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- Author or Editor: Keith A. Brewster x
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Abstract
An objective method of determining and correcting phase or position errors in numerical weather prediction is described and tested in a radar data observing system simulation experiment (OSSE) addressing the forecasting of ongoing thunderstorms. Such phase or position errors are common in numerical forecasts at grid resolutions of 2–20 km (meso-γ scale). It is proposed that the process of correcting a numerical forecast field can be simplified if such errors are addressed directly. An objective method of determining the phase error in the forecast by searching for a field of shift vectors that minimizes a squared-error difference from high-resolution observations is described.
Three methods of applying a phase error correction to a forecast model are detailed. The first applies the entire correction at the initial time, the second in discrete steps during an assimilation window, and the third applies the correction continuously through the model's horizontal advection process.
It is shown that the phase correction method is effective in producing an analysis field that agrees with the data yet preserves the structure developed by the model. The three methods of assimilating the correction in the forecast are successful, and a long-term positive effect on the thunderstorm simulation is achieved in the simulations, even as the modeled storms go through a cycle of decline and regeneration.
Abstract
An objective method of determining and correcting phase or position errors in numerical weather prediction is described and tested in a radar data observing system simulation experiment (OSSE) addressing the forecasting of ongoing thunderstorms. Such phase or position errors are common in numerical forecasts at grid resolutions of 2–20 km (meso-γ scale). It is proposed that the process of correcting a numerical forecast field can be simplified if such errors are addressed directly. An objective method of determining the phase error in the forecast by searching for a field of shift vectors that minimizes a squared-error difference from high-resolution observations is described.
Three methods of applying a phase error correction to a forecast model are detailed. The first applies the entire correction at the initial time, the second in discrete steps during an assimilation window, and the third applies the correction continuously through the model's horizontal advection process.
It is shown that the phase correction method is effective in producing an analysis field that agrees with the data yet preserves the structure developed by the model. The three methods of assimilating the correction in the forecast are successful, and a long-term positive effect on the thunderstorm simulation is achieved in the simulations, even as the modeled storms go through a cycle of decline and regeneration.
Abstract
A scheme to correct phase errors in numerical model forecasts using Doppler radar, radiosonde, profiler, and surface data is demonstrated to improve forecasts in a complex severe thunderstorm situation. The technique is designed to directly address forecast phase errors or initial position errors as part of a data assimilation strategy. In the demonstration the phase error correction is applied near the time of initial cell development and the forecast results are compared to the uncorrected forecast and forecasts made using an analysis created at the time of the observations. Forecasts are verified qualitatively for the position of thunderstorm cells and quantitatively for accumulated precipitation. It is shown that the scheme can successfully correct errors in thunderstorm locations and it has a positive influence on the subsequent forecast. The advantage of the phase correction over the control lasts for about 3 h despite storm dissipation and regeneration, and interactions among multiple storms.
Abstract
A scheme to correct phase errors in numerical model forecasts using Doppler radar, radiosonde, profiler, and surface data is demonstrated to improve forecasts in a complex severe thunderstorm situation. The technique is designed to directly address forecast phase errors or initial position errors as part of a data assimilation strategy. In the demonstration the phase error correction is applied near the time of initial cell development and the forecast results are compared to the uncorrected forecast and forecasts made using an analysis created at the time of the observations. Forecasts are verified qualitatively for the position of thunderstorm cells and quantitatively for accumulated precipitation. It is shown that the scheme can successfully correct errors in thunderstorm locations and it has a positive influence on the subsequent forecast. The advantage of the phase correction over the control lasts for about 3 h despite storm dissipation and regeneration, and interactions among multiple storms.
Abstract
An interesting swirl in the cloud base of a severe thunderstorm near Denver, Colorado, is documented with photographs and Doppler radar velocity measurements. The swirl, which produced two funnel clouds, may have been an eddy of a weak midlevel mesocyclone or a result of surface vorticity stretching by the storm's intense updraft.
Abstract
An interesting swirl in the cloud base of a severe thunderstorm near Denver, Colorado, is documented with photographs and Doppler radar velocity measurements. The swirl, which produced two funnel clouds, may have been an eddy of a weak midlevel mesocyclone or a result of surface vorticity stretching by the storm's intense updraft.
Abstract
Doppler radars offer unique data from which it is possible to estimate the turbulent eddy dissipation rates, ε. If the inertial subrange extends to lengths longer than the radar resolution volume size, ε can be obtained from the Doppler spectrum width. Spatial spectra of mean Doppler velocities can also yield ε estimates but only if a significant portion of the analysis length is contained within the inertial subrange. We compare dissipation rate estimates obtained with the two independent measurement techniques. At close range and vertical incidence, agreement between the two independent estimates of ε is within 10%. Furthermore, the slope of the spatial energy densities is very close to −5/3 predicted by Kolmogorov. The energy input is mainly from buoyancy-driven updrafts and the transition wavelength (about 3 km) between the input scale and the inertial subrange is consistent with the updraft-downdraft circulation cell, which is about 10 km. For a more distant storm at a range of 60 km, the filtering of mean velocities by the resolution volume precludes precise estimation of ε from spatial spectra of mean velocities.
Abstract
Doppler radars offer unique data from which it is possible to estimate the turbulent eddy dissipation rates, ε. If the inertial subrange extends to lengths longer than the radar resolution volume size, ε can be obtained from the Doppler spectrum width. Spatial spectra of mean Doppler velocities can also yield ε estimates but only if a significant portion of the analysis length is contained within the inertial subrange. We compare dissipation rate estimates obtained with the two independent measurement techniques. At close range and vertical incidence, agreement between the two independent estimates of ε is within 10%. Furthermore, the slope of the spatial energy densities is very close to −5/3 predicted by Kolmogorov. The energy input is mainly from buoyancy-driven updrafts and the transition wavelength (about 3 km) between the input scale and the inertial subrange is consistent with the updraft-downdraft circulation cell, which is about 10 km. For a more distant storm at a range of 60 km, the filtering of mean velocities by the resolution volume precludes precise estimation of ε from spatial spectra of mean velocities.
Abstract
On 24 May 2011, Oklahoma experienced an outbreak of tornadoes, including one rated EF5 on the enhanced Fujita (EF) scale and two rated EF4. The extensive observation network in this area makes this an ideal case to examine the impact of using five different microphysics parameterization schemes, including single-, double-, and triple-moment microphysics, in an efficient high-resolution data assimilation system suitable for nowcasting and short-term forecasting with low latencies. Additionally, the real-time configuration of the 1-km ARPS, which assimilated increments produced by 3DVAR with cloud analysis using incremental analysis updating (IAU), had success providing a good baseline forecast. ARPS forecasts of 0–2 h are verified using observation-point, neighborhood, and object-based verification techniques. The object-based verification technique uses updraft helicity fields to represent mesocyclone centers, which are verified against tornado locations from three supercells of interest. Varying levels of success in the forecasts are found and appear to be dependent on the complexity of the storm interaction, with early forecasts of isolated storms exhibiting the most success. Verification scores indicate that the multimoment microphysics schemes tend to produce better forecasts of tornadic supercells. However, some of the forecasts from the single-moment microphysics schemes perform as well as or better than the forecasts from the multimoment microphysics schemes.
Abstract
On 24 May 2011, Oklahoma experienced an outbreak of tornadoes, including one rated EF5 on the enhanced Fujita (EF) scale and two rated EF4. The extensive observation network in this area makes this an ideal case to examine the impact of using five different microphysics parameterization schemes, including single-, double-, and triple-moment microphysics, in an efficient high-resolution data assimilation system suitable for nowcasting and short-term forecasting with low latencies. Additionally, the real-time configuration of the 1-km ARPS, which assimilated increments produced by 3DVAR with cloud analysis using incremental analysis updating (IAU), had success providing a good baseline forecast. ARPS forecasts of 0–2 h are verified using observation-point, neighborhood, and object-based verification techniques. The object-based verification technique uses updraft helicity fields to represent mesocyclone centers, which are verified against tornado locations from three supercells of interest. Varying levels of success in the forecasts are found and appear to be dependent on the complexity of the storm interaction, with early forecasts of isolated storms exhibiting the most success. Verification scores indicate that the multimoment microphysics schemes tend to produce better forecasts of tornadic supercells. However, some of the forecasts from the single-moment microphysics schemes perform as well as or better than the forecasts from the multimoment microphysics schemes.
Abstract
The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas–Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case. Data denial experiments tested the impact of high-frequency (5 min) assimilation of nonconventional data on the timing and location of CI and subsequent storm evolution. Results showed nonconventional observations were necessary to capture details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations in sensitive areas along the dryline gradient. Accurate metadata were found to be crucial toward the future application of nonconventional observations in high-resolution assimilation and forecast systems.
Abstract
The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas–Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case. Data denial experiments tested the impact of high-frequency (5 min) assimilation of nonconventional data on the timing and location of CI and subsequent storm evolution. Results showed nonconventional observations were necessary to capture details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations in sensitive areas along the dryline gradient. Accurate metadata were found to be crucial toward the future application of nonconventional observations in high-resolution assimilation and forecast systems.
Abstract
Several mesoscale data analysis systems are reviewed, of which one is then adapted and applied to the complex terrain of northwest Utah and the western United States. The analysis system relies on the simple, but computationally efficient, successive correction methodology. Near-real-time three-dimensional mesoscale analyses are produced hourly over northwest Utah at 1-km horizontal resolution while analyses are produced every 15 min for surface fields over northwest Utah and the western United States. Surface analyses over the western United States are also generated at 0000 and 1200 UTC to help to initialize 36-h mesoscale model forecasts. Comparisons between the 1-km three-dimensional analyses and the background three-dimensional analysis provided by the National Centers for Environmental Prediction Rapid Update Cycle, version 2 (RUC-2), indicate that, where surface and upper-air observations are abundant, the local analysis adds information beyond that of simply interpolating the background (RUC-2) data to the high-resolution analysis grid.
Abstract
Several mesoscale data analysis systems are reviewed, of which one is then adapted and applied to the complex terrain of northwest Utah and the western United States. The analysis system relies on the simple, but computationally efficient, successive correction methodology. Near-real-time three-dimensional mesoscale analyses are produced hourly over northwest Utah at 1-km horizontal resolution while analyses are produced every 15 min for surface fields over northwest Utah and the western United States. Surface analyses over the western United States are also generated at 0000 and 1200 UTC to help to initialize 36-h mesoscale model forecasts. Comparisons between the 1-km three-dimensional analyses and the background three-dimensional analysis provided by the National Centers for Environmental Prediction Rapid Update Cycle, version 2 (RUC-2), indicate that, where surface and upper-air observations are abundant, the local analysis adds information beyond that of simply interpolating the background (RUC-2) data to the high-resolution analysis grid.
Abstract
A 3-h intermittent data assimilation system (Mesoscale Analysis and Prediction System—MAPS) configured in isentropic coordinates was developed and implemented in real-time operation. The major components of the system are data ingest, objective quality control of the observation, objective analysis, and a primitive equation forecast model, all using isentropic coordinates to take advantage of the improved resolution near frontal zones and greater spatial coherence of data that this coordinate system provides. Each 3-h forecast becomes the background for the subsequent analysis; in this manner, a four-dimensional set of observations can be assimilated.
The primary asynoptic data source used in current real-time operation of this system is air-craft data, most of it automated. Data from wind profilers, surface observations, and radiosondes are also included in MAPS.
Statistics were collected over the last half of 1989 and into 1990 to study the performance of MAPS and compare it with that of the Regional Analysis and Forecast System (RAFS), which is run operationally at the National Meteorological Center (NMC). Analyses generally fit mandatory-level observations more closely in MAPS than in RAFS. Three-hour forecasts from MAPS, incorporating asynoptic aircraft reports, improve on 12-h MAPS forecasts valid at the same time for all levels and variables, and also improve on 12-h RAFS forecasts of upper-level winds. This result is due to the quality and volume of the aircraft data as well as the effectiveness of the isentropic data assimilation used. Forecast fields at other levels are slightly poorer than those from RAFS. This may be largely due to the lack of diabatic and boundary-layer physics for the MAPS model used in this test period.
Abstract
A 3-h intermittent data assimilation system (Mesoscale Analysis and Prediction System—MAPS) configured in isentropic coordinates was developed and implemented in real-time operation. The major components of the system are data ingest, objective quality control of the observation, objective analysis, and a primitive equation forecast model, all using isentropic coordinates to take advantage of the improved resolution near frontal zones and greater spatial coherence of data that this coordinate system provides. Each 3-h forecast becomes the background for the subsequent analysis; in this manner, a four-dimensional set of observations can be assimilated.
The primary asynoptic data source used in current real-time operation of this system is air-craft data, most of it automated. Data from wind profilers, surface observations, and radiosondes are also included in MAPS.
Statistics were collected over the last half of 1989 and into 1990 to study the performance of MAPS and compare it with that of the Regional Analysis and Forecast System (RAFS), which is run operationally at the National Meteorological Center (NMC). Analyses generally fit mandatory-level observations more closely in MAPS than in RAFS. Three-hour forecasts from MAPS, incorporating asynoptic aircraft reports, improve on 12-h MAPS forecasts valid at the same time for all levels and variables, and also improve on 12-h RAFS forecasts of upper-level winds. This result is due to the quality and volume of the aircraft data as well as the effectiveness of the isentropic data assimilation used. Forecast fields at other levels are slightly poorer than those from RAFS. This may be largely due to the lack of diabatic and boundary-layer physics for the MAPS model used in this test period.
Abstract
During the summers of 2016 and 2017, the Center for Analysis and Prediction of Storms (CAPS) ran real-time storm-scale ensemble forecasts (SSEFs) in support of the Hydrometeorology Testbed (HMT) Flash Flood and Intense Rainfall (FFaIR) experiment. These forecasts, using WRF-ARW and Nonhydrostatic Mesoscale Model on the B-grid (NMMB) in 2016, and WRF-ARW and GFDL Finite Volume Cubed-Sphere Dynamical Core (FV3) in 2017, covered the contiguous United States at 3-km horizontal grid spacing, and supported the generation and evaluation of precipitation forecast products, including ensemble probabilistic products. Forecasts of 3-h precipitation accumulation are evaluated. Overall, the SSEF produces skillful 3-h accumulated precipitation forecasts, with ARW members generally outperforming NMMB members and the single FV3 member run in 2017 outperforming ARW members; these differences are significant at some forecast hours. Statistically significant differences exist in the performance, in terms of bias and ETS, among subensembles of members sharing common microphysics and PBL schemes. Year-to-year consistency is higher for PBL subensembles than for microphysical subensembles. Probability-matched (PM) ensemble mean forecasts outperform individual members, while the simple ensemble mean exhibits substantial bias. A newly developed localized probability-matched (LPM) ensemble mean product was produced in 2017; compared to the simple ensemble mean and the conventional PM mean, the LPM mean exhibits improved retention of small-scale structures, evident in both 2D forecast fields and variance spectra. Probabilistic forecasts of precipitation exceeding flash flood guidance (FFG) or thresholds associated with recurrence intervals (RI) ranging from 10 to 100 years show utility in predicting regions of flooding threat, but generally overpredict the occurrence of such events; however, they may still be useful in subjective flash flood risk assessment.
Abstract
During the summers of 2016 and 2017, the Center for Analysis and Prediction of Storms (CAPS) ran real-time storm-scale ensemble forecasts (SSEFs) in support of the Hydrometeorology Testbed (HMT) Flash Flood and Intense Rainfall (FFaIR) experiment. These forecasts, using WRF-ARW and Nonhydrostatic Mesoscale Model on the B-grid (NMMB) in 2016, and WRF-ARW and GFDL Finite Volume Cubed-Sphere Dynamical Core (FV3) in 2017, covered the contiguous United States at 3-km horizontal grid spacing, and supported the generation and evaluation of precipitation forecast products, including ensemble probabilistic products. Forecasts of 3-h precipitation accumulation are evaluated. Overall, the SSEF produces skillful 3-h accumulated precipitation forecasts, with ARW members generally outperforming NMMB members and the single FV3 member run in 2017 outperforming ARW members; these differences are significant at some forecast hours. Statistically significant differences exist in the performance, in terms of bias and ETS, among subensembles of members sharing common microphysics and PBL schemes. Year-to-year consistency is higher for PBL subensembles than for microphysical subensembles. Probability-matched (PM) ensemble mean forecasts outperform individual members, while the simple ensemble mean exhibits substantial bias. A newly developed localized probability-matched (LPM) ensemble mean product was produced in 2017; compared to the simple ensemble mean and the conventional PM mean, the LPM mean exhibits improved retention of small-scale structures, evident in both 2D forecast fields and variance spectra. Probabilistic forecasts of precipitation exceeding flash flood guidance (FFG) or thresholds associated with recurrence intervals (RI) ranging from 10 to 100 years show utility in predicting regions of flooding threat, but generally overpredict the occurrence of such events; however, they may still be useful in subjective flash flood risk assessment.
Abstract
To help inform physics configuration decisions and help design and optimize a multi-physics Rapid Refresh Forecasting System (RRFS) ensemble to be used operationally by the National Weather Service, five FV3-LAM-based convection allowing forecasts were run on 35 cases between October 2020 and March 2021. These forecasts used ∼3-km grid spacing on a CONUS domain with physics configurations including Thompson, NSSL, and Ferrier–Aligo microphysics schemes, Noah, RUC, and NoahMP land surface models, and MYNN-EDMF, K-EDMF, and TKE-EDMF PBL schemes. All forecasts were initialized from the 0000 UTC GFS analysis and run for 84 h. Also, a subset of 8 cases were run with 15 combinations of physics options, also including the Morrison–Gettelman microphysics and Shin–Hong PBL schemes, to help attribute behaviors to individual schemes and isolate the main contributors of forecast errors. Evaluations of both sets of forecasts find that the CONUS-wide 24-h precipitation > 1 mm is positively biased across all five forecasts. NSSL microphysics displays a low bias in QPF along the Gulf Coast. Analyses show that it produces smaller raindrops prone to evaporation. Additionally, TKE-EDMF PBL in combination with Thompson microphysics displays a positive bias in precipitation over the Great Lakes and in the ocean near Florida due to higher latent heat fluxes calculated over water. Furthermore, the K-EDMF PBL scheme produces temperature errors that result in a negative bias in snowfall over the southern Mountain West. Finally, recommendations for which physics schemes to use in future suites and the RRFS ensemble are discussed.
Abstract
To help inform physics configuration decisions and help design and optimize a multi-physics Rapid Refresh Forecasting System (RRFS) ensemble to be used operationally by the National Weather Service, five FV3-LAM-based convection allowing forecasts were run on 35 cases between October 2020 and March 2021. These forecasts used ∼3-km grid spacing on a CONUS domain with physics configurations including Thompson, NSSL, and Ferrier–Aligo microphysics schemes, Noah, RUC, and NoahMP land surface models, and MYNN-EDMF, K-EDMF, and TKE-EDMF PBL schemes. All forecasts were initialized from the 0000 UTC GFS analysis and run for 84 h. Also, a subset of 8 cases were run with 15 combinations of physics options, also including the Morrison–Gettelman microphysics and Shin–Hong PBL schemes, to help attribute behaviors to individual schemes and isolate the main contributors of forecast errors. Evaluations of both sets of forecasts find that the CONUS-wide 24-h precipitation > 1 mm is positively biased across all five forecasts. NSSL microphysics displays a low bias in QPF along the Gulf Coast. Analyses show that it produces smaller raindrops prone to evaporation. Additionally, TKE-EDMF PBL in combination with Thompson microphysics displays a positive bias in precipitation over the Great Lakes and in the ocean near Florida due to higher latent heat fluxes calculated over water. Furthermore, the K-EDMF PBL scheme produces temperature errors that result in a negative bias in snowfall over the southern Mountain West. Finally, recommendations for which physics schemes to use in future suites and the RRFS ensemble are discussed.
