T. S. V. Vijaya Kumar

Real-Time Multianalysis-Multimodel Superensemble Forecasts of Precipitation Using TRMM and SSM/I Products

This paper addresses real-time precipitation forecasts from a multianalysis‐multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis‐multimodel system studied here. In this paper, ‘‘multimodel’’ refers to different models whose forecasts are being assimilated for the construction of the superensemble. ‘‘Multianalysis’’ refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a ‘‘best’’ rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis‐multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis‐multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model’s skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually biasremoved models. The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1‐3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

A multi-model superensemble algorithm for seasonal climate prediction using DEMETER forecasts

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 multimodel 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.

Multimodel Superensemble Forecasting of Tropical Cyclones in the Pacific

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.

Real-Time Multimodel Superensemble Forecasts of Atlantic Tropical Systems of 1999

Abstract In this paper, Atlantic hurricane forecasts for the year 1999 are addressed. The methodology for these forecasts is called the multimodel superensemble. This statistical method makes use of the real-time forecasts provided by a number of operational and research models to construct the superensemble forecasts. This method divides the forecast time line into two phases: a training phase and a forecast combining phase. The training phase includes an inventory of past applicable hurricane forecasts, each by the multimodels. The model biases of position and intensity errors of past forecasts are summarized via a simple linear multiple regression of these forecasts against the best-observed estimates of position and intensity. These statistics are next passed on to future forecasts of the multimodels in order to forecast the hurricanes of 1999. This method was first tested for the hurricanes of 1998 with considerable success, with some of those results summarized here. Those statistics were refined fo...

The Hurricane Intensity Issue

Abstract The intensity issue of hurricanes is addressed in this paper using the angular momentum budget of a hurricane in storm-relative cylindrical coordinates and a scale-interaction approach. In the angular momentum budget in storm-relative coordinates, a large outer angular momentum of the hurricane is depleted continually along inflowing trajectories. This depletion occurs via surface and planetary boundary layer friction, model diffusion, and “cloud torques”; the latter is a principal contributor to the diminution of outer angular momentum. The eventual angular momentum of the parcel near the storm center determines the storm’s final intensity. The scale-interaction approach is the familiar energetics in the wavenumber domain where the eddy and zonal kinetic energy on the hurricane scale offer some insights on its intensity. Here, however, these are cast in storm-centered local cylindrical coordinates as a point of reference. The wavenumbers include azimuthally averaged wavenumber 0, principal hurri...

Improved Skill for the Anomaly Correlation of Geopotential Heights at 500 hPa

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 „ 2 of the geopotential based on the daily forecasts of the geopotential fields at the 500hPa 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.

Hydrometeor structure of a composite monsoon depression using the TRMM radar

This paper addresses the use of a satellite-based radar for obtaining the composite structure(from several monsoon depressions) of the distribution of precipitation elements in the horizontaland the vertical. This composting is based on the use of a simple elliptical layout ofcoordinates along the major and minor axes of each storm as it passed over north central India.This satellite, called the Tropical Rainfall Measuring Mission (TRMM), carries onboard amicrowave instrument known as the Precipitation Radar (PR). The vertical structure ofhydrometeors provided by the radar is somewhat of the same quality as the ground-basedDoppler radar units. The PR could identify many features such as the melting layers, height ofconvection, extent of anvils and types of precipitation over different sections of the compositedmonsoon depression. Furthermore, the asymmetric nature of surface rainfall that intensifiesaround the composited monsoon depression has also been mapped, which could provide severalmore details than was possible from other satellite-based estimates. It is found that the mostintense precipitation occurs in the south-southwest region of the monsoon depression. Thepreponderance of stratiform rain and the coverage of fewer deep convective elements, especiallyover the orographic upslope region, are some other noticeable features obtained using theTRMM PR. The stratiform rain was noted to arise where the melting layers (in the radarreflectivity signatures) were located near 5 km. In those few occasions where tall rain cloudswere discernible, the cloud tops were seen to extend all the way from 12 to 15 km. Rainfallamounts across the composite monsoon depression range from 10 to 100 mm d-1. The 3—4 dpassage time of one of those disturbances resulted in local rainfall totals of the order of 200—300 mm d-1.

Determination of Forecast Errors Arising from Different Components of Model Physics and Dynamics

This paper addresses a procedure to extract error estimates for the physical and dynamical components of a forecast model. This is a two-step process in which contributions to the forecast tendencies from individual terms of the model equations are first determined using an elaborate bookkeeping of the forecast. The second step regresses these estimates of tendencies from individual terms of the model equations against the observed total tendencies. This process is executed separately for the entire horizontal and vertical transform grid points of a global model. The summary of results based on the corrections to the physics and dynamics provided by the regression coefficients highlights the component errors of the model arising from its formulation. This study provides information on geographical and vertical distribution of forecast errors contributed by features such as nonlinear advective dynamics, the rest of the dynamics, deep cumulus convection, large-scale condensation physics, radiative processes, and the rest of physics. Several future possibilities from this work are also discussed in this paper.

High resolution numerical weather prediction over the Indian subcontinent

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.

On the weakening of Hurricane Lili, October 2002

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 0000 UTC to a category 1 storm on October 3, 2002 at 1300 UTC. 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.