Assessing spatial precipitation uncertainties in a convective-scale ensemble

New techniques have recently been developed to quantify the location-dependent spatial agreement between ensemble members, and the spatial spread-skill relationship. In this paper a summer of convection permitting ensemble forecasts are analysed to better understand the factors influencing location-dependent spatial agreement of precipitation fields and the spatial spread-skill relationship over the UK. The aim is to further investigate the agreement scale method, and to highlight the information that could be extracted for a more long-term routine model evaluation. Overall, for summer 2013, the UK 2.2km-resolution ensemble system was found to be reasonably well spread spatially, although there was a tendency for the ensemble to be over confident in the location of precipitation. For the forecast lead times considered (up to 36 hrs) a diurnal cycle was seen in the spatial agreement and in the spatial spread-skill relationship: the forecast spread and error did not increase noticeably with forecast lead time. Both the spatial agreement, and the spatial spread-skill, were dependent on the fractional coverage and average intensity of precipitation. A poor spread-skill relationship was associated with a low fractional coverage of rain and low average rain rates. The times with a smaller fractional coverage, or lower intensity, of precipitation were found to have lower spatial agreement. The spatial agreement was found to be location dependant, with higher confidence in the location of precipitation to the northwest of the UK

A new method for the characterisation and verification of local spatial predictability for convective scale ensembles

The use of kilometre-scale ensembles in operational forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood based method is presented for evaluating and characterising the local predictability variations from convective scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, S^A) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs (S^A(mm)_ij), with that between members and radar observations (S^A(mo)_ij), this approach allows the location-dependent spatial spread-skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealised experiment. To demonstrate the methods in an operational context the S^A(mm)_ij and S^A(mo)_ij are calculated for six convective cases run with the Met Office UK Ensemble Prediction System. The S^A(mm)_ij highlight predictability differences between cases, which can be linked to physical processes. Maps of S^A(mm)_ij are found to summarise the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and for model interpretation. Comparison of S^A(mm)_ij and S^A(mo)_ij demonstrates the case-by-case and temporal variability of the spatial spread-skill, which can again be linked to physical processes

Convective-scale perturbation growth across the spectrum of convective regimes

Convection-permitting ensembles have led to improved forecasts of many atmospheric phenomena. However, to fully utilize these forecasts the dependence of predictability on synoptic conditions needs to be understood. In this study, convective regimes are diagnosed based on a convective timescale which identifies the degree to which convection is in equilibrium with the large-scale forcing. Six convective cases are examined in a convection-permitting ensemble constructed using the Met Office Unified Model. The ensemble members were generated using small-amplitude buoyancy perturbations added into the boundary layer, which can be considered to represent turbulent fluctuations close to the gridscale. Perturbation growth is shown to occur on different scales with an order of magnitude difference between the regimes (O(1 km) for cases closer to non-equilibrium convection and O(10 km) for cases closer to equilibrium convection). This difference reflects the fact that cell locations are essentially random in the equilibrium events after the first 12 h of the forecast, indicating a more rapid upscale perturbation growth compared to the non-equilibrium events. Furthermore, large temporal variability is exhibited in all perturbation growth diagnostics for the nonequilibrium regime. Two boundary condition driven cases are also considered and show similar characteristics to the non-equilibrium cases, implying that caution is needed to interpret the timescale when initiation is not within the domain. Further understanding of perturbation growth within the different regimes could lead to a better understanding of where ensemble design improvements can be made beyond increasing the model resolution and could improve interpretation of forecasts

Development and Application of a Simplified Coplane Wind Retrieval Algorithm Using Dual-Beam Airborne Doppler Radar Observations for Tropical Cyclone Prediction

Abstract Based on established coplane methodology, a simplified three-dimensional wind retrieval algorithm is proposed to derive two-dimensional wind vectors from radial velocity observations by the tail Doppler radars on board the NOAA P3 hurricane reconnaissance aircraft. Validated against independent in situ flight-level and dropsonde observations before and after genesis of Hurricane Karl (2010), each component of the retrieved wind vectors near the aircraft track has an average error of approximately 1.5 m s−1, which increases with the scanning angle and distance away from the aircraft track. Simulated radial velocities derived from a convection-permitting simulation of Karl are further used to systematically quantify errors of the simplified coplane algorithm. The accuracy of the algorithm is strongly dependent on the time between forward and backward radar scans and to a lesser extent, the zero vertical velocity assumption at large angles relative to a plane parallel with the aircraft wings. A proof-of-concept experiment assimilating the retrieved wind vectors with an ensemble Kalman filter shows improvements in track and intensity forecasts similar to assimilating radial velocity super observations or the horizontal wind vectors from the analysis retrievals provided by the Hurricane Research Division of NOAA. Future work is needed to systematically evaluate this simplified coplane algorithm with proper error characteristics for TC initialization and prediction through a large number of events to establish statistical significance.</jats:p

Practical and Intrinsic Predictability of Severe and Convective Weather at the Mesoscales

Abstract This study explores both the practical and intrinsic predictability of severe convective weather at the mesoscales using convection-permitting ensemble simulations of a squall line and bow echo event during the Bow Echo and Mesoscale Convective Vortex (MCV) Experiment (BAMEX) on 9–10 June 2003. Although most ensemble members—initialized with realistic initial condition uncertainties smaller than the NCEP Global Forecast System Final Analysis (GFS FNL) using an ensemble Kalman filter—forecast broad areas of severe convection, there is a large variability of forecast performance among different members, highlighting the limit of practical predictability. In general, the best-performing members tend to have a stronger upper-level trough and associated surface low, producing a more conducive environment for strong long-lived squall lines and bow echoes, once triggered. The divergence in development is a combination of a dislocation of the upper-level trough, surface low with corresponding marginal environmental differences between developing and nondeveloping members, and cold pool evolution by deep convection prior to squall line formation. To further explore the intrinsic predictability of the storm, a sequence of sensitivity experiments was performed with the initial condition differences decreased to nearly an order of magnitude smaller than typical analysis and observation errors. The ensemble forecast and additional sensitivity experiments demonstrate that this storm has a limited practical predictability, which may be further improved with more accurate initial conditions. However, it is possible that the true storm could be near the point of bifurcation, where predictability is intrinsically limited. The limits of both practical and intrinsic predictability highlight the need for probabilistic and ensemble forecasts for severe weather prediction.</jats:p

Diurnal Radiation Cycle Impact on the Pregenesis Environment of Hurricane Karl (2010)

Abstract Through convection-permitting ensemble and sensitivity experiments, this study examines the impact of the diurnal radiation cycle on the pregenesis environment of Hurricane Karl (2010). It is found that the pregenesis environmental stability and the intensity of deep moist convection can be considerably modulated by the diurnal extremes in radiation. Nighttime destabilization of the local and large-scale environment through radiative cooling may promote deep moist convection and increase the genesis potential, likely enhancing the intensity of the resultant tropical cyclones. Modified longwave and shortwave radiation experiments found tropical cyclone development to be highly sensitive to the periodic cycle of heating and cooling, with suppressed formation in the daytime-only and no-radiation experiments and quicker intensification compared with the control for nighttime-only experiments.</jats:p

A Multiple-Model Convection-Permitting Ensemble Examination of the Probabilistic Prediction of Tropical Cyclones: Hurricanes Sandy (2012) and Edouard (2014)

Abstract This study examines a multimodel comparison of regional-scale convection-permitting ensembles including both physics and initial condition uncertainties for the probabilistic prediction of Hurricanes Sandy (2012) and Edouard (2014). The model cores examined include COAMPS-TC, HWRF, and WRF-ARW. Two stochastic physics schemes were also applied using the WRF-ARW model. Each ensemble was initialized with the same initial condition uncertainties represented by the analysis perturbations from a WRF-ARW-based real-time cycling ensemble Kalman filter. It is found that single-core ensembles were capable of producing similar ensemble statistics for track and intensity for the first 36–48 h of model integration, with biases in the ensemble mean evident at longer forecast lead times along with increased variability in spread. The ensemble spread of a multicore ensemble with members sampled from single-core ensembles was generally as large or larger than any constituent model, especially at longer lead times. Systematically varying the physic parameterizations in the WRF-ARW ensemble can alter both the forecast ensemble mean and spread to resemble the ensemble performance using a different forecast model. Compared to the control WRF-ARW experiment, the application of the stochastic kinetic energy backscattering scheme had minimal impact on the ensemble spread of track and intensity for both cases, while the use of stochastic perturbed physics tendencies increased the ensemble spread in track for Sandy and in intensity for both cases. This case study suggests that it is important to include model physics uncertainties for probabilistic TC prediction. A single-core multiphysics ensemble can capture the ensemble mean and spread forecasted by a multicore ensemble for the presented case studies.</jats:p

Impact of Sea Surface Temperature Forcing on Weeks 3 and 4 Forecast Skill in the NCEP Global Ensemble Forecasting System

Abstract The Global Ensemble Forecasting System (GEFS) is being extended from 16 to 35 days to cover the subseasonal period, bridging weather and seasonal forecasts. In this study, the impact of SST forcing on the extended-range land-only global 2-m temperature, continental United States (CONUS) accumulated precipitation, and MJO skill are explored with version 11 of the GEFS (GEFSv11) under various SST forcing configurations. The configurations consist of 1) the operational GEFS 90-day e-folding time of the observed real-time global SST (RTG-SST) anomaly relaxed to climatology, 2) an optimal AMIP configuration using the observed daily RTG-SST analysis, 3) a two-tier approach using the CFSv2-predicted daily SST, and 4) a two-tier approach using bias-corrected CFSv2-predicted SST, updated every 24 h. The experimental period covers the fall of 2013 and the winter of 2013/14. The results indicate that there are small differences in the ranked probability skill scores (RPSSs) between the various SST forcing experiments. The improvements in forecast skill of the Northern Hemisphere 2-m temperature and precipitation for weeks 3 and 4 are marginal, especially for North America. The bias-corrected CFSv2-predicted SST experiment generally delivers superior performance with statistically significant improvement in spatially and temporally aggregated 2-m temperature RPSSs over North America. Improved representation of the SST forcing (AMIP) increased the forecast skill for MJO indices up through week 2, but there is no significant improvement of the MJO forecast skill for weeks 3 and 4. These results are obtained over a short period with weak MJO activity and are also subject to internal model weaknesses in representing the MJO. Additional studies covering longer periods with upgraded model physics are warranted.</jats:p