State University Global Spectral Model Research Articles

The impact of the diurnal cycle on the MJO over the Maritime Continent: a modeling study assimilating TRMM rain rate into global analysis

In the present study, we use modeling experiments to investigate the impact of the diurnal cycle on the Madden-Julian Oscillation (MJO) during the Australian summer. Physical initialization and a nudging technique enable us to assimilate the observed Tropical Rainfall Measuring Mission (TRMM) rain rate and atmospheric variables from the National Centers for Environmental Prediction—National Center for Atmospheric Research Reanalysis 2 (R2) into the Florida State University Global Spectral Model (FSUGSM), resulting in a realistic simulation of the MJO. Model precipitation is also significantly improved by TRMM rain rate observation via the physical initialization. We assess the influence of the diurnal cycle on the MJO by modifying the diurnal component during the model integration. Model variables are nudged toward the daily averaged values from R2. Globally suppressing the diurnal cycle (NO_DIURNAL) exerts a strong impact on the Maritime Continent. The mean state of precipitation increases and intraseasonal variability becomes stronger over the region. It is well known that MJO weakens as it passes over the Maritime Continent. However, the MJO maintains its strength in the NO_DIURNAL experiment, and the diminution of diurnal signals during the integration does not change the propagating speed of the MJO. We suggest that diminishing the diurnal cycle in NO_DIURNAL consumes less moist static energy (MSE), which is required to trigger both diurnal and intraseasonal convection. Thus, the remaining MSE may play a major role along with larger convective instability and stronger lower level moisture convergence in intensifying the MJO over the Maritime Continent in the model simulation.

Observing System Simulation Experiment for Global Precipitation Mission

From the suite of future Global Precipitation Mission (GPM) satellites we have selected 11 of the possible contributors to the NASA’s International precipitation measurement program. The Observing System Simulation Experiments (OSSE) presented here explores the predictive usefulness of this suite of satellites. In order to carry out such experiments a Nature Run based on results from a state of the model is required. For that purpose we have selected recent past runs from the European Center for Medium Range Forecasts (ECMWF). These were designated as special data sets for OSSEs in partnership between NASA, NCEP/EMC, and NOAA. In order to test the usefulness of these future GPM-based precipitation measurements we first identify the typical orbits of eleven satellites. Along these orbital tracks we generate proxy precipitation data sets from the ECMWF Nature Run. This method of extraction of precipitation data set from a Nature Run is described in this paper. This methodology also requires a fraternal twin model (different from the Nature Run) in which the usefulness of the proposed GPM proxy data sets from the Nature Run are systematically evaluated in a forecast mode. The procedure for incorporation of the rainfall data sets is called the rain rate initialization. Data from one or more satellites are sequentially introduced into the fraternal twin model (which is the Florida State University Global Spectral Model) during the initialization phase for a number of experiments. After the initialization of such precipitation data sets, forecast experiments are carried out with the fraternal twin. The question asked is, as we introduce more and more GPM satellites how close do the forecasts from the fraternal twin approach the Nature Run? The results from this experimentation show that very promising improvements for short-range precipitation forecast skills are attainable from the proposed suite of GPM satellites.

Improved Forecasts of the Diurnal Cycle in the Tropics Using Multiple Global Models. Part I: Precipitation

Abstract 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.

Improved Forecasts of the Diurnal Cycle in the Tropics Using Multiple Global Models. Part II: Asian Summer Monsoon

Abstract 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.

Evaluation of several different planetary boundary layer schemes within a single model, a unified model and a multimodel superensemble

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.Asingle 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.

Prediction of the Diurnal Cycle Using a Multimodel Superensemble. Part II: Clouds

Abstract This study addresses the issue of cloud parameterization in general circulation models utilizing a twofold approach. Four versions of the Florida State University (FSU) global spectral model (GSM) were used, including four different cloud parameterization schemes in order to construct ensemble forecasts of cloud covers. Next, a superensemble approach was used to combine these model forecasts based on their past performance. It was shown that it is possible to substantially reduce the 1–5-day forecast errors of phase and amplitude of the diurnal cycle of clouds from the use of a multimodel superensemble. Further, the statistical information generated in the construction of a superensemble was used to develop a unified cloud parameterization scheme for a single model. This new cloud scheme, when implemented in the FSU GSM, carried a higher forecast accuracy compared to those of the individual cloud schemes and their ensemble mean for the diurnal cycle of cloud cover up to day 5 of the forecasts. This results in a 5–10 W m−2 improvement in the root-mean-square error to the upward longwave and shortwave flux at the top of the atmosphere, especially over deep convective regions. It is shown that while the multimodel superensemble is still the best product in forecasting the diurnal cycle of clouds, a unified cloud parameterization scheme, implemented in a single model, also provides higher forecast accuracy compared to the individual cloud models. Moreover, since this unified scheme is an integral part of the model, the forecast accuracy of the single model improves in terms of radiative fluxes and thus has greater impacts on weather and climate time scales. This new cloud scheme will be tested in real-time simulations.

Adaptive use of research aircraft data sets for hurricane forecasts

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.

Simulation of South-Asian Summer Monsoon in a GCM

Major characteristics of Indian summer monsoon climate are analyzed using simulations from the upgraded version of Florida State University Global Spectral Model (FSUGSM). The Indian monsoon has been studied in terms of mean precipitation and low-level and upper-level circulation patterns and compared with observations. In addition, the model's fidelity in simulating observed monsoon intraseasonal variability, interannual variability and teleconnection patterns is examined. The model is successful in simulating the major rainbelts over the Indian monsoon region. However, the model exhibits bias in simulating the precipitation bands over the South China Sea and the West Pacific region. Seasonal mean circulation patterns of low-level and upper-level winds are consistent with the model's precipitation pattern. Basic features like onset and peak phase of monsoon are realistically simulated. However, model simulation indicates an early withdrawal of monsoon. Northward propagation of rainbelts over the Indian continent is simulated fairly well, but the propagation is weak over the ocean. The model simulates the meridional dipole structure associated with the monsoon intraseasonal variability realistically. The model is unable to capture the observed interannual variability of monsoon and its teleconnection patterns. Estimate of potential predictability of the model reveals the dominating influence of internal variability over the Indian monsoon region.

Dependence of Simulation of Boreal Summer Tropical Intraseasonal Oscillations on the Simulation of Seasonal Mean

Abstract The link between realism in simulation of the seasonal mean precipitation and summer tropical intraseasonal oscillations and their dependence on cumulus parameterization schemes is investigated using the Florida State University Global Spectral Model (FSUGSM). Forty-member model ensemble simulations of the northern summer season are generated for three different cumulus parameterization schemes [namely, Arakawa–Schubert (Naval Research Laboratory; NRL), Zhang and McFarlane (National Center for Atmospheric Research; NCAR), and Emanuel (Massachusetts Institute of Technology; MIT)]. The MIT scheme simulates the regional pattern of seasonal mean precipitation over the Indian monsoon region well but has large systematic bias in simulating the precipitation over the western Pacific and the Maritime Continent. Although the simulation of details of regional distribution of precipitation over the Indian monsoon region by the NRL and NCAR schemes is not accurate, they simulate the spatial pattern of precipitation over the tropical Indo–Pacific domain closer to observation. The NRL scheme seems to captures the observed northward and eastward propagation of intraseasonal precipitation anomalies realistically. However, the simulations of the NCAR and MIT schemes are dominated by a westward propagating component. The westward propagating mode seen in the model as well as observations is indicated to be an equatorial Rossby wave modified by the northern summer mean flow. An examination of the relationship between simulation of the model climatology and eastward propagating character of monsoon intraseasonal oscillations (ISOs) in a limited sample shows that the scheme that simulates better seasonal mean pattern of rainfall over the tropical Indo–Pacific domain also simulates better intraseasonal variance and more realistic eastward propagation of monsoon ISOs. Among the parameters known to be important for meridional propagation of the summer monsoon ISOs, the meridional gradient of mean humidity in the lower atmosphere seems to be crucial in determining the northward propagation in the equatorial Indian Ocean (between 10°S and 10°N). For better prediction of the seasonal mean Indian monsoon, therefore, the model climatology should have minimum bias not only over the Indian monsoon region but also over the entire Indo–Pacific basin.

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.

Analysis of the singular vectors of the full-physics Florida State University Global Spectral Model

Analysis of the singular vectors of the full-physics Florida State University Global Spectral Model

Analysis of the singular vectors of the full-physics Florida State University Global Spectral Model

Analysis of singular vectors (SVs) is performed on the Florida State University Global Spectral Model, which includes linearized full physics of the atmosphere. It is demonstrated that the physical processes, especially precipitation, fundamentally affect the leading SVs. When the SVs are coupled with the precipitation geographically, their growth rates increase substantially and their structures change significantly. The physical processes however have little impact on growth rates or structures of the SVs that are geographically independent of precipitation. Furthermore, it is shown that spatial filtering along with the projection operator that projects the flow winds to the rotational wind (designed as a simple initialization process) improves the structural features of SVs and is found to mitigate spurious modes.

An Examination of a Model's Components during Tropical Cyclone Recurvature

Abstract 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 shall...

An Empirical Cumulus Parameterization Scheme for a Global Spectral Model

Realistic vertical heating and drying profiles in a cumulus scheme is important for obtaining accurate weather forecasts. A new empirical cumulus parameterization scheme based on a procedure to improve the vertical distribution of heating and moistening over the tropics is developed. The empirical cumulus parameterization scheme (ECPS) utilizes profiles of Tropical Rainfall Measuring Mission (TRMM) based heating and moistening derived from the European Centre for Medium- Range Weather Forecasts (ECMWF) analysis. A dimension reduction technique through rotated principal component analysis (RPCA) is performed on the vertical profiles of heating (Q1) and drying (Q2) over the convective regions of the tropics, to obtain the dominant modes of variability. Analysis suggests that most of the variance associated with the observed profiles can be explained by retaining the first three modes. The ECPS then applies a statistical approach in which Q1 and Q2 are expressed as a linear combination of the first three dominant principal components which distinctly explain variance in the troposphere as a function of the prevalent large-scale dynamics. The principal component (PC) score which quantifies the contribution of each PC to the corresponding loading profile is estimated through a multiple screening regression method which yields the PC score as a function of the large-scale variables. The profiles of Q1 and Q2 thus obtained are found to match well with the observed profiles. The impact of the ECPS is investigated in a series of short range (1-3 day) prediction experiments using the Florida State University global spectral model (FSUGSM, T126L14). Comparisons between short range ECPS forecasts and those with the modified Kuo scheme show a very marked improvement in the skill in ECPS forecasts. This improvement in the forecast skill with ECPS emphasizes the importance of incorporating realistic vertical distributions of heating and drying in the model cumulus scheme. This also suggests that in the absence of explicit models for convection, the proposed statistical scheme improves the modeling of the vertical distribution of heating and moistening in areas of deep convection.

Short‐ to medium‐range superensemble precipitation forecasts using satellite products: 1. Deterministic forecasting

As a comprehensive extension of the previous multimodel/multianalysis superensemble (SE) studies of rainfall forecasts, the benefits and prospects of the SE precipitation forecasts are explored using satellite products. Three different precipitation ensemble configurations are first established from a great number of numerical experiments. These configurations are multianalysis (MA), multicumulus‐scheme (MC), and multimodel (MM) configurations. A set of MA ensemble comes from the use of several different satellite‐derived rain rates through the physical initialization procedure within the Florida State University Global Spectral Model (FSUGSM) system. Six different state‐of‐the art cumulus parameterization schemes are incorporated into the FSUGSM in order to introduce the MC ensemble configuration. The MM configuration is composed of an FSU control forecast and those provided by five operational numerical weather prediction centers. In addition to the original technique, a possible deterministic SE enhancement technique (regression dynamic linear model) is then proposed and applied to the above three configurations of ensemble members as well as all of them together. The impact of a higher resolution family of models on the performance of SE forecasts is extensively investigated by repeating the above procedure with T170 resolution precipitation forecasts. Results show that short‐ to medium‐range SE forecasts are generally superior in skill to various conventional forecasts. A notably improved (∼20%) quantitative precipitation forecast is exhibited by the newly proposed SE technique. The MM configuration proved to be the most effective ensemble prediction system. Although a higher‐resolution SE forecast requires a large amount of computing time, it turns out that the impact is significant not only in skill scores but also in resolving mesoscale‐based convective disturbances.

A new approach to the cumulus parameterization issue

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 theNWPcontext, 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 geographically. It is our premise that even if a new breakthrough in cumulus parameterization were to occur from the development of a new scheme, that scheme, at best, may only achieve a skill ranking of number three for precipitation forecasts. The first place, we noted, still belongs to a multi-model superensemble, based on the optimal combination of six separate models. The second place belongs to the single model that utilizes a strung out weighted sum of many cumulus parameterization schemes within it. The individual member models have larger precipitation forecast errors compared to the two above. The skills, here, are evaluated using standard metrics such as correlations, root mean square errors and the equitable threat scores; finally we also present the vertical profiles of the apparent heat source and the apparent moisture sink that also confirm these above findings.

A Fractional Approach to the Factor Separation Method

A fractional study of the factor separation (FS) approach is performed. A fractional version of the FS method is discussed. The revised approach allows a determination of role of the acting physical mechanisms as well as that of potential nonlinearity of the modeling system responses. Application of the approach is demonstrated based on the simulations of an eastern Mediterranean weather development during 1‐2 November 1994, performed with the Florida State University global spectral model. Thirteen model simulations with varying intensity of the turbulent surface fluxes in the runs were performed for the analysis. Two locations selected for sensitivity analysis of the model results represent the processes over the Red Sea and the Mediterranean Sea. It is shown that the variation of the FS results obtained in the experiments with different intensity of factors may be in some cases significant. The degree of variability of the FS results obtained in the experiments with varying intensity of the factors under the analysis provides useful information on sufficiency (or insufficiency) of available simulation results for an improved evaluation of the role of the acting factors in a meteorological process.

Chemical compositions in convective and nonconvective regions during PEM‐Tropics B

Four DC‐8 flights during Pacific Exploratory Mission (PEM)‐Tropics B are analyzed in terms of geographical location, meteorological conditions, and chemical compositions. The meteorological conditions are determined using the meteorological analysis of the Florida State University Global Spectral Model, trajectory analysis, and in situ measurements. Typical undisturbed meteorological conditions are found over the northeastern Pacific between 30° and 15°N and southeast of Hawaiian Islands during flights 5 and 6. Disturbed meteorological conditions are found over western and central equatorial Pacific especially over the Intertropical Convergence Zone (ITCZ) and the South Pacific Convergence Zone (SPCZ) during flights 7 and 10. Comparison of the chemical concentrations between disturbed and undisturbed regions revealed remarkable differences. The distribution of the chemical constituents is found to be closely linked to the atmospheric state. Large amounts of pollutants are found to be capped at lower levels due to climatologically persistent trade wind inversion in the undisturbed regions. Transient convective systems are found to be the primary entity that destroys the inversion, causing the vertical transport of the chemicals. The disturbed regions displayed more homogeneous vertical distributions of chemicals resulting from low‐level convergence and associated convection. Trajectory analysis showed that in undisturbed regions centered north of Hawaiian Islands, and in disturbed regions, the interaction between lower‐level easterlies and upper‐level westerlies plays a significant role on atmospheric chemistry over these regions.

Adaptive Use of TRMM Observations for Tropical Precipitation Forecasts.

In order to assess and improve the short-range tropical precipitation forecasts, an adaptive use of TRMM (Tropical Rainfall Measuring Mission) rainfall via a physical initialization (PI) technique is explored. An EOF-based perturbation method is first employed to generate 25 initial ensemble members. From these initial ensemble analyses, twelve-hour forecasts are performed. Predicted 12-hour accumulated rainfall fields are then used to find sensitive (adaptive) regions, owing to initial condition uncertainties in terms of the rainfall forecast error variance. To shorten the computing time, the 14-level Florida State University Global Spectral Model at a resolution of T106 (FSUGSM T106L14) is employed in the above procedure. Next, based on a map of the rainfall forecast error variance, higher resolution T170 experiments are carried out from the adaptively initialized states. The rainfall forecast skill of the adaptive PI experiments is compared to that of the normal mode initialization (NMI), and the regular PI experiments. In the regular PI, it is performed over the whole tropics. Our results indicate that the adaptive PI of the TRMM rainfall over regions of large rainfall variances due to initial condition uncertainties, may potentially improve 3-day rainfall forecast skills over the global tropics. In addition, more notable improvements are evident over the adaptive regions, themselves.

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.