A cloudiness transition in a marine boundary layer

Boundary layer cloudiness plays several important roles in the energy budget of the earth. Low level stratocumulus are highly reflective clouds which reduce the net incoming shortwave radiation at the earth's surface. Climatically, the transition to a small area fraction of scattered cumulus clouds occurs as the air flows over warmer water. Although these clouds reflect less sunlight, they still play an important role in the boundary layer equilibrium by transporting water vapor upwards, and enhancing the surface evaporation. The First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) included a marine stratocumulus experiment off the southern California coast from June 29 to July 19, 1987. The objectives of this experiment were to study the controls on fractional cloudiness, and to assess the role of cloud-top entrainment instability (CTEI) and mesoscale structure in determining cloud type. The focus is one research day, July 7, 1987, when coordinated aircraft missions were flown by four research aircraft, centered on a LANDSAT scene at 1830 UTC. The remarkable feature of this LANDSAT scene is the transition from a clear sky in the west through broken cumulus to solid stratocumulus in the east. The dynamic and thermodynamic structure of this transition in cloudiness is analyzed using data from the NCAR Electra. By averaging the aircraft data, the internal structure of the different cloud regimes is documented, and it is shown that the transition between broken cumulus and stratocumulus is associated with a change in structure with respect to the CTEI condition. However, this results not from sea surface temperature changes, but mostly from a transition in the air above the inversion, and the breakup appears to be at a structure on the unstable side of the wet virtual adiabat

Thermodynamic structure of the stratocumulus-capped boundary layer on 7 July, 1987

As part of project First ISCCP Regional Experiment (FIRE), a mission was carried out on 7 July 1987 to study the thermodynamic structure of a boundary layer which is in transition from a clear to a cloudy state. The National Center for Aeronautical Research (NCAR) Electra flew a pattern in tight coordination with the NASA ER-2 aircraft near 122 West, 31.6 North off the coast of California. A description is given here of the thermodynamic structure. The purpose is to derive the entrainment rate and the fluxes of the thermodynamic variables. To this end researchers represent the data in conserved variable diagrams

An analysis of the Bayesian track labelling problem

In multi-target tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature, but its exact mathematical formulation, in terms of Bayesian statistics, has not been yet looked at in detail. Doing so, however, may help us to understand how Bayes-optimal track labelling should be performed or numerically approximated. Moreover, it can help us to better understand and tackle some practical difficulties associated with the MTT problem, in particular the so-called ``mixed labelling'' phenomenon that has been observed in MTT algorithms. In this memorandum, we rigorously formulate the optimal track labelling problem using Finite Set Statistics (FISST), and look in detail at the mixed labeling phenomenon. As practical contributions of the memorandum, we derive a new track extraction formulation with some nice properties and a statistic associated with track labelling with clear physical meaning. Additionally, we show how to calculate this statistic for two well-known MTT algorithms

SMC methods to avoid self-resolving for online Bayesian parameter estimation

Abstract—The particle filter is a powerful filtering technique that is able to handle a broad scope of nonlinear problems. However, it has also limitations: a standard particle filter is unable to handle, for instance, systems that include static variables (parameters) to be estimated together with the dynamic states. This limitation is due to the well-known “self-resolving” phenomenon, which is caused by the gradual loss of information that occurs during the resampling steps. In the context of online Bayesian parameter estimation, some approaches to handle this problem have proposed, such as adding artificial dynamics to the parameter model. However, these approaches typically both introduce new parameters (e.g. the intensity of artificial process noise) and inherent biases to the estimation problem. In this paper, we will give a give a look at two Sequential Monte Carlo techniques that do not rely on biasing the system model: the Autonomous Multiple Model particle filter and the Rao-Blackwellized Marginal particle filter. These approaches are not new, but have not been applied yet to the problem of online Bayesian parameter estimation for non-structured models. We will derive suitable adaptations of these methods for this problem and evaluate them using simulations. I

A Bayesian solution to multi-target tracking problems with mixed labelling

In Multi-Target Tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature and has been previously formulated using Bayesian recursion. However, the existing literature lacks an appropriate measure of uncertainty related to the assigned labels which has sound mathematical basis and clear practical meaning (to the user). This is especially important in a situation where targets move in close proximity with each other and thereafter separate again. Because, in such a situation it is well-known that there will be confusion on target identities, also known as “mixed labelling‿. In this paper, we provide a mathematical characterization of the labelling uncertainties present in Bayesian multi-target tracking and labelling (MTTL) problems and define measures of labelling uncertainties with clear physical interpretation. The introduced uncertainty measures can be used to find the optimal track label assignment, and evaluate track labelling performance. We also analyze in details the mixed labelling phenomenon in the presence of two targets. In addition, we propose a new Sequential Monte Carlo (SMC) algorithm, the Labelling Uncertainty Aware Particle Filter (LUA-PF), for the multi target tracking and labelling problem that can provide good estimates of the uncertainty measures. We validate this using simulation and show that the proposed method performs much better when compared with the performance of the SIR multi-target SMC filter

Particle filter based MAP state estimation: A comparison

MAP estimation is a good alternative to MMSE for certain applications involving nonlinear non Gaussian systems. Recently a new particle filter based MAP estimator has been derived. This new method extracts the MAP directly from the output of a running particle filter. In the recent past, a Viterbi algorithm based MAP sequence estimator has been developed. In this paper, we compare these two methods for estimating the current state and the numerical results show that the former performs better

Labeling Uncertainty in Multitarget Tracking

In multitarget tracking, the problem of track labeling (assigning labels to tracks) is an ongoing research topic. The existing literature, however, lacks an appropriate measure of uncertainty related to the assigned labels that has a sound mathematical basis as well as clear practical meaning to the user. This is especially important in a situation where well separated targets move in close proximity with each other and thereafter separate again; in such a situation, it is well known that there will be confusion on target identities, also known as "mixed labeling." In this paper, we specify comprehensively the necessary assumptions for a Bayesian formulation of the multitarget tracking and labeling (MTTL) problem to be meaningful. We provide a mathematical characterization of the labeling uncertainties with clear physical interpretation. We also propose a novel labeling procedure that can be used in combination with any existing (unlabeled) MTT algorithm to obtain a Bayesian solution to the MTTL problem. One advantage of the resulting solution is that it readily provides the labeling uncertainty measures. Using the mixed labeling phenomenon in the presence of two targets as our test bed, we show with simulation results that an unlabeled multitarget sequential Monte Carlo (M-SMC) algorithm that employs sequential importance resampling (SIR) augmented with our labeling procedure performs much better than its "naive" extension, the labeled SIR M-SMC filter

A Bayesian analysis of the mixed labelling phenomenon in two-target tracking

In mulit-target tracking and labelling (MTTL), mixed labelling corresponds to a situation where there is ambiguity in labelling, i.e. in the assignment of labels to locations (where a "location" here means simply an unlabelled single-target state. The phenomenon is well-known in literature, and known to occur in the situation where targets move in close proximity to each other and afterwards separate. The occurrence of mixed labelling has been empirically observed using particle filter implementations of the Bayesian MTTL recursion. In this memorandum, we will instead demonstrate the occurrence of mixed labelling (in the situation of closely spaced targets) using only the Bayesian recursion itself, for a scenario containing two targets and no target births or deaths. We will also show how mixed labelling generally persists after the targets become well-separated, and how mixed labelling might not happen when the unlabelled single-target state contains non-kinematic quantities