A5084604416- Meteorological Phenomena and Simulations
- Climate variability and models
- Tropical and Extratropical Cyclones Research
- Atmospheric and Environmental Gas Dynamics
- Oceanographic and Atmospheric Processes
- Wind and Air Flow Studies
- Precipitation Measurement and Analysis
- Geophysics and Gravity Measurements
- Ocean Waves and Remote Sensing
- Atmospheric aerosols and clouds
- Solar and Space Plasma Dynamics
- Geomagnetism and Paleomagnetism Studies
- Cryospheric studies and observations
- Forecasting Techniques and Applications
- Reservoir Engineering and Simulation Methods
- Underwater Acoustics Research
- Methane Hydrates and Related Phenomena
- Data Visualization and Analytics
- Geological and Geophysical Studies
- Neural Networks and Applications
- Nuclear reactor physics and engineering
- Advanced Computational Techniques and Applications
- Ionosphere and magnetosphere dynamics
- Geology and Paleoclimatology Research
- Coastal and Marine Dynamics
The University of Melbourne
2018-2024
ARC Centre of Excellence for Climate System Science
2018-2024
SA Technologies (United States)
2023
Bureau of Meteorology
2021-2022
United States Naval Research Laboratory
2009-2020
Stennis Space Center
2020
Naval Research Laboratory Marine Meteorology Division
2008-2019
Texas A&M University
2018
National Academies of Sciences, Engineering, and Medicine
2018
United States Department of the Navy
2010
A suboptimal Kalman filter called the ensemble transform (ET KF) is introduced. Like other filters, it provides a framework for assimilating observations and also estimating effect of on forecast error covariance. It differs from filters in that uses transformation normalization to rapidly obtain prediction covariance matrix associated with particular deployment observational resources. This rapidity enables quickly assess ability large number future feasible sequences networks reduce...
Ensemble data assimilation methods assimilate observations using state-space estimation and low-rank representations of forecast analysis error covariances. A key element such is the transformation ensemble into an with appropriate statistics. This may be performed stochastically by treating as random variables, or deterministically requiring that updated perturbations satisfy Kalman filter covariance equation. Deterministic updates are implementations square root filters. The nonuniqueness...
Ensemble forecasting is increasingly accepted as a powerful tool to improve early warnings for high-impact weather. Recently, ensembles combining forecasts from different systems have attracted considerable level of interest. The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Globa l (TIGGE) project, prominent contribution THORPEX, has been initiated enable advanced research demonstration the multimodel ensemble concept pave way toward operational...
This study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), first landfalling in United States since end 2005 season most rapidly intensifying near-landfall storm U.S. history. caused extensive damage along southeast Texas coast but was poorly predicted by operational models forecasters. It found that EnKF after...
The ensemble transform Kalman filter (ETKF) forecast scheme is introduced and compared with both a simple masked breeding scheme. Instead of directly multiplying each perturbation constant or regional rescaling factor as in the form schemes, ETKF transforms perturbations into analysis by transformation matrix. This matrix chosen to ensure that ensemble-based error covariance would be equal true if raw were data assimilation optimal. For small ensembles (∼100), computational expense...
Suppose that the geographical and temporal resolution of observational network could be changed on a daily basis. Of all possible deployments resources, which particular deployment would minimize expected forecast error? The ensemble transform technique answers such questions by using nonlinear forecasts to rapidly construct ensemble-based approximations prediction error covariance matrices associated with wide range different resources. From these matrices, estimates each distinct resources...
New methods to center the initial ensemble perturbations on analysis are introduced and compared with commonly used centering method of positive–negative paired perturbations. In new method, one linearly dependent perturbation is added a set independent ensure that sum equals zero; covariance calculated from equal error estimated by perturbations, all equally likely. The illustrated applying it transform Kalman filter (ETKF) forecast scheme, resulting called spherical simplex ETKF ensemble....
Abstract The effect on weather forecast performance of incorporating ensemble covariances into the initial covariance model four-dimensional variational data assimilation (4D-Var) Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) is investigated. This NAVDAS-AR-hybrid scheme linearly combines static NAVDAS-AR background error with a derived from an 80-member flow-dependent ensemble. members are generated using transform technique...
The objectives and preliminary results of an interagency field program, the North Pacific Experiment (NORPEX), which took place between 14 January 27 February 1998, are described. NORPEX represents effort to directly address issue observational sparsity over basin, is a major contributing factor in short-range (less than 4 days) forecast failures for land-falling winter-season storms that affect United States, Canada, Mexico. special observations collected include approximately 700 targeted...
In this paper, the effects of targeted dropsonde observations on operational global numerical weather analyses and forecasts made at National Centers for Environmental Prediction (NCEP) are evaluated. The data were collected during 1999 Winter Storm Reconnaissance field program locations that found optimal by ensemble transform technique reducing specific forecast errors over continental United States Alaska. Two parallel analysis–forecast cycles compared; one assimilates all operationally...
Abstract A hybrid ensemble transform Kalman filter (ETKF)–optimum interpolation (OI) analysis scheme is described and compared with an square root (EnSRF) scheme. two-layer primitive equation model was used under perfect-model assumptions. simplified observation network used, the OI method utilized a static background error covariance constructed from large inventory of historical forecast errors. The updated mean using hybridized background-error covariance. perturbations in were by ETKF...
Abstract Many ensemble Kalman filter (EnKF) data assimilation (DA) schemes reduce the effect of spurious correlations caused by small size multiplying moderation functions . Moderation envelop true error correlation functions. Ideal would adapt to variations in movement and width Here, we describe a new method which flow‐dependent are built from powers smoothed correlations. The approach imparts function information retained Spurious attenuated raising them power. Simple systems were used...
In atmospheric data assimilation (DA), observations over a 6–12-h time window are used to estimate the state. Non-adaptive moderation or localization functions widely in ensemble DA reduce amplitude of spurious correlations. These inappropriate (1) if true error correlation move comparable distance length scale and/or (2) widths highly flow dependent. A method for generating that with and also adapt width function is given. The uses correlations raised power (ECO-RAP). gallery periodic...
Abstract A widely used observation space covariance localization method is shown to adversely affect satellite radiance assimilation in ensemble Kalman filters (EnKFs) when compared model localization. The two principal problems are that distance and location not well defined for integrated measurements, neighboring channels typically have broad, overlapping weighting functions, which produce true, nonzero correlations can incorrectly eliminate. limitations of the illustrated a 1D conceptual...
Abstract Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This typically done by collating model from different research institutions internationally assumption that they constitute independent samples. Heuristically, however, several factors undermine this assumption: shared treatment processes between models, observed data evaluation, even code. Here, “perfect model”...
Abstract In a strongly coupled data assimilation (DA), cross-fluid covariance is specified that allows measurements from fluid (e.g., atmosphere) to directly impact analysis increments in target ocean). The exhaustive solution this DA problem calls for where all available can influence grid points fluids. Solution of such large algebraic computationally expensive, often substantial rewrite existing fluid-specific systems, and, as shown paper, be avoided. proposed interface solver assumes...
Abstract This paper describes the new global Navy Earth System Prediction Capability (Navy‐ESPC) coupled atmosphere‐ocean‐sea ice prediction system developed at Naval Research Laboratory (NRL) for operational forecasting timescales of days to subseasonal. Two configurations are validated: (1) a low‐resolution 16‐member ensemble and (2) high‐resolution deterministic system. The Navy‐ESPC became in August 2020, this is first time NRL partner, Fleet Numerical Meteorology Oceanography Center,...
Abstract Global climate models (GCMs) are commonly downscaled to understand future local change. The high computational cost of regional (RCMs) limits how many GCMs can be dynamically downscaled, restricting uncertainty assessment. While statistical downscaling is cheaper, its validity in a changing unclear. We combine these approaches build an emulator leveraging the merits dynamical and downscaling. A machine learning model developed for each coarse grid cell predict fine variables, using...
The practical application of the ensemble transform Kalman filter (ET KF), used in recent Winter Storm Reconnaissance (WSR) programs by National Centers for Environmental Prediction (NCEP), is described. ET KF assesses value targeted observations taken at future times improving forecasts preselected critical events. It based on a serial assimilation framework that makes it an order magnitude faster than its predecessor, technique. speed enabled several different forecast scenarios to be...
Perturbations of the classical Eady model are treated in terms system's two intrinsic baroclinic edge waves. This provides a simple quantitative example wave coupling interpretation quasigeostrophic instability and compact framework for examining rudiments upper level–lower level dynamical interaction. The reformulation consolidates extends series earlier theoretical results: existence transient growth at wavenumbers beyond cutoff scale, disparity between different measures maximum...
Abstract It is shown that Bretherton's view of baroclinic instability as the interaction two counter‐propagating Rossby waves (CRWs) can be extended to a general zonal flow and dynamical system based on material conservation potential vorticity (PV). The CRWs have zero tilt with both altitude latitude are constructed from pair growing decaying normal modes. One CRW has generally large amplitude in regions positive meridional PV gradient propagates westwards relative such regions. Conversely,...
A new method of combining dynamical and statistical ensembles for the purpose improving ensemble reliability underdispersive is introduced. The involves adding independent sets N random four-dimensional ‘dressing’ perturbations to each K members a forecast obtain an N×K dressed ensemble. mathematically constrains stochastic process used generate dressing so that it removes seasonally averaged errors in second moment measures originally ensembles. random-number generator experiment with...
Abstract Airborne adaptive observations have been collected for more than two decades in the neighborhood of tropical cyclones, to attempt improve short-range forecasts cyclone track. However, only simple subjective strategies used, and utility objective remains unexplored. Two techniques that used extensively midlatitude observing programs, current strategy based on ensemble deep-layer mean (DLM) wind variance, are compared quantitatively using metrics. The transform Kalman filter (ETKF)...