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This study examines the impact of rain-rate initialization (RINIT), microphysical modifications, and cloud torques (in the context of angular momentum) on hurricane intensity forecasts using a mesoscale model [[the Advanced Research Weather Research and Forecasting model (ARW-WRF)]] at a cloud-resolving resolution of 2.7 km. The numerical simulations are performed in a triple-nested manner (25, 8.3, and 2.7 km) for Hurricane Dennis of 2005. Unless mentioned otherwise, all the results discussed are from the innermost grid with finest resolution (2.7 km). It is found that the model results obtained from the RINIT technique demonstrated robust improvement in hurricane structure, track, and intensity forecasts compared to the control experiment (CTRL; i.e., without RINIT). Thereafter, using RINIT initial conditions datasets three sensitive experiments are designed by modifying specific ice microphysical parameters (i.e., temperature-independent snow intercept parameter, doubling number of concentrations of ice, and ice crystal diameter) within the explicit parameterization scheme [[i.e., the WRF Single-Moment 6-class (WSM6)]]. It is shown that the experiment with enhanced ice mass concentration and temperature-independent snow intercept parameter produces the strongest and weakest storms, respectively. The results suggest that the distributions of hydrometeors are also impacted by the limited changes introduced in the microphysical scheme (e.g., the quantitative amount of snow drastically reduced to 0.1--0.2 g kg−−1 when the intercept parameter of snow is made independent of temperature). It is noted that the model holds ice at a warmer temperature for a longer time with a temperature-independent intercept parameter. These variations in hydrometeor distribution in the eyewall region of the storm affect diabatic heating and vertical velocity structure and modulated the storm intensity. However, irrespective of the microphysical changes the quantitative amount of graupel hydrometeors remained nearly unaffected. Finally, the indirect effect of microphysical modifications on storm intensity through angular momentum and cloud torques is examined. A formulation to predict the short-term changes in the storm intensity using a parcel segment angular momentum budget method is developed. These results serve to elucidate the indirect impact of microphysical modifications on tropical cyclone intensity changes through modulation in cloud torque magnitude. [ABSTRACT FROM AUTHOR]

The availability of daily observed rainfall estimates at a resolution of 0.5° × 0.5° latitude–longitude from a collection of over 2100 rain gauge sites over India provided the possibility for carrying out 5-day precipitation forecasts using a downscaling and a multimodel superensemble methodology. This paper addresses the forecast performances and regional distribution of predicted monsoon rains from the downscaling and from the addition of a multimodel superensemble. The extent of rainfall prediction improvements that arise above those of a current suite of operational models are discussed. The design of two algorithms one for downscaling and the other for the construction of multimodel superensembles are both based on the principle of least squares minimization of errors. That combination is shown to provide a robust forecast product through day 5 of the forecast for regional rains over the Indian monsoon region. The equitable threat scores from the downscaled superensemble over India well exceed those noted from the conventional superensemble and member models at current operational large-scale resolution. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

This paper examines how combining training-set forecasts from two separate oceanic basins affects the resulting tropical cyclone track and intensity forecasts in a particular oceanic basin. Atlantic and eastern Pacific training sets for 2002 and 2003 are combined and used to forecast 2004 eastern Pacific tropical cyclones in a real-time setting. These experiments show that the addition of Atlantic training improves the 2004 eastern Pacific forecasts. Finally, a detailed study of training-set and real-time model biases is completed in an effort to determine why cross-oceanic training may have helped in this instance. [ABSTRACT FROM AUTHOR]

This study uses an adaptive observational strategy for hurricane forecasting. It shows the impacts of Lidar Atmospheric Sensing Experiment (LASE) and dropsonde data sets from Convection and Moisture Experiment (CAMEX) field campaigns on hurricane track and intensity forecasts. The following cases are used in this study: Bonnie, Danielle and Georges of 1998 and Erin, Gabrielle and Humberto of 2001. A single model run for each storm is carried out using the Florida State University Global Spectral Model (FSUGSM) with the European Center for Medium Range Weather Forecasts (ECMWF) analysis as initial conditions, in addition to 50 other model runs where the analysis is randomly perturbed for each storm. The centers of maximum variance of the DLM heights are located from the forecast error variance fields at the 84-hr forecast. Back correlations are then performed using the centers of these maximum variances and the fields at the 36-hr forecast. The regions having the highest correlations in the vicinity of the hurricanes are indicative of regions from where the error growth emanates and suggests the need for additional observations. Data sets are next assimilated in those areas that contain high correlations. Forecasts are computed using the new initial conditions for the storm cases, and track and intensity skills are then examined with respect to the control forecast. The adaptive strategy is capable of identifying sensitive areas where additional observations can help in reducing the hurricane track forecast errors. A reduction of position error by approximately 52% for day 3 of forecast (averaged over 7 storm cases) over the control runs is observed. The intensity forecast shows only a slight positive impact due to the model’s coarse resolution. [ABSTRACT FROM AUTHOR]

This paper addresses the forecasts of latent heat fluxes from five different formulations of the planetary boundary layer (PBL). Different formulations are deployed within the Florida State University global spectral model. Hundreds of short range forecast experiments are carried out using daily data sets for summer 2002 with each model. The primary goal of this study is to compare the performance of the diverse family of PBL algorithms for the latent heat fluxes within the PBL. Benchmark fluxes are calculated from the vertical integrals of Yanai's formulation of the apparent moisture sink and a precipitation using Physical Initialization. This provides indirectly observed estimates of the vertical fluxes of latent heat in the PBL. This comparison reveals that no single scheme shows a global spread of improvement over other models for forecasts of latent heat fluxes in the PBL. Among these diverse models the turbulent kinetic energy based closure provides somewhat better results. The construction of a multimodel superensemble provides a synthesis of these different PBL formulations and shows improved forecasts of the surface fluxes. A single unified model utilizing weighted PBL algorithms where all the five schemes are retained within a single model shows some promise for improving a single model. [ABSTRACT FROM AUTHOR]

Climate variations in the Caribbean, largely manifest in rainfall activity, have important consequences for the large-scale water budget, natural vegetation, and land use in the region. The wet and dry seasons will be defined, and the important roles played by the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) in modulating the rainfall during these seasons will be discussed. The seasonal climate forecasts in this paper are made by 13 state of the art coupled atmosphere-ocean general circulation models (CGCMs) and by the Florida State University Synthetic Superensemble (FSUSSE), whose forecasts are obtained by a weighted combination of the individual CGCM forecasts based on a training period. The success of the models in simulating the observed 1989–2001 climatology of the various forecast parameters will be examined and linked to the models’ success in predicting the seasonal climate for individual years. Seasonal forecasts will be examined for precipitation, sea-surface temperature (SST), 2-meter air temperature, and 850 hPa u- and v-wind components during the period 1989–2001. Evaluation metrics include root mean square (RMS) error and Brier skill score. It will be shown that the FSUSSE is superior to the individual CGCMs and their ensemble mean both in simulating the 1989–2001 climatology for the various parameters and in predicting the seasonal climate of the various parameters for individual years. The seasonal climate forecasts of the FSUSSE and of the ensemble mean of the 13 state of the art CGCMs will be evaluated for years (during the period 1989–2001) that have particular ENSO and NAO signals that are known to influence Caribbean weather, particularly the rainfall. It will be shown that the FSUSSE provides superior forecasts of rainfall, SST, 2-meter air temperature, and 850 hPa u- and v-wind components during dry summers that are modulated by negative SOI and/or positive NAO indices. Such summers have become a feature of a twenty-year pattern of drought in the Caribbean region. The results presented in this paper will show that the FSUSSE is a valuable tool for forecasting rainfall and other atmospheric and oceanic variables during such periods of drought. [ABSTRACT FROM AUTHOR]

Modeling the geographical distribution of the phase and amplitude of the diurnal change is a challenging problem. This paper addresses the issues of modeling the diurnal mode of precipitation over the Tropics. Largely an early morning precipitation maximum over the oceans and an afternoon rainfall maximum over land areas describe the first-order diurnal variability. However, large variability in phase and amplitude prevails even within the land and oceanic areas. This paper addresses the importance of a multimodel superensemble for much improved prediction of the diurnal mode as compared to what is possible from individual models. To begin this exercise, the skills of the member models, the ensemble mean of the member models, a unified cloud model, and the superensemble for the prediction of total rain as well as its day versus night distribution were examined. Here it is shown that the distributions of total rain over the earth (tropical belt) and over certain geographical regions are predicted reasonably well (RMSE less than 18%) from the construction of a multimodel superensemble. This dataset is well suited for addressing the diurnal change. The large errors in phase of the diurnal modes in individual models usually stem from numerous physical processes such as the cloud radiation, shallow and deep cumulus convection, and the physics of the planetary boundary layer. The multimodel superensemble is designed to reduce such systematic errors and provide meaningful forecasts. That application for the diurnal mode appears very promising. This paper examines some of the regions such as the Tibetan Plateau, the eastern foothills of the Himalayas, and the Amazon region of South America that are traditionally difficult for modeling the diurnal change. In nearly all of these regions, errors in phase and amplitude of the diurnal mode of precipitation increase with the increased length of forecasts. Model forecast errors on the order of 6–12 h for phase and 50% for the amplitude are often seen from the member models. The multimodel superensemble reduces these errors and provides a close match (RMSE < 6 h) to the observed phase. The percent of daily rain and their phases obtained from the multimodel superensemble at 3-hourly intervals for different regions of the Tropics showed a closer match (pattern correlation about 0.4) with the satellite estimates. This is another area where the individual member models conveyed a much lower skill. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

The intraseasonal oscillation of Tropical Convergence Zones (TCZ) over the Indo-Pacific region has been studied. Both meridional and zonal propagating modes with timescales of about 40 days are prominent over the Indian and West Pacific regions. The mechanisms governing both these modes are reviewed. It is found that ocean--atmospheric interaction plays an important role in modulating these modes. The role of oceans is further highlighted in a series of seasonal forecasts using a coupled ocean--atmosphere model. It is found that forecasts of intraseasonal oscillations show significant improvement upon assimilation of sub-surface ocean data from the ARGO (a global programme to observe sub-surface profiles in the oceans) array of floats. [ABSTRACT FROM AUTHOR]

This paper addresses physical initialization of precipitation rates for a mesoscale numerical weather prediction model. This entails a slight modification of the vertical profile of the humidity variable that provides a close match between the satellite and model-based rain rates. This is based on the premise that the rain rate from a cumulus parameterization scheme such as the Arakawa–Schubert scheme is most sensitive to the vertical profiles of moist static stability. It is possible to adjust the vertical profile of moisture by a small linear perturbation by making it wetter (or drier) in the lower levels and the opposite at levels immediately above. This can provide a change in the moist static stability in order to achieve the desired rain rate. The procedure is invoked in a preforecast period between hours -24 and 0 following Krishnamurti et al. The present study is the authors’ first attempt to bring in this feature in a mesoscale model. They first noted that the procedure does indeed provide a much closer match between the satellite estimate of initial rain and that from the physical initialization for a mesoscale model. They have examined the impacts of this procedure for the initialization and short-range forecasts of a monsoon rainfall event and a hurricane. In both of these examples it became possible to improve the forecasts of rains compared with those from control runs that did not include the initialization of rains. Among these two examples, the results for the monsoon forecasts that deployed a uniform resolution of 25 km and the Grell and Devenyi scheme over the entire domain had the largest positive impact. The hurricane forecasts example also show improvement over the control run but with less impact, which may be due to heavy rains from explicit clouds in the nonhydrostatic model. Here the results did convey a strong positive impact from the use of the physical initialization; however, forecasts of very heavy rains carry smaller equitable threat scores. These require development of a more robust precipitation initialization procedure. [ABSTRACT FROM AUTHOR]

This study examines the predictability of weather over several regions in Africa using a multimodel superensemble technique developed at the Florida State University, which is an objective means of combining daily forecasts from multilevel global models. It is referred to as FSUSE and up to 7 different models are used to construct the superensemble. The benchmark reanalysis fields used are the precipitation data sets from CMORPH and all other global fields from ECMWF daily operational analysis. The FSUSE works by using multiple linear regression to derive weights from a comparison of each member model forecast to the benchmark analysis during a training period of the most recent 120 days, and these weights are passed to the forecast phase. This procedure removes the bias of each model and allows for an optimal linear combination of the individual model forecasts by taking account of the relative skill of each model to give a consensus forecast that is superior to the ensemble mean and all the members. Results show that bad models and poor analysis fields used during the training phase degrade the skill of the FSUSE. In the forecasts of rainfall events over all regions of Africa, the FSUSE root-mean-square (R M S) error, equitable threat skill score (E T S), and bias on the daily forecasts of rainfall were invariably superior to the best member model. The skills deteriorate as the forecast lead time in days increases, with the degradation being most significant beyond day 3. In all cases, the bias score of the FSUSE was approximately 1, while the anomaly correlation scores were to the order of 0.9. These scores indicate the robustness of the FSUSE forecasts. Over East Africa, the FSUSE forecasts were consistent with the spatial-temporal pattern of the Intertropical Convergence Zone (ITCZ), the main rain bearing synoptic mechanism across tropical Africa. Thus, in addition to superior forecasts, the use of FSUSE based data sets may provide a better understanding of the dynamical processes within the ITCZ over the region. These results could be further improved if the daily series of operational analysis had included gauge data and if the resolution were higher. It is hardly possible to get uniformly consistent and continuous daily observations over these diverse regions of Africa. However, given the availability of the satellite based estimates of daily rainfall, such as CMORPH and global analysis that are exchanged very fast nowadays, the FSUSE scheme for numerical weather predictions (N W P) provides useful medium range weather forecasts in real-time. [ABSTRACT FROM AUTHOR]

This paper deals with seasonal climate forecasting using as many as 13 coupled ocean–atmosphere models. An analysis of the individual model, multimodel ensemble, and FSU synthetic superensemble (FSUSSE) climate forecasts was performed for monthly and seasonal forecasts of precipitation, sea surface temperature (SST) and surface air temperature over the Euro-Mediterranean region including the land areas of Europe, North Africa and the Near East, during the period 1989–2001. In the FSUSSE methodology, forecasts are obtained by a weighted combination of the individual coupled ocean–atmosphere model forecasts based on a training period. The set of 13 individual climate forecast models utilized in this research is comprised of the seven models in the European suite of DEMETER models, a suite of four Florida State University models, the Australian POAMA model and the NCAR CCM3 model. The FSUSSE forecasts of seasonal precipitation anomalies were found to have the lowest root mean square (RMS) errors in comparison to the models in the multimodel ensemble, and their ensemble mean. However, the anomaly correlation (AC) coefficient results for seasonal precipitation anomaly forecasts by the FSUSSE were less impressive. The equitable threat scores for the FSUSSE forecasts of seasonal precipitation were found to be better than the various models in the multimodel ensemble, but those scores for the forecasts of positive seasonal anomalies were found to be worse than most of the models in the multimodel ensemble. The FSUSSE seasonal forecasts for SST and surface air temperature for the season considered (winter) were found to be excellent for both AC coefficients and RMS errors in the forecast anomalies. [ABSTRACT FROM AUTHOR]

In this study, the Florida State University Global Spectral Model (FSUGSM), in association with a high-resolution nested regional spectral model (FSUNRSM), is used for short-range weather forecasts over the Indian domain. Three-day forecasts for each day of August 1998 were performed using different versions of the FSUGSM and FSUNRSM and were compared with the observed fields (analysis) obtained from the European Center for Medium Range Weather Forecasts (ECMWF). The impact of physical initialization (a procedure that assimilates observed rain rates into the model atmosphere through a set of reverse algorithms) on rainfall forecasts was examined in detail. A very high nowcasting skill for precipitation is obtained through the use of high-resolution physical initialization applied at the regional model level. Higher skills in wind and precipitation forecasts over the Indian summer monsoon region are achieved using this version of the regional model with physical initialization. A relatively new concept, called the 'multimodel/multianalysis superensemble' is described in this paper and is applied for the wind and precipitation forecasts over the Indian subcontinent. Large improvement in forecast skills of wind at 850 hPa level over the Indian subcontinent is shown possible through the use of the multimodel superensemble. The multianalysis superensemble approach that uses the latest satellite data from the Tropical Rainfall Measuring Mission (TRMM) and the Defense Meteorological Satellite Program (DMSP) has shown significant improvement in the skills of precipitation forecasts over the Indian monsoon region. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Objective combination schemes of predictions from different models have been applied to seasonal climate forecasts. These schemes are successful in producing a deterministic forecast superior to individual member models and better than the multi-model ensemble mean forecast. Recently, a variant of the conventional superensemble formulation was created to improve skills for seasonal climate forecasts, the Florida State University (FSU) Synthetic Superensemble. The idea of the synthetic algorithm is to generate a new data set from the predicted multimodel datasets for multiple linear regression. The synthetic data is created from the original dataset by finding a consistent spatial pattern between the observed analysis and the forecast data set. This procedure is a multiple linear regression problem in EOF space. The main contribution this paper is to discuss the feasibility of seasonal prediction based on the synthetic superensemble approach and to demonstrate that the use of this method in coupled models dataset can reduce the errors of seasonal climate forecasts over South America. In this study, a suite of FSU coupled atmospheric oceanic models was used. In evaluation the results from the FSU synthetic superensemble demonstrate greater skill for most of the variables tested here. The forecast produced by the proposed method out performs other conventional forecasts. These results suggest that the methodology and database employed are able to improve seasonal climate prediction over South America when compared to the use of single climate models or from the conventional ensemble averaging. The results show that anomalous conditions simulated over South America are reasonably realistic. The negative (positive) precipitation anomalies for the summer monsoon season of 1997/98 (2001/02) were predicted by Synthetic Superensemble formulation quite well. In summary, the forecast produced by the Synthetic Superensemble approach outperforms the other conventional forecasts. [ABSTRACT FROM AUTHOR]

In this paper, a multi-model ensemble approach with statistical correction for seasonal precipitation forecasts using a coupled DEMETER model data set is presented. Despite the continuous improvement of coupled models, they have serious systematic errors in terms of the mean, the annual cycle and the interannual variability; consequently, the predictive skill of extended forecasts remains quite low. One of the approaches to the improvement of seasonal prediction is the empirical weighted multi-model ensemble, or superensemble, combination. In the superensemble approach, the different model forecasts are statistically combined during the training phase using multiple linear regression, with the skill of each ensemble member implicitly factored into the superensemble forecast. The skill of a superensemble relies strongly on the past performance of the individual member models used in its construction. The algorithm proposed here involves empirical orthogonal function (EOF) filtering of the actual data set prior to the construction of a multi-model ensemble or superensemble as an alternative solution for seasonal prediction. This algorithm generates a new data set from the input multi-model data set by finding a consistent spatial pattern between the observed analysis and the individual model forecast. This procedure is a multiple linear regression problem in the EOF space. The newly generated EOF-filtered data set is then used as an input data set for the construction of a multi-model ensemble and superensemble. The skill of forecast anomalies is assessed using statistics of categorical forecast, spatial anomaly correlation and root mean square (RMS) errors. The various verifications show that the unbiased multi-model ensemble of DEMETER forecasts improves the prediction of spatial patterns (i.e. the anomaly correlation), but it shows poor skill in categorical forecast. Due to the removal of seasonal mean biases of the different models, the forecast errors of the bias-corrected multi-model ensemble and superensemble are already quite small. Based on the anomaly correlation and RMS measures, the forecasts produced by the proposed method slightly outperform the other conventional forecasts. [ABSTRACT FROM AUTHOR]

The skill of the FSU Superensemble technique as applied to global numerical weather prediction is evaluated extensively in this paper. The global mass and motion fields for year 2000 and precipitation over the domain 55?S to 55?N for year 2001, as predicted by the Superensemble, the ensemble member models, and the mean of the ensemble members, are evaluated by standard statistical measures of skill to determine the performance of the Superensemble in relation to the other models. The member models are global forecast models from 5 of the world’s operational forecast centers in addition to the FSU global spectral model. For precipitation 5 additional versions of the FSU global model are utilized in the ensemble, as defined by different initial conditions provided by various physical initialization algorithms. Statistical parameters calculated for the mass and motion fields include root mean square (RMS) error, systematic error (or bias), and anomaly correlation. These are applied to the mean sea level pressure, 500?hPa heights, and the wind fields at 850?hPa and 200?hPa. Statistical parameters that were calculated for precipitation include RMS error, correlation, equitable threat score (ETS), and a special definition of bias appropriate for the precipitation field. For the mass and motion fields the performance of the Superensemble was considered for the annual global case, as well as for each hemisphere (north and south) and for each of the four seasons. For precipitation only the annual case was considered over the domain cited above.For the mass and motion fields the RMS calculations showed the Superensemble to be superior (to have the smallest total forecast error) in all comparisons to the ensemble member models, and to be superior to the ensemble mean in the vast majority of comparisons. Performance in comparison to the other models was generally better in the Southern Hemisphere than in the Northern Hemisphere, and better in the transition seasons of fall and spring than in the extreme seasons of winter and summer. The Superensemble had the best success with mean sea level pressure, followed in order by 500?hPa geopotential heights, 850?hPa winds, and 200?hPa winds.In the calculations of 500?hPa geopotential height anomaly correlation the Superensemble had higher scores in all comparisons to the ensemble member models, as well as higher scores in the majority of comparisons to the ensemble mean. As with the RMS error results, the Superensemble performed better in the Southern Hemisphere than in the Northern Hemisphere, and better in fall than in summer, in comparison to the other models. The superior anomaly correlation scores of the Superensemble attest to the ability of the model to forecast daily perturbations from the climatological means, perturbations that are associated with transient synoptic scale features, given the horizontal resolution in the forecast models.In terms of systematic error reduction the Superensemble produces its most impressive results. Annual global mean sea-level pressure systematic errors for day 5 forecasts are generally in the range of ±1?hPa (compared to errors as high as 8?hPa in other models), and day 2 forecasts of 500?hPa geopotential height produced systematic errors generally in the range of ±10 meters (compared to errors as high as 60 meters in other models). The Superensemble was able to reduce systematic errors in forecasts of a variety of important features in the global mass and motion fields: surface equatorial trough, wave amplitude in geopotential heights at 500?hPa, trade winds and Somali Jet at 850?hPa, mid-latitude westerlies, subtropical jet, and Tropical Easterly Jet (TEJ) at 200?hPa.In terms of forecasting precipitation the Superensemble outperforms all ensemble member models and the ensemble mean in terms of RMS error, correlation coefficient, equitable threat score, and bias. The superior correlation scores indicate that the Superensemble is more reliable than the other models in predicting perturbations in the area distribution of precipitation, perturbations that are essentially associated with migrant synoptic scale disturbances, considering the horizontal resolution of the forecast models.The Superensemble is a valuable tool for significantly improving upon the global model forecasts of the world’s operational forecast centers. These forecasts are used daily as important guidance in making weather forecasts in all regions of the world. This paper will demonstrate that the Superensemble improves upon the ensemble member model forecasts: (1) in a statistical sense considering broad areas of the globe, (2) in a synoptic climatology sense through focus on the improved forecasts of climatological features seen in the global mass and motion fields, (3) in a synoptic sense through use of anomaly correlation and correlation coefficient where improvement is demonstrated in the forecasts of perturbations from mean fields which are essentially associated with transient synoptic scale disturbances. [ABSTRACT FROM AUTHOR]

Dramatic examples of forecast failures in global models of moderate resolution (i.e., T106) have been shown to occur during periods of the negative phase of the Pacific/North American (PNA) pattern in the Northern Hemisphere winter. Specifically, in these periods forecast skills at 500?hPa as measured by the standard anomaly correlation index dropped to rather low values by days 4 and 5 of the forecasts. This paper examines systematically some of the factors that may have contributed to the failure of these model forecasts.In particular, strong winds approaching intensities on the order of 100?m?s-1 south of Japan at the 200?hPa level were degraded by the initialization and data assimilation procedures of the models. These observed winds were found to be supergradient in nature and representative of the anomalous solution of the gradient wind equation. Procedures such as the multivariate optimum interpolation (with its geostrophic constraints) and the normal-mode initialization including several vertical modes apparently were factors that led to the degradation of these strong winds in the initial model states. In this paper, an analysis of these factors is presented, and it is shown that uninitialized analyses (with no constraints) based on a simple successive correction procedure can retain the strong winds evident in the observations. Forecasts thus performed appear to retain wave trains, a characteristic feature of negative PNA initial states, leading to a significant improvement in forecast skill. [ABSTRACT FROM AUTHOR]

This paper addresses the weakening of Hurricane Lili of October 2002 just before it made landfall in Louisiana. This hurricane weakened from a category 4 storm on October 3, 2002 at 0000utcto a category 1 storm on October 3, 2002 at 1300utc. This sudden drop in intensity has been a subject of considerable interest. In this paper we explore a forecast model diagnostic approach that explores the contribution to the hurricane intensity changes arising from a number of dynamical and physical possibilities. Running several versions of a global model at very high resolution, the relative contribution to the intensity drop of Lili arising from cooler sea surface temperatures, dry air advection into the storm, advective non-linear dynamics, non-advective dynamics, and shallow and deep cumulus convection was examined. This line of inquiry led to the conclusion that dry air advection from the north into the storm and the slightly cold sea surface temperatures were not the primary contribution to the observed pressure rise by 22 hPa. The primary contribution to the pressure rise was found to be the‘rest of dynamics’ (the non-advective dynamics). The shallow convection contributed slightly to an overall cooling, i.e. a weakening of the warm core of Lili. The effects of deep cumulus convection appeared to be opposite, i.e. towards maintaining a strong storm. A primary term in the‘rest of dynamics’, the advection of Earth's angular momentum into the storm, is identified as a major contributor for the intensity change in the analysis. This feature resembles an intrusion of dry air into the core of the storm. This intrusion contributes to a reduction of spin and an overall rapid weakening of the hurricane. The angular momentum partitioning appears quite revealing on the sudden demise of Lili. [ABSTRACT FROM AUTHOR]

The main goal of this study is to investigate the relative contributions from the components of dynamics and physics of a forecast model, toward the understanding of the recurvature dynamics of hurricanes. A number of experiments were conducted using the Florida State University Global Spectral Model (FSU GSM), run at a global resolution of 126 waves. The method of physical initialization was used to ‘spin up’ the model, 24 h prior to the 5-day forecast period to better define the initial water vapor, sensible heat fluxes, and rainfall rates. The usage of the FSU GSM employed a partitioning of the dynamics and physics into separate components, that assumes a residue-free budget of the models' components. The model dynamics were broken down into a nonlinear advective component and also a linear dynamics (rest of the dynamics) partition. The model physics were partitioned into four components: deep convective heating, large-scale precipitation (nonconvective stable rain), total radiation, and shallow convection and surface fluxes. A total of four cases were examined, two each for Hurricanes Cindy and Dennis—1200 UTC 26 and 27 August, and 1200 UTC 28 and 29 August, occurring during the 1999 Atlantic hurricane season. The series of model runs were formulated to examine the tropical cyclone forecast tracks, suppressing one or more of the partitions for each time step, through day 5 of a forecast. Initial experiments coupling both the nonlinear advective and the linear dynamics (summed to equal the ‘total dynamics’) found that the total dynamics component resulted in a weakly recurving track for each of the storm cases. The addition of the physics components incrementally sharpened the recurving track through time. While the full model dynamics was used as a baseline, the results of this study indicated that the deep convective heating (also referred to as deep convection) and total dynamics combined to produce a recurving track for both storms, for 50% of the four examined cases. The remaining cases required that the shallow convection and surface fluxes partition be included along with the deep convection and total dynamics. It was found that incremental improvements occurred with both the deep convective heating and shallow convection and surface fluxes partitions, however, the additions of the large-scale precipitation and radiation partitions did not significantly improve the forecast track relative to the full model, and their resulting magnitudes were significantly smaller than the rest. [ABSTRACT FROM AUTHOR]

This paper addresses the anomaly correlation of the 500-hPa geopotential heights from a suite of global multimodels and from a model-weighted ensemble mean called the superensemble. This procedure follows a number of current studies on weather and seasonal climate forecasting that are being pursued. This study includes a slightly different procedure from that used in other current experimental forecasts for other variables. Here a superensemble for the ∇[SUP2] of the geopotential based on the daily forecasts of the geopotential fields at the 500-hPa level is constructed. The geopotential of the superensemble is recovered from the solution of the Poisson equation. This procedure appears to improve the skill for those scales where the variance of the geopotential is large and contributes to a marked improvement in the skill of the anomaly correlation. Especially large improvements over the Southern Hemisphere are noted. Consistent day-6 forecast skill above 0.80 is achieved on a day to day basis. The superensemble skills are higher than those of the best model and the ensemble mean. For days 1-6 the percent improvement in anomaly correlations of the superensemble over the best model are 0.3, 0.8, 2.25, 4.75, 8.6, and 14.6, respectively, for the Northern Hemisphere. The corresponding numbers for the Southern Hemisphere are 1.12, 1.66, 2.69, 4.48, 7.11, and 12.17. Major improvement of anomaly correlation skills is realized by the superensemble at days 5 and 6 of forecasts. The collective regional strengths of the member models, which is reflected in the proposed superensemble, provide a useful consensus product that may be useful for future operational guidance. [ABSTRACT FROM AUTHOR]

Using currently available operational forecast datasets on the tracks and intensities of tropical cyclones over the Pacific Ocean for the years 1998, 1999, and 2000 a multimodel superensemble has been constructed following the earlier work of the authors on the Atlantic hurricanes. The models included here include forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction/Environmental Modeling Center [NCEP/EMC, the Aviation (AVN) and Medium-Range Forecast (MRF) Models], the U.S. Navy [Naval Operational Global Atmospheric Prediction System, (NOGAPS)], the U.K. Met Office (UKMO), and the Japan Meteorological Agency (JMA). The superensemble methodology includes a collective bias estimation from a training phase in which a multiple-regression-based least squares minimization principle for the model forecasts with respect to the observed measures is employed. This is quite different from a simple bias correction, whereby a mean value is simply shifted. These bias estimates are described by separate weights at every 12 h during the forecasts for each of the member models. Superensemble forecasts for track and intensity are then constructed up to 144 h into the future using these weights. Some 100 past forecasts of tropical cyclone days are used to define the training phase for each basin. The findings presented herein show a marked improvement for the tracks and intensities of forecasts from the proposed multimodel superensemble as compared to the forecasts from member models and the ensemble mean. This note includes detailed statistics on the Pacific Ocean tropical cyclone forecasts for the years 1998, 1999, and 2000. [ABSTRACT FROM AUTHOR]

A high-resolution nested regional spectral model and an ensemble prediction system are combined to forecast the track, intensity, and flooding precipitation arising from Typhoon Winnie of August 1997, which eventually reached supertyphoon status. The prediction of floods is operationally challenging since rainfall distributions can have a high degree of spatial and temporal variability. Rare event probabilities, however, can be estimated more readily via ensemble forecasting. This technique is used to evaluate a typhoon flood event in which rainfall amounts greater than 200 mm led to landslides and major flooding of crops. Seven-member ensembles were generated using an EOF-based technique. An experiment was conducted with a regional model resolution of 0.5° latitude. A Mercator transform grid with a grid mesh size of approximately 55 km in the east–west and 48 km in the north–south was employed. The results indicated very accurate track and intensity forecasts for both the control and ensemble mean. Track position errors remained below 150 km through 72 h, while intensity errors were approximately 5 m s[sup -1] at landfall. Qualitatively, the overall 5-day precipitation patterns appeared realistic and compared favorably with the observed data, while, quantitatively, the correlation coefficient was near 0.6. For stations near and north of where Winnie made landfall, ensemble-based predictions performed well. While the ensemble mean often underestimated the heaviest rainfall totals by approximately 25%–50%, the maximum values within the ensemble spread either exceeded or came within 10%–15% of the station totals. Finally, in a related experiment the horizontal resolution was increased to 0.25° latitude. Even though more precipitation was produced, especially in northeastern China, the ensemble mean was similar to the 0.5° latitude simulation. [ABSTRACT FROM AUTHOR]