K. D. Whitehall

Bio: K. D. Whitehall is an academic researcher from California Institute of Technology. The author has contributed to research in topics: North Atlantic oscillation & Mesoscale meteorology. The author has an hindex of 7, co-authored 13 publications receiving 131 citations. Previous affiliations of K. D. Whitehall include Jet Propulsion Laboratory & University of Zululand.

SciSpark: Applying in-memory distributed computing to weather event detection and tracking

Abstract: In this paper we present SciSpark, a Big Data framework that extends Apache™ Spark for scaling scientific computations. The paper details the initial architecture and design of SciSpark. We demonstrate how SciSpark achieves parallel ingesting and partitioning of earth science satellite and model datasets. We also illustrate the usability and extensibility of SciSpark by implementing aspects of the Grab 'em Tag 'em Graph 'em (GTG) algorithm using SciSpark and its Map Reduce capabilities. GTG is a topical automated method for identifying and tracking Mesoscale Convective Complexes in satellite infrared datasets.

Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets

Abstract: Mesoscale convective systems are high impact convectively driven weather systems that contribute large amounts to the precipitation daily and monthly totals at various locations globally. As such, an understanding of the lifecycle, characteristics, frequency and seasonality of these convective features is important for several sectors and studies in climate studies, agricultural and hydrological studies, and disaster management. This study explores the applicability of graph theory to creating a fully automated algorithm for identifying mesoscale convective systems and determining their precipitation characteristics from satellite datasets. Our results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.

Exploring the relationship between the North Atlantic oscillation and rainfall patterns in Barbados

Abstract: The North Atlantic oscillation (NAO) is a large-scale seesawing movement of atmospheric mass between the North Atlantic subtropical high-pressure system and the Icelandic low-pressure system. This phenomenon has been acknowledged as the dominant mode of winter climate variability in the temperate latitudes of the North Atlantic region. In this study, an attempt is made to identify the effect of the NAO on the rainfall pattern in the Eastern Caribbean, based on data from a representative station in that subregion. Rainfall data taken from a southeastern coastal station in Barbados are used to explore the relationship between the behavior of the NAO (using standardized monthly indexes) and the rainfall pattern at that station. In the period considered (1950–2004), the NAO is shown to have a very significant effect on the monthly variability of rainfall in Barbados during both El Nino and La Nina periods. Copyright © 2006 Royal Meteorological Society

Warming of an elevated layer over Africa

Abstract: This paper analyzes trends of temperatures over Africa and seeks to quantify the most significant processes. Observations of air temperature reveal significant warming trends in the 925–600 hPa layer over tropical west Africa and the east Atlantic. This is related to the influence of desert dust and biomass burning emissions on the atmospheric energy budget. We calculate a net radiative absorption of ∼− 20 W m − 2. The southern (northern) plume is rich in short-lived greenhouse gases (dust aerosols), and the atmospheric response, according to a simplified radiative transfer model, is a >3°C heating of the 2–4 km layer. The observed pattern of warming coincides with a mixture of dust, black carbon and short-lived greenhouse gases in space, time and height. Physical forcing provides a secondary source of regional warming, with sinking motions over the Sahel. The elevated warm layer stabilizes the lower atmosphere over and west of Africa, so drying trends may be anticipated.

The properties of mesoscale convective systems in Indonesia detected using the grab ‘em tag ‘em graph ‘em (GTG) algorithm

Abstract: A mesoscale convective system (MCS) is organized thunderstorms with connected anvils, which has a significant impact on the global climate. By focusing on MCSs over the Maritime Continent of Indonesia, this study aims to gain a better understanding on the properties of the MCSs over the study area. The “Grab ‘em Tag ‘em Graph ‘em” (GTG) tracking algorithm is applied to hourly Multi-functional Transport Satellite-1R data for two years to observe the distribution of MCSs and the evolution of MCSs along their lifetime. The results of MCS identification by using GTG are combined with CloudSat data products to study the vertical structure of the MCSs at various MCS life stages: developing, mature, and dissipating. The distribution of MCSs over Indonesia has a seasonal variation and distinct diurnal cycle. The life stages of the observed MCSs are characterized by distinct cloud microphysics at each stage. In the developing stage, the upper level of the MCS raining region shows the presence of precipitating ice particles. As the MCS progresses to the mature stage, the proportion of the raining area becomes small and the intensity of rain is reduced, accompanied by increasing occurrence of small-sized ice particles at the upper level. In the dissipating stage, large hydrometeors no longer exist at the upper part of the raining region. Within the MCS anvils, the dissipating stage shows a more uniform distribution of ice-particle effective radius compared to that shown by the developing and

Big data analytics on Apache Spark

Abstract: Apache Spark has emerged as the de facto framework for big data analytics with its advanced in-memory programming model and upper-level libraries for scalable machine learning, graph analysis, streaming and structured data processing. It is a general-purpose cluster computing framework with language-integrated APIs in Scala, Java, Python and R. As a rapidly evolving open source project, with an increasing number of contributors from both academia and industry, it is difficult for researchers to comprehend the full body of development and research behind Apache Spark, especially those who are beginners in this area. In this paper, we present a technical review on big data analytics using Apache Spark. This review focuses on the key components, abstractions and features of Apache Spark. More specifically, it shows what Apache Spark has for designing and implementing big data algorithms and pipelines for machine learning, graph analysis and stream processing. In addition, we highlight some research and development directions on Apache Spark for big data analytics.

Changes in extreme temperature and precipitation in the Caribbean region, 1961–2010

Abstract: A workshop was held at the University of the West Indies, Jamaica, in May 2012 to build capacity in climate data rescue and to enhance knowledge about climate change in the Caribbean region. Scientists brought their daily observational surface temperature and precipitation data from weather stations for an assessment of quality and homogeneity and for the calculation of climate indices helpful for studying climate change in their region. This study presents the trends in daily and extreme temperature and precipitation indices in the Caribbean region for records spanning the 1961–2010 and 1986–2010 intervals. Overall, the results show a warming of the surface air temperature at land stations. In general, the indices based on minimum temperature show stronger warming trends than indices calculated from maximum temperature. The frequency of warm days, warm nights and extreme high temperatures has increased while fewer cool days, cool nights and extreme low temperatures were found for both periods. Changes in precipitation indices are less consistent and the trends are generally weak. Small positive trends were found in annual total precipitation, daily intensity, maximum number of consecutive dry days and heavy rainfall events particularly during the period 1986–2010. Correlations between indices and the Atlantic multidecadal oscillation (AMO) index suggest that temperature variability and, to a lesser extent, precipitation extremes are related to the AMO signal of the North Atlantic surface sea temperatures: stronger associations are found in August and September for the temperature indices and in June and October for some of the precipitation indices.

Sub-regional Precipitation Climate of the Caribbean and Relationships With ENSO and NAO

Abstract: [1] Thirty-five meteorological stations encompassing the Caribbean region (Cuba, Bahamas, Jamaica, Dominican Republic, Puerto Rico, US Virgin Islands, St. Maarten, and Barbados) were analyzed over the time interval 1951–1981 to assess regional precipitation patterns and their relationships with the North Atlantic Oscillation (NAO) and El Nino-Southern Oscillation (ENSO). Application of factor analysis to these series revealed the existence of four geographically distinct precipitation regions, (C1) western Cuba and northwestern Bahamas, (C2) Jamaica, eastern Cuba, and southeastern Bahamas, (C3) Dominican Republic and northwestern Puerto Rico, and (C4) eastern Puerto Rico, US Virgin Islands, St. Maarten, and Barbados. This regionalization is related to different annual cycles and interannual fluctuations of rainfall. The annual cycle is more unimodal and largest in the northwest Caribbean (C1) and becomes increasingly bimodal toward lower latitudes (C4) as expected. Year-to-year variations of precipitation are compared with two well-known climatic indices. The ENSO relationship, represented by Nino 3.4 sea surface temperatures (SST), is positive and stable at all lags, but tends to reverse over the SE Caribbean (C4) in late summer. The NAO influence is weak and seasonally dependent. Early summer rainfall in the northwest Caribbean (C1) increases under El Nino conditions. Clusters 2 and 3 are less influenced by the global predictors and more regional in character.

Subregional precipitation climate of the Caribbean and relationships with ENSO and NAO

Abstract: [1] Thirty-five meteorological stations encompassing the Caribbean region (Cuba, Bahamas, Jamaica, Dominican Republic, Puerto Rico, US Virgin Islands, St. Maarten, and Barbados) were analyzed over the time interval 1951–1981 to assess regional precipitation patterns and their relationships with the North Atlantic Oscillation (NAO) and El Nino-Southern Oscillation (ENSO). Application of factor analysis to these series revealed the existence of four geographically distinct precipitation regions, (C1) western Cuba and northwestern Bahamas, (C2) Jamaica, eastern Cuba, and southeastern Bahamas, (C3) Dominican Republic and northwestern Puerto Rico, and (C4) eastern Puerto Rico, US Virgin Islands, St. Maarten, and Barbados. This regionalization is related to different annual cycles and interannual fluctuations of rainfall. The annual cycle is more unimodal and largest in the northwest Caribbean (C1) and becomes increasingly bimodal toward lower latitudes (C4) as expected. Year-to-year variations of precipitation are compared with two well-known climatic indices. The ENSO relationship, represented by Nino 3.4 sea surface temperatures (SST), is positive and stable at all lags, but tends to reverse over the SE Caribbean (C4) in late summer. The NAO influence is weak and seasonally dependent. Early summer rainfall in the northwest Caribbean (C1) increases under El Nino conditions. Clusters 2 and 3 are less influenced by the global predictors and more regional in character.

A review of observed and projected changes in climate for the islands in the Caribbean

Abstract: Resumen Los cambios climaticos observados y proyectados tienen implicaciones socioeconomicas importantes para las islas del Caribe. El objetivo de este articulo es presentar informacion esencial sobre el cambio climatico —basada en estudios previos, observaciones disponibles y simulaciones de modelos climaticos— a escalas espaciales relevantes para las islas caribenas. Se utilizan datos del modelo general de circulacion (GCM) incluidos en el Proyecto de Comparacion de Modelos Acoplados fase 3 (CMIP3), asi como del modelo climatico regional (RCM) del Centro Hadley del Reino Unido, para aportar tanto informacion actual como informacion basada en proyecciones sobre precipitaciones y temperatura en estados insulares especifcos. Se utilizan observaciones reticuladas de estaciones y datos satelitales para estudiar el clima del siglo XX y evaluar el desempeno de los modelos climaticos. Con un enfoque centrado en la precipitacion, tambien se analizan factores como la temperatura superficial del mar, la presion al nivel del mar y los vientos que influyen en las variaciones estacionales de la precipitacion. La media del ensamble del CMIP3 y el RCM captan satisfactoriamente las peculiaridades de la circulacion atmosferica de gran escala en la region, pero no asi el ciclo estacional bimodal caracteristico de la precipitacion. La aridez en epocas de lluvias prevista en escenarios de cambio climatico en la region se ha abordado en estudios previos, pero la magnitud de la variacion es muy incierta en las simulaciones tanto del GCM como del RCM. La disminucion proyectada es mayor al inicio de la temporada de lluvias y suprime la sequia del medio verano en el Caribe occidental. Las simulaciones del RCM muestran avances respecto del GCM, sobre todo por sus mejores representaciones de la extension territorial, pero su desempeno depende en gran medida de la conduccion del GCM. El presente estudio destaca la necesidad de contar con observaciones de alta resolucion y comparar simulaciones de modelos climaticos para entender a fondo el cambio climatico y su impacto en las pequenas islas del Caribe.