellite technologies, an increasing number of NWP systems have been upgraded to include capabilities to analyze and predict the concentration of trace gases. This dissertation explores some of the specific issues that have to be addressed for an efficient modeling of the concentration of the trace gases. These issues include modeling the effects of convective mixing on the concentration of the trace gases and the multivariate assimilation of space-based observations of the concentration of the trace gases. In this dissertation, we assimilate observations of the concentration of trace gases with an implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system on the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) NWP model. We use a modified version of the NCEP GFS model that was operational in 2004 at resolution T62/L28. We modify the model by adding parameterization for the process of convective mixing of the trace gases. We consider two specific trace gases: ozone (O3) and carbon monoxide (CO). We incorporate these gases into the model by using 3-dimensional time-dependent O3 and CO production-loss values from the Real-time Air Quality Modeling System (RAQMS) global chemical model. The O3 observations we assimilate are from the Solar Backscatter UltraViolet generation 2 (SBUV/2) satellite instrument (version 8) flown on the NOAA 16 and 17 satellites. The CO observations we assimilate are from the Measurements Of Pollution In The Troposphere (MOPITT) instrument (version 3) flown on the NASA TERRA satellite. We also develop a new observation operator for the assimilation of retrievals with the LETKF. We carry out numerical experiment for the period between 000UTC 1 July 2004 to 000UTC 15 August in the summer of 2004. The analysis and forecast impact of the assimilation of trace gas observations on the meteorological fields is assessed by comparing the analyses and forecasts to the high resolution operational NCEP GFS analyses and to radiosonde observations. The analysis and forecast impact on the trace gas fields is assessed by comparing the analyzed and predicted fields to observations collected during the Intercontinental Chemical Transport Experiment (INTEX-A) field mission. The INTEX-A field mission was conducted to characterize composition of pollution over North America, thus providing us with ozonesonde and aircraft based verification data. We find that adding the process of convective mixing to the parameterization package of the model and the assimilation of observations of the trace gases improves the analysis and forecast of the concentration of the trace gases. In particular, our system is more accurate in quantifying the concentration of O3 in the troposphere than the original NCEP GFS. Also, our system is competitive with the state-of-the-art RAQMS atmospheric chemical model in analyzing the concentration of O3 and CO throughout the full atmospheric model column. The assimilation of O3 and CO observations has a mixed impact on the analysis and forecast of the meteorological fields. We find that most of the negative impact on the meteorological fields can be eliminated, without much reduction to the positive impact on the trace gas fields, by inflating the prescribed variance of the trace gas observations. The appendices of this dissertation reproduces two papers on related research. The first paper covers the northward front movement and rising surface temperatures in the great planes. The second paper covers the assessment of predictability with a Local Ensemble Kalman Filter.Item VARIATIONAL DATA ASSIMILATION OF SOIL MOISTURE INFORMATION(2005-04-20) Grunmann, Pablo Javier; Kalnay, Eugenia E; Mitchell, Kenneth E; Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research examines the feasibility of using observations of land surface temperatures (in principle available from satellite observations) to initialize soil moisture (which is not available on a continental scale). This problem is important because it is known that wrong soil moisture initial conditions can negatively affect the skill of numerical weather prediction models. Since this problem requires the availability of a good soil model, considerable effort was devoted to the improvement of several aspects of the NCEP Noah land surface model and its numerical properties (reliability, efficiency, updates and differentiability). When tested against the experimental station data at Champaign, IL collected by Dr. Tilden Meyers of NOAA/ARL, where the surface fluxes, precipitation, and surface temperature were available, the Noah model forced with observed downward radiative surface fluxes and near-surface meteorology, including precipitation, was able to reproduce the observations quite well. A method for data assimilation was developed and tested, in a manner similar to 4-dimensional variational assimilation (4D-Var) in the sense of applying the temporal behavior of the observed variable but with a single spatial dimension (land surface models are typically “column models”, as they do not usually compute horizontal derivatives). The results show that it is indeed possible to assimilate land surface temperature and use it to correct soil moisture initial conditions, which may manifest significant errors if, for example, the precipitation forcing the model is significantly biased. This is true, however, only if the surface forcings besides precipitation are essentially correct. When surface forcing come from the North American Land Data Assimilation System (NLDAS) as they would be available for operational use over the US, the results are not satisfactory. This is because the assimilation changes the soil moisture to correct for problems in the simulated land surface temperature that are at least partially due to other sources of errors, such as the surface radiative fluxes. We suggest that in order to succeed in the soil moisture initialization, more (and more accurate) observations are needed in order to constrain the dependence of the observation part of the cost function solely on soil moisture.