Meteorological Studies With the Phased Array Weather Radar and Data Assimilation Using the Ensemble Kalman Filter (original) (raw)
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1998
The lack of good ocean surface data on the one-mile or more scale is a major stumbling block for proper evaluation of proposed Mobile O shore Base MOB conceptual designs . A general model and associated software for stationary, linear, statistical, directional wave systems has been available for some time. If that model can appropriately be used for design of marine mega-structures, then there is no problem in proceeding with conceptual MOB designs and their evaluations. However, it is believed that storm waves might be locally stationary, but on a larger area scale might be nonstationary with patches of clusters of high waves, giving local variances inconsistent with stationary, linear, statistical, directional wave systems. There is a strong need for more knowledge concerning nonlinearity and coherence of storm waves on the mega-structure scale. Figure 1: An artist's conception of the Mobile O shore Base MOB.
A new methodology for the extension of the impact of data assimilation on ocean wave prediction
Ocean Dynamics, 2009
It is a common fact that the majority of today's wave assimilation platforms have a limited, in time, ability of affecting the final wave prediction, especially that of long-period forecasting systems. This is mainly due to the fact that after "closing" the assimilation window, i.e., the time that the available observations are assimilated into the wave model, the latter continues to run without any external information. Therefore, if a systematic divergence from the observations occurs, only a limited portion of the forecasting period will be improved. A way of dealing with this drawback is proposed in this study: A combination of two different statistical tools-Kolmogorov-Zurbenko and Kalman filters-is employed so as to eliminate any systematic error of (a first run of) the wave model results. Then, the obtained forecasts are used as artificial observations that can be assimilated to a follow-up model simulation inside the forecasting period. The method was successfully applied to an open sea area (Pacific Ocean) for significant wave height forecasts using the wave model WAM and six different buoys as observational stations. The results were encouraging and led to the extension of the assimilation impact to the entire forecasting period as well as to a significant reduction of the forecast bias.
Ocean Modelling, 2006
This paper compares contending advanced data assimilation algorithms using the same dynamical model and measurements. Assimilation experiments use the ensemble Kalman filter (EnKF), the ensemble Kalman smoother (EnKS) and the representer method involving a nonlinear model and synthetic measurements of a mesoscale eddy. Twin model experiments provide the "truth" and assimilated state. The difference between truth and assimilation state is a mispositioning of an eddy in the initial state affected by a temporal shift. The systems are constructed to represent the dynamics, error covariances. and data density as similarly as possible, though because of the differing assumptions in the system derivations subtle differences do occur. The results reflect some of these differences in the tangent linear assumption made in the representer adjoint and the temporal covariance of the EnKF, which does not correct initial condition errors. These differences are assessed through the accuracy of each method as a function of measurement density. Results indicate that these methods are comparably accurate for sufficiently dense measurement networks; and each is able to correct the position of a purposefully misplaced mesoscale eddy. As measurement density is decreased, the EnKS and the representer method retain accuracy longer than the EnKF. While the representer method is more accurate than the sequential methods within the time period covered by the observations (particularly during the first part of the assimilation time), the representer method is less accurate during later times and during the forecast time period for sparse networks as the tangent linear assumption becomes less accurate. Furthermore, the representer method proves to be significantly more costly (2-4 times) than the EnKS and EnKF even with only a few outer iterations of the iterated indirect representer method.
Development of a Mesoscale Ensemble Data Assimilation System at the Naval Research Laboratory
Weather and Forecasting, 2013
An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system’s computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-...
2013
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Proceedings of the Human Factors and …, 2001
Research has shown that spatial disorientation often occurs when pilots transition between real-world visual cues and head-down attitude instruments. Recent studies investigating the opto-kinetic cervical reflex (OKCR) indicate that when pilots transition between these two types of visual cues, they are also transitioning between frames of reference. Limited research has been conducted investigating pilots' response during transitions between real-world visual cues and helmet-mounted display (HMD) attitude symbology. Eleven pilots performed vertical "S" maneuvers in and out of clouds to simulate frequent transitions between visual meteorological conditions and instrument meteorological conditions using both primary flight symbology on an HMD and traditional head-down primary flight instruments. Because pilots focused primarily on the symbology during the task, the OKCR was not found. Results also revealed that pilots were better able to maintain commanded vertical velocity when using the HMD compared to the head-down instruments, which is attributed to the head-up location of the symbology. Having the HMD symbology superimposed on the real-world visual scene can provide additional visual cues that pilots can use to perform their task more efficiently.
2010
The quality of convective-scale ensemble forecasts, initialized from analysis ensembles obtained through the assimilation of radar observations using an ensemble Kalman filter (EnKF), is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. This work is the companion to Part I, which focused on the quality of analyses during the 60-min analysis period. Here, the focus is on 30-min ensemble forecasts initialized at the end of that period. As in Part I, the Weather Research and Forecasting (WRF) model is employed as a simplified cloud model at 2-km horizontal grid spacing. Various observationspace and state-space verification metrics, computed both for ensemble means and individual ensemble members, are employed to assess the quality of ensemble forecasts comparatively across cases. While the cases exhibit noticeable differences in predictability, the forecast skill in each case, as measured by various metrics, decays on a time scale of tens of minutes. The ensemble spread also increases rapidly but significant outlier members or clustering among members are not encountered. Forecast quality is seen to be influenced to varying degrees by the respective initial soundings. While radar data assimilation is able to partially mitigate some of the negative effects in some situations, the supercell case, in particular, remains difficult to predict even after 60 min of data assimilation.
Coupled Tropical Cyclone-ocean Modeling for Transition to Operational Predictive Capabilities
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