Evaluation of Long-Term SSM/I-Based Precipitation Records over Land

The record of global precipitation mapping using Special Sensor Microwave Imager (SSM/I) measurements now extends over two decades. Similar measurements, albeit with different retrieval algorithms, are to be used in the Global Precipitation Measurement (GPM) mission as part of a constellation to map global precipitation with a more frequent data refresh rate. Remotely sensed precipitation retrievals are prone to both magnitude (precipitation intensity) and phase (position) errors. In this study, the ground-based radar precipitation product from the Next Generation Weather Radar stage-IV (NEXRAD-IV) product is used to evaluate a new metric of error in the long-term SSM/I-based precipitation records. The new metric quantifies the proximity of two multidimensional datasets. Evaluation of the metric across the years shows marked seasonality and precipitation intensity dependence. Drifts and changes in the instrument suite are also evident. Additionally, the precipitation retrieval errors conditional on an estimate of background surface soil moisture are estimated. The dynamic soil moisture can produce temporal variability in surface emissivity, which is a source of error in retrievals. Proper filtering has been applied in the analysis to differentiate between the detection error and the retrieval error. The identification of the different types of errors and their dependence on season, intensity, instrument, and surface conditions provide guidance to the development of improved retrieval algorithms for use in GPM constellation-based precipitation data products

Support for the predictive validity of the multifactor offender readiness model (MORM) : forensic patients' readiness and engagement with therapeutic groups

BACKGROUND: Treatment non-engagement in forensic health settings has ethical and economic implications. The multifactor offender readiness model (MORM) provides a framework for assessing treatment readiness across person, programme and contexts. AIMS: To answer the following question: Are the internal factors of the MORM associated with likelihood of engagement in groups by patients in forensic mental health services? METHOD: In a retrospective design, associations were investigated between internal factors of the MORM, measured as part of assessment for group participation, and the outcomes of treatment refusal, treatment dropout and treatment completion. RESULTS: One hundred and eighteen male patients in a high security hospital consecutively referred for group treatment agreed to participate. Internal factors of the MORM associated with treatment refusals included: psychopathic cognition, negative self-evaluation/affect and effective goal-seeking strategies. Those associated with dropouts included emotional dysregulation, low competencies to engage and low levels of general distress. MORM factors associated with completion included: low motivation, ineffective goal-seeking strategies, absence of psychopathic cognition, high levels of general distress and competency to engage. CONCLUSIONS: Internal factors of the MORM could be useful contributors to decisions about treatment readiness for hospitalised male offender-patients. Up to one in three programmes offered were refused, so clinical use of the MORM to aid referral decisions could optimise the most constructive use of resources for every individual

Characterization of precipitation product errors across the United States using multiplicative triple collocation

Validation of precipitation estimates from various products is a challenging problem, since the true precipitation is unknown. However, with the increased availability of precipitation estimates from a wide range of instruments (satellite, ground-based radar, and gauge), it is now possible to apply the triple collocation (TC) technique to characterize the uncertainties in each of the products. Classical TC takes advantage of three collocated data products of the same variable and estimates the mean squared error of each, without requiring knowledge of the truth. In this study, triplets among NEXRAD-IV, TRMM 3B42RT, GPCP 1DD, and GPI products are used to quantify the associated spatial error characteristics across a central part of the continental US. Data are aggregated to biweekly accumulations from January 2002 through April 2014 across a 2° × 2° spatial grid. This is the first study of its kind to explore precipitation estimation errors using TC across the US. A multiplicative (logarithmic) error model is incorporated in the original TC formulation to relate the precipitation estimates to the unknown truth. For precipitation application, this is more realistic than the additive error model used in the original TC derivations, which is generally appropriate for existing applications such as in the case of wind vector components and soil moisture comparisons. This study provides error estimates of the precipitation products that can be incorporated into hydrological and meteorological models, especially those used in data assimilation. Physical interpretations of the error fields (related to topography, climate, etc.) are explored. The methodology presented in this study could be used to quantify the uncertainties associated with precipitation estimates from each of the constellations of GPM satellites. Such quantification is prerequisite to optimally merging these estimates

Regionally Strong Feedbacks Between the Atmosphere and Terrestrial Biosphere

The terrestrial biosphere and atmosphere interact through a series of feedback loops. Variability in terrestrial vegetation growth and phenology can modulate fluxes of water and energy to the atmosphere, and thus affect the climatic conditions that in turn regulate vegetation dynamics. Here we analyze satellite observations of solar-induced fluorescence, precipitation, and radiation using a multivariate statistical technique. We find that biosphere-atmosphere feedbacks are globally widespread and regionally strong: they explain up to 30 of precipitation and surface radiation variance. Substantial biosphere-precipitation feedbacks are often found in regions that are transitional between energy and water limitation, such as semi-arid or monsoonal regions. Substantial biosphere-radiation feedbacks are often present in several moderately wet regions and in the Mediterranean, where precipitation and radiation increase vegetation growth. Enhancement of latent and sensible heat transfer from vegetation accompanies this growth, which increases boundary layer height and convection, affecting cloudiness, and consequently incident surface radiation. Enhanced evapotranspiration can increase moist convection, leading to increased precipitation. Earth system models underestimate these precipitation and radiation feedbacks mainly because they underestimate the biosphere response to radiation and water availability. We conclude that biosphere-atmosphere feedbacks cluster in specific climatic regions that help determine the net CO2 balance of the biosphere

An Adaptive Tangent Feature Perspective of Neural Networks

In order to better understand feature learning in neural networks, we propose a framework for understanding linear models in tangent feature space where the features are allowed to be transformed during training. We consider linear transformations of features, resulting in a joint optimization over parameters and transformations with a bilinear interpolation constraint. We show that this optimization problem has an equivalent linearly constrained optimization with structured regularization that encourages approximately low rank solutions. Specializing to neural network structure, we gain insights into how the features and thus the kernel function change, providing additional nuance to the phenomenon of kernel alignment when the target function is poorly represented using tangent features. We verify our theoretical observations in the kernel alignment of real neural networks.Comment: 14 pages, 3 figures. Appeared at the First Conference on Parsimony and Learning (CPAL 2024

Nonlinear self-action of light through biological suspensions

It is commonly thought that biological media cannot exhibit an appreciable nonlinear optical response. We demonstrate, for the first time to our knowledge, a tunable optical nonlinearity in suspensions of cyanobacteria that leads to robust propagation and strong self-action of a light beam. By deliberately altering the host environment of the marine bacteria, we show experimentally that nonlinear interaction can result in either deep penetration or enhanced scattering of light through the bacterial suspension, while the viability of the cells remains intact. A theoretical model is developed to show that a nonlocal nonlinearity mediated by optical forces (including both gradient and forward-scattering forces) acting on the bacteria explains our experimental observation