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20
Adaptive Sampling With the Ensemble Transform . . .
, 2001
"... A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filt ..."
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Cited by 321 (19 self)
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A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filters in that it uses ensemble transformation and a normalization to rapidly obtain the prediction error covariance matrix associated with a particular deployment of observational resources. This rapidity enables it to quickly assess the ability of a large number of future feasible sequences of observational networks to reduce forecast error variance. The ET KF was used by the National Centers for Environmental Prediction in the Winter Storm Reconnaissance missions of 1999 and 2000 to determine where aircraft should deploy dropwindsondes in order to improve 2472h forecasts over the continental United States. The ET KF may be applied to any wellconstructed set of ensemble perturbations. The ET KF
2001: Idealized Adaptive Observation Strategies for Improving Numerical Weather Prediction
 J. Atmos. Sci
, 1998
"... Adaptive sampling uses information about individual atmospheric situations to identify regions where additional observations are likely to improve weather forecasts of interest. The observation network could be adapted for a wide range of forecasting goals, and it could be adapted either by allocat ..."
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Cited by 26 (2 self)
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Adaptive sampling uses information about individual atmospheric situations to identify regions where additional observations are likely to improve weather forecasts of interest. The observation network could be adapted for a wide range of forecasting goals, and it could be adapted either by allocating existing observations differently or by adding observations from programmable platforms to the existing network. In this study, observing strategies are explored in a simulated idealized system with a threedimensional quasigeostrophic model and a realistic data assimilation scheme. Using simple error norms, idealized adaptive observations are compared to nonadaptive observations for a range of observation densities. The results presented show that in this simulated system, the influence of both adaptive and nonadaptive observations depends strongly on the observation density. For sparse observation networks, the simple adaptive strategies tested are beneficial: adaptive observations can, on average, reduce analysis and forecast errors more than the same number of nonadaptive observations, and they can reduce errors by a given amount using fewer observational resources. In contrast, for dense observation networks it is much more difficult to benefit from adapting observations, at least for the data assimilation method used here. The results suggest that the adaptive strategies tested are most effective when the observations are adapted regularly and frequently, giving the data assimilation system as many opportunities as possible to reduce errors as they evolve. They also indicate that ensemblebased estimates of initial condition errors may be useful for adaptive observations. Further study is needed to understand the extent to which the results from this idealized study apply to more complex, more realistic systems. 1.
Path planning of autonomous underwater vehicles (AUVs) for adaptive sampling
, 2005
"... Abstract—The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constrain ..."
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Cited by 22 (4 self)
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Abstract—The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constraints of the available observing network. Examples of objectives are better oceanic understanding, to improve forecast quality, or to sample regions of high interest. This work provides a new pathplanning scheme for the adaptive sampling problem. We define the pathplanning problem in terms of an optimization framework and propose a method based on mixed integer linear programming (MILP). The mathematical goal is to find the vehicle path that maximizes the line integral of the uncertainty of field estimates along this path. Sampling this path can improve the accuracy of the field estimates the most. While achieving this objective, several constraints must be satisfied and are implemented. They relate to vehicle motion, intervehicle coordination, communication, collision avoidance, etc. The MILP formulation is quite powerful to handle different problem constraints and flexible enough to allow easy extensions of the problem. The formulation covers single and multiplevehicle cases as well as singleand multipleday formulations. The need for a multipleday formulation arises when the ocean sampling mission is optimized for several days ahead. We first introduce the details of the formulation, then elaborate on the objective function and constraints, and finally, present a varied set of examples to illustrate the applicability of the proposed method. Index Terms—Adaptive sampling, Autonomous Ocean Sampling Network (AOSN), autonomous underwater vehicle (AUV), data
Assimilation of Standard and Targeted Observations within the Unstable Subspace of the ObservationAnalysisForecast Cycle System
 J. Atmos. Sci
"... In this paper it is shown that the flowdependent instabilities that develop within an observation–analysis– forecast (OAF) cycle and that are responsible for the background error can be exploited in a very simple way to assimilate observations. The basic idea is that, in order to minimize the analy ..."
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Cited by 16 (9 self)
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In this paper it is shown that the flowdependent instabilities that develop within an observation–analysis– forecast (OAF) cycle and that are responsible for the background error can be exploited in a very simple way to assimilate observations. The basic idea is that, in order to minimize the analysis and forecast errors, the analysis increment must be confined to the unstable subspace of the OAF cycle solution. The analysis solution here formally coincides with that of the classical threedimensional variational solution with the background error covariance matrix estimated in the unstable subspace. The unstable directions of the OAF system solution are obtained by breeding initially random perturbations of the analysis but letting the perturbed trajectories undergo the same process as the control solution, including assimilation of all the available observations. The unstable vectors are then used both to target observations and for the assimilation design. The approach is demonstrated in an idealized environment using a simple model, simulated standard observations over land with a single adaptive observation over the ocean. In the application a simplified form is adopted of the analysis solution and a single unstable vector at each analysis time whose amplitude is determined by means of the adaptive observation. The remarkable reduction of the analysis and forecast error obtained by
2006: Verification region selection and data assimilation for adaptive sampling
"... Adaptive or targeted observations supplement routine observations at a prespecified targeting time. Adaptive observation locations are selected to supplement routine observations in an attempt to minimize the forecast error variance of a future target forecast within some predefined verification re ..."
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Cited by 4 (2 self)
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Adaptive or targeted observations supplement routine observations at a prespecified targeting time. Adaptive observation locations are selected to supplement routine observations in an attempt to minimize the forecast error variance of a future target forecast within some predefined verification region (VR) at some predefined verification time. Ideally, the VR is placed in a location where unusually large forecast errors are likely. Here, we compare three methods of selecting VRs. A climatological method based on seasonal averages of forecast errors. An unconditioned method based on verification time ensemble spread and a conditioned method based on an Ensemble Transform Kalman Filter (ETKF) estimate of forecast error variance given the routine observations to be taken at the targeting time. To test the effectiveness of the three approaches, Observation System Simulation Experiments (OSSEs) on a chaotic barotropic flow were performed using an imperfect model. To test the sensitivity of our results to the type of forecast error covariance model used in the data assimilation (DA) scheme, two types of DA schemes were tested: An isotropic DA scheme and a hybrid DA scheme. For isotropic DA, correlations between vorticity forecast errors at any two points were solely a function of the distance between the points. For hybrid DA, the
Preface
, 2004
"... THORPEX is an international research programme to accelerate improvements in the accuracy of 1day to 2week highimpact weather forecasts. These improvements will lead to substantial benefits for humanity, as we respond to the weather related challenges of the 21st century. THORPEX research Subpro ..."
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Cited by 2 (0 self)
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THORPEX is an international research programme to accelerate improvements in the accuracy of 1day to 2week highimpact weather forecasts. These improvements will lead to substantial benefits for humanity, as we respond to the weather related challenges of the 21st century. THORPEX research Subprogrammes address: i) globaltoregional influences on the evolution and predictability of weather systems; ii) global observingsystem design and demonstration; iii) targeting and assimilation of observations; iv) societal, economic, and environmental benefits of improved forecasts. THORPEX establishes an organisational framework that addresses weather research and forecast problems whose solutions will be accelerated through international collaboration among academic institutions, operational forecast centres and users of forecast products.
2007: Comparing adjoint and ensemble sensitivity analysis with applications to observation targeting
"... The sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors. Ensemble sensitivity is defined here by linear regression of analysis errors onto a given forecast metric. We show that ensemble sensitivity is p ..."
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The sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors. Ensemble sensitivity is defined here by linear regression of analysis errors onto a given forecast metric. We show that ensemble sensitivity is proportional to the projection of the analysiserror covariance onto the adjoint sensitivity field. Furthermore, the ensemble sensitivity approach proposed here involves a small calculation that is easy to implement. Ensemble and adjointbased sensitivity fields are compared for a representative wintertime flow pattern near the West Coast of North America for a 90member ensemble of independent initial conditions derived from an ensemble Kalman filter. The forecast metric is taken for simplicity to be the 24hr forecast of sealevel pressure at a single point in western Washington state. Results show that adjoint and ensemble sensitivities are very different in terms of location, scale, and magnitude. Adjoint sensitivity fields reveal mesoscale lowertropospheric structures that tilt strongly upshear, whereas ensemble sensitivity fields emphasize synopticscale features that tilt modestly throughout the troposphere and are associated with significant weather features at the initial time. Optimal locations for targeting can easily be determined from ensemble sensitivity, and results indicate that the primary targeting locations are located away from regions of greatest adjoint and ensemble sensitivity. We show that this method of targeting is similar to previous ensemblebased methods that estimate forecasterror variance reduction, but easily allows for the application of statistical confidence measures to deal with sampling error. 1 1.
An Observing System Experiment with the West Coast Picket Fence
, 2000
"... Analyses and forecasts from a modern data assimilation and modeling system are used to evaluate the impact of a special rawinsonde dataset of 3h soundings at seven sites interspersed with the seven regular sites along the West Coast (to form a socalled picket fence to intercept all transiting circ ..."
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Analyses and forecasts from a modern data assimilation and modeling system are used to evaluate the impact of a special rawinsonde dataset of 3h soundings at seven sites interspersed with the seven regular sites along the West Coast (to form a socalled picket fence to intercept all transiting circulations) plus special 6h rawinsondes over the National Weather Service Western Region. Whereas four intensive observing periods (IOPs) are available, only two representative IOPs (IOP3 and IOP4) are described here. The special observations collected during each 12h cycle are analyzed with the National Centers for Environmental Prediction (NCEP) Eta Data Assimilation System in a cold start from the NCEP–National Center for Atmospheric Research reanalyses as the initial condition. Forecasts up to 48 h with and without the special picket fence observations are generated by the 32km horizontal resolution Eta Model with 45 vertical levels. The picket fence observations had little impact in some cases with smooth environmental flow. In other cases, relatively large initial increments were introduced offshore of the picket fence observations. However, these increments usually damped as they translated downstream. During IOP3, the increments amplified east of the Rocky Mountains after only 24 h. Even though initially small, the increments in IOP4 grew rapidly to 500mb height increments;20–25 m with accompanying meridional wind increments of 5–8 m s21 that contributed to
WMO/CAS/WWW SIXTH INTERNATIONAL WORKSHOP on TROPICAL CYCLONES Topic 3.3: Targeted observation and data assimilation in track prediction Rapporteur:
"... The objective of this report is to document recent progress since IWTC5 on the topic related to the Targeted observation and data assimilation in track prediction. The report begins by reviewing the background of targeted observations, followed by an introduction to the techniques specifically used ..."
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The objective of this report is to document recent progress since IWTC5 on the topic related to the Targeted observation and data assimilation in track prediction. The report begins by reviewing the background of targeted observations, followed by an introduction to the techniques specifically used for targeted observations and data assimilation to improve tropical cyclone track prediction. These
SUMMARY
"... Prepared using fldauth.cls [Version: 2002/09/18 v1.01] Effect of random perturbations on adaptive observation techniques ..."
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Prepared using fldauth.cls [Version: 2002/09/18 v1.01] Effect of random perturbations on adaptive observation techniques