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- Author or Editor: T. N. Krishnamurti x
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Abstract
An arbitrary isobaric surface and the level of non-divergence constitute the two levels of the proposed model. The vorticity equation consistent with the quasi-geostrophic assumptions is applied at two levels. The thermodynamic energy equation for adiabatic motion is applied to the layer between the two levels. A parabolic profile for the vertical motion is assumed. The vertical motion is eliminated between the vorticity and the thermodynamic energy equations to obtain prognostic equations. The prognostic equations are solved by a generalized graphical forecast scheme of successive approximations. Forecasts and forecast errors in a selected meteorological situation are discussed.
Abstract
An arbitrary isobaric surface and the level of non-divergence constitute the two levels of the proposed model. The vorticity equation consistent with the quasi-geostrophic assumptions is applied at two levels. The thermodynamic energy equation for adiabatic motion is applied to the layer between the two levels. A parabolic profile for the vertical motion is assumed. The vertical motion is eliminated between the vorticity and the thermodynamic energy equations to obtain prognostic equations. The prognostic equations are solved by a generalized graphical forecast scheme of successive approximations. Forecasts and forecast errors in a selected meteorological situation are discussed.
Abstract
The equation for steady two-dimensional mountain waves is expressed in the isentropic coordinates. An elliptic equation for the finite amplitude vertical motion field is solved by a numerical marching scheme in atmospheres with varying shear and stability.
Abstract
The equation for steady two-dimensional mountain waves is expressed in the isentropic coordinates. An elliptic equation for the finite amplitude vertical motion field is solved by a numerical marching scheme in atmospheres with varying shear and stability.
Abstract
In this paper the elements of a monsoon system are defined, and its oscillations are determined from spectral analysis of long observational records. The elements of the monsoon system include pressure of the monsoon trough, pressure of the Mascarene high, cross-equatorial low-level jet, Tibetan high, tropical easterly jet, monsoon cloud cover, monsoon rainfall, dry static stability of the lower troposphere, and moist static stability of the lower troposphere. The summer monsoon months over India during normal monsoon rainfall years are considered as guidelines in the selection of data for the period of this study. The salient result of this study is that there seems to exist a quasi-biweekly oscillation in almost all of the elements of the monsoon system. For some of these elements, such as the surface pressure field, monsoon rainfall, low-level cross-equatorial jet and monsoon cloudiness, the amplitude of this oscillation in quasi-biweekly range is very pronounced. For the spectral representation of the time series, the product of the spectral density times frequency is used as the ordinate and the log of the frequency as the abscissa. Dominant modes are also found in the shorter time scales (<6 days). A sequential ordering of elements of the monsoon systems for the quasi-biweekly oscillation is carried out in terms of their respective phase angle. The principal result here is that soon after the maximum dry and moist static instabilities are realized in the stabilizing phase, there occur in sequence an intensification of the monsoon trough, satellite brightness, Mascarene high, Tibetan high and the tropical easterly jet. Soon after that the rainfall maximum over central India, arising primarily from monsoon depressions, is found to be a maximum.
In the second part of this paper we offer some plausible mechanisms for these quasi-biweekly oscillations.
Abstract
In this paper the elements of a monsoon system are defined, and its oscillations are determined from spectral analysis of long observational records. The elements of the monsoon system include pressure of the monsoon trough, pressure of the Mascarene high, cross-equatorial low-level jet, Tibetan high, tropical easterly jet, monsoon cloud cover, monsoon rainfall, dry static stability of the lower troposphere, and moist static stability of the lower troposphere. The summer monsoon months over India during normal monsoon rainfall years are considered as guidelines in the selection of data for the period of this study. The salient result of this study is that there seems to exist a quasi-biweekly oscillation in almost all of the elements of the monsoon system. For some of these elements, such as the surface pressure field, monsoon rainfall, low-level cross-equatorial jet and monsoon cloudiness, the amplitude of this oscillation in quasi-biweekly range is very pronounced. For the spectral representation of the time series, the product of the spectral density times frequency is used as the ordinate and the log of the frequency as the abscissa. Dominant modes are also found in the shorter time scales (<6 days). A sequential ordering of elements of the monsoon systems for the quasi-biweekly oscillation is carried out in terms of their respective phase angle. The principal result here is that soon after the maximum dry and moist static instabilities are realized in the stabilizing phase, there occur in sequence an intensification of the monsoon trough, satellite brightness, Mascarene high, Tibetan high and the tropical easterly jet. Soon after that the rainfall maximum over central India, arising primarily from monsoon depressions, is found to be a maximum.
In the second part of this paper we offer some plausible mechanisms for these quasi-biweekly oscillations.
Abstract
Forecasting tropical storm intensities is a very challenging issue. In recent years, dynamical models have improved considerably. However, for intensity forecasts more improvement is necessary. Dynamical models have different kinds of biases. Considering a multimodel consensus could eliminate some of the biases resulting in improved intensity forecasts as compared to the individual models. Apart from the ensemble mean, the construction of multimodel consensuses has always contributed to somewhat improved forecasts. The Florida State University (FSU) multimodel superensemble is one that, over the years, has systematically provided improved forecasts for hurricanes, numerical weather prediction, and seasonal climate forecasts. The present study considers an artificial neural network (ANN), based on biological principles, for the construction of a multimodel ensemble. ANN has been used for constructing multimodel consensus forecasts for tropical cyclone intensities. This study uses the generalized regression neural network (GRNN) method for the construction of consensus intensity forecasts for the Atlantic basin. Hurricane seasons 2012–16 are considered. Results show that with only five input models improved guidance for tropical storm intensities may be obtained. The consensus using GRNN mostly outperforms all the models included in the study and the ensemble mean. Forecast errors at the longer forecast leads are considerably less for this multimodel superensemble based on the generalized regression neural network. The skill and correlations of different models along with the developed consensus are provided in our analysis. Results suggest that this consensus forecast may be used for operational guidance and for planning and emergency evacuation management. Possibilities for future improvements of the consensus based on new advances in statistical algorithms are also indicated.
Abstract
Forecasting tropical storm intensities is a very challenging issue. In recent years, dynamical models have improved considerably. However, for intensity forecasts more improvement is necessary. Dynamical models have different kinds of biases. Considering a multimodel consensus could eliminate some of the biases resulting in improved intensity forecasts as compared to the individual models. Apart from the ensemble mean, the construction of multimodel consensuses has always contributed to somewhat improved forecasts. The Florida State University (FSU) multimodel superensemble is one that, over the years, has systematically provided improved forecasts for hurricanes, numerical weather prediction, and seasonal climate forecasts. The present study considers an artificial neural network (ANN), based on biological principles, for the construction of a multimodel ensemble. ANN has been used for constructing multimodel consensus forecasts for tropical cyclone intensities. This study uses the generalized regression neural network (GRNN) method for the construction of consensus intensity forecasts for the Atlantic basin. Hurricane seasons 2012–16 are considered. Results show that with only five input models improved guidance for tropical storm intensities may be obtained. The consensus using GRNN mostly outperforms all the models included in the study and the ensemble mean. Forecast errors at the longer forecast leads are considerably less for this multimodel superensemble based on the generalized regression neural network. The skill and correlations of different models along with the developed consensus are provided in our analysis. Results suggest that this consensus forecast may be used for operational guidance and for planning and emergency evacuation management. Possibilities for future improvements of the consensus based on new advances in statistical algorithms are also indicated.
Abstract
With current computational limitations, the accuracy of high-resolution precipitation forecasts has limited temporal and spatial resolutions. However, with the recent development of the superensemble technique, the potential to improve precipitation forecasts at the regional resolution exists. The purpose of this study is to apply the superensemble technique to regional precipitation forecasts to generate more accurate forecasts pinpointing exact locations and intensities of strong precipitation systems. This study will determine the skill of a regional superensemble forecast out to 60 h by examining its equitable threat score and its false alarm ratio. The regional superensemble consists of 12–60-h daily quantitative precipitation forecasts from six models. Five are independent operational models, and one comes from the physically initialized Florida State University regional spectral model. The superensemble forecasts are verified during the summer 2003 season over the southeastern United States using merged River Forecast Center stage-IV radar–gauge and satellite analyses. Precipitation forecasts were skillful in outperforming the operational models at all model times. Precipitation results were stratified by time of day to allow detections of the diurnal cycle. As expected, warm season daytime precipitation is commonly forced by convection, which is difficult to accurately model. Major synoptic regimes, including subtropical highs, midlatitude troughs/fronts, and tropical cyclones, were examined to determine the skill of the superensemble under various synoptic conditions.
Abstract
With current computational limitations, the accuracy of high-resolution precipitation forecasts has limited temporal and spatial resolutions. However, with the recent development of the superensemble technique, the potential to improve precipitation forecasts at the regional resolution exists. The purpose of this study is to apply the superensemble technique to regional precipitation forecasts to generate more accurate forecasts pinpointing exact locations and intensities of strong precipitation systems. This study will determine the skill of a regional superensemble forecast out to 60 h by examining its equitable threat score and its false alarm ratio. The regional superensemble consists of 12–60-h daily quantitative precipitation forecasts from six models. Five are independent operational models, and one comes from the physically initialized Florida State University regional spectral model. The superensemble forecasts are verified during the summer 2003 season over the southeastern United States using merged River Forecast Center stage-IV radar–gauge and satellite analyses. Precipitation forecasts were skillful in outperforming the operational models at all model times. Precipitation results were stratified by time of day to allow detections of the diurnal cycle. As expected, warm season daytime precipitation is commonly forced by convection, which is difficult to accurately model. Major synoptic regimes, including subtropical highs, midlatitude troughs/fronts, and tropical cyclones, were examined to determine the skill of the superensemble under various synoptic conditions.
Abstract
This study addresses seasonal forecasts of rains over India using the following components: high-resolution rain gauge–based rainfall data covering the years 1987–2001, rain-rate initialization, four global atmosphere–ocean coupled models, a regional downscaling of the multimodel forecasts, and a multimodel superensemble that includes a training and a forecast phase at the high resolution over the internal India domain. The results of monthly and seasonal forecasts of rains for the member models and for the superensemble are presented here. The main findings, assessed via the use of RMS error, anomaly correlation, equitable threat score, and ranked probability skill score, are (i) high forecast skills for the downscaled superensemble-based seasonal forecasts compared to the forecasts from the direct use of large-scale model forecasts were possible; (ii) very high scores for rainfall forecasts have been noted separately for dry and wet years, for different regions over India and especially for heavier rains in excess of 15 mm day−1; and (iii) the superensemble forecast skills exceed that of the benchmark observed climatology. The availability of reliable measures of high-resolution rain gauge–based rainfall was central for this study. Overall, the proposed algorithms, added together, show very promising results for the prediction of monsoon rains on the seasonal time scale.
Abstract
This study addresses seasonal forecasts of rains over India using the following components: high-resolution rain gauge–based rainfall data covering the years 1987–2001, rain-rate initialization, four global atmosphere–ocean coupled models, a regional downscaling of the multimodel forecasts, and a multimodel superensemble that includes a training and a forecast phase at the high resolution over the internal India domain. The results of monthly and seasonal forecasts of rains for the member models and for the superensemble are presented here. The main findings, assessed via the use of RMS error, anomaly correlation, equitable threat score, and ranked probability skill score, are (i) high forecast skills for the downscaled superensemble-based seasonal forecasts compared to the forecasts from the direct use of large-scale model forecasts were possible; (ii) very high scores for rainfall forecasts have been noted separately for dry and wet years, for different regions over India and especially for heavier rains in excess of 15 mm day−1; and (iii) the superensemble forecast skills exceed that of the benchmark observed climatology. The availability of reliable measures of high-resolution rain gauge–based rainfall was central for this study. Overall, the proposed algorithms, added together, show very promising results for the prediction of monsoon rains on the seasonal time scale.
Abstract
This paper provides new information on the low-level (850 hPa) structure and behavior of African easterly waves (AEWs) and relates this information to previous studies. Individual AEWs that occurred during June–September of 2001 are studied by a synoptic approach that employs Hovmöller diagrams, wave track maps, and case studies. The focus is on two AEW regimes in the lower troposphere over North Africa: a dry regime to the north of the African easterly jet (AEJ) coincident with the surface position of the monsoon trough near 20°N, and a wet regime to the south of the jet coincident with the near-equatorial rainbelt near 10°N. The following issues are addressed: the origin of the waves seen in the two wave regimes, relation of the wave activity to the mean positions of the surface monsoon trough and the 600–700-hPa AEJ, collocation of the tracks of the two wave regimes off the African coast, and diversity in low-level wave behavior that includes merging, splitting, and dissipation of the cyclonic vorticity centers associated with the wave troughs. The relationship between the waves following the two tracks is examined as well as the relationship between the low-level wave activity and Atlantic tropical cyclogenesis in 2001. It is shown that the two wave regimes can interact, and that both regimes were instrumental in Atlantic tropical cyclogenesis in 2001.
Abstract
This paper provides new information on the low-level (850 hPa) structure and behavior of African easterly waves (AEWs) and relates this information to previous studies. Individual AEWs that occurred during June–September of 2001 are studied by a synoptic approach that employs Hovmöller diagrams, wave track maps, and case studies. The focus is on two AEW regimes in the lower troposphere over North Africa: a dry regime to the north of the African easterly jet (AEJ) coincident with the surface position of the monsoon trough near 20°N, and a wet regime to the south of the jet coincident with the near-equatorial rainbelt near 10°N. The following issues are addressed: the origin of the waves seen in the two wave regimes, relation of the wave activity to the mean positions of the surface monsoon trough and the 600–700-hPa AEJ, collocation of the tracks of the two wave regimes off the African coast, and diversity in low-level wave behavior that includes merging, splitting, and dissipation of the cyclonic vorticity centers associated with the wave troughs. The relationship between the waves following the two tracks is examined as well as the relationship between the low-level wave activity and Atlantic tropical cyclogenesis in 2001. It is shown that the two wave regimes can interact, and that both regimes were instrumental in Atlantic tropical cyclogenesis in 2001.
Abstract
Several modeling studies have shown that the south Asian monsoon region has the lowest skill for seasonal forecasts compared with many other domains of the world. This paper demonstrates that a multimodel synthetic superensemble approach, when constructed with any set of coupled atmosphere–ocean models, can provide improved skill in seasonal climate prediction compared with single-member models or their ensemble mean for the south Asian summer monsoon region. However, performance of the superensemble tends to improve when a better set of input member models are used. As many as 13 state-of-the-art coupled atmosphere–ocean models were used in the synthetic superensemble algorithm. The merit of this technique lies in assigning differential weights to the member models. The rms errors, anomaly correlations, case studies of extreme events, and probabilistic skill scores are used here to assess these forecast skills. It was found that over the south Asian region the seasonal forecasts from the superensemble are, in general, superior to the forecasts of the individual member models, and their bias-removed ensemble mean at a significance level of 95% or more (based on a Student's t test) during the 13 yr of forecasts. Moreover, the skill of the superensemble was found to be better than those of the ensemble mean over smaller domains as well as during extreme events that were monitored, especially during the switch on and off of the Indian Ocean dipole, which seems to modulate the Indian monsoon rainfall. The results of this paper suggest that the superensemble provides somewhat consistent forecasts on the seasonal time scale. This methodology needs to be tested for real-time seasonal climate forecasting over the south Asian region.
Abstract
Several modeling studies have shown that the south Asian monsoon region has the lowest skill for seasonal forecasts compared with many other domains of the world. This paper demonstrates that a multimodel synthetic superensemble approach, when constructed with any set of coupled atmosphere–ocean models, can provide improved skill in seasonal climate prediction compared with single-member models or their ensemble mean for the south Asian summer monsoon region. However, performance of the superensemble tends to improve when a better set of input member models are used. As many as 13 state-of-the-art coupled atmosphere–ocean models were used in the synthetic superensemble algorithm. The merit of this technique lies in assigning differential weights to the member models. The rms errors, anomaly correlations, case studies of extreme events, and probabilistic skill scores are used here to assess these forecast skills. It was found that over the south Asian region the seasonal forecasts from the superensemble are, in general, superior to the forecasts of the individual member models, and their bias-removed ensemble mean at a significance level of 95% or more (based on a Student's t test) during the 13 yr of forecasts. Moreover, the skill of the superensemble was found to be better than those of the ensemble mean over smaller domains as well as during extreme events that were monitored, especially during the switch on and off of the Indian Ocean dipole, which seems to modulate the Indian monsoon rainfall. The results of this paper suggest that the superensemble provides somewhat consistent forecasts on the seasonal time scale. This methodology needs to be tested for real-time seasonal climate forecasting over the south Asian region.
Abstract
This study illustrates the capability of the ensemble technique to improve hurricane forecasts in the Florida State University Global Spectral Model. A perturbation method for hurricane ensemble prediction is proposed. The perturbation method consists of perturbing hurricane initial position and the large-scale environment in which the storm is embedded. The position perturbation is done by displacing the observed hurricane toward different directions by a small distance. The empirical orthogonal function (EOF) analysis is used to find fast-growing modes in the initial state. It is shown that the model forecasts, in terms of both hurricane track and other physical variables, are very sensitive to the hurricane initial position, intensity, and its large-scale environment. The results also show the EOF-based perturbations are the fast-growing modes and can be used to reduce the initial uncertainty in the analysis.
The hurricane forecast obtained from ensemble statistics lead to a large improvement in the track forecasts. For the intensity forecasts, the ensemble prediction provides several statistical methods to display the forecasts. The statistical mean from individual ensemble members provide an overview of the forecast. The spatial distribution of the probability of predicted variables make it possible to find the most likely weather pattern.
Abstract
This study illustrates the capability of the ensemble technique to improve hurricane forecasts in the Florida State University Global Spectral Model. A perturbation method for hurricane ensemble prediction is proposed. The perturbation method consists of perturbing hurricane initial position and the large-scale environment in which the storm is embedded. The position perturbation is done by displacing the observed hurricane toward different directions by a small distance. The empirical orthogonal function (EOF) analysis is used to find fast-growing modes in the initial state. It is shown that the model forecasts, in terms of both hurricane track and other physical variables, are very sensitive to the hurricane initial position, intensity, and its large-scale environment. The results also show the EOF-based perturbations are the fast-growing modes and can be used to reduce the initial uncertainty in the analysis.
The hurricane forecast obtained from ensemble statistics lead to a large improvement in the track forecasts. For the intensity forecasts, the ensemble prediction provides several statistical methods to display the forecasts. The statistical mean from individual ensemble members provide an overview of the forecast. The spatial distribution of the probability of predicted variables make it possible to find the most likely weather pattern.
Abstract
An empirical planetary boundary layer cloud parameterization has been developed for The Florida State University coupled ocean–atmosphere model to improve low-level clouds in the model. The scheme diagnoses the clouds by combining the PBL depth, ground wetness, and the relative humidity. Winter and summer simulations between 1987 and 1989 suggest an improvement in the low cloud representation compared to the International Satellite Cloud Climatology Project analysis. When implemented in the model, this parameterization results in positive impacts on shortwave fluxes and low-level circulation, particularly along the west coasts of the North and South American continents. Enhanced mechanical forcing at the ocean surface improves the SST representation in the eastern Pacific Ocean basin. Warm versus cold phase ENSO variability of the east Pacific SSTs are also improved during the seasonal simulations. Furthermore, the near-coast diurnal fluctuation of the low cloud is comparable to observations.
Abstract
An empirical planetary boundary layer cloud parameterization has been developed for The Florida State University coupled ocean–atmosphere model to improve low-level clouds in the model. The scheme diagnoses the clouds by combining the PBL depth, ground wetness, and the relative humidity. Winter and summer simulations between 1987 and 1989 suggest an improvement in the low cloud representation compared to the International Satellite Cloud Climatology Project analysis. When implemented in the model, this parameterization results in positive impacts on shortwave fluxes and low-level circulation, particularly along the west coasts of the North and South American continents. Enhanced mechanical forcing at the ocean surface improves the SST representation in the eastern Pacific Ocean basin. Warm versus cold phase ENSO variability of the east Pacific SSTs are also improved during the seasonal simulations. Furthermore, the near-coast diurnal fluctuation of the low cloud is comparable to observations.
