Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.     

Phasing of segmented mirrors: a new algorithm for piston detection

The point spread function of a segmented-mirror telescope is severely affected by segment misalignment, which can nullify the performance of adaptive optics systems. The piston and tilt of each segment must be precisely adjusted in relation to the other segments. Furthermore, the direct detection of the alignment error with natural stars would be desirable in order to monitor the errors during astronomical observation.
We have studied the lost information of the piston error caused by the presence of atmospheric turbulence in the measurements of curvature, and present a new algorithm for obtaining the local piston using the curvature sensor. A phase-wrapping effect is shown as responsible for the loss of curvature information and so the piston errors can no longer adequately be mapped; this happens not only in the presence of atmospheric turbulence, but also in its absence.
Good results are obtained using a new iterative method for obtaining the local piston error map. In the presence of atmospheric perturbation, the turbulent phase information obtained from a Shack–Hartmann sensor is introduced in our new iterative method. We propose a hybrid sensor composed of a curvature sensor and a Shack–Hartmann sensor, in order to complete all the information for the phasing. This design takes a short computation time and could be used in real time inside an adaptive optics system, where tilt and piston errors must be corrected.     

Optimal Compensation and Implementation for Adaptive Optics Systems

This paper develops a compensation algorithm based on Linear–Quadratic–Gaussian (LQG) control system design whose parameters are determined (in part) by a model of the atmosphere. The model for the atmosphere is based on the open-loop statistics of the atmosphere as observed by the wavefront sensor, and is identified from these using an auto-regressive, moving average (ARMA) model. The (LQG) control design is compared with an existing compensation algorithm for a simulation developed at ESO that represents the operation of MACAO adaptive optics system on the 8.2 m telescopes at Paranal, Chile. This revised version was published online in July 2006 with corrections to the Cover Date.     

Belemnites from the lower middle Cenomanian of Hoppenstedt, northern Germany: significance and integrated correlation

The belemnite species Praeactinocamax primus (Arkhangelsky, 1912) and Belemnocamax boweri Crick, 1910 are described from the Cenomanian of the abandoned limestone quarry section of Hoppenstedt (Sachsen-Anhalt, northern Germany). They co-occur in the upper part of a prominent tripartite bioclastic limestone bed associated with the ammonite Acanthoceras rhotomagense, indicating the primus Event of the lower middle Cenomanian A. rhotomagense ammonite Zone. An integrated stratigraphical calibration including carbon stable isotope correlation to southern England suggests that the belemnite event horizon at Hoppenstedt occupies exactly the same chronostratigraphical position as elsewhere, highlighting the strictly isochronous character of the primus Event across northwestern Europe. Furthermore, stratigraphical gaps in the Hoppenstedt succession are evaluated.     

An operational multiscale system for hazards prediction, mapping, and response

By definition, a crisis is a situation that requires assistance to be managed. Hence, response to a crisis involves the merging of local and non-local emergency response personnel. In this situation, it is critical that each participant: (1) know the roles and responsibilities of each of the other participants; (2) know the capabilities of each of the participants; and (3) have a common basis for action. For many types of natural disasters, this entails having a common operational picture of the unfolding events, including detailed information on the weather, both current and forecasted, that may impact on either the emergency itself or on response activities. The Consequences Assessment Tool Set (CATS) is a comprehensive package of hazard prediction models and casualty and damage assessment tools that provides a linkage between a modeled or observed effect and the attendant consequences for populations, infrastructure, and resources, and, hence, provides the common operational picture for emergency response. The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is an atmospheric simulation system that links the latest methods in computational fluid dynamics and high-resolution gridding technologies with numerical weather prediction to provide specific weather analysis and forecast capability that can be merged into the geographic information system framework of CATS. This paper documents the problem of emergency response as an end-to-end system and presents the integrated CATS–OMEGA system as a prototype of such a system that has been used successfully in a number of different situations.     

Calibration of JASON-1 Altimeter over Lake Erie

This article describes absolute calibration results for both JASON-1 and TOPEX Side B (TSB) altimeters obtained at the Lake Erie calibration site, Marblehead, Ohio, USA. Using 15 overflights, the estimated JASON altimeter bias at Marblehead is 58 ± 38 mm, with an uncertainty of 19 mm based on detailed error analysis. Assuming that the TSB bias is negligible, relative bias estimates using both data from the TSB-JASON formation flight period and data from 48 water level gauges around the entire Great Lakes confirmed the Marblehead results. Global analyses using both the formation flight data and dual-satellite (TSB and JASON) crossovers yield a similar relative bias estimate of 146 ± 59 mm, which agrees well with open ocean absolute calibration results obtained at Harvest, Corsica, and Bass Strait (e.g., Watson et al. 2003). We find that there is a strong dependence of bias estimates on the choice of sea state bias (SSB) models. Results indicate that the invariant JASON instrument bias estimated oceanwide is 71 mm, with additional biases of 76 mm or 28 mm contributed by the choice of Collecte Localisation Satellites (CLS) SSB or Center for Space Research (CSR) SSB model, respectively. Similar analysis in the Great Lakes yields the invariant JASON instrument bias at 19 mm, with the SSB contributed biases at 58 mm or 13 mm, respectively. The reason for the discrepancy is currently unknown and warrants further investigation. Finally, comparison of the TOPEX/POSEIDON mission (1992-2002) data with the Great Lakes water level gauge measurements yields a negligible TOPEX altimeter drift of 0.1 mm/yr.     

Robust adaptive control of underactuated ships on a linear course with comfort

This paper addresses an important problem in ship control application—the robust stabilization of underactuated ships on a linear course with comfort. Specifically, we develop a multivariable controller to stabilize ocean surface ships without a sway actuator on a linear course and to reduce roll and pitch simultaneously. The controller adapts to unknown parameters of the ship and constant environmental disturbances induced by wave, ocean current and wind. It is also robust to time-varying environmental disturbances, time-varying change in ship parameters and other motions of the ship such as surge and heave. The roll and pitch can be made arbitrarily small while the heading angle and sway are kept to be in reasonably small bounds. The controller development is based on Lyapunov’s direct method and backstepping technique. A Lipschitz continuous projection algorithm is used to update the estimate of the unknown parameters to avoid the parameters’ drift due to time-varying environmental disturbances. Simulations on a full-scale catamaran illustrate the effectiveness of our proposed controller.