John K. Williams

Hurricanes and Global Warming: Results from Downscaling IPCC AR4 Simulations

Changes in tropical cyclone activity are among the more potentially consequential results of global climate change, and it is therefore of considerable interest to understand how anthropogenic climate change may affect such storms. Global climate models are currently used to estimate future climate change, but the current generation of models lacks the horizontal resolution necessary to resolve the intense inner core of tropical cyclones. Here we review a new technique for inferring tropical cyclone climatology from the output of global models, extend it to predict genesis climatologies (rather than relying on historical climatology), and apply it to current and future climate states simulated by a suite of global models developed in support of the most recent Intergovernmental Panel on Climate Change report. This new technique attacks the horizontal resolution problem by using a specialized, coupled ocean-atmosphere hurricane model phrased in angular momentum coordinates, which provide a high resolution ...

Neural correlates of the emergence of consciousness of thirst

Thirst was induced by rapid i.v. infusion of hypertonic saline (0.51 M at 13.4 ml/min). Ten humans were neuroimaged by positron-emission tomography (PET) and four by functional MRI (fMRI). PET images were made 25 min after beginning infusion, when the sensation of thirst began to enter the stream of consciousness. The fMRI images were made when the maximum rate of increase of thirst occurred. The PET results showed regional cerebral blood flow changes similar to those delineated when thirst was maximal. These loci involved the phylogenetically ancient areas of the brain. fMRI showed activation in the anterior wall of the third ventricle, an area that is key in the genesis of thirst but is not an area revealed by PET imaging. Thus, this region plays as major a role in thirst for humans as for animals. Strong activations in the brain with fMRI included the anterior cingulate, parahippocampal gyrus, inferior and middle frontal gyri, insula, and cerebellum. When the subjects drank water to satiation, thirst declined immediately to baseline. A precipitate decline in intensity of activation signal occurred in the anterior cingulate area (Brodmann area 32) putatively related to consciousness of thirst. The intensity of activation in the anterior wall of the third ventricle was essentially unchanged, which is consistent with the fact that a significant time (15–20 min) would be needed before plasma Na concentration changed as a result of water absorption from the gut.

Recent Advances in the Understanding of Near-Cloud Turbulence

Anyone who has flown in a commercial aircraft is familiar with turbulence. Unexpected encounters with turbulence pose a safety risk to airline passengers and crew, can occasionally damage aircraft, and indirectly increase the cost of air travel. Deep convective clouds are one of the most important sources of turbulence. Cloud-induced turbulence can occur both within clouds and in the surrounding clear air. Turbulence associated with but outside of clouds is of particular concern because it is more difficult to discern using standard hazard identification technologies (e.g., satellite and radar) and thus is often the source of unexpected turbulence encounters. Although operational guidelines for avoiding near-cloud turbulence exist, they are in many ways inadequate because they were developed before the governing dynamical processes were understood. Recently, there have been significant advances in the understanding of the dynamics of near-cloud turbulence. Using examples, this article demonstrates how the...

Metabolic and neuroendocrine responses to RXFP3 modulation in the central nervous system.

Neuroanatomical studies have shown relaxin‐3 neurons, primarily found in the rodent nucleus incertus (NI), project widely into a large number of areas expressing the relaxin‐3 receptor (RXFP3), and these data suggest relaxin‐3/RXFP3 signaling modulates sensory, emotional, and neuroendocrine processing. The similar distribution of this receptor–ligand pair in the rat, mouse, and monkey brain suggests that experimental findings obtained in lower species will translate to higher species. A role for relaxin‐3 and RXFP3 in modulating stress responses is strongly suggested by the expression of corticotropin‐releasing factor R1 (CRF‐R1) by NI cells, increased relaxin‐3 expression in the NI after stress or CRF injection, and hormonal responses to intracerebroventricular (i.c.v.) relaxin‐3 injection. Recent data are consistent with a further role for this ligand–receptor pair in modulating memory. In addition, relaxin‐3 has been reported to modulate feeding and body weight control. Acute or chronic central (i.c.v. or intraparaventricular) injections of relaxin‐3 have shown a consistent stimulatory effect on food consumption while relaxin was inactive, suggesting the phagic effect of relaxin‐3 is mediated by RXFP3. We have confirmed the role of RXFP3 in modulating feeding and body weight by using a selective RXFP3 agonist (R3/I5) and antagonist [R3(Δ23–27)R/I5], collecting feeding, body weight, hormone, and body composition data. In addition, we have preliminary body weight and magnetic resonance imaging data from relaxin‐3 knockout mice, which on a 129S5:B6 background are smaller and leaner than congenic controls. These data suggest relaxin‐3, acting through RXFP3, is involved in coordinating stress, learning and memory, and feeding responses as predicted on the basis of neuroanatomy.

Probabilistic 0–1-h Convective Initiation Nowcasts that Combine Geostationary Satellite Observations and Numerical Weather Prediction Model Data

AbstractThe Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the...

Using spatiotemporal relational random forests to improve our understanding of severe weather processes

Major severe weather events can cause a significant loss of life and property. We seek to revolutionize our understanding of and our ability to predict such events through the mining of severe weather data. Because weather is inherently a spatiotemporal phenomenon, mining such data requires a model capable of representing and reasoning about complex spatiotemporal dynamics, including temporally and spatially varying attributes and relationships. We introduce an augmented version of the Spatiotemporal Relational Random Forest, which is a random forest that learns with spatiotemporally varying relational data. Our algorithm maintains the strength and performance of random forests but extends their applicability, including the estimation of variable importance, to complex spatiotemporal relational domains. We apply the augmented Spatiotemporal Relational Random Forest to three severe weather data sets. These are: predicting atmospheric turbulence across the continental United States, examining the formation of tornadoes near strong frontal boundaries, and understanding the spatial evolution of drought across the southern plains of the United States. The results on such a wide variety of real-world domains demonstrate the extensive applicability of the Spatiotemporal Relational Random Forest. Our long-term goal is to significantly improve the ability to predict and warn about severe weather events. We expect that the tools and techniques we develop will be applicable to a wide range of complex spatiotemporal phenomena.

Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning

Severe weather, including tornadoes, thunderstorms, wind, and hail annually cause significant loss of life and property. We are developing spatiotemporal machine learning techniques that will enable meteorologists to improve the prediction of these events by improving their understanding of the fundamental causes of the phenomena and by building skillful empirical predictive models. In this paper, we present significant enhancements of our Spatiotemporal Relational Probability Trees that enable autonomous discovery of spatiotemporal relationships as well as learning with arbitrary shapes. We focus our evaluation on two real-world case studies using our technique: predicting tornadoes in Oklahoma and predicting aircraft turbulence in the United States. We also discuss how to evaluate success for a machine learning algorithm in the severe weather domain, which will enable new methods such as ours to transfer from research to operations, provide a set of lessons learned for embedded machine learning applications, and discuss how to field our technique.

Sources of Error in Dual-Wavelength Radar Remote Sensing of Cloud Liquid Water Content

Abstract Dual-wavelength ratio (DWR) techniques offer the prospect of producing high-resolution mapping of cloud microphysical properties, including retrievals of cloud liquid water content (LWC) from reflectivity measured by millimeter-wavelength radars. Unfortunately, noise and artifacts in the DWR require smoothing to obtain physically realistic values of LWC with a concomitant loss of resolution. Factors that cause inaccuracy in the retrieved LWC include uncertainty in gas and liquid water attenuation coefficients, Mie scattering due to large water droplets or ice particles, corruption of the radar reflectivities by noise and nonatmospheric returns, and artifacts due to mismatched radar illumination volumes. The error analysis presented here consists of both analytic and heuristic arguments; it is illustrated using data from the Mount Washington Icing Sensors Project (MWISP) and from an idealized simulation. In addition to offering insight into design considerations for a DWR system, some results sugg...

Spatiotemporal Relational Random Forests

We introduce and validate Spatiotemporal Relational Random Forests, which are random forests created with spatiotemporal relational probability trees. We build on the documented success of random forests by bringing spatiotemporal capabilities to the trees, enabling them to identify critical spatial, temporal, and spatiotemporal features in the data. We validate our results on simulated data and real-world convectively-induced turbulence data from a commercial airline flying in the continental United States.

Combining observations and model data for short-term storm forecasting

This paper describes the use of a machine learning data fusion methodology to support development of an automated short-term thunderstorm forecasting system for aviation users. Information on current environmental conditions is combined with observations of current storms and derived indications of the onset of rapid change. Predictor data include satellite radiances and rates of change, satellite-derived cloud type, ground weather station measurements, land surface and climatology data, numerical weather prediction model fields, and radar-derived storm intensity and morphology. The machine learning methodology creates an ensemble of decision trees that can serve as a forecast logic to provide both deterministic and probabilistic estimates of thunderstorm intensity. It also provides evaluation of predictor importance, facilitating selection of a minimal skillful set of predictor variables and providing a tool to help determine what weather regimes may require specialized forecast logic. This work is sponsored by the Federal Aviation Administrations Aviation Weather Research Program. Its aim is to contribute to the development of the Consolidated Storm Prediction for Aviation (CoSPA) system, which is being developed in collaboration with the MIT Lincoln Laboratory and the NOAA Earth System Research Laboratorys Global Systems Division. CoSPA is scheduled to become part of the NextGen Initial Operating Capability by 2012.