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Xuguang Wang

Abstract

A hybrid ensemble transform Kalman filter (ETKF)–three-dimensional variational data assimilation (3DVAR) system developed for the Weather Research and Forecasting Model (WRF) was studied for the forecasts of the tracks of two major hurricanes, Ike and Gustav, in 2008 over the Gulf of Mexico. The impacts of the flow-dependent ensemble covariance generated by the ETKF were revealed by comparing the forecasts, analyses, and analysis increments generated by the hybrid data assimilation method with those generated by the 3DVAR that used the static background covariance. The root-mean-square errors of the track forecasts by the hybrid data assimilation (DA) method were smaller than those by the 3DVAR for both Ike and Gustav. Experiments showed that such improvements were due to the use of the flow-dependent covariance provided by the ETKF ensemble in the hybrid DA system. Detailed diagnostics further revealed that the increments produced by the hybrid and the 3DVAR were different for both the analyses of the hurricane itself and its environment. In particular, it was found that the hybrid, using the flow-dependent covariance that gave the hurricane-specific error covariance estimates, was able to systematically adjust the position of the hurricane during the assimilation whereas the 3DVAR was not. The study served as a pilot study to explore and understand the potential of the hybrid method for hurricane data assimilation and forecasts. Caution needs to be taken to extrapolate the results to operational forecast settings.

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Aaron Johnson
and
Xuguang Wang

Abstract

A real-time GSI-based and ensemble-based data assimilation (DA) and forecast system was implemented at the University of Oklahoma during the 2015 Plains Elevated Convection at Night (PECAN) experiment. Extensive experiments on the configuration of the cycled DA and on both the DA and forecast physics ensembles were conducted using retrospective cases to optimize the system design for nocturnal convection. The impacts of radar DA between 1200 and 1300 UTC, as well as the frequency and number of DA cycles and the DA physics configuration, extend through the following night. Ten-minute cycling of radar DA leads to more skillful forecasts than both more and less frequent cycling. The Thompson microphysics scheme for DA better analyzes the effects of morning convection on environmental moisture than WSM6, which improves the convection forecast the following night. A multi-PBL configuration during DA leads to less skillful short-term forecasts than even a relatively poorly performing single-PBL scheme. Deterministic and ensemble forecast physics configurations are also evaluated. Thompson microphysics and the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL provide the most skillful nocturnal precipitation forecasts. A well thought out multiphysics configuration is shown to provide advantages over evenly distributing three of the best-performing microphysics and PBL schemes or a fixed MYNN/Thompson ensemble. This is shown using objective and subjective verification of precipitation and nonprecipitation variables, including convective initiation. Predictions of the low-level jet are sensitive to the PBL scheme, with the best scheme being variable and time dependent. These results guided the implementation and verification of a real-time ensemble DA and forecast system for PECAN.

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Aaron Johnson
,
Xuguang Wang
, and
Samuel Degelia

Abstract

Multiscale ensemble-based data assimilation and forecasts were performed in real time during the Plains Elevated Convection At Night (PECAN) field experiment. A 20-member ensemble of forecasts at 4-km grid spacing was initialized daily at both 1300 and 1900 UTC, together with a deterministic forecast at 1-km grid spacing initialized at 1300 UTC. The configuration of the GSI-based data assimilation and forecast system was guided by results presented in Part I of this two-part study. The present paper describes the implementation of the real-time system and the extensive forecast products that were generated to support the unique interests of PECAN researchers. Subjective and objective verification of the real-time forecasts from 1 June through 15 July 2015 is conducted, with an emphasis on nocturnal mesoscale convective systems (MCSs), nocturnal convective initiation (CI), nocturnal low-level jets (LLJs), and bores on the nocturnal stable layer. Verification of nocturnal precipitation during overnight hours, a proxy for MCSs, shows both greater skill and spread for the 1300 UTC forecasts than the 1900 UTC forecasts. Verification against observed soundings reveals that the forecast LLJs systematically peak, veer, and dissipate several hours before the observations. Comparisons with bores that passed over an Atmospheric Emitted Radiance Interferometer reveal an ability to predict borelike features that is greatly improved at 1-km, compared with 4-km, grid spacing. Objective verification of forecast CI timing reveals strong sensitivity to the PBL scheme but an overall unbiased ensemble.

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Aaron Johnson
,
Xuguang Wang
,
Yongming Wang
,
Anthony Reinhart
,
Adam J. Clark
, and
Israel L. Jirak

Abstract

An object-based probabilistic (OBPROB) forecasting framework is developed and applied, together with a more traditional neighborhood-based framework, to convection-permitting ensemble forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability (MAP) laboratory during the 2017 and 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Case studies from 2017 are used for parameter tuning and demonstration of methodology, while the 2018 ensemble forecasts are systematically verified. The 2017 case study demonstrates that the OBPROB forecast product can provide a unique tool to operational forecasters that includes convective-scale details such as storm mode and morphology, which are typically lost in neighborhood-based methods, while also providing quantitative ensemble probabilistic guidance about those details in a more easily interpretable format than the more commonly used paintball plots. The case study also demonstrates that objective verification metrics reveal different relative performance of the ensemble at different forecast lead times depending on the verification framework (i.e., object versus neighborhood) because of the different features emphasized by object- and neighborhood-based evaluations. Both frameworks are then used for a systematic evaluation of 26 forecasts from the spring of 2018. The OBPROB forecast verification as configured in this study shows less sensitivity to forecast lead time than the neighborhood forecasts. Both frameworks indicate a need for probabilistic calibration to improve ensemble reliability. However, lower ensemble discrimination for OBPROB than the neighborhood-based forecasts is also noted.

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Aaron Johnson
,
Xuguang Wang
,
Yongming Wang
,
Anthony Reinhart
,
Adam J. Clark
, and
Israel L. Jirak

Abstract

An object-based probabilistic (OBPROB) forecasting framework is developed and applied, together with a more traditional neighborhood-based framework, to convection-permitting ensemble forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability (MAP) laboratory during the 2017 and 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Case studies from 2017 are used for parameter tuning and demonstration of methodology, while the 2018 ensemble forecasts are systematically verified. The 2017 case study demonstrates that the OBPROB forecast product can provide a unique tool to operational forecasters that includes convective-scale details such as storm mode and morphology, which are typically lost in neighborhood-based methods, while also providing quantitative ensemble probabilistic guidance about those details in a more easily interpretable format than the more commonly used paintball plots. The case study also demonstrates that objective verification metrics reveal different relative performance of the ensemble at different forecast lead times depending on the verification framework (i.e., object versus neighborhood) because of the different features emphasized by object- and neighborhood-based evaluations. Both frameworks are then used for a systematic evaluation of 26 forecasts from the spring of 2018. The OBPROB forecast verification as configured in this study shows less sensitivity to forecast lead time than the neighborhood forecasts. Both frameworks indicate a need for probabilistic calibration to improve ensemble reliability. However, lower ensemble discrimination for OBPROB than the neighborhood-based forecasts is also noted.

Free access
Junjun Hu
,
Nusrat Yussouf
,
David D. Turner
,
Thomas A. Jones
, and
Xuguang Wang

Abstract

Due to lack of high spatial and temporal resolution boundary layer (BL) observations, the rapid changes in the near-storm environment are not well represented in current convective-scale numerical models. Better representation of the near-storm environment in model initial conditions will likely further improve the forecasts of severe convective weather. This study investigates the impact of assimilating high temporal resolution BL retrievals from two ground-based remote sensing instruments for short-term forecasts of a tornadic supercell event on 13 July 2015 during the Plains Elevated Convection At Night field campaign. The instruments are the Atmospheric Emitted Radiance Interferometer (AERI) that retrieves thermodynamic profiles and the Doppler lidar (DL) that measures horizontal wind profiles. Six sets of convective-scale ensemble data assimilation (DA) experiments are performed: two control experiments that assimilate conventional and WSR-88D radar observations using either relaxation-to-prior-spread (RTPS) or the adaptive inflation (AI) technique and four experiments similar to the control but that assimilate either DL or AERI or both observations in addition to all other observations that are in the control experiments. Results indicate a positive impact of AERI and DL observations in forecasting convective initiation (CI) and early evolution of the supercell storm. The experiment that employs the AI technique to assimilate BL observations in DA enhances the humidity in the near-storm environment and low-level convergence, which in turn helps forecasting CI. The forecast improvement is most pronounced during the first ~3 h. Results also indicate that the AERI observations have a larger impact compared to DL in predicting CI.

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Brett Roberts
,
Burkely T. Gallo
,
Israel L. Jirak
,
Adam J. Clark
,
David C. Dowell
,
Xuguang Wang
, and
Yongming Wang

Abstract

The High Resolution Ensemble Forecast v2.1 (HREFv2.1), an operational convection-allowing model (CAM) ensemble, is an “ensemble of opportunity” wherein forecasts from several independently designed deterministic CAMs are aggregated and postprocessed together. Multiple dimensions of diversity in the HREFv2.1 ensemble membership contribute to ensemble spread, including model core, physics parameterization schemes, initial conditions (ICs), and time lagging. In this study, HREFv2.1 forecasts are compared against the High Resolution Rapid Refresh Ensemble (HRRRE) and the Multiscale data Assimilation and Predictability (MAP) ensemble, two experimental CAM ensembles that ran during the 5-week Spring Forecasting Experiment (SFE) in spring 2018. The HRRRE and MAP are formally designed ensembles with spread achieved primarily through perturbed ICs. Verification in this study focuses on composite radar reflectivity and updraft helicity to assess ensemble performance in forecasting convective storms. The HREFv2.1 shows the highest overall skill for these forecasts, matching subjective real-time impressions from SFE participants. Analysis of the skill and variance of ensemble member forecasts suggests that the HREFv2.1 exhibits greater spread and more effectively samples model uncertainty than the HRRRE or MAP. These results imply that to optimize skill in forecasting convective storms at 1–2-day lead times, future CAM ensembles should employ either diverse membership designs or sophisticated perturbation schemes capable of representing model uncertainty with comparable efficacy.

Free access
Corey K. Potvin
,
Jacob R. Carley
,
Adam J. Clark
,
Louis J. Wicker
,
Patrick S. Skinner
,
Anthony E. Reinhart
,
Burkely T. Gallo
,
John S. Kain
,
Glen S. Romine
,
Eric A. Aligo
,
Keith A. Brewster
,
David C. Dowell
,
Lucas M. Harris
,
Israel L. Jirak
,
Fanyou Kong
,
Timothy A. Supinie
,
Kevin W. Thomas
,
Xuguang Wang
,
Yongming Wang
, and
Ming Xue

Abstract

The 2016–18 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFE) featured the Community Leveraged Unified Ensemble (CLUE), a coordinated convection-allowing model (CAM) ensemble framework designed to provide empirical guidance for development of operational CAM systems. The 2017 CLUE included 81 members that all used 3-km horizontal grid spacing over the CONUS, enabling direct comparison of forecasts generated using different dynamical cores, physics schemes, and initialization procedures. This study uses forecasts from several of the 2017 CLUE members and one operational model to evaluate and compare CAM representation and next-day prediction of thunderstorms. The analysis utilizes existing techniques and novel, object-based techniques that distill important information about modeled and observed storms from many cases. The National Severe Storms Laboratory Multi-Radar Multi-Sensor product suite is used to verify model forecasts and climatologies of observed variables. Unobserved model fields are also examined to further illuminate important intermodel differences in storms and near-storm environments. No single model performed better than the others in all respects. However, there were many systematic intermodel and intercore differences in specific forecast metrics and model fields. Some of these differences can be confidently attributed to particular differences in model design. Model intercomparison studies similar to the one presented here are important to better understand the impacts of model and ensemble configurations on storm forecasts and to help optimize future operational CAM systems.

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