scholarly journals Impact of VORTEX2 Observations on Analyses and Forecasts of the 5 June 2009 Goshen County, Wyoming, Supercell

2016 ◽  
Vol 144 (1) ◽  
pp. 429-449 ◽  
Author(s):  
Timothy A. Supinie ◽  
Youngsun Jung ◽  
Ming Xue ◽  
David J. Stensrud ◽  
Michael M. French ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Phased Array ◽  
Weather Radar ◽  
Data Sources ◽  
Square Root ◽  
Low Level ◽  
X Band ◽  
Negative Impacts ◽  
The Impact ◽  
Square Root Filter

Abstract Several data assimilation and forecast experiments are undertaken to determine the impact of special observations taken during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) on forecasts of the 5 June 2009 Goshen County, Wyoming, supercell. The data used in these experiments are those from the Mobile Weather Radar, 2005 X-band, Phased Array (MWR-05XP); two mobile mesonets (MM); and several mobile sounding units. Data sources are divided into “routine,” including those from operational Weather Surveillance Radar-1988 Dopplers (WSR-88Ds) and the Automated Surface Observing System (ASOS) network, and “special” observations from the VORTEX2 project. VORTEX2 data sources are denied individually from a total of six ensemble square root filter (EnSRF) data assimilation and forecasting experiments. The EnSRF data assimilation uses 40 ensemble members on a 1-km grid nested inside a 3-km grid. Each experiment assimilates data every 5 min for 1 h, followed by a 1-h forecast. All experiments are able to reproduce the basic evolution of the supercell, though the impact of the VORTEX2 observations was mixed. The VORTEX2 sounding data decreased the mesocyclone intensity in the latter stages of the forecast, consistent with observations. The MWR-05XP data increased the forecast vorticity above approximately 1 km AGL in all experiments and had little impact on forecast vorticity below 1 km AGL. The MM data had negative impacts on the intensity of the low-level mesocyclone, by decreasing the vertical vorticity and indirectly by decreasing the buoyancy of the inflow.

scholarly journals High-Speed Volumetric Observation of a Wet Microburst Using X-Band Phased Array Weather Radar in Japan

2016 ◽  
Vol 144 (10) ◽  
pp. 3749-3765 ◽  
Author(s):  
Toru Adachi ◽  
Kenichi Kusunoki ◽  
Satoru Yoshida ◽  
Ken-ichiro Arai ◽  
Tomoo Ushio
Keyword(s):  
High Speed ◽  
Phased Array ◽  
Three Dimensional ◽  
Weather Radar ◽  
Ground Level ◽  
Dimensional Structure ◽  
Convection Cell ◽  
Low Level ◽  
X Band ◽  
Traffic Operation

This paper reports a high-speed volumetric observation of a wet microburst event using X-band phased array weather radar (PAWR) in Japan. On 10 September 2014, PAWR observed the three-dimensional structure of a convection cell, which had a vertical extent of 5–6 km and a horizontal dimension of 2–10 km, moving toward the east-northeast. At 2310 Japan standard time (JST), a precipitation core with a radar reflectivity of >40 dBZ appeared at 3–5 km above ground level. The core then increased in size and intensity and rapidly descended to the ground. During this time, a reflectivity notch associated with midlevel inflow was initially formed near the top of the precipitation core and, subsequently, at lower altitudes. A strong low-level outflow with a radial divergence of >4 × 10−3 s−1 appeared just below the notch at around 2321 JST. The outflow lasted for approximately 13 min and eventually disappeared after 2333 JST along with dissipation of the causative storm cell. These results suggest that, in addition to hydrometeor loading, evaporative cooling due to the entrainment of midlevel relatively dry air played an additional role in driving a strong downdraft. The preceding signatures including descending precipitation core, reflectivity notch, and midlevel convergence observed by PAWR are useful precursors to forecast the occurrence of low-level wind shear 5–10 min ahead, which is important for safe air traffic operation.

scholarly journals Reexamining the Vertical Development of Tornadic Vortex Signatures in Supercells

2013 ◽  
Vol 141 (12) ◽  
pp. 4576-4601 ◽  
Author(s):  
Michael M. French ◽  
Howard B. Bluestein ◽  
Ivan PopStefanija ◽  
Chad A. Baldi ◽  
Robert T. Bluth
Keyword(s):  
Phased Array ◽  
Doppler Radar ◽  
Weather Radar ◽  
Low Level ◽  
The Past ◽  
Level Data ◽  
X Band ◽  
Order Of Magnitude ◽  
Vertical Development ◽  
Better Than

Abstract Observations from a hybrid phased-array Doppler radar, the Mobile Weather Radar, 2005 X-band, Phased Array (MWR-05XP), were used to investigate the vertical development of tornadic vortex signatures (TVSs) during supercell tornadogenesis. Data with volumetric update times of ∼10 s, an order of magnitude better than that of most other mobile Doppler radars, were obtained up to storm midlevels during the formation of three tornadoes. It is found that TVSs formed upward with time during tornadogenesis for two cases. In a third case, missing low-level data prevented a complete time–height analysis of TVS development; however, TVS formation occurred first near the ground and then at storm midlevels several minutes later. These results are consistent with the small number of volumetric mobile Doppler radar tornadogenesis cases from the past ∼10 years, but counter to studies prior to that, in which a descending TVS was observed in roughly half of tornado cases utilizing Weather Surveillance Radar-1988 Doppler (WSR-88D) data. A comparative example is used to examine the possible effects relatively long WSR-88D volumetric update times have on determining the mode of tornadogenesis.

scholarly journals Impact of enriched analyses on regional numerical forecasts over southeastern South America during SALLJEX

2014 ◽  
Vol 29 (3) ◽  
pp. 315-330
Author(s):  
Yanina García Skabar ◽  
Matilde Nicolini
Keyword(s):  
Data Assimilation ◽  
South America ◽  
Atmospheric Modeling ◽  
South American ◽  
The South ◽  
Low Level ◽  
Positive Effects ◽  
Accumulated Rainfall ◽  
Low Level Jet ◽  
The Impact

During the warm season 2002-2003, the South American Low-Level Jet Experiment (SALLJEX) was carried out in southeastern South America. Taking advantage of the unique database collected in the region, a set of analyses is generated for the SALLJEX period assimilating all available data. The spatial and temporal resolution of this new set of analyses is higher than that of analyses available up to present for southeastern South America. The aim of this paper is to determine the impact of assimilating data into initial fields on mesoscale forecasts in the region, using the Brazilian Regional Atmospheric Modeling System (BRAMS) with particular emphasis on the South American Low-Level Jet (SALLJ) structure and on rainfall forecasts. For most variables, using analyses with data assimilated as initial fields has positive effects on short term forecast. Such effect is greater in wind variables, but not significant in forecasts longer than 24 hours. In particular, data assimilation does not improve forecasts of 24-hour accumulated rainfall, but it has slight positive effects on accumulated rainfall between 6 and 12 forecast hours. As the main focus is on the representation of the SALLJ, the effect of data assimilation in its forecast was explored. Results show that SALLJ is fairly predictable however assimilating additional observation data has small impact on the forecast of SALLJ timing and intensity. The strength of the SALLJ is underestimated independently of data assimilation. However, Root mean square error (RMSE) and BIAS values reveal the positive effect of data assimilation up to 18-hours forecasts with a greater impact near higher topography.

Improving the Accuracy of Rain Rate Estimates Using X-Band Phased-Array Weather Radar Network

2018 ◽  
Vol 56 (12) ◽  
pp. 6986-6994 ◽  
Author(s):  
Hiroshi Kikuchi ◽  
Tomoo Ushio ◽  
Fumihiko Mizutani ◽  
Masakazu Wada
Keyword(s):  
Rain Rate ◽  
Phased Array ◽  
Weather Radar ◽  
Radar Network ◽  
X Band

Prediction of Tornado-Like Vortex (TLV) Embedded in the 8 May 2003 Oklahoma City Tornadic Supercell Initialized from the Subkilometer Grid Spacing Analysis Produced by the Dual-Resolution GSI-Based EnVar Data Assimilation System

2020 ◽  
Vol 148 (7) ◽  
pp. 2909-2934
Author(s):  
Yongming Wang ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Potential Temperature ◽  
Intensity Variation ◽  
Oklahoma City ◽  
Grid Spacing ◽  
Near Surface ◽  
Low Level ◽  
Cold Pools ◽  
The Impact

Abstract Explicit forecasts of a tornado-like vortex (TLV) require subkilometer grid spacing because of their small size. Most previous TLV prediction studies started from interpolated kilometer grid spacing initial conditions (ICs) rather than subkilometer grid spacing ICs. The tornadoes embedded in the 8 May 2003 Oklahoma City tornadic supercell are used to understand the impact of IC resolution on TLV predictions. Two ICs at 500-m and 2-km grid spacings are, respectively, produced through an efficient dual-resolution (DR) and a single-coarse-resolution (SCR) EnVar ingesting a 2-km ensemble. Both experiments launch 1-h forecasts at 500-m grid spacing. Diagnostics of data assimilation (DA) cycling reveal DR produces stronger and broader rear-flank cold pools, more intense downdrafts and updrafts with finer scales, and more hydrometeors at high altitudes through accumulated differences between two DA algorithms. Relative differences in DR, compared to SCR, include the integration from higher-resolution analyses, the update for higher-resolution backgrounds, and the propagation of ensemble perturbations along higher-resolution model trajectory. Predictions for storm morphology and cold pools are more realistic in DR than in SCR. The DR-TLV tracks match better with the observed tornado tracks than SCR-TLV in timing of intensity variation, and in duration. Additional experiments suggest 1) the analyzed kinematic variables strongly influence timing of intensity variation through affecting both low-level rear-flank outflow and midlevel updraft; 2) potential temperature analysis by DR extends the second track’s duration consistent with enhanced low-level stretching, delayed broadening large-scale downdraft, and (or) increased near-surface baroclinic vorticity supply; and 3) hydrometeor analyses have little impact on TLV predictions.

scholarly journals Model-driven optimization of coastal sea observatories through data assimilation in a finite element hydrodynamic model (SHYFEM v. 7_5_65)

2021 ◽  
Vol 14 (1) ◽  
pp. 645-659
Author(s):  
Christian Ferrarin ◽  
Marco Bajo ◽  
Georg Umgiesser
Keyword(s):  
Finite Element ◽  
Data Assimilation ◽  
Water Level ◽  
Tide Gauge ◽  
Monitoring Network ◽  
Monitoring Site ◽  
Square Root ◽  
Monitoring Networks ◽  
Lagoon Of Venice ◽  
Square Root Filter

Abstract. Monitoring networks aims at capturing the spatial and temporal variability of one or several environmental variables in a specific environment. The optimal placement of sensors in an ocean or coastal observatory should maximize the amount of collected information and minimize the development and operational costs for the whole monitoring network. In this study, the problem of the design and optimization of ocean monitoring networks is tackled throughout the implementation of data assimilation techniques in the Shallow water HYdrodynamic Finite Element Model (SHYFEM). Two data assimilation methods – nudging and ensemble square root filter – have been applied and tested in the Lagoon of Venice (Italy), where an extensive water level monitoring network exists. A total of 29 tide gauge stations were available, and the assimilation of the observations results in an improvement of the performance of the SHYFEM model, which went from an initial root mean square error (RMSE) on the water level of 5.8 cm to a final value of about 2.1 and 3.2 cm for each of the two data assimilation methods. In the monitoring network optimization procedure, by excluding just one tide gauge at a time and always the station that contributes less to the improvement of the RMSE, a minimum number of tide gauges can be found that still allow for a successful description of the water level variability. Both data assimilation methods allow identifying the number of stations and their distribution that correctly represent the state variable in the investigated system. However, the more advanced ensemble square root filter has the benefit of keeping a physically and mass-conservative solution of the governing equations, which results in a better reproduction of the hydrodynamics over the whole system. In the case of the Lagoon of Venice, we found that, with the help of a process-based and observation-driven numerical model, two-thirds of the monitoring network can be dismissed. In this way, if some of the stations must be decommissioned due to a lack of funding, an a priori choice can be made, and the importance of a single monitoring site can be evaluated. The developed procedure may also be applied to the continuous monitoring of other ocean variables, like sea temperature and salinity.

scholarly journals Model-driven optimization of coastal sea observatories through data assimilation in a finite element hydrodynamic model (SHYFEM v.7_5_65)

2020 ◽  
Author(s):  
Christian Ferrarin ◽  
Marco Bajo ◽  
Georg Umgiesser
Keyword(s):  
Finite Element ◽  
Data Assimilation ◽  
Water Level ◽  
Tide Gauge ◽  
Monitoring Network ◽  
Monitoring Site ◽  
Square Root ◽  
Monitoring Networks ◽  
Lagoon Of Venice ◽  
Square Root Filter

Abstract. Monitoring networks aims at capturing the spatial and temporal variability of one or several environmental variables in a specific environment. The optimal placement of sensors in an ocean or coastal observatory should maximize the amount of collected information and minimize the development and operational costs for the whole monitoring network. In this study, the problem of the design and optimization of ocean monitoring networks is tackled throughout the implementation of data assimilation techniques in the Shallow water Hydrodynamic Finite Element Model (SHYFEM). Two data assimilation methods – Nudging and Ensemble Square Root Filter – have been applied and tested in the Lagoon of Venice (Italy), where an extensive water level monitoring network exists. A total of 29 tide gauge stations were available and the assimilation of the observations result in an improvement of the performance of the SHYFEM model that went from an initial root mean square error (RMSE) on the water level of 5.8 cm to a final value of about 2.1 and 3.2 cm for the two data assimilation methods, respectively. In the monitoring network optimization procedure, by excluding just one tide gauge at a time, and always the station that contributes less to the improvement of the RMSE, a minimum number of tide gauges can be found that still allow for a successful description of the water level variability. Both data assimilation methods allow identifying the number of stations and their distribution that correctly represent the state variable in the investigated system. However, the more advanced Ensemble Square Root Filter has the benefit of keeping a physically and mass conservative solution of the governing equations, which results in a better reproduction of the hydrodynamics over the whole system. In the case of the Lagoon of Venice, we found that, with the help of a process-based and observation-driven numerical model, two-thirds of the monitoring network can be dismissed. In this way, if some of the stations must be decommissioned due to a lack of funding, an a-priori choice can be made, and the importance of the single monitoring site can be evaluated. The developed procedure may also be applied to the continuous monitoring of other ocean variables, like sea temperature and salinity.

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