D. W. Shin

Improving Tropical Precipitation Forecasts from a Multianalysis Superensemble

Abstract This paper utilizes forecasts from a multianalysis system to construct a superensemble of precipitation forecasts. This method partitions the computations into two time lines. The first of those is a control (or a training) period and the second is a forecast period. The multianalysis is derived from a physical initialization–based data assimilation of “observed rainfall rates.” The different members of the reanalysis are produced by using different rain-rate algorithms for physical initialization. The basic rain-rate datasets are derived from satellites’ microwave radiometers, including those from the Tropical Rainfall Measuring Mission (TRMM) satellites and the Special Sensor Microwave Imager (SSM/I) data from three current U.S. Air Force Defense Meteorological Satellite Program (DMSP) satellites. During the training period, 155 experiments were conducted to find the relationship between forecasts from the multianalysis dataset and the best “observed” estimates of daily rainfall totals. This re...

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.

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...

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.

Assessing Maize and Peanut Yield Simulations with Various Seasonal Climate Data in the Southeastern United States

A comprehensive evaluation of crop yield simulations with various seasonal climate data is performed to improve the current practice of crop yield projections. The El Nino-Southern Oscillation (ENSO)-based historical data are commonly used to predict the upcoming season crop yields over the southeastern United States. In this study, eight different seasonal climate datasets are generated using the combinations of two global models, a regional model, and a statistical downscaling technique. One of the global models and the regional model are run with two different convective schemes. These datasets are linked to maize and peanut dynamic models to assess their impacts on crop yield simulations and are then compared with the ENSO- based approach. Improvement of crop yield simulations with the climate model data is varying, depending on the model configuration and the crop type. Although using the global climate model data directly provides no improvement, the dynamically and statistically downscaled data show increased skill in the crop yield sim- ulations. A statistically downscaled operational seasonal climate model forecast shows statistically significant (at the 5% level) interannual predictability in the peanut yield simulation. Since the yield amount simulated by the dynamical crop model is highly sensitive to wet/dry spell sequences (water stress) during the growing season, fidelity in simulating the precipitation variability is essential.

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...

A Numerical Study on the Formation Mechanism of a Mesoscale Low during East-Asia Winter Monsoon

Mesoscale low is often observed over the downstream region of the East Sea (or, northwest coast off the Japan Islands) during East-Asia winter monsoon. The low system causes a heavy snowfall at the region. A series of numerical experiments were conducted with the aid of a regional model (MM5 ver. 3.5) to examine the formation mechanism of the mesoscale low. The following results were obtained: 1) A well-developed mesoscale low was simulated by the regional model under real topography, NCEP reanalysis, and OISST; 2) The mesoscale low was simulated under a zonally averaged SST without topography. This implies that the meridional gradient of SST is the main factor in the formation of a mesoscale low; 3) A thermal contrast (

ENSO Teleconnection Pattern Changes over the Southeastern United States under a Climate Change Scenario in CMIP5 Models

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