The Japan Meteorological Agency (JMA) Typhoon Ensemble Prediction System (TEPS) and its performance are described. In February 2008, JMA started an operation of TEPS that was designed for providing skillful tropical cyclone (TC) track predictions in both deterministic and probabilistic ways. TEPS consists of 1 nonperturbed prediction and 10 perturbed predictions based on the lower-resolution version (TL319L60) of the JMA Global Spectral Model (GSM; TL959L60) and a global analysis for JMA/GSM. A singular vector method is employed to create initial perturbations. Focusing on TCs in the western North Pacific Ocean and the South China Sea (08-608N, 1008E-1808), TEPS runs 4 times a day, initiated at 0000, 0600, 1200, and 1800 UTC with a prediction range of 132 h. The verifications of TEPS during the quasi-operational period from May to December 2007 indicate that the ensemble mean track predictions statistically have better performance as compared with the control (nonperturbed) predictions: the error reduction in the 5-day predictions is 40 km on average. Moreover, it is found that the ensemble spread of tracks is an indicator of position error, indicating that TEPS will be useful in presenting confidence information on TC track predictions. For 2008 when TEPS was in operational use, however, it was also found that the ensemble mean was significantly worse than the deterministic model (JMA/GSM) out to 84 h.

[1] Results are presented from an intercomparison of atmospheric general circulation model (AGCM) simulations of tropical convection during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE). The distinct cloud properties, precipitation, radiation, and vertical diabatic heating profiles associated with three different monsoon regimes (wet, dry, and break) from available observations are used to evaluate 9 AGCM forecasts initialized daily from realistic global analyses. All models captured well the evolution of large-scale circulation and thermodynamic fields, but cloud properties differed substantially among models. Compared with the relatively well simulated top-heavy heating structures during the wet and break period, most models had difficulty in depicting the bottom-heavy heating profiles associated with cumulus congestus during the dry period. The best performing models during this period were the ones whose convection scheme was most responsive to the free tropospheric humidity. Compared with the large impact of cloud and convective parameterizations on model cloud and precipitation characteristics, resolution has relatively minor impact on simulated cloud properties. However, one feature that was influenced by resolution in several models was the diurnal cycle of precipitation. Peaking at a different time from convective precipitation, large-scale precipitation generally increases in high resolution forecasts and modulates the total precipitation diurnal cycle. Overall, the study emphasizes the need for convection parameterizations that are more responsive to environmental conditions as well as the substantial diversity among large-scale cloud and precipitation schemes in current AGCMs. This experiment has demonstrated itself to be a very useful test bed for those developing cloud and convection schemes for AGCMs.

In this study we derive the environmental lapse rate (ELR) from vertical profiles of temperature in the lower troposphere, applying it to downscale air temperature of the new European Centre For Medium-Range Weather Forecasts (ECMWF) reanalysis ERA5, which replaces ERA-Interim (ERAI). We focus over the western U.S. region, a data-rich area with observations of daily maximum and minimum temperature (Global Historical Climatology Network) and snow depth and soil temperature. Observations indicate an ELR of −4.5 K·km −1 in the region, lower than the commonly used −6.5 K·km −1 . ERA5 ELR agrees with the observational estimates, with some overestimation in winter and limitations in the diurnal variability. The elevation correction of ERA5 temperature using different ELR showed the benefits of deriving ELR fields from ERA5 vertical profiles, when compared with a constant ELR. Simulations with the ECMWF land surface model, at 9-km resolution, driven by ERA5 using different ELR corrections showed the added value of the methodology, but the impact of different ELR corrections is limited. However, the validity of the downscaling method in reducing temperature to station altitude suggests that there is sufficient generality for application at kilometer and subkilometer resolutions. By comparing the estimated representativity errors of observations with reanalysis, the improvements from ERAI to ERA5 are mainly visible in the random component of the error. Large systematic biases remain, which require further attention from the modeling and data assimilation, and limit the potential benefits of ELR corrections.

[1] Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.Citation: Davies, L., et al. (2013), A single-column model ensemble approach applied to the TWP-ICE experiment,

As the LES correctly reproduces the observed growth of the boundary layer, the gradual development of shallow clouds, the initiation of deep convection and the development of cold pools, it provides a basis to evaluate in detail the representation of the diurnal cycle of convection by the other models and to test the hypotheses underlying convective parametrizations. Most SCMs have difficulty in representing the timing of convective initiation and rain intensity, although substantial modifications to boundary-layer and deep-convection parametrizations lead to improvements. The SCMs also fail to represent the mid-level troposphere moistening during the shallow convection phase, which we analyse further. Nevertheless, beyond differences in timing of deep convection, the SCM models reproduce the sensitivity to initial and boundary conditions simulated in the LES regarding boundary-layer characteristics, and often the timing of convection triggering.