Synoptic-scale controls of persistent low temperature and icy weather over southern China in January 2008

In January 2008, central and southern China experienced persistent low temperatures, freezing rain, and snow. The large-scale conditions associated with the occurrence and development of these snowstorms are examined in order to identify the key synoptic controls leading to this event. Three main factors are identified: 1) the persistent blocking high over Siberia, which remained quasi-stationary around 65°E for 3 weeks, led to advection of dry and cold Siberian air down to central and southern China; 2) a strong persistent southwesterly flow associated with the western Pacific subtropical high led to enhanced moisture advection from the Bay of Bengal into central and southern China; and 3) the deep inversion layer in the lower troposphere associated with the extended snow cover over most of central and southern China. The combination of these three factors is likely responsible for the unusual severity of the event, and hence a long return period

New age estimates for the Palaeolithic assemblages and Pleistocene succession of Casablanca, Morocco

Marine and aeolian Quaternary sediments from Casablanca, Morocco were dated using the optically stimulated luminescence (OSL) signal of quartz grains. These sediments form part of an extensive succession spanning the Pleistocene, and contain a rich faunal and archaeological record, including an Acheulian lithic assemblage from before the Brunhes–Matayama boundary, and a Homo erectus jaw from younger cave deposits. Sediment samples from the sites of Reddad Ben Ali, Oulad J’mel, Sidi Abderhamane and Thomas Quarries have been dated, in order to assess the upper limits of OSL. The revision of previously measured mammalian tooth enamel electron spin resonance (ESR) dates from the Grotte des Rhinocéros, Oulad Hamida Quarry 1, incorporating updated environmental dose rate measurements and attenuation calculations, also provide chronological constraint for the archaeological material preserved at Thomas Quarries. Several OSL age estimates extend back to around 500,000 years, with a single sample providing an OSL age close to 1 Ma in magnetically reversed sediments. These luminescence dates are some of the oldest determined, and their reliability is assessed using both internal criteria based on stratigraphic consistency, and external lithostratigraphic, morphostratigraphic and independent chronological constraints. For most samples, good internal agreement is observed using single aliquot regenerative-dose OSL measurements, while multiple aliquot additive-dose measurements generally have poorer resolution and consistency. Novel slow-component and component-resolved OSL approaches applied to four samples provide significantly enhanced dating precision, and an examination of the degree of signal zeroing at deposition. A comparison of the OSL age estimates with the updated ESR dates and one U-series date demonstrate that this method has great potential for providing reliable age estimates for sediments of this antiquity. We consider the cause of some slight age inversion observed at Thomas Quarries, and provide recommendations for further luminescence dating within this succession.

Automated identification and tracking of polar-cap plasma patches at solar minimum

A method of automatically identifying and tracking polar-cap plasma patches, utilising data inversion and feature-tracking methods, is presented. A well-established and widely used 4-D ionospheric imaging algorithm, the Multi-Instrument Data Assimilation System (MIDAS), inverts slant total electron content (TEC) data from ground-based Global Navigation Satellite System (GNSS) receivers to produce images of the free electron distribution in the polar-cap ionosphere. These are integrated to form vertical TEC maps. A flexible feature-tracking algorithm, TRACK, previously used extensively in meteorological storm-tracking studies is used to identify and track maxima in the resulting 2-D data fields. Various criteria are used to discriminate between genuine patches and "false-positive" maxima such as the continuously moving day-side maximum, which results from the Earth's rotation rather than plasma motion. Results for a 12-month period at solar minimum, when extensive validation data are available, are presented. The method identifies 71 separate structures consistent with patch motion during this time. The limitations of solar minimum and the consequent small number of patches make climatological inferences difficult, but the feasibility of the method for patches larger than approximately 500 km in scale is demonstrated and a larger study incorporating other parts of the solar cycle is warranted. Possible further optimisation of discrimination criteria, particularly regarding the definition of a patch in terms of its plasma concentration enhancement over the surrounding background, may improve results.

Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. Specifically, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalisation of Hammerstein channels.

Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3~data

A method has been developed to estimate Aerosol Optical Depth (AOD), Fine Mode Fraction (FMF) and Single Scattering Albedo (SSA) over land surfaces using simulated Sentinel-3 data. The method uses inversion of a coupled surface/atmosphere radiative transfer model, and includes a general physical model of angular surface reflectance. An iterative process is used to determine the optimum value of the aerosol properties providing the best fit of the corrected reflectance values for a number of view angles and wavelengths with those provided by the physical model. A method of estimating AOD using only angular retrieval has previously been demonstrated on data from the ENVISAT and PROBA-1 satellite instruments, and is extended here to the synergistic spectral and angular sampling of Sentinel-3 and the additional aerosol properties. The method is tested using hyperspectral, multi-angle Compact High Resolution Imaging Spectrometer (CHRIS) images. The values obtained from these CHRIS observations are validated using ground based sun-photometer measurements. Results from 22 image sets using the synergistic retrieval and improved aerosol models show an RMSE of 0.06 in AOD, reduced to 0.03 over vegetated targets.

Improvements in Aerosol Optical Depth Estimation Using Multiangle CHRIS/PROBA Images

A method has been developed to estimate aerosol optical depth (AOD) over land surfaces using high spatial resolution, hyperspectral, and multiangle Compact High Resolution Imaging Spectrometer (CHRIS)/Project for On Board Autonomy (PROBA) images. The CHRIS instrument is mounted aboard the PROBA satellite and provides up to 62 bands. The PROBA satellite allows pointing to obtain imagery from five different view angles within a short time interval. The method uses inversion of a coupled surface/atmosphere radiative transfer model and includes a general physical model of angular surface reflectance. An iterative process is used to determine the optimum value providing the best fit of the corrected reflectance values for a number of view angles and wavelengths with those provided by the physical model. This method has previously been demonstrated on data from the Advanced Along-Track Scanning Radiometer and is extended here to the spectral and angular sampling of CHRIS/PROBA. The values obtained from these observations are validated using ground-based sun-photometer measurements. Results from 22 image sets show an rms error of 0.11 in AOD at 550 nm, which is reduced to 0.06 after an automatic screening procedure.

Foehn jets over the Larsen C ice shelf, Antarctica

Previously unknown foehn jets have been identified to the east of the Antarctic Peninsula (AP) above the Larsen C Ice Shelf. These jets have major implications for the east coast of the AP, a region of rapid climatic warming and where two large sections of ice shelf have collapsed in recent years. During three foehn events across the AP, leeside warming and drying is seen in new aircraft observations and simulated well by the Met Office Unified Model (MetUM) at ∼1.5 km grid spacing. In case A, weak southwesterly flow and an elevated upwind inversion characterise a highly nonlinear flow regime with upwind flow blocking. In case C strong northwesterly winds characterise a relatively linear case with little upwind flow blocking. Case B resides somewhere between the two in flow regime linearity. The foehn jets – apparent in aircraft observations where available and MetUM simulations of all three cases – are mesoscale features (up to 60 km in width) originating from the mouths of leeside inlets. Through back trajectory analysis they are identified as a type of gap flow. In cases A and B the jets are distinct, being strongly accelerated relative to the background flow, and confined to low levels above the Larsen C Ice Shelf. They resemble the ‘shallow foehn’ of the Alps. Case C resembles a case of ‘deep foehn’, with the jets less distinct. The foehn jets are considerably cooler and moister relative to adjacent regions of calmer foehn air. This is due to a dampened foehn effect in the jet regions: in case A the jets have lower upwind source regions, and in the more linear case C there is less diabatic warming and precipitation along jet trajectories due to the reduced orographic uplift across the mountain passes.

Single-carrier frequency domain equalisation for hammerstein communication systems using complex-valued neural networks

Single-carrier (SC) block transmission with frequency-domain equalisation (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high bandwidthefficiency and high power-efficiency systems, the channel can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. This paper advocates a complex-valued (CV) B-spline neural network based nonlinear SC-FDE scheme for Hammerstein channels. Specifically, We model the nonlinear HPA, which represents the CV static nonlinearity of the Hammerstein channel, by a CV B-spline neural network, and we develop two efficient alternating least squares schemes for estimating the parameters of the Hammerstein channel, including both the channel impulse response coefficients and the parameters of the CV B-spline model. We also use another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse B-spline neural network model obtained in time domain. Extensive simulation results are included to demonstrate the effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels.

The evolution of the Sun's open magnetic flux: I. a single bipole

In this paper the origin and evolution of the Sun’s open magnetic flux are considered for single magnetic bipoles as they are transported across the Sun. The effects of magnetic flux transport on the radial field at the surface of the Sun are modeled numerically by developing earlier work by Wang, Sheeley, and Lean (2000). The paper considers how the initial tilt of the bipole axis (α) and its latitude of emergence affect the variation and magnitude of the surface and open magnetic flux. The amount of open magnetic flux is estimated by constructing potential coronal fields. It is found that the open flux may evolve independently from the surface field for certain ranges of the tilt angle. For a given tilt angle, the lower the latitude of emergence, the higher the magnitude of the surface and open flux at the end of the simulation. In addition, three types of behavior are found for the open flux depending on the initial tilt angle of the bipole axis. When the tilt is such that α ≥ 2◦ the open flux is independent of the surface flux and initially increases before decaying away. In contrast, for tilt angles in the range −16◦ < α < 2◦ the open flux follows the surface flux and continually decays. Finally, for α ≤ −16◦ the open flux first decays and then increases in magnitude towards a second maximum before decaying away. This behavior of the open flux can be explained in terms of two competing effects produced by differential rotation. Firstly, differential rotation may increase or decrease the open flux by rotating the centers of each polarity of the bipole at different rates when the axis has tilt. Secondly, it decreases the open flux by increasing the length of the polarity inversion line where flux cancellation occurs. The results suggest that, in order to reproduce a realistic model of the Sun’s open magnetic flux over a solar cycle, it is important to have accurate input data on the latitude of emergence of bipoles along with the variation of their tilt angles as the cycle progresses.

Friction in mid-latitude cyclones: an Ekman-PV mechanism

The mechanism by which the atmospheric boundary layer reduces the intensity of mid-latitude cyclones is investigated. It is demonstrated that two alternative theories, Ekman pumping and the baroclinic potential vorticity (PV) mechanism, in fact act in union to maximize the spin-down. Ekman pumping aids the ventilation of PV from the boundary layer, and shapes the resulting PV anomaly into one of increased static stability. PV inversion techniques are used to demonstrate how this anomaly reduces the coupling between the upper- and lower-levels within the cyclone, reducing the growth rate.

A diabatically-generated potential vorticity structure near the extratropical tropopause in three simulated extratropical cyclones

The structure of near-tropopause potential vorticity (PV) acts as a primary control on the evolution of extratropical cyclones. Diabatic processes such as the latent heating found in ascending moist warm conveyor belts modify PV. A dipole in diabatically-generated PV (hereafter diabatic PV) straddling the extratropical tropopause, with the positive pole above the negative pole, was diagnosed in a recently published analysis of a simulated extratropical cyclone. This PV dipole has the potential to significantly modify the propagation of Rossby waves and the growth of baroclinically-unstable waves. This previous analysis was based on a single case study simulated with 12-km horizontal grid spacing and parameterized convection. Here, the dipole is investigated in three additional cold-season extratropical cyclones simulated in both convection-parameterizing and convection-permitting model configurations. A diabatic PV dipole across the extratropical tropopause is diagnosed in all three cases. The amplitude of the dipole saturates approximately 36 hours from the time diabatic PV is accumulated. The node elevation of the dipole varies between 2-4 PVU in the three cases, and the amplitude of the system-averaged dipole varies between 0.2-0.4 PVU. The amplitude of the negative pole is similar in the convection-parameterizing and convection-permitting simulations. The positive pole, which is generated by long-wave radiative cooling, is weak in the convection-permitting simulations due to the small domain size which limits the accumulation of diabatic tendencies within the interior of the domain. The possible correspondence between the diabatic PV dipole and the extratropical tropopause inversion layer is discussed.

Comments on 'Current GCMs' Unrealistic Negative Feedback in the Arctic'

In contrast to prior studies showing a positive lapse-rate feedback associated with the Arctic inversion, Boé et al. reported that strong present-day Arctic temperature inversions are associated with stronger negative longwave feedbacks and thus reduced Arctic amplification in the model ensemble from phase 3 of the Coupled Model Intercomparison Project (CMIP3). A permutation test reveals that the relation between longwave feedbacks and inversion strength is an artifact of statistical self-correlation and that shortwave feedbacks have a stronger correlation with intermodel spread. The present comment concludes that the conventional understanding of a positive lapse-rate feedback associated with the Arctic inversion is consistent with the CMIP3 model ensemble.

Nonlinear equalization of Hammerstein OFDM systems

A practical orthogonal frequency-division multiplexing (OFDM) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. In this contribution, we advocate a novel nonlinear equalization scheme for OFDM Hammerstein systems. We model the nonlinear HPA, which represents the static nonlinearity of the OFDM Hammerstein channel, by a B-spline neural network, and we develop a highly effective alternating least squares algorithm for estimating the parameters of the OFDM Hammerstein channel, including channel impulse response coefficients and the parameters of the B-spline model. Moreover, we also use another B-spline neural network to model the inversion of the HPA’s nonlinearity, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalization of the OFDM Hammerstein channel can then be accomplished by the usual one-tap linear equalization as well as the inverse B-spline neural network model obtained. The effectiveness of our nonlinear equalization scheme for OFDM Hammerstein channels is demonstrated by simulation results.

B-spline neural network based single-carrier frequency domain equalisation for Hammerstein channels

A practical single-carrier (SC) block transmission with frequency domain equalisation (FDE) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such Hammerstein channels, the standard SC-FDE scheme no longer works. We propose a novel Bspline neural network based nonlinear SC-FDE scheme for Hammerstein channels. In particular, we model the nonlinear HPA, which represents the complex-valued static nonlinearity of the Hammerstein channel, by two real-valued B-spline neural networks, one for modelling the nonlinear amplitude response of the HPA and the other for the nonlinear phase response of the HPA. We then develop an efficient alternating least squares algorithm for estimating the parameters of the Hammerstein channel, including the channel impulse response coefficients and the parameters of the two B-spline models. Moreover, we also use another real-valued B-spline neural network to model the inversion of the HPA’s nonlinear amplitude response, and the parameters of this inverting B-spline model can be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse Bspline neural network model obtained in time domain. The effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels is demonstrated in a simulation study.

Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier

High bandwidth-efficiency quadrature amplitude modulation (QAM) signaling widely adopted in high-rate communication systems suffers from a drawback of high peak-toaverage power ratio, which may cause the nonlinear saturation of the high power amplifier (HPA) at transmitter. Thus, practical high-throughput QAM communication systems exhibit nonlinear and dispersive channel characteristics that must be modeled as a Hammerstein channel. Standard linear equalization becomes inadequate for such Hammerstein communication systems. In this paper, we advocate an adaptive B-Spline neural network based nonlinear equalizer. Specifically, during the training phase, an efficient alternating least squares (LS) scheme is employed to estimate the parameters of the Hammerstein channel, including both the channel impulse response (CIR) coefficients and the parameters of the B-spline neural network that models the HPA’s nonlinearity. In addition, another B-spline neural network is used to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard LS algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Nonlinear equalisation of the Hammerstein channel is then accomplished by the linear equalization based on the estimated CIR as well as the inverse B-spline neural network model. Furthermore, during the data communication phase, the decision-directed LS channel estimation is adopted to track the time-varying CIR. Extensive simulation results demonstrate the effectiveness of our proposed B-Spline neural network based nonlinear equalization scheme.