On the use of time series concepts and spectral and cross-spectral analyses in the study of long-range forecasting problems

Monthly values of sea surface temperature anomalies and wind anomalies for an area of the North Atlantic Ocean are studied as a vector Gaussian process. The covariances and the spectra and cross-spectra provide information on the nature of the interaction of ocean and atmosphere. The long period fluctuations are found to be fairly coherent. Indications are that the sea surface temperature can be predicted for one month into the future on the basis of past values with a 50 % reduction of variance. Prediction problems are discussed

Probabilities and statistics for backscatter estimates obtained by a scatterometer with applications to new scatterometer design data

The values of the Normalized Radar Backscattering Cross Section (NRCS), sigma (o), obtained by a scatterometer are random variables whose variance is a known function of the expected value. The probability density function can be obtained from the normal distribution. Models for the expected value obtain it as a function of the properties of the waves on the ocean and the winds that generated the waves. Point estimates of the expected value were found from various statistics given the parameters that define the probability density function for each value. Random intervals were derived with a preassigned probability of containing that value. A statistical test to determine whether or not successive values of sigma (o) are truly independent was derived. The maximum likelihood estimates for wind speed and direction were found, given a model for backscatter as a function of the properties of the waves on the ocean. These estimates are biased as a result of the terms in the equation that involve natural logarithms, and calculations of the point estimates of the maximum likelihood values are used to show that the contributions of the logarithmic terms are negligible and that the terms can be omitted

NSCAT high-resolution surface wind measurements in Typhoon Violet

NASA scatterometer (NSCAT) measurements of the western Pacific Supertyphoon Violet are presented for revolutions 478 and 485 that occurred in September 1996. A tropical cyclone planetary boundary layer numerical, model, which uses conventional meteorological and geostationary cloud data, is used to estimate the winds at 10-m elevation in the cyclone. These model winds are then compared with the winds inferred from the NSCAT backscatter data by means of a novel approach that allows a wind speed to be recovered from each individual backscatter cell. This spatial adaptive (wind vector) retrieval algorithm employs several unique steps. The backscatter values are first regrouped in terms of closest neighbors in sets of four. The maximum likelihood estimates of speed and direction are then used to obtain speeds and directions for each group. Since the cyclonic flow around the tropical cyclone is known, NSCAT wind direction alias selection is easily accomplished. The selected wind directions are then used to convert each individual backscatter value to a wind speed. The results are compared to the winds obtained from the tropical cyclone boundary layer model. The NSCAT project baseline geophysical model function, NSCAT 1, was found to yield wind speeds that were systematically too low, even after editing for suspected rain areas of the cyclone. A new geophysical model function was developed using conventional NSCAT data and airborne Ku band scatterometer measurements in an Atlantic hurricane. This new model uses the neural network method and yields substantially better agreement with the winds obtained from the boundary layer model according to the statistical tests that were used

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

A unified mathematical theory for the analysis, propagation, and refraction of storm generated ocean surface waves /

pt.2 (1952