Retrievals of sea surface temperature from infrared imagery: origin and form of systematic errors

We show that retrievals of sea surface temperature from satellite infrared imagery are prone to two forms of systematic error: prior error (familiar from the theory of atmospheric sounding) and error arising from nonlinearity. These errors have different complex geographical variations, related to the differing geographical distributions of the main geophysical variables that determine clear-sky brightness-temperatures over the oceans. We show that such errors arise as an intrinsic consequence of the form of the retrieval (rather than as a consequence of sub-optimally specified retrieval coefficients, as is often assumed) and that the pattern of observed errors can be simulated in detail using radiative-transfer modelling. The prior error has the linear form familiar from atmospheric sounding. A quadratic equation for nonlinearity error is derived, and it is verified that the nonlinearity error exhibits predominantly quadratic behaviour in this case.

Using cubic splines to mitigate systematic errors in GPS relative positioning

Systematic errors can have a significant effect on GPS observable. In medium and long baselines the major systematic error source are the ionosphere and troposphere refraction and the GPS satellites orbit errors. But, in short baselines, the multipath is more relevant. These errors degrade the accuracy of the positioning accomplished by GPS. So, this is a critical problem for high precision GPS positioning applications. Recently, a method has been suggested to mitigate these errors: the semiparametric model and the penalised least squares technique. It uses a natural cubic spline to model the errors as a function which varies smoothly in time. The systematic errors functions, ambiguities and station coordinates, are estimated simultaneously. As a result, the ambiguities and the station coordinates are estimated with better reliability and accuracy than the conventional least square method.

Mitigating systematic errors for single frequency GPS receivers employing a penalized least squares methodology

Among the positioning systems that compose GNSS (Global Navigation Satellite System), GPS has the capability of providing low, medium and high precision positioning data. However, GPS observables may be subject to many different types of errors. These systematic errors can degrade the accuracy of the positioning provided by GPS. These errors are mainly related to GPS satellite orbits, multipath, and atmospheric effects. In order to mitigate these errors, a semiparametric model and the penalized least squares technique were employed in this study. This is similar to changing the stochastical model, in which error functions are incorporated and the results are similar to those in which the functional model is changed instead. Using this method, it was shown that ambiguities and the estimation of station coordinates were more reliable and accurate than when employing a conventional least squares methodology.

Modifying the stochastic model to mitigate GPS systematic errors in relative positioning

The GPS observables are subject to several errors. Among them, the systematic ones have great impact, because they degrade the accuracy of the accomplished positioning. These errors are those related, mainly, to GPS satellites orbits, multipath and atmospheric effects. Lately, a method has been suggested to mitigate these errors: the semiparametric model and the penalised least squares technique (PLS). In this method, the errors are modeled as functions varying smoothly in time. It is like to change the stochastic model, in which the errors functions are incorporated, the results obtained are similar to those in which the functional model is changed. As a result, the ambiguities and the station coordinates are estimated with better reliability and accuracy than the conventional least square method (CLS). In general, the solution requires a shorter data interval, minimizing costs. The method performance was analyzed in two experiments, using data from single frequency receivers. The first one was accomplished with a short baseline, where the main error was the multipath. In the second experiment, a baseline of 102 km was used. In this case, the predominant errors were due to the ionosphere and troposphere refraction. In the first experiment, using 5 minutes of data collection, the largest coordinates discrepancies in relation to the ground truth reached 1.6 cm and 3.3 cm in h coordinate for PLS and the CLS, respectively, in the second one, also using 5 minutes of data, the discrepancies were 27 cm in h for the PLS and 175 cm in h for the CLS. In these tests, it was also possible to verify a considerable improvement in the ambiguities resolution using the PLS in relation to the CLS, with a reduced data collection time interval. © Springer-Verlag Berlin Heidelberg 2007.

Characterization of electromagnetic fields in the aSPECT spectrometer and reduction of systematic errors

Das aSPECT Spektrometer wurde entworfen, um das Spektrum der Protonen beimrnZerfall freier Neutronen mit hoher Präzision zu messen. Aus diesem Spektrum kann dann der Elektron-Antineutrino Winkelkorrelationskoeffizient "a" mit hoher Genauigkeit bestimmt werden. Das Ziel dieses Experiments ist es, diesen Koeffizienten mit einem absoluten relativen Fehler von weniger als 0.3% zu ermitteln, d.h. deutlich unter dem aktuellen Literaturwert von 5%.rnrnErste Messungen mit dem aSPECT Spektrometer wurden an der Forschungsneutronenquelle Heinz Maier-Leibnitz in München durchgeführt. Jedoch verhinderten zeitabhängige Instabilitäten des Meßhintergrunds eine neue Bestimmung von "a".rnrnDie vorliegende Arbeit basiert hingegen auf den letzten Messungen mit dem aSPECTrnSpektrometer am Institut Laue-Langevin (ILL) in Grenoble, Frankreich. Bei diesen Messungen konnten die Instabilitäten des Meßhintergrunds bereits deutlich reduziert werden. Weiterhin wurden verschiedene Veränderungen vorgenommen, um systematische Fehler zu minimieren und um einen zuverlässigeren Betrieb des Experiments sicherzustellen. Leider konnte aber wegen zu hohen Sättigungseffekten der Empfängerelektronik kein brauchbares Ergebnis gemessen werden. Trotzdem konnten diese und weitere systematische Fehler identifiziert und verringert, bzw. sogar teilweise eliminiert werden, wovon zukünftigernStrahlzeiten an aSPECT profitieren werden.rnrnDer wesentliche Teil der vorliegenden Arbeit befasst sich mit der Analyse und Verbesserung der systematischen Fehler, die durch das elektromagnetische Feld aSPECTs hervorgerufen werden. Hieraus ergaben sich vielerlei Verbesserungen, insbesondere konnten die systematischen Fehler durch das elektrische Feld verringert werden. Die durch das Magnetfeld verursachten Fehler konnten sogar soweit minimiert werden, dass nun eine Verbesserung des aktuellen Literaturwerts von "a" möglich ist. Darüber hinaus wurde in dieser Arbeit ein für den Versuch maßgeschneidertes NMR-Magnetometer entwickelt und soweit verbessert, dass nun Unsicherheiten bei der Charakterisierung des Magnetfeldes soweit reduziert wurden, dass sie für die Bestimmung von "a" mit einer Genauigkeit von mindestens 0.3% vernachlässigbar sind.

Correction of systematic errors in Wide Area Multilateration

This work presents a method to estimate and correct slow time-dependent position errors due to non perfect ground station synchronization and tropospheric propagation. It uses opportunity traffic emissions, i.e. signals transmitted from the aircrafts within the coverage zone. This method is used to overcome the difficulty of installing reference beacons simultaneously visible by all the base stations in a given Wide Area Multilateration (WAM) system.

A systematic approach to identify the sources of tropical SST errors in coupled models using the adjustment of initialised experiments

Understanding the sources of systematic errors in climate models is challenging because of coupled feedbacks and errors compensation. The developing seamless approach proposes that the identification and the correction of short term climate model errors have the potential to improve the modeled climate on longer time scales. In previous studies, initialised atmospheric simulations of a few days have been used to compare fast physics processes (convection, cloud processes) among models. The present study explores how initialised seasonal to decadal hindcasts (re-forecasts) relate transient week-to-month errors of the ocean and atmospheric components to the coupled model long-term pervasive SST errors. A protocol is designed to attribute the SST biases to the source processes. It includes five steps: (1) identify and describe biases in a coupled stabilized simulation, (2) determine the time scale of the advent of the bias and its propagation, (3) find the geographical origin of the bias, (4) evaluate the degree of coupling in the development of the bias, (5) find the field responsible for the bias. This strategy has been implemented with a set of experiments based on the initial adjustment of initialised simulations and exploring various degrees of coupling. In particular, hindcasts give the time scale of biases advent, regionally restored experiments show the geographical origin and ocean-only simulations isolate the field responsible for the bias and evaluate the degree of coupling in the bias development. This strategy is applied to four prominent SST biases of the IPSLCM5A-LR coupled model in the tropical Pacific, that are largely shared by other coupled models, including the Southeast Pacific warm bias and the equatorial cold tongue bias. Using the proposed protocol, we demonstrate that the East Pacific warm bias appears in a few months and is caused by a lack of upwelling due to too weak meridional coastal winds off Peru. The cold equatorial bias, which surprisingly takes 30 years to develop, is the result of an equatorward advection of midlatitude cold SST errors. Despite large development efforts, the current generation of coupled models shows only little improvement. The strategy proposed in this study is a further step to move from the current random ad hoc approach, to a bias-targeted, priority setting, systematic model development approach.

On-board correction of systematic error of Earth sensors

Infrared Earth sensors are used in spacecraft for attitude sensing. Their accuracy is limited by systematic and random errors. Dominant sources of systematic errors are analyzed for a typical scanning infrared Earth sensor used in a remote-sensing satellite in a 900-km sun-synchronous orbit. The errors considered arise from 1) seasonable variation of infrared radiation, 2) oblate shape of the Earth, 3) ambient temperature of sensors, 4) changes in spin/scan period, and 5) misalignment of the axis of the sensors. Simple relations are derived using least-squares curve fitting for onboard correction of these errors. With these, it is possible to improve the accuracy of attitude determination by eight fold and achieve performance comparable to ground-based post-facto attitude computation.

Systematic Uncertainties Associated with the Cosmological Analysis of the First PAN-STARRS1 Type Ia Supernova Sample

We probe the systematic uncertainties from the 113 Type Ia supernovae (SN Ia) in the Pan-STARRS1 (PS1) sample along with 197 SN Ia from a combination of low-redshift surveys. The companion paper by Rest et al. describes the photometric measurements and cosmological inferences from the PS1 sample. The largest systematic uncertainty stems from the photometric calibration of the PS1 and low-z samples. We increase the sample of observed Calspec standards from 7 to 10 used to define the PS1 calibration system. The PS1 and SDSS-II calibration systems are compared and discrepancies up to ∼0.02 mag are recovered. We find uncertainties in the proper way to treat intrinsic colors and reddening produce differences in the recovered value of w up to 3%. We estimate masses of host galaxies of PS1 supernovae and detect an insignificant difference in distance residuals of the full sample of 0.037 ± 0.031 mag for host galaxies with high and low masses. Assuming flatness and including systematic uncertainties in our analysis of only SNe measurements, we find w = -1.120^+0.360_-0.206(Stat)^+0.269_-0.291(Sys). With additional constraints from Baryon acoustic oscillation, cosmic microwave background (CMB) (Planck) and H₀ measurements, we find w = -1.166^+0.072_-0.069 and Ω_m = 0.280^+0.013_-0.012 (statistical and systematic errors added in quadrature). The significance of the inconsistency with w = -1 depends on whether we use Planck or Wilkinson Microwave Anisotropy Probe measurements of the CMB: w_{BAO+H0+SN+WMAP} = -1.124^+0.083_-0.065.