Kent H. Knopfmeier

Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar Data Experiments

AbstractThis first part of a two-part study on storm-scale radar and satellite data assimilation provides an overview of a multicase study conducted as part of the NOAA Warn-on-Forecast (WoF) project. The NSSL Experimental WoF System for ensembles (NEWS-e) is used to produce storm-scale analyses and forecasts of six diverse severe weather events from spring 2013 and 2014. In this study, only Doppler reflectivity and radial velocity observations (and, when available, surface mesonet data) are assimilated into a 36-member, storm-scale ensemble using an ensemble Kalman filter (EnKF) approach. A series of 1-h ensemble forecasts are then initialized from storm-scale analyses during the 1-h period preceding the onset of storm reports. Of particular interest is the ability of these 0–1-h ensemble forecasts to reproduce the low-level rotational characteristics of supercell thunderstorms, as well as other convective hazards. For the tornado-producing thunderstorms considered in this study, ensemble probabilistic f...

Storm-Scale Data Assimilation and Ensemble Forecasts for the 27 April 2011 Severe Weather Outbreak in Alabama

AbstractAs part of NOAA’s Warn-on-Forecast (WoF) initiative, a multiscale ensemble-based assimilation and prediction system is developed using the WRF-ARW model and DART assimilation software. To evaluate the capabilities of the system, retrospective short-range probabilistic storm-scale (convection allowing) ensemble analyses and forecasts are produced for the 27 April 2011 Alabama severe weather outbreak. Results indicate that the storm-scale ensembles are able to analyze the observed storms with strong low-level rotation at approximately the correct locations and to retain the supercell structures during the 0–1-h forecasts with reasonable accuracy. The system predicts the low-level mesocyclones of significant isolated tornadic supercells that align well with the locations of radar-derived rotation. For cases with multiple interacting storms in close proximity, the system tends to produce more variability in mesocyclone forecasts from one initialization time to the next until the observations show the ...

Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part II: Combined Radar and Satellite Data Experiments

AbstractThis research represents the second part of a two-part series describing the development of a prototype ensemble data assimilation system for the Warn-on-Forecast (WoF) project known as the NSSL Experimental WoF System for ensembles (NEWS-e). Part I describes the NEWS-e design and results from radar reflectivity and radial velocity data assimilation for six severe weather events occurring during 2013 and 2014. Part II describes the impact of assimilating satellite liquid and ice water path (LWP and IWP, respectively) retrievals from the GOES Imager along with the radar observations. Assimilating LWP and IWP observations may improve thermodynamic conditions at the surface over the storm-scale domain through better analysis of cloud coverage in the model compared to radar-only experiments. These improvements sometimes corresponded to an improved analysis of supercell storms leading to better forecasts of low-level vorticity. This positive impact was most evident for events where convection is not on...

Assimilation of Satellite Infrared Radiances and Doppler Radar Observations during a Cool Season Observing System Simulation Experiment

AbstractAn observing system simulation experiment is used to examine the impact of assimilating water vapor–sensitive satellite infrared brightness temperatures and Doppler radar reflectivity and radial velocity observations on the analysis accuracy of a cool season extratropical cyclone. Assimilation experiments are performed for four different combinations of satellite, radar, and conventional observations using an ensemble Kalman filter assimilation system. Comparison with the high-resolution “truth” simulation indicates that the joint assimilation of satellite and radar observations reduces errors in cloud properties compared to the case in which only conventional observations are assimilated. The satellite observations provide the most impact in the mid- to upper troposphere, whereas the radar data also improve the cloud analysis near the surface and aloft as a result of their greater vertical resolution and larger overall sample size. Errors in the wind field are also significantly reduced when rada...

Forecast Evaluation of an Observing System Simulation Experiment Assimilating Both Radar and Satellite Data

AbstractIn the first part of this study, Jones et al. compared the relative skill of assimilating simulated radar reflectivity and radial velocity observations and satellite 6.95-μm brightness temperatures TB and found that both improved analyses of water vapor and cloud hydrometeor variables for a cool-season, high-impact weather event across the central United States. In this study, the authors examine the impact of the observations on 1–3-h forecasts and provide additional analysis of the relationship between simulated satellite and radar data observations to various water vapor and cloud hydrometeor variables. Correlation statistics showed that the radar and satellite observations are sensitive to different variables. Assimilating 6.95-μm TB primarily improved the atmospheric water vapor and frozen cloud hydrometeor variables such as ice and snow. Radar reflectivity proved more effective in both the lower and midtroposphere with the best results observed for rainwater, graupel, and snow. The impacts o...

Influence of Mesonet Observations on the Accuracy of Surface Analyses Generated by an Ensemble Kalman Filter

AbstractThe expansion of surface mesoscale networks (mesonets) across the United States provides a high-resolution observational dataset for meteorological analysis and prediction. To clarify the impact of mesonet data on the accuracy of surface analyses, 2-m temperature, 2-m dewpoint, and 10-m wind analyses for 2-week periods during the warm and cold seasons produced through an ensemble Kalman filter (EnKF) approach are compared to surface analyses created by the Real-Time Mesoscale Analysis (RTMA). Results show in general a similarity between the EnKF analyses and the RTMA, with the EnKF exhibiting a smoother appearance with less small-scale variability. Root-mean-square (RMS) innovations are generally lower for temperature and dewpoint from the RTMA, implying a closer fit to the observations. Kinetic energy spectra computed from the two analyses reveal that the EnKF analysis spectra match more closely to the spectra computed from observations and numerical models in earlier studies. Data-denial experim...

Collaborative Efforts between the United States and United Kingdom to Advance Prediction of High-Impact Weather

AbstractIn recent years, a growing partnership has emerged between the Met Office and the designated U.S. national centers for expertise in severe weather research and forecasting, that is, the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory (NSSL) and the NOAA Storm Prediction Center (SPC). The driving force behind this partnership is a compelling set of mutual interests related to predicting and understanding high-impact weather and using high-resolution numerical weather prediction models as foundational tools to explore these interests.The forum for this collaborative activity is the NOAA Hazardous Weather Testbed, where annual Spring Forecasting Experiments (SFEs) are conducted by NSSL and SPC. For the last decade, NSSL and SPC have used these experiments to find ways that high-resolution models can help achieve greater success in the prediction of tornadoes, large hail, and damaging winds. Beginning in 2012, the Met Office became a contributing partner in ann...

Application of Two Spatial Verification Methods to Ensemble Forecasts of Low-Level Rotation

AbstractTwo spatial verification methods are applied to ensemble forecasts of low-level rotation in supercells: a four-dimensional, object-based matching algorithm and the displacement and amplitude score (DAS) based on optical flow. Ensemble forecasts of low-level rotation produced using the National Severe Storms Laboratory (NSSL) Experimental Warn-on-Forecast System are verified against WSR-88D single-Doppler azimuthal wind shear values interpolated to the model grid. Verification techniques are demonstrated using four 60-min forecasts issued at 15-min intervals in the hour preceding development of the 20 May 2013 Moore, Oklahoma, tornado and compared to results from two additional forecasts of tornadic supercells occurring during the springs of 2013 and 2014.The object-based verification technique and displacement component of DAS are found to reproduce subjectively determined forecast characteristics in successive forecasts for the 20 May 2013 event, as well as to discriminate in subjective forecast ...

Impact of MPEX Upsonde Observations on Ensemble Analyses and Forecasts of the 31 May 2013 Convective Event over Oklahoma

AbstractThis study examines the impact of assimilating three radiosonde profiles obtained from ground-based mobile systems during the Mesoscale Predictability Experiment (MPEX) on analyses and convection-permitting model forecasts of the 31 May 2013 convective event over Oklahoma. These radiosonde profiles (in addition to standard observations) are assimilated into a 36-member mesoscale ensemble using an ensemble Kalman filter (EnKF) before embedding a convection-permitting (3 km) grid and running a full ensemble of 9-h forecasts. This set of 3-km forecasts is compared to a control run that does not assimilate the MPEX soundings. The analysis of low- to midlevel moisture is impacted the most by the assimilation, but coherent mesoscale differences in temperature and wind are also seen, primarily downstream of the location of the soundings. The ensemble of forecasts of convection on the 3-km grid are improved the most in the first three hours of the forecast in a region where the analyzed position of low-le...

Impact of Assimilating Preconvective Upsonde Observations on Short-Term Forecasts of Convection Observed during MPEX

AbstractThis study examines the impact of assimilating preconvective radiosonde observations obtained by mobile sounding systems on short-term forecasts of convection. Ensemble data assimilation is performed on a mesoscale (15 km) grid and the resulting analyses are downscaled to produce forecasts on a convection-permitting grid (3 km). The ensembles of forecasts are evaluated through their depiction of radar reflectivity compared to observed radar reflectivity. Examination of fractions skill scores over eight cases shows that, for four of the cases, assimilation of radiosonde observations nearby to subsequent convection has a positive impact on the initiation and early evolution during the first 3–4 h of the forecasts, even for the smallest resolvable scales of the 3-km grid. For the four cases in which positive impacts near the smallest resolvable scales of the grid are not seen, analysis of the changes to the preconvective environment suggests that suboptimal locations of the soundings compared to the ...