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Stephen S. Weygandt and Nelson L. Seaman

Abstract

To quantitatively assess numerical predictive skill for synoptic and mesoscale features as a function of horizontal grid resolution, a series of experiments is conducted using the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model. For eight cases of continental cyclogenesis, 72-h integrations are examined using grids of 160, 80, and 26.7 km. First, we briefly examine error statistics for synoptic-scale cyclones and anticyclones. Next, a detailed analysis of model errors for mesoscale features is presented. A bandpass filtering technique, based on the Barnes objective analysis scheme, is used to separate perturbation quantities associated with the mesoscale features from the synoptic-scale fields. Error statistics are then compiled for various mesoscale features, including the intensity of mesolows, damming ridges, and postfrontal troughs, and the thermal gradients, propagation speed, and vertical velocity maxima associated with surface cold fronts. Finally, the accuracy of the predicted precipitation fields, produced using the Anthes-Kuo cumulus parameterization, is examined.

Objective verification reveals that forecast skill does not improve uniformly for all types of mesoscale features as horizontal grid resolution is increased, although the general trend is for reduced errors as expected. Improvements do occur on both the 80- and 27-km grids for all geographically related mesoscale features (such as orographic lee troughs). A similar improvement is seen for propagating mesoscale features (such as postfrontal troughs) and synoptic-scale cyclones as the grid length is reduced from 160 to 80 km. However, when the grid length is further reduced to 27 km, mean absolute errors and mean position errors actually increase for both classes of features. This greater variability in model performance suggests that as grid resolution is enhanced, other factors such as the accuracy of model physics and initial conditions become increasingly important.

The effect on precipitation bias and threat scores in these experiments is positive (reduced errors) when resolution is improved from 160 to 80 km but is generally insignificant or negative for the 27-km grid. Based on these results, the Anthes-Kuo convective parameterization used in these experiments is not recommended for application on grids of about 30 km or less.

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Stephen S. Weygandt, Alan Shapiro, and Kelvin K. Droegemeier

Abstract

In this two-part study, a single-Doppler parameter retrieval technique is developed and applied to a real-data case to provide initial conditions for a short-range prediction of a supercell thunderstorm. The technique consists of the sequential application of a single-Doppler velocity retrieval (SDVR), followed by a variational velocity adjustment, a thermodynamic retrieval, and a moisture specification step. By utilizing a sequence of retrievals in this manner, some of the difficulties associated with full-model adjoints (possible solution nonuniqueness and large computational expense) can be circumvented. In Part I, the SDVR procedure and present results from its application to a deep-convective storm are discussed. Part II focuses on the thermodynamic retrieval and subsequent numerical prediction.

For the SDVR, Shapiro's reflectivity conservation-based method is adapted by applying it in a moving reference frame. Verification of the retrieved wind fields against corresponding dual-Doppler analyses indicates that the best skill scores are obtained for a reference frame moving with the mean wind, which effectively reduces the problem to a perturbation retrieval. A decomposition of the retrieved wind field into mean and perturbation components shows that the mean wind accounts for a substantial portion of the total retrieved azimuthal velocity. At low levels, where the retrieval skill scores are especially good, the retrieved perturbation azimuthal velocity is mostly associated with the polar component of vorticity. Missing from the retrieved fields (compared to the dual-Doppler analysis) is most of the low-level azimuthal convergence. Consistent with this result, most of the retrieved updraft is associated with convergence of the perturbation radial velocity, which is calculated from the observed radial velocity and directly used in the wind retrieval.

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Stephen S. Weygandt, Alan Shapiro, and Kelvin K. Droegemeier

Abstract

In this two-part study, a single-Doppler parameter retrieval technique is developed and applied to a real-data case to provide model initial conditions for a short-range prediction of a supercell thunderstorm. The technique consists of the sequential application of a single-Doppler velocity retrieval (SDVR), followed by a variational velocity adjustment, a thermodynamic retrieval, and a moisture specification step. In Part I, the SDVR procedure is described and results from its application to a supercell thunderstorm are presented. In Part II, results from the thermodynamic retrieval and the numerical model prediction for this same case are presented. For comparison, results from parallel sets of experiments using dual-Doppler-derived winds and winds obtained from the simplified velocity retrieval described in Part I are also shown.

Following the SDVR, the retrieved wind fields (available only within the storm volume) are blended with a base-state background field obtained from a proximity sounding. The blended fields are then variationally adjusted to preserve anelastic mass conservation and the observed radial velocity. A Gal-Chen type thermodynamic retrieval procedure is then applied to the adjusted wind fields. For all experiments (full retrieval, simplified retrieval, and dual Doppler), the resultant perturbation pressure and potential temperature fields agree qualitatively with expectations for a deep-convective storm. An analysis of the magnitude of the various terms in the vertical momentum equation for both the full retrieval and dual-Doppler experiments indicates a reasonable agreement with predictions from linear theory. In addition, the perturbation pressure and vorticity fields for both the full retrieval and dual-Doppler experiments are in reasonable agreement with linear theory predictions for deep convection in sheared flow.

Following a simple moisture specification step, short-range numerical predictions are initiated for both retrieval experiments and the dual-Doppler experiment. In the full single-Doppler retrieval and dual-Doppler cases, the general storm evolution and deviant storm motion are reasonably well predicted for a period of about 35 minutes. In contrast, the storm initialized using the simplified wind retrieval decays too rapidly, indicating that the additional information obtained by the full wind retrieval (primarily low-level polar vorticity) is vital to the success of the numerical prediction. Sensitivity experiments using the initial fields from the full retrieval indicate that the predicted storm evolution is strongly dependent on the initial moisture fields. Overall, the numerical prediction results suggest at least some degree of short-term predictability for this storm and provide an impetus for continued development of single-Doppler retrieval procedures.

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Haidao Lin, Stephen S. Weygandt, Stanley G. Benjamin, and Ming Hu

Abstract

Assimilation of satellite radiance data in limited-area, rapidly updating weather model/assimilation systems poses unique challenges compared to those for global model systems. Principal among these is the severe data restriction posed by the short data cutoff time. Also, the limited extent of the model domain reduces the spatial extent of satellite data coverage and the lower model top of regional models reduces the spectral usage of radiance data especially for infrared data. These three factors impact the quality of the feedback to the bias correction procedures, making the procedures potentially less effective. Within the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) hourly updating prediction system, satellite radiance data are assimilated using the standard procedures within the Gridpoint Statistical Interpolation (GSI) analysis package. Experiments for optimizing the operational implementation of radiance data into RAP and for improving benefits of radiance data have been performed. The radiance data impact for short-range forecasts has been documented to be consistent and statistically significantly positive in systematic RAP retrospective runs using real-time datasets. The radiance data impact has also been compared with conventional observation datasets within RAP. The configuration for RAP satellite radiance assimilation evaluated here is that implemented at the National Centers for Environmental Prediction (NCEP) in August 2016.

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Ming Hu, Stanley G. Benjamin, Therese T. Ladwig, David C. Dowell, Stephen S. Weygandt, Curtis R. Alexander, and Jeffrey S. Whitaker

Abstract

The Rapid Refresh (RAP) is an hourly updated regional meteorological data assimilation/short-range model forecast system running operationally at NOAA/National Centers for Environmental Prediction (NCEP) using the community Gridpoint Statistical Interpolation analysis system (GSI). This paper documents the application of the GSI three-dimensional hybrid ensemble–variational assimilation option to the RAP high-resolution, hourly cycling system and shows the skill improvements of 1–12-h forecasts of upper-air wind, moisture, and temperature over the purely three-dimensional variational analysis system. Use of perturbation data from an independent global ensemble, the Global Data Assimilation System (GDAS), is demonstrated to be very effective for the regional RAP hybrid assimilation. In this paper, application of the GSI-hybrid assimilation for the RAP is explained. Results from sensitivity experiments are shown to define configurations for the operational RAP version 2, the ratio of static and ensemble background error covariance, and vertical and horizontal localization scales for the operational RAP version 3. Finally, a 1-week RAP experiment from a summer period was performed using a global ensemble from a winter period, suggesting that a significant component of its multivariate covariance structure from the ensemble is independent of time matching between analysis time and ensemble valid time.

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Kefeng Zhu, Yujie Pan, Ming Xue, Xuguang Wang, Jeffrey S. Whitaker, Stanley G. Benjamin, Stephen S. Weygandt, and Ming Hu

Abstract

A regional ensemble Kalman filter (EnKF) system is established for potential Rapid Refresh (RAP) operational application. The system borrows data processing and observation operators from the gridpoint statistical interpolation (GSI), and precalculates observation priors using the GSI. The ensemble square root Kalman filter (EnSRF) algorithm is used, which updates both the state vector and observation priors. All conventional observations that are used in the operational RAP GSI are assimilated. To minimize computational costs, the EnKF is run at ⅓ of the operational RAP resolution or about 40-km grid spacing, and its performance is compared to the GSI using the same datasets and resolution. Short-range (up to 18 h, the RAP forecast length) forecasts are verified against soundings, surface observations, and precipitation data. Experiments are run with 3-hourly assimilation cycles over a 9-day convectively active retrospective period from spring 2010. The EnKF performance was improved by extensive tuning, including the use of height-dependent covariance localization scales and adaptive covariance inflation. When multiple physics parameterization schemes are employed by the EnKF, forecast errors are further reduced, especially for relative humidity and temperature at the upper levels and for surface variables. The best EnKF configuration produces lower forecast errors than the parallel GSI run. Gilbert skill scores of precipitation forecasts on the 13-km RAP grid initialized from the 3-hourly EnKF analyses are consistently better than those from GSI analyses.

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Yujie Pan, Kefeng Zhu, Ming Xue, Xuguang Wang, Ming Hu, Stanley G. Benjamin, Stephen S. Weygandt, and Jeffrey S. Whitaker

Abstract

A coupled ensemble square root filter–three-dimensional ensemble-variational hybrid (EnSRF–En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

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Haidao Lin, Stephen S. Weygandt, Agnes H. N. Lim, Ming Hu, John M. Brown, and Stanley G. Benjamin

Abstract

This study describes the initial application of radiance bias correction and channel selection in the hourly updated Rapid Refresh model. For this initial application, data from the Atmospheric Infrared Sounder (AIRS) are used; this dataset gives atmospheric temperature and water vapor information at higher vertical resolution and accuracy than previously launched low-spectral resolution satellite systems. In this preliminary study, data from AIRS are shown to add skill to short-range weather forecasts over a relatively data-rich area. Two 1-month retrospective runs were conducted to evaluate the impact of assimilating clear-sky AIRS radiance data on 1–12-h forecasts using a research version of the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) regional mesoscale model already assimilating conventional and other radiance [AMSU-A, Microwave Humidity Sounder (MHS), HIRS-4] data. Prior to performing the assimilation, a channel selection and bias-correction spinup procedure was conducted that was appropriate for the RAP configuration. RAP forecasts initialized from analyses with and without AIRS data were verified against radiosonde, surface atmosphere, precipitation, and satellite radiance observations. Results show that the impact from AIRS radiance data on short-range forecast skill in the RAP system is small but positive and statistically significant at the 95% confidence level. The RAP-specific channel selection and bias correction procedures described in this study were the basis for similar applications to other radiance datasets now assimilated in version 3 of RAP implemented at NOAA’s National Centers for Environmental Prediction (NCEP) in August 2016.

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Stanley G. Benjamin, Dezsö Dévényi, Stephen S. Weygandt, Kevin J. Brundage, John M. Brown, Georg A. Grell, Dongsoo Kim, Barry E. Schwartz, Tatiana G. Smirnova, Tracy Lorraine Smith, and Geoffrey S. Manikin

Abstract

The Rapid Update Cycle (RUC), an operational regional analysis–forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic–sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed.

A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.

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David J. Stensrud, Ming Xue, Louis J. Wicker, Kevin E. Kelleher, Michael P. Foster, Joseph T. Schaefer, Russell S. Schneider, Stanley G. Benjamin, Stephen S. Weygandt, John T. Ferree, and Jason P. Tuell

The National Oceanic and Atmospheric Administration's (NOAA's) National Weather Service (NWS) issues warnings for severe thunderstorms, tornadoes, and flash floods because these phenomena are a threat to life and property. These warnings are presently based upon either visual confirmation of the phenomena or the observational detection of proxy signatures that are largely based upon radar observations. Convective-scale weather warnings are unique in the NWS, having little reliance on direct numerical forecast guidance. Because increasing severe thunderstorm, tornado, and flash-flood warning lead times are a key NOAA strategic mission goal designed to reduce the loss of life, injury, and economic costs of these high-impact weather phenomena, a new warning paradigm is needed in which numerical model forecasts play a larger role in convective-scale warnings. This new paradigm shifts the warning process from warn on detection to warn on forecast, and it has the potential to dramatically increase warning lead times.

A warn-on-forecast system is envisioned as a probabilistic convective-scale ensemble analysis and forecast system that assimilates in-storm observations into a high-resolution convection-resolving model ensemble. The building blocks needed for such a system are presently available, and initial research results clearly illustrate the value of radar observations to the production of accurate analyses of convective weather systems and improved forecasts. Although a number of scientific and cultural challenges still need to be overcome, the potential benefits are significant. A probabilistic convective-scale warn-on-forecast system is a vision worth pursuing.

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