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Abstract
Using a conceptual forecasting format that is similar to some in current operational use, trade-offs between climate forecast quality and economic value are examined from the perspective of the forecast user. Various scenarios for climate forecast quality are applied to corn production in east-central Illinois. A stochastic dynamic programming model is used to obtain the expected value of the various scenarios. As anticipated, the results demonstrate that the entire structure of the forecast format interacts to determine the economic value of that system. Additional results indicate two possible preferred directions for research concerning climate forecasting and economic applications such as corn production in Illinois. First, increasing forecast quality by decreasing the error between the observed condition and the forecast condition may be preferred to increasing quality by increasing the number of predictions in the correct category. Second, corn producers may prefer research to increase the quality of forecasts for “poorer” climatic conditions over research directed toward increasing the quality of forecasts for “good” conditions.
Abstract
Using a conceptual forecasting format that is similar to some in current operational use, trade-offs between climate forecast quality and economic value are examined from the perspective of the forecast user. Various scenarios for climate forecast quality are applied to corn production in east-central Illinois. A stochastic dynamic programming model is used to obtain the expected value of the various scenarios. As anticipated, the results demonstrate that the entire structure of the forecast format interacts to determine the economic value of that system. Additional results indicate two possible preferred directions for research concerning climate forecasting and economic applications such as corn production in Illinois. First, increasing forecast quality by decreasing the error between the observed condition and the forecast condition may be preferred to increasing quality by increasing the number of predictions in the correct category. Second, corn producers may prefer research to increase the quality of forecasts for “poorer” climatic conditions over research directed toward increasing the quality of forecasts for “good” conditions.
Abstract
An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June–September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble.
For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed–predicted correlation r (SSClim) being +0.81 (65%) for 1970–2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society.
Abstract
An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June–September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble.
For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed–predicted correlation r (SSClim) being +0.81 (65%) for 1970–2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society.
Abstract
A set of regional climate scenarios is constructed for two study regions in North America using a combination of GCM output and synoptic–dynamical reasoning. The approach begins by describing the structure and components of a climate scenario and identifying the dynamical determinants of large-scale and regional climate. Expert judgement techniques are used to categorize the tendencies of these elements in response to increased greenhouse forcing in climate model studies. For many of the basic dynamical elements, tendencies are ambiguous, and changes in sign (magnitude, position) can usually be argued in either direction. A set of climate scenarios is produced for winter and summer, emphasizing the interrelationships among dynamical features, and adjusting GCM results on the basis of known deficiences in GCM simulations of the dynamical features. The scenarios are qualitative only, consistent with the level of precision afforded by the uncertainty in understanding of the dynamics, and in order to provide an outline of the reasoning and chain of contingencies on which the scenarios are based. The three winter scenarios outlined correspond roughly to a north–south displacement of the stationary wave pattern, to an increase in amplitude of the pattern, and to a shift in phase of the pattern. These scenarios illustrate that small changes in the dynamics can lead to large changes in regional climate in some regions, while other regions are apparently insensitive to some of the large changes in dynamics that can be plausibly hypothesized. The dynamics of summer regional climate changes are even more difficult to project, though thermodynamic considerations allow some more general conclusions to be reached in this season. Given present uncertainties it is difficult to constrain regional climate projections.
Abstract
A set of regional climate scenarios is constructed for two study regions in North America using a combination of GCM output and synoptic–dynamical reasoning. The approach begins by describing the structure and components of a climate scenario and identifying the dynamical determinants of large-scale and regional climate. Expert judgement techniques are used to categorize the tendencies of these elements in response to increased greenhouse forcing in climate model studies. For many of the basic dynamical elements, tendencies are ambiguous, and changes in sign (magnitude, position) can usually be argued in either direction. A set of climate scenarios is produced for winter and summer, emphasizing the interrelationships among dynamical features, and adjusting GCM results on the basis of known deficiences in GCM simulations of the dynamical features. The scenarios are qualitative only, consistent with the level of precision afforded by the uncertainty in understanding of the dynamics, and in order to provide an outline of the reasoning and chain of contingencies on which the scenarios are based. The three winter scenarios outlined correspond roughly to a north–south displacement of the stationary wave pattern, to an increase in amplitude of the pattern, and to a shift in phase of the pattern. These scenarios illustrate that small changes in the dynamics can lead to large changes in regional climate in some regions, while other regions are apparently insensitive to some of the large changes in dynamics that can be plausibly hypothesized. The dynamics of summer regional climate changes are even more difficult to project, though thermodynamic considerations allow some more general conclusions to be reached in this season. Given present uncertainties it is difficult to constrain regional climate projections.
Abstract
Monthly sea level pressure (SLP) data from the National Centers for Environmental Prediction reanalysis for 1948–99 are used to develop a seasonally and geographically varying “mobile” index of the North Atlantic oscillation (NAOm). NAOm is defined as the difference between normalized SLP anomalies at the locations of maximum negative correlation between the subtropical and subpolar North Atlantic SLP. The subtropical nodal point migrates westward and slightly northward into the central North Atlantic from winter to summer. The NAOm index is robust across datasets, and correlates more highly than EOF coefficients with historical measures of westerly wind intensity across North Atlantic midlatitudes. As measured by this “mobile index,” the NAO’s nodes maintain their correlation from winter to summer to a greater degree than traditional NAO indices based on fixed stations in the eastern North Atlantic (Azores, Lisbon, Iceland). When the NAOm index is extended back to 1873, its annual values during the late 1800s are strongly negative due to negative contributions from all seasons, amplifying fluctuations present in traditional winter-only indices. In contrast, after the mid-1950s, the values for different seasons sufficiently offset each other to make the annually averaged excursions of NAOm smaller than those of winter-only indices. Global teleconnection fields show that the wider influence of the NAO—particularly in the western North Atlantic, eastern North America, and Arctic—is more apparent during spring–summer–autumn when the NAOm is used to characterize the NAO. Thus, the mobile index should be useful in NAO investigations that involve seasonality.
Abstract
Monthly sea level pressure (SLP) data from the National Centers for Environmental Prediction reanalysis for 1948–99 are used to develop a seasonally and geographically varying “mobile” index of the North Atlantic oscillation (NAOm). NAOm is defined as the difference between normalized SLP anomalies at the locations of maximum negative correlation between the subtropical and subpolar North Atlantic SLP. The subtropical nodal point migrates westward and slightly northward into the central North Atlantic from winter to summer. The NAOm index is robust across datasets, and correlates more highly than EOF coefficients with historical measures of westerly wind intensity across North Atlantic midlatitudes. As measured by this “mobile index,” the NAO’s nodes maintain their correlation from winter to summer to a greater degree than traditional NAO indices based on fixed stations in the eastern North Atlantic (Azores, Lisbon, Iceland). When the NAOm index is extended back to 1873, its annual values during the late 1800s are strongly negative due to negative contributions from all seasons, amplifying fluctuations present in traditional winter-only indices. In contrast, after the mid-1950s, the values for different seasons sufficiently offset each other to make the annually averaged excursions of NAOm smaller than those of winter-only indices. Global teleconnection fields show that the wider influence of the NAO—particularly in the western North Atlantic, eastern North America, and Arctic—is more apparent during spring–summer–autumn when the NAOm is used to characterize the NAO. Thus, the mobile index should be useful in NAO investigations that involve seasonality.
Abstract
The formation of a recirculating subsurface core in an internal solitary wave (ISW) of depression, shoaling over realistic bathymetry, is explored through fully nonlinear and nonhydrostatic two-dimensional simulations. The computational approach is based on a high-resolution/accuracy deformed spectral multidomain penalty-method flow solver, which employs the recorded bathymetry, background current, and stratification profile in the South China Sea. The flow solver is initialized using a solution of the fully nonlinear Dubreil–Jacotin–Long equation. During shoaling, convective breaking precedes core formation as the rear steepens and the trough decelerates, allowing heavier fluid to plunge forward, forming a trapped core. This core-formation mechanism is attributed to a stretching of a near-surface background vorticity layer. Since the sign of the vorticity is opposite to that generated by the propagating wave, only subsurface recirculating cores can form. The onset of convective breaking is visualized, and the sensitivity of the core properties to changes in the initial wave, near-surface background shear, and bottom slope is quantified. The magnitude of the near-surface vorticity determines the size of the convective-breaking region, and the rapid increase of local bathymetric slope accelerates core formation. If the amplitude of the initial wave is increased, the subsequent convective-breaking region increases in size. The simulations are guided by field data and capture the development of the recirculating subsurface core. The analyzed parameter space constitutes a baseline for future three-dimensional simulations focused on characterizing the turbulent flow engulfed within the convectively unstable ISW.
Abstract
The formation of a recirculating subsurface core in an internal solitary wave (ISW) of depression, shoaling over realistic bathymetry, is explored through fully nonlinear and nonhydrostatic two-dimensional simulations. The computational approach is based on a high-resolution/accuracy deformed spectral multidomain penalty-method flow solver, which employs the recorded bathymetry, background current, and stratification profile in the South China Sea. The flow solver is initialized using a solution of the fully nonlinear Dubreil–Jacotin–Long equation. During shoaling, convective breaking precedes core formation as the rear steepens and the trough decelerates, allowing heavier fluid to plunge forward, forming a trapped core. This core-formation mechanism is attributed to a stretching of a near-surface background vorticity layer. Since the sign of the vorticity is opposite to that generated by the propagating wave, only subsurface recirculating cores can form. The onset of convective breaking is visualized, and the sensitivity of the core properties to changes in the initial wave, near-surface background shear, and bottom slope is quantified. The magnitude of the near-surface vorticity determines the size of the convective-breaking region, and the rapid increase of local bathymetric slope accelerates core formation. If the amplitude of the initial wave is increased, the subsequent convective-breaking region increases in size. The simulations are guided by field data and capture the development of the recirculating subsurface core. The analyzed parameter space constitutes a baseline for future three-dimensional simulations focused on characterizing the turbulent flow engulfed within the convectively unstable ISW.
Abstract
Bridging the gap between rapidly moving scientific research and specific forecasting tools, Meteorology of Tropical West Africa: The Forecasters’ Handbook gives unprecedented access to the latest science for the region’s forecasters, researchers, and students and combines this with pragmatic approaches to forecasting. It is set to change the way tropical meteorology is learned and will serve to drive demand for new forecasting tools. The Forecasters’ Handbook builds upon the legacy of the African Monsoon Multidisciplinary Analysis (AMMA) project, making the latest science applicable to forecasting in the region. By bringing together, at the outset, researchers and forecasters from across the region, and linking to applications, user communities, and decision-makers, The Forecasters’ Handbook provides a template for finding much needed solutions to critical issues such as building resilience to weather hazards and climate change in West Africa.
Abstract
Bridging the gap between rapidly moving scientific research and specific forecasting tools, Meteorology of Tropical West Africa: The Forecasters’ Handbook gives unprecedented access to the latest science for the region’s forecasters, researchers, and students and combines this with pragmatic approaches to forecasting. It is set to change the way tropical meteorology is learned and will serve to drive demand for new forecasting tools. The Forecasters’ Handbook builds upon the legacy of the African Monsoon Multidisciplinary Analysis (AMMA) project, making the latest science applicable to forecasting in the region. By bringing together, at the outset, researchers and forecasters from across the region, and linking to applications, user communities, and decision-makers, The Forecasters’ Handbook provides a template for finding much needed solutions to critical issues such as building resilience to weather hazards and climate change in West Africa.
The Canopy Horizontal Array Turbulence Study (CHATS) took place in spring 2007 and is the third in the series of Horizontal Array Turbulence Study (HATS) experiments. The HATS experiments have been instrumental in testing and developing subfilterscale (SFS) models for large-eddy simulation (LES) of planetary boundary layer (PBL) turbulence. The CHATS campaign took place in a deciduous walnut orchard near Dixon, California, and was designed to examine the impacts of vegetation on SFS turbulence. Measurements were collected both prior to and following leafout to capture the impact of leaves on the turbulence, stratification, and scalar source/sink distribution. CHATS utilized crosswind arrays of fast-response instrumentation to investigate the impact of the canopy-imposed distribution of momentum extraction and scalar sources on SFS transport of momentum, energy, and three scalars. To directly test and link with PBL parameterizations of canopy-modified turbulent exchange, CHATS also included a 30-m profile tower instrumented with turbulence instrumentation, fast and slow chemical sensors, aerosol samplers, and radiation instrumentation. A highresolution scanning backscatter lidar characterized the turbulence structure above and within the canopy; a scanning Doppler lidar, mini sodar/radio acoustic sounding system (RASS), and a new helicopter-observing platform provided details of the PBL-scale flow. Ultimately, the CHATS dataset will lead to improved parameterizations of energy and scalar transport to and from vegetation, which are a critical component of global and regional land, atmosphere, and chemical models. This manuscript presents an overview of the experiment, documents the regime sampled, and highlights some preliminary key findings.
The Canopy Horizontal Array Turbulence Study (CHATS) took place in spring 2007 and is the third in the series of Horizontal Array Turbulence Study (HATS) experiments. The HATS experiments have been instrumental in testing and developing subfilterscale (SFS) models for large-eddy simulation (LES) of planetary boundary layer (PBL) turbulence. The CHATS campaign took place in a deciduous walnut orchard near Dixon, California, and was designed to examine the impacts of vegetation on SFS turbulence. Measurements were collected both prior to and following leafout to capture the impact of leaves on the turbulence, stratification, and scalar source/sink distribution. CHATS utilized crosswind arrays of fast-response instrumentation to investigate the impact of the canopy-imposed distribution of momentum extraction and scalar sources on SFS transport of momentum, energy, and three scalars. To directly test and link with PBL parameterizations of canopy-modified turbulent exchange, CHATS also included a 30-m profile tower instrumented with turbulence instrumentation, fast and slow chemical sensors, aerosol samplers, and radiation instrumentation. A highresolution scanning backscatter lidar characterized the turbulence structure above and within the canopy; a scanning Doppler lidar, mini sodar/radio acoustic sounding system (RASS), and a new helicopter-observing platform provided details of the PBL-scale flow. Ultimately, the CHATS dataset will lead to improved parameterizations of energy and scalar transport to and from vegetation, which are a critical component of global and regional land, atmosphere, and chemical models. This manuscript presents an overview of the experiment, documents the regime sampled, and highlights some preliminary key findings.
