Impact of Stochastic Physics and Model Resolution on the Simulation of Tropical Cyclones in Climate GCMs

The role of model resolution in simulating geophysical vortices with the characteristics of realistic tropical cyclones (TCs) is well established. The push for increasing resolution continues, with general circulation models (GCMs) starting to use sub-10-km grid spacing. In the same context it has been suggested that the use of stochastic physics (SP) may act as a surrogate for high resolution, providing some of the benefits at a fraction of the cost. Either technique can reduce model uncertainty, and enhance reliability, by providing a more dynamic environment for initial synoptic disturbances to be spawned and to grow into TCs. We present results from a systematic comparison of the role of model resolution and SP in the simulation of TCs, using EC-Earth simulations from project Climate-SPHINX, in large ensemble mode, spanning five different resolutions. All tropical cyclonic systems, including TCs, were tracked explicitly. As in previous studies, the number of simulated TCs increases with the use of higher resolution, but SP further enhances TC frequencies by ~30%, in a strikingly similar way. The use of SP is beneficial for removing systematic climate biases, albeit not consistently so for interannual variability; conversely, the use of SP improves the simulation of the seasonal cycle of TC frequency. An investigation of the mechanisms behind this response indicates that SP generates both higher TC (and TC seed) genesis rates, and more suitable environmental conditions, enabling a more efficient transition of TC seeds into TCs. These results were confirmed by the use of equivalent simulations with the HadGEM3-GC31 GCM.

The Impact of Stochastic Physics-Based Hybrid GSI/EnKF Data Assimilation on Hurricane Forecasts Using EMC Operational Hurricane Modeling System

The National Oceanic and Atmospheric Administration’s (NOAA) cloud-permitting high-resolution operational Hurricane Weather and Research Forecasting (HWRF) model includes the sophisticated hybrid grid-point statistical interpolation (GSI) and Ensemble Kalman Filter (EnKF) data assimilation (DA) system, which allows assimilating high-resolution aircraft observations in tropical cyclone (TC) inner core regions. In the operational HWRF DA system, the flow-dependent background error covariance matrix is calculated from the HWRF self-cycled 40-member ensemble. This DA system has proved to provide improved initial TC structure and therefore improved TC track and intensity forecasts. However, the uncertainties from the model physics are not taken into account in the FY2017 version of the HWRF DA system. In order to further improve the HWRF DA system, the stochastic physics perturbations are introduced in the HWRF DA, including the cumulus convection scheme, the planetary boundary layer (PBL) scheme, and model surface physics (drag coefficient), for HWRF-based ensembles. This study shows that both TC initial conditions and TC track and intensity forecast skills are improved by adding stochastic model physics in the HWRF self-cycled DA system. It was found that the improvements in the TC initial conditions and forecasts are the results of ensemble spread increases which realistically represent the model background error covariance matrix in HWRF DA. For all 2016 Atlantic storms, the TC track and intensity forecast skills are improved by about ~3% and 6%, respectively, compared to the control experiment. The case study shows that the stochastic physics in HWRF DA is especially helpful for those TCs that have inner-core high-resolution aircraft observations, such as tail Doppler radar (TDR) data.

Tropical Cyclones in European Seasonal Forecast Models

<p>This study assesses the representation of Tropical Cyclones (TC) in an ensemble of seasonal forecast models from five different centres (ECMWF, UK Met Office, DWD, CMCC, Météo-France). Northern Hemispheric Tropical Cyclones are identified using a widely applied objective Tropical Cyclone tracking algorithm based on relative vorticity fields. Analyses for three different aspects are carried out: 1) assessment of the skill of the ensemble to predict  the TC frequencies over different ocean basins, 2) analyse the dependency between the model's ability to represent TCs and large-scale biases and 3) assess the impact of stochastic physics and horizontal resolution on TC frequency.</p><p>For the July to October season all seasonal forecast models initialized in June are skilful in predicting the observed inter-annual variability of TC frequency over the North Atlantic (NA). Similarly, the models initialized in May show significant skill over the Western North Pacific (WNP) for the season from June to October. Further to these significant positive correlations over the NA, it is found that most models are also able to discriminate between inactive and active seasons over this region. However, despite these encouraging results, especially  for skill over the NA, most models suffer from large biases. These biases are not only related to biases in the large-scale circulation but also to the representation of intrinsic model uncertainties and the relatively coarse resolution of current seasonal forecasts. At ECMWF model uncertainty is accounted for by the use of stochastic physics, which has been shown to improve forecasts on seasonal time-scales in previous studies. Using a set of simulations conducted with the ECMWF SEAS5 model, the effects of stochastic physics and resolution on the representation of Tropical Cyclones on seasonal time-scales are assessed. Including stochastic physics increases the number of TCs over all ocean basins, but especially over the North Atlantic and Western North Pacific.</p>

A Comparison of Methods to Sample Model Errors for Convection-Allowing Ensemble Forecasts in the Setting of Multiscale Initial Conditions Produced by the GSI-Based EnVar Assimilation System

A gridpoint statistical interpolation (GSI)-based hybrid ensemble–variational (EnVar) scheme was extended for convective scales—including radar reflectivity assimilation—and implemented in real-time spring forecasting experiments. This study compares methods to address model error during the forecast under the context of multiscale initial condition error sampling provided by the EnVar system. A total of 10 retrospective cases were used to explore the optimal design of convection-allowing ensemble forecasts. In addition to single-model single-physics (SMSP) configurations, ensemble forecast experiments compared multimodel (MM) and multiphysics (MP) approaches. Stochastic physics was also applied to MP for further comparison. Neighborhood-based verification of precipitation and composite reflectivity showed each of these model error techniques to be superior to SMSP configurations. Comparisons of MM and MP approaches had mixed findings. The MM approach had better overall skill in heavy-precipitation forecasts; however, MP ensembles had better skill for light (2.54 mm) precipitation and reduced ensemble mean error of other diagnostic fields, particularly near the surface. The MM experiment had the largest spread in precipitation, and for most hours in other fields; however, rank histograms and spaghetti contours showed significant clustering of the ensemble distribution. MP plus stochastic physics was able to significantly increase spread with time to be competitive with MM by the end of the forecast. The results generally suggest that an MM approach is best for early forecast lead times up to 6–12 h, while a combination of MP and stochastic physics approaches is preferred for forecasts beyond 6–12 h.

A Unified Scheme of Stochastic Physics and Bias Correction in an Ensemble Model to Reduce Both Random and Systematic Errors

This study experimented with a unified scheme of stochastic physics and bias correction within a regional ensemble model [Global and Regional Assimilation and Prediction System–Regional Ensemble Prediction System (GRAPES-REPS)]. It is intended to improve ensemble prediction skill by reducing both random and systematic errors at the same time. Three experiments were performed on top of GRAPES-REPS. The first experiment adds only the stochastic physics. The second experiment adds only the bias correction scheme. The third experiment adds both the stochastic physics and bias correction. The experimental period is one month from 1 to 31 July 2015 over the China domain. Using 850-hPa temperature as an example, the study reveals the following: 1) the stochastic physics can effectively increase the ensemble spread, while the bias correction cannot. Therefore, ensemble averaging of the stochastic physics runs can reduce more random error than the bias correction runs. 2) Bias correction can significantly reduce systematic error, while the stochastic physics cannot. As a result, the bias correction greatly improved the quality of ensemble mean forecasts but the stochastic physics did not. 3) The unified scheme can greatly reduce both random and systematic errors at the same time and performed the best of the three experiments. These results were further confirmed by verification of the ensemble mean, spread, and probabilistic forecasts of many other atmospheric fields for both upper air and the surface, including precipitation. Based on this study, we recommend that operational numerical weather prediction centers adopt this unified scheme approach in ensemble models to achieve the best forecasts.