Florida 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.

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.

Reduction of forecast error for global numerical weather prediction by The Florida State University (FSU) Superensemble

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.

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.

November 2, 1994, severe storms in the southeastern Mediterranean

The available data archive over the Middle East is complemented with mesoscale fields from simulations for 1–2 November 1994 event of hazardous rains in the southeastern Mediterranean with the aim of exploring the mechanisms of this extraordinary phenomena. The associated severe weather was not predicted at all by the present operational models. A global/limited area modeling system is used for numerical simulation of the process. The system consists of the Florida State University (FSU) Global Spectral Model (FSUGSM) and the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) MM5 model. The accuracy of the numerical simulation is found very sensitive to the moist processes in the model. The development was a consequence of intensive non-adiabatic processes caused by northward propagation of large quantities of warm and moist tropical air. Local terrain of northeastern Africa also played a significant role in the development of a Mesoscale Convective System (MCS) in the Mediterranean region on November 2, 1994. Over Israel, the system had a character of relatively large-scale trough with a narrow frontal system accompanied by rapidly developing small cyclonic vortices in the eastern Mediterranean.

Performance of 4D-Var with Different Strategies for the Use of Adjoint Physics with the FSU Global Spectral Model

A set of four-dimensional variational data assimilation (4D-Var) experiments were conducted using both a standard method and an incremental method in an identical twin framework. The full physics adjoint model of the Florida State University global spectral model (FSUGSM) was used in the standard 4D-Var, while the adjoint of only a few selected physical parameterizations was used in the incremental method. The impact of physical processes on 4D-Var was examined in detail by comparing the results of these experiments. The inclusion of full physics turned out to be significantly beneficial in terms of assimilation error to the lower troposphere during the entire minimization process. The beneficial impact was found to be primarily related to boundary layer physics. The precipitation physics in the adjoint model also tended to have a beneficial impact after an intermediate number (50) of minimization iterations. Experiment results confirmed that the forecast from assimilation analyses with the full physics adjoint model displays a shorter precipitation spinup period. The beneficial impact on precipitation spinup did not result solely from the inclusion of the precipitation physics in the adjoint model, but rather from the combined impact of several physical processes. The inclusion of full physics in the adjoint model exhibited a detrimental impact on the rate of convergence at an early stage of the minimization process, but did not affect the final convergence. A truncated Newton-like incremental approach was introduced for examining the possibility of circumventing the detrimental aspects using the full physics in the adjoint model in 4D-Var but taking into account its positive aspects. This algorithm was based on the idea of the truncated Newton minimization method and the sequential cost function incremental method introduced by Courtier et al., consisting of an inner loop and an outer loop. The inner loop comprised the incremental method, while the outer loop consisted of the standard 4D-Var method using the full physics adjoint. The limited-memory quasi-Newton minimization method (L-BFGS) was used for both inner and outer loops, while information on the Hessian of the cost function was jointly updated at every iteration in both loops. In an experiment with a two-cycle truncated Newton-like incremental approach, the assimilation analyses turned out to be better than those obtained from either the standard 4D-Var or the incremental 4D-Var in all aspects examined. The CPU time required by this two-cycle approach was larger by 35% compared with that required by the incremental 4D-Var without almost any physics in the adjoint model, while the CPU time required by the standard 4D-Var with the full physics adjoint model was more than twice that required by the incremental 4D-Var. Finally, several hypotheses concerning the impact of using standard 4D-Var full physics on minimization convergence were advanced and discussed.

Improving Precipitation Forecasts over the Global Tropical Belt

This study explores the nowcasting and short-range forecasting (up to 3 days) skills of rainfall over the tropics using a high resolution global model. Since the model-predicted rainfall is very sensitive to model parameters, four key model parameters were first selected. They are the Asselin filter coefficient, the fourth order horizontal diffusion coefficient, the surface moisture flux coefficient, and the vertical diffusion coefficient. The optimal values were defined as those which contributed to the best one day rainfall forecasts in the present study. In order to demonstrate and improve the precipitation forecast skill, several numerical experiments were designed using the 14-level Florida State University Global Spectral Model (FSUGSM) at a resolution of T106. Comparisons were also made of the short-range forecasts obtained from a control experiment subjected to normal mode initialization (NMI) versus experiments based on physical initialization (PI). The latter experiments were integrated using the original FSUGSM and a modified version. This modified FSUGSM was developed here by applying a reverse cumulus parameterization alorithm to the regular forecast model, which restructures the vertical humidity distribution and constrains the large-scale model’s moisture error growth during the model integration. An improved short-range rainfall prediction skill was achieved from the modified FSUGSM in this study. The results showed a better agreement between model-based and observed rainfall intensity and pattern.

Case Study of Erin Using the FSU Nested Regional Spectral Model

Abstract A case study of Hurricane Erin of the 1995 storm season is presented using the recently developed Florida State University (FSU) Nested Regional Spectral Model. The nested regional spectral model uses a perturbation technique similar to that used in the National Centers for Environmental Prediction and European Centre for Medium-Range Weather Forecasts regional spectral models, but with a number of differences such as the use of a Mercator projection. The perturbations are deviations from the FSU Global Spectral Model (FSUGSM) results and are spectrally represented with π-periodic trigonometric basis functions. The perturbations are relaxed at the boundary to approach the global model results. The perturbation time tendencies are solved using a semi-implicit time integration scheme similar to that used in the FSUGSM. The regional model has the same sigma-coordinate vertical structure and physics as the FSUGSM. Implicit horizontal diffusion and time filtering of the perturbations is included. Erin...

フロリダ州立大学全球スペクトルモデルに組み込んだ雲予測スキームとその影響

An explicit cloud predicting scheme is developed for the Florida State University Global Spectral Model (FSU-GSM). Both cloud water and cloud cover are prognostic variables in the scheme, but they are not advected in the current version. Cloud liquid water and cloud ice are distinguished by a prescribed function of temperature. Source terms are considered for convective cloud as well as stratiform cloud. The predicted cloud water content and cloud cover are provided to the radiation scheme. The model produces reasonable amounts of cloud cover and cloud water. Clouds originating from convection are important for cloud cover and cloud water except for the polar region. In the tropics, clouds originating from convection account for approximately half of the total cloud cover, and the majority of the high cloud coverage. They also produce half of the cloud water in the mid-latitudes, and are an essential source of the cloud water in the tropics. The outgoing longwave radiation in the tropics is improved by the explicit scheme, because the scheme includes the clouds originating from the convection. High clouds from convection are essential in the tropics, and have a large influence on the outgoing longwave radiation.

Impact of physical initialization on cloud forecasts

The impact of initial data on cloud forecasts by the Florida State University Global Spectral Model (FSUGSM) has been investigated. This work has shown that improving the information content of the initial data by physical initialization has a very strong, positive impact on cloud forecasts. Model spin-up of clouds is considerably reduced. There is an overall better representation of high, middle, low, and total clouds over the tropics and there is a discernible improvement in the prediction of clouds. A strong correlation between cloud shortwave forcing and longwave forcing has been noted in model forecasts with the physically initialized data. This result compares very well with observations from the Earth Radiation Budget Experiment (ERBE).

Explosive East Coast Cyclogenesis: Numerical Experimentation and Model-Based Diagnostics

Numerical experimentation of explosive east-coast cyclogenesis is performed using the Florida State University Global Spectral Model (FSUGSM). The three cases examined here are the Presidents' Day storm of February 18-19, 1979 and the North Atlantic and Pacific bombs of January 18-20, 1979 which formed off the east coasts of the United States and Japan, respectively. The use of a global model provides a framework for studying the phenomena on the 3-5 day time scale. The forecast verifications of the numerical experiments indicate that the FSUGSM was able to adequately predict the phase, intensity, and synoptic-scale structure. These results justify the use of model data for diagnostic studies of the bomb. The model data are used to quantify the role of the adiabatic and diabatic forcing in the explosive cyclogenetic process, using surface pressure tendency to gage development.