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The self-consistent estimator is commonly used for estimating a survival function with interval-censored data. Recent studies on interval censoring have focused on case 2 interval censoring, which does not involve exact observations, and double censoring, which involves only exact, right-censored or left-censored observations. In this paper, we consider an interval censoring scheme that involves exact, left-censored, right-censored and strictly interval-censored observations. Under this censoring scheme, we prove that the self-consistent estimator is strongly consistent under certain regularity conditions.

This chapter contains sections titled: Introduction Formal derivation of the posterior distribution References

Ground‐based observations show that persistent liquid‐containing Arctic clouds occur frequently and have a dominant influence on Arctic surface radiative fluxes. Yet, without a hemispheric multi‐year perspective, the climate relevance of these intriguing Arctic cloud observations was previously unknown. In this study, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations are used to document cloud phase over the Arctic basin (60–82°N) during a five‐year period (2006–2011). Over Arctic ocean‐covered areas, low‐level liquid‐containing clouds are prevalent in all seasons, especially in Fall. These new CALIPSO observations provide a unique and climate‐relevant constraint on Arctic cloud processes. Evaluation of one climate model using a lidar simulator suggests a lack of liquid‐containing Arctic clouds contributes to a lack of “radiatively opaque” states. The surface radiation biases found in this one model are found in multiple models, highlighting the need for improved modeling of Arctic cloud phase. Key Points New CALIPSO‐GOCCP observations show ubiquitous liquid‐containing Arctic clouds Insufficient liquid‐containing Arctic cloud leads to radiation biases in models Reproducing observed cloud phase is an important target for model improvement

ABSTRACT With the unprecedented photometric precision of the Kepler spacecraft, significant systematic and stochastic errors on transit signal levels are observable in the Kepler photometric data. These errors, which include discontinuities, outliers, systematic trends, and other instrumental signatures, obscure astrophysical signals. The presearch data conditioning (PDC) module of the Kepler data analysis pipeline tries to remove these errors while preserving planet transits and other astrophysically interesting signals. The completely new noise and stellar variability regime observed in Kepler data poses a significant problem to standard cotrending methods. Variable stars are often of particular astrophysical interest, so the preservation of their signals is of significant importance to the astrophysical community. We present a Bayesian maximum a posteriori (MAP) approach, where a subset of highly correlated and quiet stars is used to generate a cotrending basis vector set, which is in turn used to establish a range of \"reasonable\" robust fit parameters. These robust fit parameters are then used to generate a Bayesian prior and a Bayesian posterior probability distribution function (PDF) which, when maximized, finds the best fit that simultaneously removes systematic effects while reducing the signal distortion and noise injection that commonly afflicts simple least-squares (LS) fitting. A numerical and empirical approach is taken where the Bayesian prior PDFs are generated from fits to the light-curve distributions themselves.

Modelling shows that the misaligned orbits of ‘hot Jupiters’ can follow naturally from disk migration in binary systems whose orbital plane is uncorrelated with the spin axes of the individual stars. Orbital disruption of 'hot Jupiter' exoplanets Recent planetary transit observations have shown that many 'hot Jupiter' exoplanets in close-in orbits are grossly misaligned with respect to the rotation axes of their host stars. This fact has cast doubt on a previously widely accepted model of disk-driven migration as the primary mechanism responsible for moving planetrs from an outer to an inner orbit. In this paper Konstantin Batygin demonstrates that disk-driven migration is not only capable of producing misaligned planets, it will produce them preferentially. The argument rests on the observation that most stars are born as binaries, and that the gravitational perturbations of the stellar companions act to twist the orbital planes of proto-planetary disks out of alignment with the rotation axes of their host stars. The existence of gaseous giant planets whose orbits lie close to their host stars (‘hot Jupiters’) can largely be accounted for by planetary migration associated with viscous evolution of proto-planetary nebulae 1 . Recently, observations of the Rossiter–McLaughlin effect 2 during planetary transits have revealed that a considerable fraction of hot Jupiters are on orbits that are misaligned with respect to the spin axes of their host stars 3 . This observation has cast doubt on the importance of disk-driven migration as a mechanism for producing hot Jupiters. Here I show that misaligned orbits can be a natural consequence of disk migration in binary systems whose orbital plane is uncorrelated with the spin axes of the individual stars 4 , 5 , 6 . The gravitational torques arising from the dynamical evolution of idealized proto-planetary disks under perturbations from massive distant bodies act to misalign the orbital planes of the disks relative to the spin poles of their host stars. As a result, I suggest that in the absence of strong coupling between the angular momentum of the disk and that of the host star, or of sufficient dissipation that acts to realign the stellar spin axis and the planetary orbits, the fraction of planetary systems (including systems of ‘hot Neptunes’ and ‘super-Earths’) whose angular momentum vectors are misaligned with respect to their host stars will be commensurate with the rate of primordial stellar multiplicity.

Because of the scarcity of meteorological observations, the precipitation climate on the Tibetan Plateau and surrounding regions (TP) has been insufficiently documented so far. In this study, the characteristics and basic features of precipitation on the TP during an 11-yr period (2001–11) are described on monthly-to-annual time scales. For this purpose, a new high-resolution atmospheric dataset is analyzed, the High Asia Reanalysis (HAR), generated by dynamical downscaling of global analysis data using the Weather Research and Forecasting (WRF) model. The HAR precipitation data at 30- and 10-km resolutions are compared with both rain gauge observations and satellite-based precipitation estimates from the Tropical Rainfall Measurement Mission (TRMM). It is found that the HAR reproduces previously reported spatial patterns and seasonality of precipitation and that the high-resolution data add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation. It is demonstrated that this process-based approach, despite some unavoidable shortcomings, can improve the understanding of the processes that lead to precipitation on the TP. Analysis focuses on precipitation amounts, type, seasonality, and interannual variability. Special attention is given to the links between the observed patterns and regional atmospheric circulation. As an example of an application of the HAR, a new classification of glaciers on the TP according to their accumulation regimes is proposed, which illustrates the strong spatial variability of precipitation seasonality. Finally, directions for future research are identified based on the HAR, which has the potential to be a useful dataset for climate, glaciological, and hydrological impact studies.

A revision to the Holland parametric approach to modeling the radial profile of winds in hurricanes is presented. The approach adopted uses information readily available from hurricane archives or in hurricane warning information and the profile can be readily incorporated into existing parametric models of the hurricane surface wind field. The original model utilized central and environmental surface pressures, maximum winds, and radius of maximum winds. In the revision a capacity to incorporate additional wind observations at some radius within the hurricane circulation was included. If surface observations are used, then a surface wind profile will result, obviating the need for deriving a boundary layer reduction from the gradient wind level. The model has considerably less sensitivity to data errors compared to the original and is shown to reproduce hurricane reconnaissance and surface wind profiles with high accuracy.

A new sea surface temperature (SST) analysis on a centennial time scale is presented. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice–SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period.

The coherent manipulation of an individual electron spin qubit bound to a single phosphorus donor atom in natural silicon provides an excellent platform on which to build a scalable quantum computer. Silicon-compatible spin qubits Because silicon devices already form the basis for integrated electronic circuits, it would be ideal if future quantum-computing architectures could also be silicon-based, thus making use of the same well-developed fabrication technologies. A promising approach to constructing silicon qubits (quantum bits), first proposed more than a decade ago, is to make use of single dopant atoms in silicon. Jarryd Pla et al . have now made a nanoelectronic silicon device in which they can read out and control the electron spin of a single phosphorous donor atom, and demonstrate exceptionally long spin coherence times (of 200 microseconds). The combination of good qubit performance and practical fabrication approach opens the door to building scalable quantum-computing circuits. A single atom is the prototypical quantum system, and a natural candidate for a quantum bit, or qubit—the elementary unit of a quantum computer. Atoms have been successfully used to store and process quantum information in electromagnetic traps 1 , as well as in diamond through the use of the nitrogen–vacancy-centre point defect 2 . Solid-state electrical devices possess great potential to scale up such demonstrations from few-qubit control to larger-scale quantum processors. Coherent control of spin qubits has been achieved in lithographically defined double quantum dots in both GaAs (refs 3–5 ) and Si (ref. 6 ). However, it is a formidable challenge to combine the electrical measurement capabilities of engineered nanostructures with the benefits inherent in atomic spin qubits. Here we demonstrate the coherent manipulation of an individual electron spin qubit bound to a phosphorus donor atom in natural silicon, measured electrically via single-shot read-out 7 , 8 , 9 . We use electron spin resonance to drive Rabi oscillations, and a Hahn echo pulse sequence reveals a spin coherence time exceeding 200 µs. This time should be even longer in isotopically enriched 28 Si samples 10 , 11 . Combined with a device architecture 12 that is compatible with modern integrated circuit technology, the electron spin of a single phosphorus atom in silicon should be an excellent platform on which to build a scalable quantum computer.

Quantifying air–sea exchanges of enthalpy and momentum is important for understanding and skillfully predicting tropical cyclone intensity, but the magnitude of the corresponding wind speed–dependent bulk exchange coefficients is largely unknown at major hurricane wind speeds greater than 50 m s−1. Since direct turbulent flux measurements in these conditions are extremely difficult, the momentum and enthalpy fluxes were deduced via absolute angular momentum and total energy budgets. An error analysis of the methodology was performed to quantify and mitigate potentially significant uncertainties resulting from unresolved budget terms and observational errors. An analysis of six missions from the 2003 Coupled Boundary Layers Air–Sea Transfer (CBLAST) field program in major hurricanes Fabian and Isabel was conducted using a new variational technique. The analysis indicates a near-surface mean drag coefficient CD of 2.4 × 10−3 with a 46% standard deviation and a mean enthalpy coefficient CK of 1.0 × 10−3 with a 40% standard deviation for wind speeds between 52 and 72 m s−1. These are the first known estimates of CK and the ratio of enthalpy to drag coefficient CK/CD in major hurricanes. The results suggest that there is no significant change in the magnitude of the bulk exchange coefficients estimated at minimal hurricane wind speeds, and that the ratio CK/CD does not significantly increase for wind speeds greater than 50 m s−1.