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1. Introduction The purpose of this paper is to create a communication tool for use by forecast developers and agricultural climate forecast users that will improve the usability and usefulness of climate information. Potential users of the graphic, in addition to crop producers and climate forecasters, include agribusiness product and service providers, extension agents, financial institutions, risk management organizations, and commodity traders. Like producers and forecasters, these users
1. Introduction The purpose of this paper is to create a communication tool for use by forecast developers and agricultural climate forecast users that will improve the usability and usefulness of climate information. Potential users of the graphic, in addition to crop producers and climate forecasters, include agribusiness product and service providers, extension agents, financial institutions, risk management organizations, and commodity traders. Like producers and forecasters, these users
property loss was considerably minimized. Thus, despite the similarity in the two storms in terms of track, intensity, and lead time, the story of devastation and casualties was fortunately not repeated. There was an obvious scientific- and policy-relevant curiosity and strategic interest in the possible reasons for this success story that leads to this assessment. 2. Performance of operational TC forecasts at IMD in the last decade We conclude that the success story seen for Phailin is not due to any
property loss was considerably minimized. Thus, despite the similarity in the two storms in terms of track, intensity, and lead time, the story of devastation and casualties was fortunately not repeated. There was an obvious scientific- and policy-relevant curiosity and strategic interest in the possible reasons for this success story that leads to this assessment. 2. Performance of operational TC forecasts at IMD in the last decade We conclude that the success story seen for Phailin is not due to any
forecasting model could provide valuable information ( Koch 2011 ; Häner and Brabant 2016 ). In the literature, several studies can be found that develop forecasting models for wheat quality with different approaches for different regions using weather parameters. For example, Pan et al. (2006) developed regression models for protein content based on field experiments in China using weather parameters. Johansson and Svensson (1998) also used field experiments from Sweden and tested not only the
forecasting model could provide valuable information ( Koch 2011 ; Häner and Brabant 2016 ). In the literature, several studies can be found that develop forecasting models for wheat quality with different approaches for different regions using weather parameters. For example, Pan et al. (2006) developed regression models for protein content based on field experiments in China using weather parameters. Johansson and Svensson (1998) also used field experiments from Sweden and tested not only the
Interannual Prediction (EUROSIP; http://cosmos.enes.org/uploads/media/TStockdale.pdf ) project, which is anticipated to further improve seasonal climate prediction skill and more importantly produce robust estimates of seasonal forecast uncertainty. Much of hydrological forecasting is based on empirical methods, like linear regression, which use initial conditions and information on future climate conditions as predictors ( Rosenberg et al. 2011 ; Pagano et al. 2009 ). However, since the introduction of
Interannual Prediction (EUROSIP; http://cosmos.enes.org/uploads/media/TStockdale.pdf ) project, which is anticipated to further improve seasonal climate prediction skill and more importantly produce robust estimates of seasonal forecast uncertainty. Much of hydrological forecasting is based on empirical methods, like linear regression, which use initial conditions and information on future climate conditions as predictors ( Rosenberg et al. 2011 ; Pagano et al. 2009 ). However, since the introduction of
) independently on the causes of ignition. The estimation of fire danger provides a valuable support for the designing of strategies related to the use and the distribution of the available fire fighting resources, which can prevent or at least minimize fire effects. The most widely used fire danger forecasting systems are the Canadian Forest Fire Danger Rating System (CFFDRS; Stocks et al. 1989 ; Alexander et al. 1996 ; Van Nest and Alexander 1999 ; Lee et al. 2002 ) and the United States’ National Fire
) independently on the causes of ignition. The estimation of fire danger provides a valuable support for the designing of strategies related to the use and the distribution of the available fire fighting resources, which can prevent or at least minimize fire effects. The most widely used fire danger forecasting systems are the Canadian Forest Fire Danger Rating System (CFFDRS; Stocks et al. 1989 ; Alexander et al. 1996 ; Van Nest and Alexander 1999 ; Lee et al. 2002 ) and the United States’ National Fire
Abstract
Complex environmental gradients in the White and Inyo Mountains in eastern California produce striking variations in vegetation assemblages over short distances. Vegetation composition is dominated by elevational gradients of temperature and precipitation, but local modifications by geologic substrate, potential insolation, slope, and topographic position create finescale mosaics. Digital elevation models, geologic maps, and field data were used to map current species distributions over 6220 km2 (622 000 ha) of the White and Inyo Mountains. Species–environment relationships of 88 plant species were modeled at a scale of 54 m using canonical correspondence analysis (CCA). CCA models were calibrated from 434 field plots and evaluated with 216 plots using kappa statistics. Vegetation responses to temperature increases of 1°–6°C were modeled by shifting species tolerances along the elevational gradient according to a standard lapse rate [3°C (500 m)−1] while all other factors were kept constant. Ranges of midelevations species tended to fragment onto local peaks, whereas the ranges of many desert species merged across a major pass. In several cases, local geologic features were identified as obstacles to species’ upslope migration. As modeled temperatures increase, species contract to small populations around White Mountain Peak (4342 m) and its north-facing slopes. It is predicted that 10 of 18 modeled alpine and subalpine species will become locally extinct if temperatures increase by 6°C. These scenarios provide a detailed set of hypotheses on the structure of current species ranges and their ability to persist through rapid climate change.
Abstract
Complex environmental gradients in the White and Inyo Mountains in eastern California produce striking variations in vegetation assemblages over short distances. Vegetation composition is dominated by elevational gradients of temperature and precipitation, but local modifications by geologic substrate, potential insolation, slope, and topographic position create finescale mosaics. Digital elevation models, geologic maps, and field data were used to map current species distributions over 6220 km2 (622 000 ha) of the White and Inyo Mountains. Species–environment relationships of 88 plant species were modeled at a scale of 54 m using canonical correspondence analysis (CCA). CCA models were calibrated from 434 field plots and evaluated with 216 plots using kappa statistics. Vegetation responses to temperature increases of 1°–6°C were modeled by shifting species tolerances along the elevational gradient according to a standard lapse rate [3°C (500 m)−1] while all other factors were kept constant. Ranges of midelevations species tended to fragment onto local peaks, whereas the ranges of many desert species merged across a major pass. In several cases, local geologic features were identified as obstacles to species’ upslope migration. As modeled temperatures increase, species contract to small populations around White Mountain Peak (4342 m) and its north-facing slopes. It is predicted that 10 of 18 modeled alpine and subalpine species will become locally extinct if temperatures increase by 6°C. These scenarios provide a detailed set of hypotheses on the structure of current species ranges and their ability to persist through rapid climate change.
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.
forecasters, since out of the four frequencies imaged, it alone had sufficient resolution to support high quality images. In particular, these images allow forecasters to locate the cloud-covered eyes or centers of low-level circulations that cannot be detected otherwise. In 85-GHz images the primary signature is the depression of brightness temperature (Tb) caused by ice scattering within deep convection and precipitating anvil clouds ( Spencer et al., 1989 ). Images of lower frequencies are dominated by
forecasters, since out of the four frequencies imaged, it alone had sufficient resolution to support high quality images. In particular, these images allow forecasters to locate the cloud-covered eyes or centers of low-level circulations that cannot be detected otherwise. In 85-GHz images the primary signature is the depression of brightness temperature (Tb) caused by ice scattering within deep convection and precipitating anvil clouds ( Spencer et al., 1989 ). Images of lower frequencies are dominated by
( Tang et al. 2005 ; Choi and Deal 2008 ), coastal New England ( Schiff and Benoit 2007 ), Pacific Northwest ( Cuo et al. 2008 ), and Southeast ( Ferguson and Suckling 1990 ). Many of these have focused on water, habitat, and other ecological quality factors (e.g., Schueler et al. 2009 ; Brabec 2009 ; Walsh et al. 2009 ; Nelson et al. 2009 ). GCM forecasts (e.g., Solomon et al. 2007 ) are increasingly available for use as input to hydrologic models. A sufficiently large number of methods for
( Tang et al. 2005 ; Choi and Deal 2008 ), coastal New England ( Schiff and Benoit 2007 ), Pacific Northwest ( Cuo et al. 2008 ), and Southeast ( Ferguson and Suckling 1990 ). Many of these have focused on water, habitat, and other ecological quality factors (e.g., Schueler et al. 2009 ; Brabec 2009 ; Walsh et al. 2009 ; Nelson et al. 2009 ). GCM forecasts (e.g., Solomon et al. 2007 ) are increasingly available for use as input to hydrologic models. A sufficiently large number of methods for
atmospheric data assimilation and forecasts, ocean reanalysis fields, and coupled climate model projections. The analysis covers thermodynamic and kinematic advection patterns contributed by the atmosphere and the background marine climate governed by the ocean. The intensity of convection in the eastern Antilles region is quantified in the period 24–25 December 2013 using 4-km Geostationary Operational Environmental Satellite (GOES) infrared cloud temperatures at 30-min interval, 25-km multisatellite
atmospheric data assimilation and forecasts, ocean reanalysis fields, and coupled climate model projections. The analysis covers thermodynamic and kinematic advection patterns contributed by the atmosphere and the background marine climate governed by the ocean. The intensity of convection in the eastern Antilles region is quantified in the period 24–25 December 2013 using 4-km Geostationary Operational Environmental Satellite (GOES) infrared cloud temperatures at 30-min interval, 25-km multisatellite
