Steven Cocke

Atlantic Basin Seasonal Hurricane Simulations

Abstract An ensemble of seasonal Atlantic hurricane simulations is conducted using The Florida State University/Center for Ocean–Atmospheric Prediction Studies (FSU–COAPS) global spectral model (Cocke and LaRow) at a resolution of T126L27 (a Gaussian grid spacing of 0.94°). Four integrations comprising the ensembles were generated using the European Centre for Medium-Range Weather Forecasts (ECMWF) time-lagged initial atmospheric conditions centered on 1 June for the 20 yr from 1986 to 2005. The sea surface temperatures (SSTs) were updated weekly using the Reynolds et al. observed data. An objective-tracking algorithm obtained from the ECMWF and modified for this model’s resolution was used to detect and track the storms. It was found that the model’s composite storm structure and track lengths are realistic. In addition, the 20-yr interannual variability was well simulated by the ensembles with a 0.78 ensemble mean rank correlation. The ensembles tend to overestimate (underestimate) the numbers of storms...

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...

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.

Impact of Shortwave Radiative Effects of Dust Aerosols on the Summer Season Heat Low over Saudi Arabia

Abstract A two-stream scattering scheme based on the delta-Eddington approximation is incorporated into the Florida State University Limited Area Model for computing the shortwave radiative fluxes due to dust aerosols over the Saudi Arabian region and to study their impact on synoptic-scale systems and the diurnal cycle over the region. The radiative properties of dust corresponding to different categories of dustiness are determined from the results of field experiments. Satellite imagery and visibility are used to determine the intensity and extent of the dust layer. Two parallel simulations, one including the radiative effects of dust aerosols and the other without them, were made over a 6-day period starting with 1200 UTC 25 June 1979 using First GARP (Global Atmospheric Research Program) Global Experiment IIIb data analyses from ECMWF. A comparison of the two experiments shows that the dust aerosol radiative heating strengthens the heat low over Saudi Arabia. Furthermore, the radiative heating of the...

The Role of an Advanced Land Model in Seasonal Dynamical Downscaling for Crop Model Application

Abstract An advanced land model [the National Center for Atmospheric Research (NCAR) Community Land Model, version 2 (CLM2)] is coupled to the Florida State University (FSU) regional spectral model to improve seasonal surface climate outlooks at very high spatial and temporal resolution and to examine its potential for crop yield estimation. The regional model domain is over the southeast United States and is run at 20-km resolution, roughly resolving the county level. Warm-season (March–September) simulations from the regional model coupled to the CLM2 are compared with those from the model with a simple land surface scheme (i.e., the original FSU model). In this comparison, two convective schemes are also used to evaluate their roles in simulating seasonal climate, primarily for rainfall. It is shown that the inclusion of the CLM2 produces consistently better seasonal climate scenarios of surface maximum and minimum temperatures, precipitation, and shortwave radiation, and hence provides superior inputs...

Seasonal rainfall predictions over the southeast United States using the Florida State University nested regional spectral model

[1] Seasonal rainfall predictions over the southeast United States using the recently developed Florida State University (FSU) nested regional spectral model are presented. The regional model is nested within the FSU coupled model, which includes a version of the Max Plank Institute Hamburg Ocean Primitive Equation model. The southeast U.S. winter has a rather strong climatic signal due to teleconnections with tropical Pacific sea surface temperatures and thus provides a good test case scenario for a modeling study. Simulations were done for 12 boreal winter seasons, from 1986 to 1997. Both the regional and global models captured the basic large-scale patterns of precipitation reasonably well when compared to observed station data. The regional model was able to predict the anomaly pattern somewhat better than the global model. The regional model was particularly more skillful at predicting the frequency of significant rainfall events, in part because of the ability to produce heavier rainfall events.

Seasonal Surface Air Temperature and Precipitation in the FSU Climate Model Coupled to the CLM2

The current Florida State University (FSU) climate model is upgraded by coupling the National Center for Atmospheric Research (NCAR) Community Land Model Version 2 (CLM2) as its land component in order to make a better simulation of surface air temperature and precipitation on the seasonal time scale, which is important for crop model application. Climatological and seasonal simulations with the FSU climate model coupled to the CLM2 (hereafter FSUCLM) are compared to those of the control (the FSU model with the original simple land surface treatment). The current version of the FSU model is known to have a cold bias in the temperature field and a wet bias in precipitation. The implementation of FSUCLM has reduced or eliminated this bias due to reduced latent heat flux and increased sensible heat flux. The role of the land model in seasonal simulations is shown to be more important during summertime than wintertime. An additional experiment that assimilates atmospheric forcings produces improved land-model initial conditions, which in turn reduces the biases further. The impact of various deep convective parameterizations is examined as well to further assess model performance. The land scheme plays a more important role than the convective scheme in simulations of surface air temperature. However, each convective scheme shows its own advantage over different geophysical locations in precipitation simulations.

Multiconvective Parameterizations as a Multimodel Proxy for Seasonal Climate Studies

Abstract A six-member multicoupled model ensemble is created by using six state-of-the-art deep atmospheric convective schemes. The six convective schemes are used inside a single model and make up the ensemble. This six-member ensemble is compared against a multianalysis ensemble, which is created by varying the initial start dates of the atmospheric component of the coupled model. Both ensembles were integrated for seven months (November–May) over a 12-yr period from 1987 to 1998. Examination of the sea surface temperature and precipitation show that while deterministic skill scores are slightly better for the multicoupled model ensemble the probabilistic skill scores favor the multimodel approach. Combining the two ensembles to create a larger ensemble size increases the probabilistic skill score compared to the multimodel. This altering physics approach to create a multimodel ensemble is seen as an easy way for small modeling centers to generate ensembles with better reliability than by only varying t...

Hurricane wind fields needed to assess risk to offshore wind farms

In their paper in PNAS, Rose et al. (1) applied a statistical model to estimate hurricane wind losses to wind turbines over a 20-y typical wind farm lifetime. They combined a county annual landfall frequency probability density function with a generalized extreme value (GEV) fit of maximum wind speeds to model the expected 20-y losses attributable to hurricane activity at four hypothetical offshore wind farm sites.

Data assimilation by artificial neural networks for the global FSU atmospheric model: Surface pressure

Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modelled system as its initial condition. This paper shows the results of a data assimilation technique using artificial neural networks (NN) to obtain the initial condition to the atmospheric general circulation model (AGCM) for the Florida State University in USA. The Local Ensemble Transform Kalman filter (LETKF) is implemented with Florida State University Global Spectral Model (FSUGSM). LETKF is a version of Kalman filter with Monte-Carlo ensembles of short-term forecasts to solve the data assimilation problem. FSUGSM is a multilevel spectral primitive equation model with a vertical sigma coordinate, at resolution T63L27. The LETKF data assimilation experiments are based in simulated observations data. For the NN data assimilation scheme, we use Multilayer Perceptron (MLP-DA) with supervised training algorithm where NN receives input vectors with their corresponding response from LETKF scheme. The surface pressure results are presented. An self-configuration method finds the optimal NN and configures the MLP-DA in this experiment. The NNs were trained with data from each month of 2001, 2002 and 2003. A experiment for data assimilation cycle using MLP-DA was performed with simulated observations for January of 2004. The results demonstrate the effectiveness of the ANN technique for atmospheric data assimilation, with similar quality to LETKF analyses.