Air quality forecast systems need reliable and accurate representations of the planetary boundary layer (PBL) to perform well. An important question is how accurately numerical weather prediction models can reproduce conditions in diverse synoptic flow types. Here, observations from the summer 2014 HygrA-CD (Hygroscopic Aerosols to Cloud Droplets) experimental campaign are used to validate simulations from the Weather Research and Forecasting (WRF) model over the complex, urban terrain of the Greater Athens Area. Three typical atmospheric flow types were identified during the 39-day campaign based on 2-day backward trajectories: Continental, Etesians, and Saharan. It is shown that the numerical model simulations differ dramatically depending on the PBL scheme, atmospheric dynamics, and meteorological parameter (e.g., 2-m air temperature). Eight PBL schemes from WRF version 3.4 are tested with daily simulations on an inner domain at 1-km grid spacing. Near-surface observations of 2-m air temperature and relative humidity and 10-m wind speed are collected from multiple meteorological stations. Estimates of the PBL height come from measurements using a multiwavelength Raman lidar, with an adaptive extended Kalman filter technique. Vertical profiles of atmospheric variables are obtained from radiosonde launches, along with PBL heights calculated using bulk Richardson number. Daytime maximum PBL heights ranged from 2.57 km during Etesian flows, to as low as 0.37 km during Saharan flows. The largest differences between model and observations are found with simulated PBL height during Saharan synoptic flows. During the daytime, campaign-averaged near-surface variables show WRF tended to have a cool, moist bias with higher simulated wind speeds than the observations, especially near the coast. It is determined that non-local PBL schemes give the most agreeable solutions when compared with observations.Peer ReviewedPostprint (published version

We evaluate planetary boundary-layer (PBL) parametrizations in the Weather Research and Forecasting (WRF) numerical model, with three connected objectives: first, for a 16-year period, we use a cluster analysis algorithm of three-day back-trajectories to determine general synoptic flow patterns over Barcelona, Spain arriving at heights of 0.5, 1.5, and 3 km; to represent the lower PBL, upper PBL, and lower free troposphere, respectively. Seven clusters are determined at each arriving altitude. Regional recirculations account for 54 % of the annual total at 0.5 km, especially in summertime. In the second objective, we assess a time-adaptive approach using an extended Kalman filter to estimate PBL height from backscatter lidar returns at 1200 UTC ± 30 min for 45 individual days during a seven-year period. PBL heights retrieved with this technique are compared with three classic methods used in the literature to estimate PBL height from lidar. The methods are validated against PBL heights calculated from daytime radiosoundings. Lidar and radiosonde estimated PBL heights are classified under objectively-determined synoptic clusters. With the final objective, WRF model-simulated PBL heights are validated against lidar estimates using eight unique PBL schemes as inputs. Evaluation of WRF model-simulated PBL heights are performed under different synoptic situations. Determination coefficients with lidar estimates indicate the non-local assymetric convective model scheme is the most reliable, with the widely-tested local Mellor-Yamada-Janjic scheme showing the weakest correlations with lidar retrievals. Overall, there is a systematic underestimation of PBL height simulated in the WRF model.

A solution based on a Kalman filter to trace the evolution of the atmospheric boundary layer (ABL) sensed by a ground-based elastic-backscatter tropospheric lidar is presented. An erf-like profile is used to model the mixing-layer top and the entrainment-zone thickness. The extended Kalman filter (EKF) enables to retrieve and track the ABL parameters based on simplified statistics of the ABL dynamics and of the observation noise present in the lidar signal. This adaptive feature permits to analyze atmospheric scenes with low signal-to-noise ratios (SNRs) without the need to resort to long-time averages or range-smoothing techniques, as well as to pave the way for future automated detection solutions. First, EKF results based on oversimplified synthetic and experimental lidar profiles are presented and compared with classic ABL estimation quantifiers for a case study with different SNR scenarios.Index Terms-Adaptive kalman filtering, laser radar, remote sensing, signal processing.

Low-frequency fluctuations (LFFs) represent a dynamical instability that occurs in semiconductor lasers when they are operated near the lasing threshold and subject to moderate optical feedback. LFFs consist of sudden power dropouts followed by gradual, stepwise recoveries. We analyze experimental time series of intensity dropouts and quantify the complexity of the underlying dynamics employing two tools from information theory, namely, Shannon's entropy and the Martín, Plastino, and Rosso statistical complexity measure. These measures are computed using a method based on ordinal patterns, by which the relative length and ordering of consecutive interdropout intervals (i.e., the time intervals between consecutive intensity dropouts) are analyzed, disregarding the precise timing of the dropouts and the absolute durations of the interdropout intervals. We show that this methodology is suitable for quantifying subtle characteristics of the LFFs, and in particular the transition to fully developed chaos that takes place when the laser's pump current is increased. Our method shows that the statistical complexity of the laser does not increase continuously with the pump current, but levels off before reaching the coherence collapse regime. This behavior coincides with that of the first-and second-order correlations of the interdropout intervals, suggesting that these correlations, and not the chaotic behavior, are what determine the level of complexity of the laser's dynamics. These results hold for two different dynamical regimes, namely, sustained LFFs and coexistence between LFFs and steady-state emission.

We describe a method to infer signatures of determinism and stochasticity in the sequence of apparently random intensity dropouts emitted by a semiconductor laser with optical feedback. The method uses ordinal time-series analysis to classify experimental data of inter-dropout-intervals (IDIs) in two categories that display statistically significant different features. Despite the apparent randomness of the dropout events, one IDI category is consistent with waiting times in a resting state until noise triggers a dropout, and the other is consistent with dropouts occurring during the return to the resting state, which have a clear deterministic component. The method we describe can be a powerful tool for inferring signatures of determinism in the dynamics of complex systems in noisy environments, at an event-level description of their dynamics.

An adaptive solution based on an Extended Kalman Filter (EKF) is proposed to estimate the Atmospheric Boundary-Layer Height (ABLH) from Frequency-Modulated Continuous-Wave (FMCW) S-band weather-radar returns. The EKF estimator departs from previous works, in which the transition interface between the Mixing-Layer (ML) and the Free-Troposphere (FT) is modeled by means of an erf-like parametric function. In contrast to lidar remote sensing where aerosols give strong backscatter returns over the whole ML, clear-air radar reflectivity returns (Bragg scattering from refractive turbulence) shows strongest returns from the ML-FT interface. In addition, they are corrupted by "insect" noise (impulsive noise associated with Rayleigh scatter-

We introduce a method, based on symbolic analysis, to characterize the temporal correlations of the spiking activity exhibited by excitable systems. The technique is applied to the experimentally observed dynamics of a semiconductor laser with optical feedback operating in the low-frequency fluctuations regime, where the laser intensity displays irregular trains of sudden dropouts that can be interpreted as excitable pulses. Symbolic analysis transforms the series of interdropout time intervals into sequences of words, which represent the local ordering of a certain (small) number of those intervals. We then focus on the transition probabilities between pairs of words, showing that certain transitions are overrepresented (resulting in others being underrepresented) with respect to the surrogate series, provided the laser injection current is above a critical value. These experimental observations are in very good agreement with numerical simulations of the delay-differential Lang-Kobayashi model that is commonly used to describe this laser system, which supports the fact that the language organization reported here is generic and not a particular feature of the specific laser employed or the experimental time series analyzed. We also present results of simulations of the phenomenological nondelayed Eguia-Mindlin-Giudici(EMG) model and find that in this model the agreement between the experiments and the simulations is good at a qualitative, but not at a quantitative, level.

This work presents a new methodology to estimate the motion-induced standard deviation and related turbulence intensity on the retrieved horizontal wind speed by means of the velocity-azimuth-display algorithm applied to the conical scanning pattern of a floating Doppler lidar. The method considers a ZephIR™300 continuous-wave focusable Doppler lidar and does not require access to individual line-of-sight radial-wind information along the scanning pattern. The method combines a software-based velocity-azimuth-display and motion simulator and a statistical recursive procedure to estimate the horizontal wind speed standard deviation—as a well as the turbulence intensity—due to floating lidar buoy motion. The motion-induced error is estimated from the simulator’s side by using basic motional parameters, namely, roll/pitch angular amplitude and period of the floating lidar buoy, as well as reference wind speed and direction measurements at the study height. The impact of buoy motion on the retrieved wind speed and related standard deviation is compared against a reference sonic anemometer and a reference fixed lidar over a 60-day period at the IJmuiden test site (the Netherlands). Individual case examples and an analysis of the overall campaign are presented. After the correction, the mean deviation in the horizontal wind speed standard deviation between the reference and the floating lidar was improved by about 70%, from 0.14 m/s (uncorrected) to −0.04 m/s (corrected), which makes evident the goodness of the method. Equivalently, the error on the estimated turbulence intensity (3–20 m/s range) reduced from 38% (uncorrected) to 4% (corrected).