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This study addresses observational and modeling sensitivity on the march of the onset isochrones of the Indian summer monsoon. The first 25 days of the passage of the isochrones of monsoon onset is of great scientific interest. Surface and satellite-based datasets are used for high-resolution modeling of the impact of the motion of the onset isochrones from Kerala to New Delhi. These include the asymmetries across the isochrone such as soil moisture and its temporal variability, moistening of the dry soil to the immediate north of the isochrone by nonconvective anvil rains, and formation of newly forming cloud elements to the immediate north of the isochrone. The region immediately north of the isochrone is shown to carry a spread of buoyancy elements. As these new elements grow, they are continually being steered by the divergent circulations of the parent isochrone to the north and eventually to the northwest. CloudSat was extremely useful for identifying the asymmetric cloud structures across the isochrone. In the modeling sensitivity studies, the authors used a mesoscale Advanced Research Weather Research and Forecasting Model (ARW-WRF) to examine days 1-25 of forecasts of the onset isochrone. Prediction experiments were first modeled during normal, dry, and wet Indian monsoons using default values of model parameters. This study was extended to determine the effects of changes in soil moisture and nonconvective rain parameterizations (the parameters suggested by the satellite observations). These sensitivity experiments show that the motion of the isochrones from Kerala to New Delhi are very sensitive to the parameterization of soil moisture and nonconvective anvil rains immediately north of the isochrone. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 FROM AUTHOR]

Recently the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) project office made available a new product called the convective–stratiform heating (CSH). These are the datasets for vertical profiles of diabatic heating rates (the apparent heat source). These observed estimates of heating are obtained from the TRMM satellite’s microwave radiances and the precipitation radar. The importance of such datasets for defining the vertical distribution of heating was largely the initiative of Dr. W.-K. Tao from NASA’s Goddard Laboratory. The need to examine how well some of the current cumulus parameterization schemes perform toward describing the amplitude and the three-dimensional distributions of heating is addressed in this paper. Three versions of the Florida State University (FSU) global atmospheric model are run that utilize different versions of cumulus parameterization schemes; namely, modified Kuo parameterization, simple Arakawa–Schubert parameterization, and Zhang–McFarlane parameterization. The Kuo-type scheme used here relies on moisture convergence and tends to overestimate the rainfall generally compared to the TRMM estimates. The other schemes used here show only a slight overestimate of rain rates compared to TRMM; those invoke mass fluxes that are less stringent in this regard in defining cloud volumes. The mass flux schemes do carry out a total moisture budget for a vertical column model and include all components of the moisture budget and are not limited to the horizontal convergence of moisture. The authors carry out a numerical experimentation that includes over a hundred experiments from each of these models; these experiments differ only in their use of the cumulus parameterization. The rest of the model physics, resolution, and initial states are kept the same for each set of 117 forecasts. The strategy for this experimentation follows the authors’ previous studies with the FSU multimodel superensemble. This includes a 100-day training and a 17-day forecast phase, both of which include a large number of forecast experiments. The training phase provides a useful statistical database for tagging the systematic errors of the respective models. The forecast phase is designed to minimize the collective bias errors of these member models. In these forecasts the authors also include the ensemble mean and the multimodel superensemble. In this paper the authors examine model errors in their representations of the heating (amplitude, vertical level of maximum, and the geographical distributions). The main message of this study is that some cumulus parameterization schemes overestimate the amplitude of heating, whereas others carry lower values. The models also exhibit large errors in the placement of the vertical level of maximum heating. Some significant errors were also found in the geographical distributions of heating. The ensemble mean largely mimics the model features and also carries some large errors. The superensemble is more selective in reducing the three-dimensional collective bias errors of the models and provides the best short range forecasts, through hour 60, for the heating. This study shows that it is possible to diagnose some of the modeling errors in the heating for individual member models and that information can be important for correcting such features. [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]

We illustrate the vast expanse and the connection of anticyclonic flows of the Tibetan high (at the 200 hPa level) to the pole centred hemispheric North Polar anticyclone at the 10 hPa level during 1988. This feature of clockwise flows appears in the form of a tilted cone that appears to connect the Asian summer monsoon to the 10 hPa high. The anticyclonic flow tapers down to the vortex of the cone near the 400 hPa level. The tropical easterly jet of the Asian summer season is found near 10°N, at the 200 hPa level. Summer monsoon rainfall resides on the poleward side of this jet where the circulation carries a large negative relative vorticity. Over these precipitating regions, large-scale upward motion contributes to a transport of negative vorticity into the lower stratosphere. We show that this region of the stratosphere carries the kinematics for a large conversion of anticyclonic shear vorticity into anticyclonic curvature vorticity. Four-dimensional parcel trajectories emanating and rising from this region slowly carve out the hemispheric anticyclonic gyre at the 10 hPa level (i.e.) the North Polar anticyclone. This kinematics plays an important role in establishing this tilted system and a connection with the 10 hPa circulation. [ABSTRACT FROM AUTHOR]

The diurnal mode of the Asian summer monsoon during active and break periods is studied using four versions of the Florida State University (FSU) global spectral model (GSM). These versions differ in the formulation of cloud parameterization schemes in the model. Observational-based estimates show that there exists a divergent circulation at 200 hPa over the Asian monsoon region in the diurnal time scale that peaks at 1200 local solar time (LST) during break monsoon and at 1800 LST during active monsoon. A circulation in the opposite direction is seen near the surface. This circulation loop is completed by vertical ascending/descending motion over the monsoon domain and its surroundings. This study shows that global models have large phase and amplitude errors for the 200-hPa velocity potential and vertical pressure velocity over the monsoon region and its surroundings. Construction of a multimodel superensemble could reduce these errors substantially out to five days in advance. This was on account of assigning differential weights to the member models based on their past performance. This study also uses a unified cloud parameterization scheme that inherits the idea of a multimodel superensemble for combining member model forecasts. The advantage of this model is that it is an integrated part of the GSM and thus can improve the forecasts of other parameters as well through improved cloud cover. It was seen that this scheme had a larger impact on forecasting the diurnal cycle of cloud cover and precipitation of the Asian summer monsoon compared to circulation. The authors show that the diurnal circulation contributes to about 10% of the rate of change of total kinetic energy of the monsoon. Therefore, forecasting this pronounced diurnal mode has important implications for the energetics of the Asian summer monsoon. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) satellite supplemented with the Defense Meteorological Satellites Program (DMSP) microwave dataset provides accurate rain-rate estimates. Furthermore, infrared radiances from the geostationary satellites provide the possibility for mapping the diurnal change of tropical rainfall. Modeling of the phase and amplitude of the tropical rainfall is the theme of this paper. The present study utilizes a suite of global multimodels that are identical in all respects except for their cumulus parameterization algorithms. Six different cumulus parameterizations are tested in this study. These include the Florida State University (FSU) Modified Kuo Scheme (KUO), Goddard Space Flight Center (GSFC) Relaxed Arakawa–Schubert Scheme (RAS1), Naval Research Laboratory–Navy Operational Global Atmospheric Prediction System (NRL–NOGAPS) Relaxed Arakawa–Schubert Scheme (RAS2), NCEP Simplified Arakawa–Schubert Scheme (SAS), NCAR Zhang–McFarlane Scheme (ZM), and NRL–NOGAPS Emanuel Scheme (ECS). The authors carried out nearly 600 experiments with these six versions of the T170 Florida State University global spectral model. These are 5-day NWP experiments where the diurnal change datasets were archived at 3-hourly intervals. This study includes the estimation of skills of the phase and amplitudes of the diurnal rain using these member models, their ensemble mean, a multimodel superensemble, and those from a single unified model. Test results are presented for the global tropics and for some specific regions where the member models show difficulty in predicting the diurnal change of rainfall. The main contribution is the considerable improvement of the modeling of diurnal rain by deploying a multimodel superensemble and by constructing a single unified model. The authors also present a comparison of these findings on the modeling of diurnal rain from another suite of multimodels that utilized different versions of cloud radiation algorithms (instead of different cumulus parameterization schemes) toward defining the suite of multimodels. The principal result is that the superensemble does provide a future forecast for the total daily rain and for the diurnal change of rain through day 5 that is superior to forecasts provided by the best model. The training of the superensemble with good observed estimates of rain, such as those from TRMM, is necessary for such forecasts. [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]

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 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]

The article describes how latent heating (LH) profiles are derived from satellite precipitation rate retrievals made with the Tropical Rainfall Measuring Mission (TRMM) satellite measurements. Latent heating is the principal source of energy in the propagation, vertical structure, creation, and growth of tropical waves. The TRMM satellite is an international cooperation effort in rainfall research involving the U.S. National Aeronautics and Space Administration (NASA) and Japan's Aerospace Exploration Agency (JAXA).

This study explores the impact on hurricane data assimilation and forecasts from the use of dropsondes and remotely sensed moisture profiles from the airborne Lidar Atmospheric Sensing Experiment (LASE) system. It is shown here that the use of these additional datasets, more than those from the conventional world weather watch, has a positive impact on hurricane predictions. The forecast tracks and intensity from the experiments show a marked improvement compared to the control experiment in which such datasets were excluded. A study of the moisture budget in these hurricanes showed enhanced evaporation and precipitation over the storm area. This resulted in these datasets making a large impact on the estimate of mass convergence and moisture fluxes, which were much smaller in the control runs. Overall this study points to the importance of high vertical resolution humidity datasets for improved model results. It is noted that the forecast impact from the moisture-profiling datasets for some of the storms is even larger than the impact from the use of dropwindsonde-based winds. [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]

A system for objectively producing daily large-scale analysis of rainfall for the Indian region has been developed and tested by using only available real-time rain gauge data and quantitative precipitation estimates from INSAT-1D IR data. The system uses a successive correction method to produce the analysis on a regular latitude–longitude grid. Quantitative precipitation estimates from the Indian National Satellite System (INSAT) operational geostationary satellite, INSAT-1D, IR data are used as the initial guess in the objective analysis method. Accumulated 24-h (daily) rainfall analyses are prepared each day by merging satellite and rain gauge data. The characteristics of the output from this analysis system have been examined by comparing the accumulated monthly observed rainfall with other available independent widely used datasets from the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) analyses. The monthly data prepared from the daily analyses are also compared with the subjectively analyzed India Meteorological Department (IMD) monthly rainfall maps. This comparison suggests that even with only the available real-time data from INSAT and rain gauge, it is possible to construct a usable large-scale rainfall map on regular latitude–longitude grids. This analysis, which uses a higher resolution and more local rain gauge data, is able to produce realistic details of the Indian summer monsoon rainfall patterns. The magnitude and distribution of orographic rainfall near the west coast of India is very different from and more realistic compared to both the GPCP and CMAP patterns. Due to the higher spatial resolution of the analysis system, the regions of heavy and light rain are demarcated clearly over the Indian landmass. Over the oceanic regions of the Arabian Sea, Bay of Bengal, and the equatorial Indian Ocean, the agreement of the analyzed rainfall at the monthly timescale is quite good compared to the other two datasets. For NWP and other model verification of large-scale rainfall, this dataset will be useful. In the field of rainfall monitoring within weather and climate research, this technique will have real-time applications with data from current (METSAT) and future (INSAT-3A and INSAT-3D) Indian geostationary satellites. [ABSTRACT FROM AUTHOR]

ABSTRACT Can the superensemble methodology provide improved precipitation forecasts by combining existing physical parameterizations? We recently addressed this question in the context of Numerical Weather Prediction (NWP). We feel, however, that the information provided here may be useful for seasonal climate modeling as well. In the NWP context, we have developed multi-model forecasts from six versions of the Florida State University global spectral model (at a horizontal resolution of 170 waves, triangular truncation). These different versions deployed six different cumulus parameterization schemes; these models were identical in all other aspects, including the initial states. Making the assumption that differences in short-range (one day) forecasts of precipitation arise largely from differences in the cumulus parameterization, a superensemble methodology, following a recent study, was deployed to assign geographically distributed weights to convective heating for the different cumulus parameterization schemes. This was done after completion of some 85 experiments for each model for the training phase of the superensemble. A new single spectral model was next designed that included the weighted sum of the six cumulus parameterization schemes strung out within this model. This model was next shown to outperform in NWP forecasts of precipitation compared to any of those models that used a single cumulus parameterization scheme. This merely suggests that no single, present scheme is superior to all other schemes over the entire tropical belt; they all seem to have some virtues over different geographical regions. This Unified collective scheme is physically based since it does carry mechanisms such as mass flux, moisture convergence, cloud detrainment, downdrafts, effects of sea surface temperature etc. that are explicitly carried within one or the other schemes. This collective scheme is, however, based on optimized weights for these processes that vary... [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]