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- Author or Editor: T. N. Krishnamurti x
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Abstract
A pronounced continental-scale diurnal mode of the Asian summer monsoon is mapped using data from recent satellites Meteosat-5 and TRMM. These datasets were available at high temporal resolutions. A result that stands out is the diurnal divergent circulation that in the afternoon hours has an ascending lobe over north-central India and has a descending lobe that reaches out radially toward central China, the southern part of China, the equatorial Indian Ocean, and the western Arabian Sea. The reverse circulation is clearly seen during the early morning hours. This diurnal pulsation of continental-scale divergent circulation appears to be an integral part of the monsoon. Another finding relates to the diurnal slowing down and speeding up of the Tibetan high circulations, especially in the southern flanks where the tropical easterly jet resides and exhibits a pulsation of intensity. The amplitude of pulsation was found to reach up to 7 m s−1. Thus this continental-scale change appears to be a pronounced feature. The phase and amplitude of various satellite datasets derived from the 90-min datasets are also displayed to confirm this major mode, that is, the diurnal oscillation of monsoon.
Abstract
A pronounced continental-scale diurnal mode of the Asian summer monsoon is mapped using data from recent satellites Meteosat-5 and TRMM. These datasets were available at high temporal resolutions. A result that stands out is the diurnal divergent circulation that in the afternoon hours has an ascending lobe over north-central India and has a descending lobe that reaches out radially toward central China, the southern part of China, the equatorial Indian Ocean, and the western Arabian Sea. The reverse circulation is clearly seen during the early morning hours. This diurnal pulsation of continental-scale divergent circulation appears to be an integral part of the monsoon. Another finding relates to the diurnal slowing down and speeding up of the Tibetan high circulations, especially in the southern flanks where the tropical easterly jet resides and exhibits a pulsation of intensity. The amplitude of pulsation was found to reach up to 7 m s−1. Thus this continental-scale change appears to be a pronounced feature. The phase and amplitude of various satellite datasets derived from the 90-min datasets are also displayed to confirm this major mode, that is, the diurnal oscillation of monsoon.
Abstract
A method for analysis of the horizontal and vertical distributions of the moisture field utilizing satellite, upper air and surface data is proposed in this paper. A brief overview of the microwave sensors on board Nimbus 5 and 6 is also presented. A technique is provided utilizing the radiosonde data sets to calibrate the satellite field of total precipitable water. Next, the calibrated satellite-derived field is utilized along with ship and coastal reports of moisture, and a vertical structure function to generate vertical distribution of moisture and thus provide a mapping of specific humidity at several levels in the troposphere. Utilizing these procedures, analyses for several case studies were performed. The resultant maps show detailed distribution of specific humidity along with some interesting climatological features. A reasonable acceptance of the available aerological data sets by the analysis scheme is demonstrated.
Abstract
A method for analysis of the horizontal and vertical distributions of the moisture field utilizing satellite, upper air and surface data is proposed in this paper. A brief overview of the microwave sensors on board Nimbus 5 and 6 is also presented. A technique is provided utilizing the radiosonde data sets to calibrate the satellite field of total precipitable water. Next, the calibrated satellite-derived field is utilized along with ship and coastal reports of moisture, and a vertical structure function to generate vertical distribution of moisture and thus provide a mapping of specific humidity at several levels in the troposphere. Utilizing these procedures, analyses for several case studies were performed. The resultant maps show detailed distribution of specific humidity along with some interesting climatological features. A reasonable acceptance of the available aerological data sets by the analysis scheme is demonstrated.
Abstract
Convective adjustment procedures remove conditional instability in a vertical sounding preserving the total energy (latent, internal and potential). It is shown that this procedure has very undesirable properties in the very first time step in numerical weather prediction, e.g., large-scale temperature and moisture distributions are greatly altered. If, on the other hand, convective adjustment is carried out on a mesoscale, as is the case for the Kuo parameterization procedure, then the large-scale conditional instability is preserved and the changes in initial data are small in the first (and subsequent) time steps. The latter procedure is used to evaluate convective precipitation in the vicinity of a squall line. A detailed heating function is designed for use in the ω equation and primitive equation prediction models. This function permits stable or unstable heating to occur in a given region and also allows for the simultaneous occurrence of both kinds of heating in the same region. Precipitation rate estimates show major improvement over earlier versions which followed only stable heating.
Abstract
Convective adjustment procedures remove conditional instability in a vertical sounding preserving the total energy (latent, internal and potential). It is shown that this procedure has very undesirable properties in the very first time step in numerical weather prediction, e.g., large-scale temperature and moisture distributions are greatly altered. If, on the other hand, convective adjustment is carried out on a mesoscale, as is the case for the Kuo parameterization procedure, then the large-scale conditional instability is preserved and the changes in initial data are small in the first (and subsequent) time steps. The latter procedure is used to evaluate convective precipitation in the vicinity of a squall line. A detailed heating function is designed for use in the ω equation and primitive equation prediction models. This function permits stable or unstable heating to occur in a given region and also allows for the simultaneous occurrence of both kinds of heating in the same region. Precipitation rate estimates show major improvement over earlier versions which followed only stable heating.
Abstract
Vertical motions in the tropics are computed for an easterly wave perturbation associated with an upper tropospheric cold core low. The winds are deduced from the pressure field by solving a five level non-geostrophic model on the IBM 7094.
The streamlines exhibit the well known properties of vortex and col points and agree quite well with the observed wind observations at the various levels. Vertical motions show the dynamical and thermodynamical interaction of the lower and upper level phenomena. Typical magnitudes of the vertical motion are around 1/10 cm sec−1. Differential vertical advection and the Laplacian of thermal advection by the rotational component of the wind contribute significantly to these organized vertical motions.
Abstract
Vertical motions in the tropics are computed for an easterly wave perturbation associated with an upper tropospheric cold core low. The winds are deduced from the pressure field by solving a five level non-geostrophic model on the IBM 7094.
The streamlines exhibit the well known properties of vortex and col points and agree quite well with the observed wind observations at the various levels. Vertical motions show the dynamical and thermodynamical interaction of the lower and upper level phenomena. Typical magnitudes of the vertical motion are around 1/10 cm sec−1. Differential vertical advection and the Laplacian of thermal advection by the rotational component of the wind contribute significantly to these organized vertical motions.
Abstract
A surface-based rainfall monitoring network reveals that summer rainfall over Taiwan exhibits a prominent diurnal variation. In this study, an attempt has been made to detect the diurnal variability of Taiwan rainfall using observations from the Tropical Rainfall Measuring Mission (TRMM) satellite. The results show that the diurnal patterns of Taiwan rainfall can be detected with TRMM Microwave Imager (TMI) observations using a satellite observation period of 36 or more days, and detected signals match reasonably with those using continuous surface observations. However, sometimes, because of the unfavorable combination of satellite sampling and the occurrence of some transient regimes in local rainfall, there is a possibility of misinterpreting the diurnal cycle. The TRMM precipitation radar sensor also reveals a diurnal cycle of convective and stratiform rainfall. The convective activity increases during the late afternoon over Taiwan, which may be the effect of convection forced by localized mass convergence caused by the sea breeze. It is of interest that TMI data indicate a significant increase of rainfall over orographic regions during the same time.
Abstract
A surface-based rainfall monitoring network reveals that summer rainfall over Taiwan exhibits a prominent diurnal variation. In this study, an attempt has been made to detect the diurnal variability of Taiwan rainfall using observations from the Tropical Rainfall Measuring Mission (TRMM) satellite. The results show that the diurnal patterns of Taiwan rainfall can be detected with TRMM Microwave Imager (TMI) observations using a satellite observation period of 36 or more days, and detected signals match reasonably with those using continuous surface observations. However, sometimes, because of the unfavorable combination of satellite sampling and the occurrence of some transient regimes in local rainfall, there is a possibility of misinterpreting the diurnal cycle. The TRMM precipitation radar sensor also reveals a diurnal cycle of convective and stratiform rainfall. The convective activity increases during the late afternoon over Taiwan, which may be the effect of convection forced by localized mass convergence caused by the sea breeze. It is of interest that TMI data indicate a significant increase of rainfall over orographic regions during the same time.
Abstract
Many low-frequency phenomena such as the Madden–Julian oscillation (MJO) or the El Niño–Southern Oscillation (ENSO) exhibit rapid growth where they appear to be undergoing a phase locking with other time scales such as the annual cycle. The purpose of this paper is to illustrate an example of phase locking of two different time scales. In this instance it is shown that during such epochs of phase locking a large increase in nonlinear energy exchange occurs from one time scale to the other. This paper utilizes the ECMWF Re-Analysis (ERA-40) datasets for the year 2001 to examine this problem. This study is a sequel to a recent modeling study where the maintenance of the MJO time scale was examined from scale interactions, especially with synoptic-scale waves with ∼2–7 day periods. It was shown that a pair of waves on the synoptic time scale can satisfy certain selection rules and undergo triad interactions (kinetic energy to kinetic energy exchanges) and transfer energy. This present study illustrates the fact that during epochs of phase locking such nonlinear interactions can become very large, thus portraying the importance of phase locking. These explosive exchanges are shown from two perspectives: an approach based on kinetic energy exchanges in the frequency domain and another that invokes the boundary layer dynamics in the frequency domain.
Abstract
Many low-frequency phenomena such as the Madden–Julian oscillation (MJO) or the El Niño–Southern Oscillation (ENSO) exhibit rapid growth where they appear to be undergoing a phase locking with other time scales such as the annual cycle. The purpose of this paper is to illustrate an example of phase locking of two different time scales. In this instance it is shown that during such epochs of phase locking a large increase in nonlinear energy exchange occurs from one time scale to the other. This paper utilizes the ECMWF Re-Analysis (ERA-40) datasets for the year 2001 to examine this problem. This study is a sequel to a recent modeling study where the maintenance of the MJO time scale was examined from scale interactions, especially with synoptic-scale waves with ∼2–7 day periods. It was shown that a pair of waves on the synoptic time scale can satisfy certain selection rules and undergo triad interactions (kinetic energy to kinetic energy exchanges) and transfer energy. This present study illustrates the fact that during epochs of phase locking such nonlinear interactions can become very large, thus portraying the importance of phase locking. These explosive exchanges are shown from two perspectives: an approach based on kinetic energy exchanges in the frequency domain and another that invokes the boundary layer dynamics in the frequency domain.
Abstract
In this study, the Florida State University Coupled Global Spectral Model (FSUCGSM) is utilized to examine the possible regulation of the warm pool SST and its contributors. The model is run for 1 yr to obtain the residue-free time evolution of the warm pool SST. The results are verified against NCEP SST analysis for the period of the model integration. The best agreement was seen over the western equatorial Pacific.
The initial analysis of the model output has suggested that the warm pool SST is derived mainly by three important types of oscillations, namely, semiannual, 10–25-, and 30–60-day oscillations. Further examination using Butterworth bandpass filter and EOF analysis has revealed that the tendency of solar radiation is the primary cause of the high-frequency oscillations (20–25 and 30–60 day) and secondary cause for the low-frequency oscillations. Moreover, the evaporative cooling is found to be the primary cause of the low-frequency oscillations and secondary cause for the high-frequency oscillations. The variations of these two forcings were found to be strongly related to convective activities. At high frequencies, convective activities are associated with equatorial waves, whereas at low frequencies such conditions are derived by the migration of the ITCZ.
In relation to the atmospheric moisture content, it was found that the cloud shortwave forcing plays the most crucial role in the solar radiation. The connection between convective activities and the changes in the evaporative cooling is found to be through the humidity deficit at low-frequency oscillations and surface wind speed at high-frequency oscillations. A careful examination of the SST–convection interaction has revealed that the warm pool SST may have an upper limit as suggested by earlier authors.
Abstract
In this study, the Florida State University Coupled Global Spectral Model (FSUCGSM) is utilized to examine the possible regulation of the warm pool SST and its contributors. The model is run for 1 yr to obtain the residue-free time evolution of the warm pool SST. The results are verified against NCEP SST analysis for the period of the model integration. The best agreement was seen over the western equatorial Pacific.
The initial analysis of the model output has suggested that the warm pool SST is derived mainly by three important types of oscillations, namely, semiannual, 10–25-, and 30–60-day oscillations. Further examination using Butterworth bandpass filter and EOF analysis has revealed that the tendency of solar radiation is the primary cause of the high-frequency oscillations (20–25 and 30–60 day) and secondary cause for the low-frequency oscillations. Moreover, the evaporative cooling is found to be the primary cause of the low-frequency oscillations and secondary cause for the high-frequency oscillations. The variations of these two forcings were found to be strongly related to convective activities. At high frequencies, convective activities are associated with equatorial waves, whereas at low frequencies such conditions are derived by the migration of the ITCZ.
In relation to the atmospheric moisture content, it was found that the cloud shortwave forcing plays the most crucial role in the solar radiation. The connection between convective activities and the changes in the evaporative cooling is found to be through the humidity deficit at low-frequency oscillations and surface wind speed at high-frequency oscillations. A careful examination of the SST–convection interaction has revealed that the warm pool SST may have an upper limit as suggested by earlier authors.
Abstract
The superensemble technique has been proven to be successful in producing a deterministic forecast superior not only to any of the individual models going into it, but also to the multimodel ensemble forecast. Research so far has been done on the superensemble as a deterministic forecast, and it has been shown that using the superensemble method leads to a significant reduction in rms errors. This paper investigates the skill of the superensemble as a probabilistic forecast, and it compares it with that of the multimodel ensemble. Using the Atmospheric Model Intercomparison Project (AMIP I) seasonal multimodel precipitation forecasts, probability forecasts are defined for the multimodel ensemble and for the multimodel superensemble. The Brier skill score of these forecasts is calculated for different thresholds of precipitation anomaly. It is shown that both the multimodel ensemble and the superensemble probability forecasts are much better than climatological forecast and that the superensemble probability forecast outperforms the multimodel bias-removed ensemble at any threshold level.
Abstract
The superensemble technique has been proven to be successful in producing a deterministic forecast superior not only to any of the individual models going into it, but also to the multimodel ensemble forecast. Research so far has been done on the superensemble as a deterministic forecast, and it has been shown that using the superensemble method leads to a significant reduction in rms errors. This paper investigates the skill of the superensemble as a probabilistic forecast, and it compares it with that of the multimodel ensemble. Using the Atmospheric Model Intercomparison Project (AMIP I) seasonal multimodel precipitation forecasts, probability forecasts are defined for the multimodel ensemble and for the multimodel superensemble. The Brier skill score of these forecasts is calculated for different thresholds of precipitation anomaly. It is shown that both the multimodel ensemble and the superensemble probability forecasts are much better than climatological forecast and that the superensemble probability forecast outperforms the multimodel bias-removed ensemble at any threshold level.
Abstract
The goal of this study is to utilize several recent developments on rainfall data collection, downscaling of available climate models, training and forecasts from such models within the framework of a multimodel superensemble, and first a detailed examination of the seasonal climatology. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state-of-the-art coupled climate models. A downscaling component, with respect to observed rainfall estimates, uses a very dense Asian rain gauge network. This feature enables the forecasts of each model to be bias corrected to a common 25-km resolution. The downscaling statistics for each model, at each grid location, are developed during a training phase of the model forecasts. This is done wherever the observed rainfall estimates are available. In the “forecast phase,” the forecasts from all of the member models use the downscaling coefficients of the “training phase.” The downscaling and the extraction of the superensemble weights are done during the training phase. This makes use of the cross-validation principle. This means that the season to be forecasted is left out of the entire forecast dataset. Thus all of the statistics for downscaling and the superensemble construction are done separately for the forecasts of each season for all the years. The forecast phase is the season that is being forecast, where the aforementioned statistics are deployed for constructing the final downscaled superensemble.
These forecasts are next used for the construction of a multimodel superensemble. The geographical distributions of the downscaling coefficients provide a first look at the systematic errors of the member model forecasts. This combination of multimodels, the vast rain gauge dataset, the downscaling, and the superensemble provides a major improvement for the rainfall climatology and anomalies for the forecast phase. One of the main results of this paper is on the improvement of rainfall climatology of the member models. The downscaled multimodel superensemble shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. This first part of the paper provides a description of the member models, the length of the training and forecast phases, the sensitivity of results as the numbers of forecast models are increased, and the skills of the downscaled climatology forecasts.
Abstract
The goal of this study is to utilize several recent developments on rainfall data collection, downscaling of available climate models, training and forecasts from such models within the framework of a multimodel superensemble, and first a detailed examination of the seasonal climatology. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state-of-the-art coupled climate models. A downscaling component, with respect to observed rainfall estimates, uses a very dense Asian rain gauge network. This feature enables the forecasts of each model to be bias corrected to a common 25-km resolution. The downscaling statistics for each model, at each grid location, are developed during a training phase of the model forecasts. This is done wherever the observed rainfall estimates are available. In the “forecast phase,” the forecasts from all of the member models use the downscaling coefficients of the “training phase.” The downscaling and the extraction of the superensemble weights are done during the training phase. This makes use of the cross-validation principle. This means that the season to be forecasted is left out of the entire forecast dataset. Thus all of the statistics for downscaling and the superensemble construction are done separately for the forecasts of each season for all the years. The forecast phase is the season that is being forecast, where the aforementioned statistics are deployed for constructing the final downscaled superensemble.
These forecasts are next used for the construction of a multimodel superensemble. The geographical distributions of the downscaling coefficients provide a first look at the systematic errors of the member model forecasts. This combination of multimodels, the vast rain gauge dataset, the downscaling, and the superensemble provides a major improvement for the rainfall climatology and anomalies for the forecast phase. One of the main results of this paper is on the improvement of rainfall climatology of the member models. The downscaled multimodel superensemble shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. This first part of the paper provides a description of the member models, the length of the training and forecast phases, the sensitivity of results as the numbers of forecast models are increased, and the skills of the downscaled climatology forecasts.
Abstract
This is the second part of a paper on the improved seasonal precipitation forecasts for the Asian monsoon using 16 atmosphere–ocean coupled models. This study utilizes a large suite of coupled atmosphere–ocean models; this second part largely addresses the skill of rainfall anomaly forecasts. These include both deterministic and probabilistic skill measures such as the RMS errors, anomaly correlations, equitable threat scores, and the Brier skill score. It was possible to improve the skills of rainfall climatology from the use of a downscaled multimodel superensemble to very high levels, and it is of interest to ask how far this methodology would go toward improving the skills of seasonal rainfall anomaly forecasts. It is possible to go through a sequence of multimodel post processing to improve upon these skills by using a dense rain gauge network over Asia, downscaling forecasts for each member model, and constructing a multimodel superensemble that benefits from the persistence of errors of the member models. This paper addresses the spinup issues of the downscaling and the superensemble results where the number of years of model data needed for training phase, for the downscaling, and for the construction of the superensemble, is addressed. In the context of cross validation, the training phase includes 14 seasons of monsoon data. The forecast phase is only one season; it is this season that was not included in the training phase each time.
The relationship between data length and the number of models needed for enhanced skills is another issue that is addressed. Seasonal climate forecasts over the larger monsoon Asia domain and over the regional belts are evaluated. The superensemble forecasts invariably have the highest skill compared to the member models globally and regionally. This is largely due to the presence of large systematic errors in models that carry low seasonal prediction skills. Such models carry persistent signatures of systematic errors, and their errors are recognized by the multimodel superensemble. The probabilistic skills show that the superensemble-based forecasts carry a much higher reliability score compared to the member models. This implies that the superensemble-based forecasts are the most reliable among all the member models. It is possible to examine the performance of models and of the superensemble during periods of heavy monsoon rainfall versus those for deficient monsoon rainfall seasons. One of the conclusions of this study is that given the uncertainties in current modeling for seasonal rainfall forecasts, post processing of multimodel forecasts, using the superensemble methodology, seems to provide the most promising results for the rainfall anomaly forecasts. These results are confirmed by an additional skill metric where the RMS errors and the correlations of forecast skills are evaluated using a normalized precipitation anomaly for the forecasts and the observed estimates.
Abstract
This is the second part of a paper on the improved seasonal precipitation forecasts for the Asian monsoon using 16 atmosphere–ocean coupled models. This study utilizes a large suite of coupled atmosphere–ocean models; this second part largely addresses the skill of rainfall anomaly forecasts. These include both deterministic and probabilistic skill measures such as the RMS errors, anomaly correlations, equitable threat scores, and the Brier skill score. It was possible to improve the skills of rainfall climatology from the use of a downscaled multimodel superensemble to very high levels, and it is of interest to ask how far this methodology would go toward improving the skills of seasonal rainfall anomaly forecasts. It is possible to go through a sequence of multimodel post processing to improve upon these skills by using a dense rain gauge network over Asia, downscaling forecasts for each member model, and constructing a multimodel superensemble that benefits from the persistence of errors of the member models. This paper addresses the spinup issues of the downscaling and the superensemble results where the number of years of model data needed for training phase, for the downscaling, and for the construction of the superensemble, is addressed. In the context of cross validation, the training phase includes 14 seasons of monsoon data. The forecast phase is only one season; it is this season that was not included in the training phase each time.
The relationship between data length and the number of models needed for enhanced skills is another issue that is addressed. Seasonal climate forecasts over the larger monsoon Asia domain and over the regional belts are evaluated. The superensemble forecasts invariably have the highest skill compared to the member models globally and regionally. This is largely due to the presence of large systematic errors in models that carry low seasonal prediction skills. Such models carry persistent signatures of systematic errors, and their errors are recognized by the multimodel superensemble. The probabilistic skills show that the superensemble-based forecasts carry a much higher reliability score compared to the member models. This implies that the superensemble-based forecasts are the most reliable among all the member models. It is possible to examine the performance of models and of the superensemble during periods of heavy monsoon rainfall versus those for deficient monsoon rainfall seasons. One of the conclusions of this study is that given the uncertainties in current modeling for seasonal rainfall forecasts, post processing of multimodel forecasts, using the superensemble methodology, seems to provide the most promising results for the rainfall anomaly forecasts. These results are confirmed by an additional skill metric where the RMS errors and the correlations of forecast skills are evaluated using a normalized precipitation anomaly for the forecasts and the observed estimates.
