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An account is given of the collection of airborne chloride particles, by airplane, on a cross country flight. The accompanying meteorological situation is described. The distribution of chloride particles found is consistent with the view that they originated in the Gulf of Mexico and in the Pacific off the coast of Mexico. The greatest abundance of chloride particles was found east of the mountains in southern California, in air that probably came from the region of a tropical storm.
An account is given of the collection of airborne chloride particles, by airplane, on a cross country flight. The accompanying meteorological situation is described. The distribution of chloride particles found is consistent with the view that they originated in the Gulf of Mexico and in the Pacific off the coast of Mexico. The greatest abundance of chloride particles was found east of the mountains in southern California, in air that probably came from the region of a tropical storm.
The first barometers in the Americas were provided by the Royal Society of London in 1677 to correspondents in the Caribbean Island of Barbados. Colonel William Sharpe of Barbados was the first person in the Americas to make daily observations of the weather using a meteorological instrument (other than a wind vane) and made the first known measurements of barometric pressure within the circulation of a hurricane on 12 August 1680. His record provides new insight into the early history of the barometer and early perceptions of tropical weather, vindicates the hypothesis that the barometer would prove useful in detecting hurricanes, and contributes to Edmund Halley's understanding of the empirical distinctions between the Tropics and temperate zones. Sharpe's name and contributions, previously unknown to the meteorological community, can now be properly recognized.
The first barometers in the Americas were provided by the Royal Society of London in 1677 to correspondents in the Caribbean Island of Barbados. Colonel William Sharpe of Barbados was the first person in the Americas to make daily observations of the weather using a meteorological instrument (other than a wind vane) and made the first known measurements of barometric pressure within the circulation of a hurricane on 12 August 1680. His record provides new insight into the early history of the barometer and early perceptions of tropical weather, vindicates the hypothesis that the barometer would prove useful in detecting hurricanes, and contributes to Edmund Halley's understanding of the empirical distinctions between the Tropics and temperate zones. Sharpe's name and contributions, previously unknown to the meteorological community, can now be properly recognized.
Abstract
A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r 2 = 0.62 (significance level p < 10–4) and a negative correlation with r 2 = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (∼300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r 2 = 0.53, p < 10–4), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r 2 = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.
Abstract
A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r 2 = 0.62 (significance level p < 10–4) and a negative correlation with r 2 = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (∼300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r 2 = 0.53, p < 10–4), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r 2 = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.
Abstract
Motivated by an attempt to augment dynamical models in predicting the Madden–Julian oscillation (MJO), and to provide a realistic benchmark to those models, the predictive skill of a multivariate lag-regression statistical model has been comprehensively explored in the present study. The predictors of the benchmark model are the projection time series of the leading pair of EOFs of the combined fields of equatorially averaged outgoing longwave radiation (OLR) and zonal winds at 850 and 200 hPa, derived using the approach of Wheeler and Hendon. These multivariate EOFs serve as an effective filter for the MJO without the need for bandpass filtering, making the statistical forecast scheme feasible for the real-time use. Another advantage of this empirical approach lies in the consideration of the seasonal dependence of the regression parameters, making it applicable for forecasts all year-round. The forecast model exhibits useful extended-range skill for a real-time MJO forecast. Predictions with a correlation skill of greater than 0.3 (0.5) between predicted and observed unfiltered (EOF filtered) fields still can be detected over some regions at a lead time of 15 days, especially for boreal winter forecasts. This predictive skill is increased significantly when there are strong MJO signals at the initial forecast time. The analysis also shows that predictive skill for the upper-tropospheric winds is relatively higher than for the low-level winds and convection signals. Finally, the capability of this empirical model in predicting the MJO is further demonstrated by a case study of a real-time “hindcast” during the 2003/04 winter. Predictive skill demonstrated in this study provides an estimate of the predictability of the MJO and a benchmark for the dynamical extended-range models.
Abstract
Motivated by an attempt to augment dynamical models in predicting the Madden–Julian oscillation (MJO), and to provide a realistic benchmark to those models, the predictive skill of a multivariate lag-regression statistical model has been comprehensively explored in the present study. The predictors of the benchmark model are the projection time series of the leading pair of EOFs of the combined fields of equatorially averaged outgoing longwave radiation (OLR) and zonal winds at 850 and 200 hPa, derived using the approach of Wheeler and Hendon. These multivariate EOFs serve as an effective filter for the MJO without the need for bandpass filtering, making the statistical forecast scheme feasible for the real-time use. Another advantage of this empirical approach lies in the consideration of the seasonal dependence of the regression parameters, making it applicable for forecasts all year-round. The forecast model exhibits useful extended-range skill for a real-time MJO forecast. Predictions with a correlation skill of greater than 0.3 (0.5) between predicted and observed unfiltered (EOF filtered) fields still can be detected over some regions at a lead time of 15 days, especially for boreal winter forecasts. This predictive skill is increased significantly when there are strong MJO signals at the initial forecast time. The analysis also shows that predictive skill for the upper-tropospheric winds is relatively higher than for the low-level winds and convection signals. Finally, the capability of this empirical model in predicting the MJO is further demonstrated by a case study of a real-time “hindcast” during the 2003/04 winter. Predictive skill demonstrated in this study provides an estimate of the predictability of the MJO and a benchmark for the dynamical extended-range models.
Abstract
Reanalysis data provide an excellent test bed for impacts prediction systems, because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM, when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields were simulated well across much of India. Correlations between observed and modeled yields, where these are significant, are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather–yield correlations vary on decadal time scales, and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather–yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.
Abstract
Reanalysis data provide an excellent test bed for impacts prediction systems, because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM, when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields were simulated well across much of India. Correlations between observed and modeled yields, where these are significant, are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather–yield correlations vary on decadal time scales, and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather–yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.
This paper documents a rare spell of severe weather in Spain that took place during the mid-nineteenth century when a tropical storm struck the southwest of the country on 29 October 1842. The use of a variety of independent documentary sources has provided unprecedented scope for the analysis of this event, allowing it to be set within its wider context, and for a judgement to be made on its tropical origin. The evidence suggests that this was similar, though stronger, to the more recent Hurricane Vince, which made landfall in Spain on 10 October 2005. This case study not only places Hurricane Vince, suggested at the time to have been unique, in its more proper long-term context, but it also demonstrates how documentary sources can improve our wider understanding of climate dynamics during historical times in the Atlantic basin.
This paper documents a rare spell of severe weather in Spain that took place during the mid-nineteenth century when a tropical storm struck the southwest of the country on 29 October 1842. The use of a variety of independent documentary sources has provided unprecedented scope for the analysis of this event, allowing it to be set within its wider context, and for a judgement to be made on its tropical origin. The evidence suggests that this was similar, though stronger, to the more recent Hurricane Vince, which made landfall in Spain on 10 October 2005. This case study not only places Hurricane Vince, suggested at the time to have been unique, in its more proper long-term context, but it also demonstrates how documentary sources can improve our wider understanding of climate dynamics during historical times in the Atlantic basin.
Abstract
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This paper draws on a range of contemporary documentary evidence from the New and Old Worlds as well as from the oceanic regions to reconstruct the trajectory and intensity of an Atlantic hurricane from August 1680. In doing so, it offers the example of one of the earliest and most comprehensive hurricane reconstructions thus far attempted. The source material includes evidence from land-based observers and some of the earliest examples of instrumental barometric data from the Caribbean and from Europe; importantly, it also calls on the written accounts offered in ships' logbooks from various parts of the Atlantic. The latter provide the opportunity of tracking the system across the otherwise data-deficient areas of the North Atlantic as it recurved toward Europe. The findings are of intrinsic interest in documenting a notable historical event. They also offer a methodological model of how such a variety of documentary sources can be drawn together and used to identify, track, and reconstruct such events from the distant past and thereby improve the chronology of hurricanes and make more reliable our interpretation of their changing frequencies.
This paper draws on a range of contemporary documentary evidence from the New and Old Worlds as well as from the oceanic regions to reconstruct the trajectory and intensity of an Atlantic hurricane from August 1680. In doing so, it offers the example of one of the earliest and most comprehensive hurricane reconstructions thus far attempted. The source material includes evidence from land-based observers and some of the earliest examples of instrumental barometric data from the Caribbean and from Europe; importantly, it also calls on the written accounts offered in ships' logbooks from various parts of the Atlantic. The latter provide the opportunity of tracking the system across the otherwise data-deficient areas of the North Atlantic as it recurved toward Europe. The findings are of intrinsic interest in documenting a notable historical event. They also offer a methodological model of how such a variety of documentary sources can be drawn together and used to identify, track, and reconstruct such events from the distant past and thereby improve the chronology of hurricanes and make more reliable our interpretation of their changing frequencies.
Abstract
A new Madden–Julian oscillation (MJO) index is developed from a combined empirical orthogonal function (EOF) analysis of meridionally averaged 200-hPa velocity potential (VP200), 200-hPa zonal wind (U200), and 850-hPa zonal wind (U850). Like the Wheeler–Hendon Real-time Multivariate MJO (RMM) index, which was developed in the same way except using outgoing longwave radiation (OLR) data instead of VP200, daily data are projected onto the leading pair of EOFs to produce the two-component index. This new index is called the velocity potential MJO (VPM) indices and its properties are quantitatively compared to RMM. Compared to the RMM index, the VPM index detects larger-amplitude MJO-associated signals during boreal summer. This includes a slightly stronger and more coherent modulation of Atlantic tropical cyclones. This result is attributed to the fact that velocity potential preferentially emphasizes the planetary-scale aspects of the divergent circulation, thereby spreading the convectively driven component of the MJO’s signal across the entire globe. VP200 thus deemphasizes the convective signal of the MJO over the Indian Ocean warm pool, where the OLR variability associated with the MJO is concentrated, and enhances the signal over the relatively drier longitudes of the equatorial Pacific and Atlantic. This work provides a useful framework for systematic analysis of the strengths and weaknesses of different MJO indices.
Abstract
A new Madden–Julian oscillation (MJO) index is developed from a combined empirical orthogonal function (EOF) analysis of meridionally averaged 200-hPa velocity potential (VP200), 200-hPa zonal wind (U200), and 850-hPa zonal wind (U850). Like the Wheeler–Hendon Real-time Multivariate MJO (RMM) index, which was developed in the same way except using outgoing longwave radiation (OLR) data instead of VP200, daily data are projected onto the leading pair of EOFs to produce the two-component index. This new index is called the velocity potential MJO (VPM) indices and its properties are quantitatively compared to RMM. Compared to the RMM index, the VPM index detects larger-amplitude MJO-associated signals during boreal summer. This includes a slightly stronger and more coherent modulation of Atlantic tropical cyclones. This result is attributed to the fact that velocity potential preferentially emphasizes the planetary-scale aspects of the divergent circulation, thereby spreading the convectively driven component of the MJO’s signal across the entire globe. VP200 thus deemphasizes the convective signal of the MJO over the Indian Ocean warm pool, where the OLR variability associated with the MJO is concentrated, and enhances the signal over the relatively drier longitudes of the equatorial Pacific and Atlantic. This work provides a useful framework for systematic analysis of the strengths and weaknesses of different MJO indices.
A Framework for Assessing Operational Madden–Julian Oscillation Forecasts
A CLIVAR MJO Working Group Project
The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group (MJOWG) has taken steps to promote the adoption of a uniform diagnostic and set of skill metrics for analyzing and assessing dynamical forecasts of the MJO. Here we describe the framework and initial implementation of the approach using real-time forecast data from multiple operational numerical weather prediction (NWP) centers. The objectives of this activity are to provide a means to i) quantitatively compare skill of MJO forecasts across operational centers, ii) measure gains in forecast skill over time by a given center and the community as a whole, and iii) facilitate the development of a multimodel forecast of the MJO. The MJO diagnostic is based on extensive deliberations among the MJOWG in conjunction with input from a number of operational centers and makes use of the MJO index of Wheeler and Hendon. This forecast activity has been endorsed by the Working Group on Numerical Experimentation (WGNE), the international body that fosters the development of atmospheric models for NWP and climate studies.
The Climate Prediction Center (CPC) within the National Centers for Environmental Prediction (NCEP) is hosting the acquisition of the forecast data, application of the MJO diagnostic, and real-time display of the standardized forecasts. The activity has contributed to the production of 1–2-week operational outlooks at NCEP and activities at other centers. Further enhancements of the diagnostic's implementation, including more extensive analysis, comparison, illustration, and verification of the contributions from the participating centers, will increase the usefulness and application of these forecasts and potentially lead to more skillful predictions of the MJO and indirectly extratropical and other weather variability (e.g., tropical cyclones) influenced by the MJO. The purpose of this article is to inform the larger scientific and operational forecast communities of the MJOWG forecast effort and invite participation from additional operational centers.
The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group (MJOWG) has taken steps to promote the adoption of a uniform diagnostic and set of skill metrics for analyzing and assessing dynamical forecasts of the MJO. Here we describe the framework and initial implementation of the approach using real-time forecast data from multiple operational numerical weather prediction (NWP) centers. The objectives of this activity are to provide a means to i) quantitatively compare skill of MJO forecasts across operational centers, ii) measure gains in forecast skill over time by a given center and the community as a whole, and iii) facilitate the development of a multimodel forecast of the MJO. The MJO diagnostic is based on extensive deliberations among the MJOWG in conjunction with input from a number of operational centers and makes use of the MJO index of Wheeler and Hendon. This forecast activity has been endorsed by the Working Group on Numerical Experimentation (WGNE), the international body that fosters the development of atmospheric models for NWP and climate studies.
The Climate Prediction Center (CPC) within the National Centers for Environmental Prediction (NCEP) is hosting the acquisition of the forecast data, application of the MJO diagnostic, and real-time display of the standardized forecasts. The activity has contributed to the production of 1–2-week operational outlooks at NCEP and activities at other centers. Further enhancements of the diagnostic's implementation, including more extensive analysis, comparison, illustration, and verification of the contributions from the participating centers, will increase the usefulness and application of these forecasts and potentially lead to more skillful predictions of the MJO and indirectly extratropical and other weather variability (e.g., tropical cyclones) influenced by the MJO. The purpose of this article is to inform the larger scientific and operational forecast communities of the MJOWG forecast effort and invite participation from additional operational centers.
