Florida State University Global Spectral 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.

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...

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.

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.

The effect of Indian Ocean warming on the Indian Monsoon: An atmospheric model study

The results from an atmospheric modeling study using the Florida State University Global Spectral Model indicate that, in years such as 1997 when the Indian Ocean SSTs are large, the Indian monsoon exhibits a typical behaviour. During that year, an extended shift of the tropical convergence zone towards the north played a role in the regional Hadley cell anomalies. The local warm boundary conditions in the northwestern Indian Ocean aided the high rainfall anomaly in Western India during the model simulations. The upper level structure, exhibited in terms of the global velocity potential is slightly shifted east for 1997, but with the correct sign. This structure shows regions of convergence over Indonesia where severe drought had occurred. The performance of the model rainfall over the equatorial Indian Ocean was uncanny for most seasons studied. Overall, the model performed best over the oceanic regions.

An intercomparison of hurricane forecasts using SSM/I and TRMM rain rate algorithm(s)

Three different Special Sensor Microwave Imager (SSM/I) rain rate algorithms are evaluated as a means of improving both the physical initialization and the hurricane forecast output of the Florida State University Global Spectral Model (FSU GSM). These SSM/I rain rate algorithms are known as Cal/Val, NESDIS, and GPROF 4.0. In addition the Tropical Rainfall Measuring Mission (TRMM) TMI – 2A12 rain rate algorithm is validated, and its impact on FSU GSM hurricane forecasts is also studied.

Impact of Parameter Estimation on the Performance of the FSU Global Spectral Model Using Its Full-Physics Adjoint

The full-physics adjoint of the Florida State University Global Spectral Model at resolution T42L12 is applied to carry out parameter estimation using an initialized analysis dataset. The three parameters, that is, the biharmonic horizontal diffusion coefficient, the ratio of the transfer coefficient of moisture to the transfer coefficient of sensible heat, and the Asselin filter coefficient, as well as the initial condition, are optimally recovered from the dataset using adjoint parameter estimation. The fields at the end of the assimilation window starting from the retrieved optimal initial conditions and the optimally identified parameter values successfully capture the main features of the analysis fields. A number of experiments are conducted to assess the effect of carrying out 4D Var assimilation on both the initial conditions and parameters, versus the effect of optimally estimating only the parameters. A positive impact on the ensuing forecasts due to each optimally identified parameter value is observed, while the maximum benefit is obtained from the combined effect of both parameter estimation and initial condition optimization. The results also show that during the ensuing forecasts, the model tends to “lose” the impact of the optimal initial condition first, while the positive impact of the optimally identified parameter values persists beyond 72 h. Moreover, the authors notice that their regional impacts are quite different.

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.

A Perturbation Method for Hurricane Ensemble Predictions

Abstract This study illustrates the capability of the ensemble technique to improve hurricane forecasts in the Florida State University Global Spectral Model. A perturbation method for hurricane ensemble prediction is proposed. The perturbation method consists of perturbing hurricane initial position and the large-scale environment in which the storm is embedded. The position perturbation is done by displacing the observed hurricane toward different directions by a small distance. The empirical orthogonal function (EOF) analysis is used to find fast-growing modes in the initial state. It is shown that the model forecasts, in terms of both hurricane track and other physical variables, are very sensitive to the hurricane initial position, intensity, and its large-scale environment. The results also show the EOF-based perturbations are the fast-growing modes and can be used to reduce the initial uncertainty in the analysis. The hurricane forecast obtained from ensemble statistics lead to a large improvement ...

Application of a GCM to Study the Surface Hydrological Budget of Amazonia

Abstract Using The Florida State University Global Spectral Model, hydrological budgets are calculated over the Amazon River basin for the boreal summer of 1979 with and without a complex biosphere model (BATS) coupled to the atmospheric model. Substantially increased precipitation and latent heat fluxes over the Amazon are noted for the BATS case, along with better maintenance of low-level flow patterns. Partitioning of the rainfall and latent heat flux into detailed component terms from BATS reveals evidence of “moisture recycling,” particularly in relation to the intercepted rainfall. Monthly variations in the component terms for precipitation, latent heat flux, and upper soil moisture are described. A total runoff efficiency of 75% is simulated by the model, while the surface runoff efficiency is about 30%. Model performance in the locality of two intensive field study areas (Para and Rondonia) of the Large-Scale Biosphere–Atmosphere Experiment in Amazonia has been examined via time series from the tw...

Tropical Cyclone Forecasts Made with the FSU Global Spectral Model

Abstract The Florida State University Global Spectral Model forecast skill is calculated for several tropical systems in the Atlantic and Pacific Ocean basins and is compared to operational forecast skill. The series of forecasts were initialized using global analyses and supplemental satellite data. Track forecast errors were calculated for the storm series for control runs, enhanced runs, and operational forecasts made for the same time periods. Cumulative results for all modeled 1995 hurricanes are summarized.

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.