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- Author or Editor: Andrew J. Monaghan x
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Real-Time Mesoscale Modeling Over Antarctica: The Antarctic Mesoscale Prediction System*
The Antarctic Mesoscale Prediction System
In support of the United States Antarctic Program (USAP), the National Center for Atmospheric Research and the Byrd Polar Research Center of The Ohio State University have created the Antarctic Mesoscale Prediction System (AMPS): an experimental, real-time mesoscale modeling system covering Antarctica. AMPS has been designed to serve flight forecasters at McMurdo Station, to support science and operations around the continent, and to be a vehicle for the development of physical parameterizations suitable for polar regions. Since 2000, AMPS has been producing high-resolution forecasts (grids to 3.3 km) with the “Polar MM5,” a version of the fifth-generation Pennsylvania State University-NCAR Mesoscale Model tuned for the polar atmosphere. Beyond its basic mission of serving the USAP flight forecasters at McMurdo, AMPS has assisted both in emergency operations to save lives and in programs to explore the extreme polar environment. The former have included a medical evacuation from the South Pole and a marine rescue from the continental margin. The latter have included scientific field campaigns and the daily activities of international Antarctic forecasters and researchers. The AMPS program has been a success in terms of advancing polar mesoscale NWP, serving critical logistical operations of the USAP, and, most visibly, assisting in emergency rescue missions to save lives. The history and performance of AMPS are described and the successes of this unique real-time mesoscale modeling system in crisis support are detailed.
In support of the United States Antarctic Program (USAP), the National Center for Atmospheric Research and the Byrd Polar Research Center of The Ohio State University have created the Antarctic Mesoscale Prediction System (AMPS): an experimental, real-time mesoscale modeling system covering Antarctica. AMPS has been designed to serve flight forecasters at McMurdo Station, to support science and operations around the continent, and to be a vehicle for the development of physical parameterizations suitable for polar regions. Since 2000, AMPS has been producing high-resolution forecasts (grids to 3.3 km) with the “Polar MM5,” a version of the fifth-generation Pennsylvania State University-NCAR Mesoscale Model tuned for the polar atmosphere. Beyond its basic mission of serving the USAP flight forecasters at McMurdo, AMPS has assisted both in emergency operations to save lives and in programs to explore the extreme polar environment. The former have included a medical evacuation from the South Pole and a marine rescue from the continental margin. The latter have included scientific field campaigns and the daily activities of international Antarctic forecasters and researchers. The AMPS program has been a success in terms of advancing polar mesoscale NWP, serving critical logistical operations of the USAP, and, most visibly, assisting in emergency rescue missions to save lives. The history and performance of AMPS are described and the successes of this unique real-time mesoscale modeling system in crisis support are detailed.
Abstract
Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.
Abstract
Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.
Abstract
The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.
Abstract
The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.
