Craig S. Schwartz

Some Practical Considerations Regarding Horizontal Resolution in the First Generation of Operational Convection-Allowing NWP

Abstract During the 2005 NOAA Hazardous Weather Testbed Spring Experiment two different high-resolution configurations of the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) model were used to produce 30-h forecasts 5 days a week for a total of 7 weeks. These configurations used the same physical parameterizations and the same input dataset for the initial and boundary conditions, differing primarily in their spatial resolution. The first set of runs used 4-km horizontal grid spacing with 35 vertical levels while the second used 2-km grid spacing and 51 vertical levels. Output from these daily forecasts is analyzed to assess the numerical forecast sensitivity to spatial resolution in the upper end of the convection-allowing range of grid spacing. The focus is on the central United States and the time period 18–30 h after model initialization. The analysis is based on a combination of visual comparison, systematic subjective verification conducted during the Spring Experiment, and objectiv...

Impact of Assimilating AMSU-A Radiances on Forecasts of 2008 Atlantic Tropical Cyclones Initialized with a Limited-Area Ensemble Kalman Filter

AbstractThe impact of assimilating radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) on forecasts of several tropical cyclones (TCs) was studied using the Weather Research and Forecasting Model (WRF) and a limited-area ensemble Kalman filter (EnKF). Analysis/forecast cycling experiments with and without AMSU-A radiance assimilation were performed over the Atlantic Ocean for the period 11 August–13 September 2008, when five named storms formed. For convenience, the radiance forward operators and bias-correction coefficients, along with the majority of quality-control decisions, were computed by a separate, preexisting variational assimilation system. The bias-correction coefficients were obtained from 3-month offline statistics and fixed during the EnKF analysis cycles. The vertical location of each radiance observation, which is required for covariance localization in the EnKF, was taken to be the level at which the AMSU-A channels’ weighting functions peaked.Deterministic 72-h WR...

Assessing Advances in the Assimilation of Radar Data and Other Mesoscale Observations within a Collaborative Forecasting-Research Environment

The impacts of assimilating radar data and other mesoscale observations in real-time, convection-allowing model forecasts were evaluated during the spring seasons of 2008 and 2009 as part of the Hazardous Weather Test Bed Spring Experiment activities. In tests of a prototype continental U.S.-scale forecast system, focusing primarily on regions with active deep convection at the initial time, assimilation of these observations had a positive impact. Daily interrogation of output by teams of modelers, forecasters, and verification experts provided additional insights into the value-added characteristics of the unique assimilation forecasts. This evaluation revealed that the positive effects of the assimilation were greatest during the first 3‐6 h of each forecast, appeared to be most pronounced with larger convective systems, and may have been related to a phase lag that sometimes developed when the convective-scale information was not assimilated. These preliminary results are currently being evaluated further using advanced objective verification techniques.

Representing Forecast Error in a Convection-Permitting Ensemble System

AbstractEnsembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have persistent problems with underdispersion. Representing initial and or lateral boundary condition uncertainty along with forecast model error provides a foundation for building a more dependable CPEFS, but the best practice for ensemble system design is not well established.Several configurations of CPEFSs are examined where ensemble forecasts are nested within a larger domain, drawing initial conditions from a downscaled, continuously cycled, ensemble data assimilation system that provides state-dependent initial condition uncertainty. The control ensemble forecast, with initial condition uncertainty only, is skillful but underdispersive. To improve the reliability of the ensemble forecasts, the control ensemble is supplemented with 1) perturbed lateral bou...

Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts

AbstractDuring the spring 2011 season, a real-time continuously cycled ensemble data assimilation system using the Advanced Research version of the Weather Research and Forecasting Model (WRF) coupled with the Data Assimilation Research Testbed toolkit provided initial and boundary conditions for deterministic convection-permitting forecasts, also using WRF, over the eastern two-thirds of the conterminous United States (CONUS). In this study the authors evaluate the mesoscale assimilation system and the convection-permitting forecasts, at 15- and 3-km grid spacing, respectively. Experiments employing different physics options within the continuously cycled ensemble data assimilation system are shown to lead to differences in the mean mesoscale analysis characteristics. Convection-permitting forecasts with a fixed model configuration are initialized from these physics-varied analyses, as well as control runs from 0.5° Global Forecast System (GFS) analysis. Systematic bias in the analysis background influen...

Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter

AbstractConvection-permitting Weather Research and Forecasting (WRF) Model forecasts with 3-km horizontal grid spacing were produced for a 50-member ensemble over a domain spanning three-quarters of the contiguous United States between 25 May and 25 June 2012. Initial conditions for the 3-km forecasts were provided by a continuously cycling ensemble Kalman filter (EnKF) analysis–forecast system with 15-km horizontal grid length. The 3-km forecasts were evaluated using both probabilistic and deterministic techniques with a focus on hourly precipitation. All 3-km ensemble members overpredicted rainfall and there was insufficient forecast precipitation spread. However, the ensemble demonstrated skill at discriminating between both light and heavy rainfall events, as measured by the area under the relative operating characteristic curve. Subensembles composed of 20–30 members usually demonstrated comparable resolution, reliability, and skill as the full 50-member ensemble. On average, deterministic forecasts ...

NCAR’s Experimental Real-Time Convection-Allowing Ensemble Prediction System

AbstractThis expository paper documents an experimental, real-time, 10-member, 3-km, convection-allowing ensemble prediction system (EPS) developed at the National Center for Atmospheric Research (NCAR) in spring 2015. The EPS is particularly unique in that continuously cycling, limited-area, mesoscale ensemble Kalman filter analyses provide diverse initial conditions. In addition to describing the EPS configurations, initial forecast assessments are presented that suggest the EPS can provide valuable severe weather guidance and skillful predictions of precipitation. The EPS output is available to operational forecasters, many of whom have incorporated the products into their toolboxes. Given such rapid embrace of an experimental system by the operational community, acceleration of convection-allowing EPS development is encouraged.

Convection-Permitting Forecasts Initialized with Continuously Cycling Limited-Area 3DVAR, Ensemble Kalman Filter, and “Hybrid” Variational–Ensemble Data Assimilation Systems

AbstractAnalyses with 20-km horizontal grid spacing were produced from parallel continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and “hybrid” variational–ensemble data assimilation (DA) systems between 0000 UTC 6 May and 0000 UTC 21 June 2011 over a domain spanning the contiguous United States. Beginning 9 May, the 0000 UTC analyses initialized 36-h Weather Research and Forecasting Model (WRF) forecasts containing a large convection-permitting 4-km nest. These 4-km 3DVAR-, EnSRF-, and hybrid-initialized forecasts were compared to benchmark WRF forecasts initialized by interpolating 0000 UTC Global Forecast System (GFS) analyses onto the computational domain. While important differences regarding mean state characteristics of the 20-km DA systems were noted, verification efforts focused on the 4-km precipitation forecasts. The 3DVAR-, hybrid-, and EnSRF-initialized 4-km precipitation forecasts performed similarly regarding general precipitation charact...

Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of Typhoon Morakot

AbstractTwo parallel experiments were designed to evaluate whether assimilating microwave radiances with a cyclic, limited-area ensemble adjustment Kalman filter (EAKF) could improve track, intensity, and precipitation forecasts of Typhoon Morakot (2009). The experiments were configured identically, except that one assimilated microwave radiances and the other did not. Both experiments produced EAKF analyses every 6 h between 1800 UTC 3 August and 1200 UTC 9 August 2009, and the mean analyses initialized 72-h Weather Research and Forecasting model forecasts. Examination of individual forecasts and average error statistics revealed that assimilating microwave radiances ultimately resulted in better intensity forecasts compared to when radiances were withheld. However, radiance assimilation did not substantially impact track forecasts, and the impact on precipitation forecasts was mixed. Overall, net positive results suggest that assimilating microwave radiances with a limited-area EAKF system is beneficial...

The Mesoscale Predictability Experiment (MPEX)

AbstractThe Mesoscale Predictability Experiment (MPEX) was conducted from 15 May to 15 June 2013 in the central United States. MPEX was motivated by the basic question of whether experimental, subsynoptic observations can extend convective-scale predictability and otherwise enhance skill in short-term regional numerical weather prediction.Observational tools for MPEX included the National Science Foundation (NSF)–National Center for Atmospheric Research (NCAR) Gulfstream V aircraft (GV), which featured the Airborne Vertical Atmospheric Profiling System mini-dropsonde system and a microwave temperature-profiling (MTP) system as well as several ground-based mobile upsonde systems. Basic operations involved two missions per day: an early morning mission with the GV, well upstream of anticipated convective storms, and an afternoon and early evening mission with the mobile sounding units to sample the initiation and upscale feedbacks of the convection.A total of 18 intensive observing periods (IOPs) were compl...