scholarly journals The Influence of Assimilated Upstream, Preconvective Dropsonde Observations on Ensemble Forecasts of Convection Initiation during the Mesoscale Predictability Experiment

2017 ◽  
Vol 145 (12) ◽  
pp. 4747-4770 ◽  
Author(s):  
Alexandra M. Keclik ◽  
Clark Evans ◽  
Paul J. Roebber ◽  
Glen S. Romine
Keyword(s):  
Short Range ◽  
Wrf Model ◽  
Skill Score ◽  
Forecast Skill ◽  
Weather Research And Forecasting ◽  
Moist Convection ◽  
Ensemble Forecasts ◽  
High Bias ◽  
Deep Moist Convection ◽  
Convection Initiation

This study tests the hypothesis that assimilating mid- to upper-tropospheric, meso- α- to synoptic-scale observations collected in upstream, preconvective environments is insufficient to improve short-range ensemble convection initiation (CI) forecast skill over the set of cases considered by the 2013 Mesoscale Predictability Experiment (MPEX) because of a limited influence upon the lower-tropospheric phenomena that modulate CI occurrence, timing, and location. The ensemble Kalman filter implementation within the Data Assimilation Research Testbed as coupled to the Advanced Research Weather Research and Forecasting (WRF) Model is used to initialize two nearly identical 30-member ensembles of short-range forecasts for each case: one initial condition set that incorporates MPEX dropsonde observations and one that excludes these observations. All forecasts for a given mission begin at 1500 UTC and are integrated for 15 h on a convection-permitting grid encompassing much of the conterminous United States. Forecast verification is conducted probabilistically using fractions skill score and deterministically using a 2 × 2 contingency table approach at multiple neighborhood sizes and spatiotemporal event-matching thresholds to assess forecast skill and support hypothesis testing. The probabilistic verification represents the first of its kind for numerical CI forecasts. Forecasts without MPEX observations have high fractions skill score and probabilities of detection on the meso- α scale but exhibit a considerable high bias for forecast CI event count. Assimilating MPEX observations has a negligible impact upon forecast skill for the cases considered, independent of verification metric, as the MPEX observations result in only subtle differences primarily manifest in the position and intensity of atmospheric features responsible for focusing and/or triggering deep, moist convection.

Convection Initiation Aided by Lake-Breeze Convergence over the Niagara Peninsula

2019 ◽  
Vol 147 (11) ◽  
pp. 3955-3979 ◽  
Author(s):  
Chun-Chih Wang ◽  
Daniel J. Kirshbaum ◽  
David M. L. Sills
Keyword(s):  
Large Scale ◽  
Inversion Layer ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Lake Breeze ◽  
Moist Convection ◽  
Warm Advection ◽  
American Science ◽  
Convection Initiation ◽  
Cloud Tops

Abstract Observations from the 2015 Environment and Climate Change Canada Pan/Parapan American Science Showcase (ECPASS) and real-case, cloud-resolving numerical simulations with the Weather Research and Forecasting (WRF) Model are used to investigate two cases of moist convection forced by lake-breeze convergence over southern Ontario (18 July and 15 August 2015). The two cases shared several characteristics, including high pressure conditions, similar morning soundings, and isolated afternoon convection along a line of lake-breeze convergence between Lakes Erie and Ontario. However, the convection was significantly stronger in the August case, with robustly deeper clouds and larger radar reflectivities than in the July case. Synoptic and mesoscale analyses of these events reveal that the key difference between them was their large-scale forcing. The July event exhibited a combination of strong warm advection and large-scale descent at midlevels (850–650 hPa), which created an inversion layer that capped cloud tops at 4–6 km. The August case exhibited similar features (large-scale descent and warm advection), but these were focused at higher levels (700–400 hPa) and weaker. As a consequence, the convection in the August case was less suppressed at midlevels and ascended deeper (reaching over 8 km). Although the subcloud updraft along the lake-breeze convergence zone was also found to be stronger in the August case, this difference was found to be an effect, rather than a cause, of stronger moist convection within the cloud layer.

scholarly journals An Observational and Modeling Study of the Processes Leading to Deep, Moist Convection in Complex Terrain

2014 ◽  
Vol 142 (8) ◽  
pp. 2687-2708 ◽  
Author(s):  
Tammy M. Weckwerth ◽  
Lindsay J. Bennett ◽  
L. Jay Miller ◽  
Joël Van Baelen ◽  
Paolo Di Girolamo ◽  
...  
Keyword(s):  
Water Vapor ◽  
Three Dimensional ◽  
Wrf Model ◽  
Convective Precipitation ◽  
Spatial And Temporal Variability ◽  
Moist Convection ◽  
Wind Fields ◽  
Vosges Mountains ◽  
Deep Moist Convection ◽  
Convection Initiation

Abstract A case study of orographic convection initiation (CI) that occurred along the eastern slopes of the Vosges Mountains in France on 6 August 2007 during the Convective and Orographically-Induced Precipitation Study (COPS) is presented. Global positioning system (GPS) receivers and two Doppler on Wheels (DOW) mobile radars sampled the preconvective and storm environments and were respectively used to retrieve three-dimensional tomographic water vapor and wind fields. These retrieved data were supplemented with temperature, moisture, and winds from radiosondes from a site in the eastern Rhine Valley. High-resolution numerical simulations with the Weather Research and Forecasting (WRF) Model were used to further investigate the physical processes leading to convective precipitation. This unique, time-varying combination of derived water vapor and winds from observations illustrated an increase in low-level moisture and convergence between upslope easterlies and downslope westerlies along the eastern slope of the Vosges Mountains. Uplift associated with these shallow, colliding boundary layer flows eventually led to the initiation of moist convection. WRF reproduced many features of the observed complicated flow, such as cyclonic (anticyclonic) flow around the southern (northern) end of the Vosges Mountains and the east-side convergent flow below the ridgeline. The WRF simulations also illustrated spatial and temporal variability in buoyancy and the removal of the lids prior to convective development. The timing and location of CI from the WRF simulations was surprisingly close to that observed.

scholarly journals Implementation of a Digital Filter Initialization in the WRF Model and Its Application in the Rapid Refresh

2015 ◽  
Vol 144 (1) ◽  
pp. 99-106 ◽  
Author(s):  
Steven E. Peckham ◽  
Tatiana G. Smirnova ◽  
Stanley G. Benjamin ◽  
John M. Brown ◽  
Jaymes S. Kenyon
Keyword(s):  
Data Assimilation ◽  
Digital Filter ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Frequency Noise ◽  
Moist Convection ◽  
Short Term ◽  
Operational Forecasts ◽  
Hourly Data ◽  
Deep Moist Convection

Abstract Because of limitations of variational and ensemble data assimilation schemes, resulting analysis fields exhibit some noise from imbalance in subsequent model forecasts. Controlling finescale noise is desirable in the NOAA’s Rapid Refresh (RAP) assimilation/forecast system, which uses an hourly data assimilation cycle. Hence, a digital filter initialization (DFI) capability has been introduced into the Weather Research and Forecasting Model and applied operationally in the RAP, for which hourly intermittent assimilation makes DFI essential. A brief overview of the DFI approach, its implementation, and some of its advantages are discussed. Results from a 1-week impact test with and without DFI demonstrate that DFI is effective at reducing high-frequency noise in short-term operational forecasts as well as providing evidence of reduced errors in the 1-h mass and momentum fields. However, DFI is also shown to reduce the strength of parameterized deep moist convection during the first hour of the forecast.

scholarly journals Relationship between the Track and Structural Evolution of Hurricane Sandy (2012) Using a Regional Ensemble

2018 ◽  
Vol 146 (12) ◽  
pp. 4279-4302 ◽  
Author(s):  
Alex M. Kowaleski ◽  
Jenni L. Evans
Keyword(s):  
Large Scale ◽  
Wrf Model ◽  
Structural Evolution ◽  
Hurricane Sandy ◽  
Weather Research And Forecasting ◽  
Extratropical Transition ◽  
Ensemble Forecasts ◽  
High Predictability ◽  
Regression Mixture ◽  
The Relationship

Abstract An ensemble of 72 Weather Research and Forecasting (WRF) Model simulations is evaluated to examine the relationship between the track of Hurricane Sandy (2012) and its structural evolution. Initial and boundary conditions are obtained from ECMWF and GEFS ensemble forecasts initialized at 0000 UTC 25 October. The 5-day WRF simulations are initialized at 0000 UTC 27 October, 48 h into the global model forecasts. Tracks and cyclone phase space (CPS) paths from the 72 simulations are partitioned into 6 clusters using regression mixture models; results from the 4 most populous track clusters are examined. The four analyzed clusters vary in mean landfall location from southern New Jersey to Maine. Extratropical transition timing is the clearest difference among clusters; more eastward clusters show later Sandy–midlatitude trough interaction, warm seclusion formation, and extratropical transition completion. However, the intercluster variability is much smaller when examined relative to the landfall time of each simulation. In each cluster, a short-lived warm seclusion forms and contracts through landfall while lower-tropospheric potential vorticity concentrates at small radii. Despite the large-scale similarity among the clusters, relevant intercluster differences in landfall-relative extratropical transition are observed. In the easternmost cluster the Sandy–trough interaction is least intense and the warm seclusion decays the most by landfall. In the second most eastward cluster Sandy retains the most intact warm seclusion at landfall because of a slightly later (relative to landfall) and weaker trough interaction compared to the two most westward clusters. Nevertheless, the remarkably similar large-scale evolution of Sandy among the four clusters indicates the high predictability of Sandy’s warm seclusion extratropical transition before landfall.

Quasi-idealized numerical simulations of processes involved in orogenic convection initiation over the Sierras de Córdoba mountains

2021 ◽  
Author(s):  
Christopher A. Davis
Keyword(s):  
Numerical Simulations ◽  
Mountain Range ◽  
Moist Convection ◽  
Low Level ◽  
Physical Mechanisms ◽  
Convergence Line ◽  
Synoptic Conditions ◽  
Deep Moist Convection ◽  
Outflow Boundary ◽  
Convection Initiation

Abstract The Sierras de Córdoba (SDC) mountain range in Argentina is a hotspot of deep moist convection initiation (CI). Radar climatology indicates that 44% of daytime CI events that occur near the SDC in spring and summer seasons and that are not associated with the passage of a cold front or an outflow boundary involve a northerly LLJ, and these events tend to preferentially occur over the southeast quadrant of the main ridge of the SDC. To investigate the physical mechanisms acting to cause CI, idealized convection-permitting numerical simulations with a horizontal grid spacing of 1 km were conducted using CM1. The sounding used for initializing the model featured a strong northerly LLJ, with synoptic conditions resembling those in a previously postulated conceptual model of CI over the region, making it a canonical case study. Differential heating of the mountain caused by solar insolation in conjunction with the low-level northerly flow sets up a convergence line on the eastern slopes of the SDC. The southern portion of this line experiences significant reduction in convective inhibition, and CI occurs over the SDC southeast quadrant. Thesimulated storm soon acquires supercellular characteristics, as observed. Additional simulations with varying LLJ strength also show CI over the southeast quadrant. A simulation without background flow generated convergence over the ridgeline, with widespread CI across the entire ridgeline. A simulation with mid- and upper-tropospheric westerlies removed indicates that CI is minimally influenced by gravity waves. We conclude that the low-level jet is sufficient to focus convection initiation over the southeast quadrant of the ridge.

scholarly journals Image Processing of Radar Mosaics for the Climatology of Convection Initiation in South China

2020 ◽  
Vol 59 (1) ◽  
pp. 65-81 ◽  
Author(s):  
Lanqiang Bai ◽  
Guixing Chen ◽  
Ling Huang
Keyword(s):  
South China ◽  
Numerical Models ◽  
Early Morning ◽  
Moist Convection ◽  
Late Night ◽  
Morning Peak ◽  
Spatial Modes ◽  
Triggering Mechanisms ◽  
Deep Moist Convection ◽  
Convection Initiation

AbstractA dataset of convection initiation (CI) is of great value in studying the triggering mechanisms of deep moist convection and evaluating the performances of numerical models. In recent years, the data quality of the operationally generated radar mosaics over China has been greatly improved, which provides an opportunity to retrieve a CI dataset from that region. In this work, an attempt is made to reveal the potential of applying a simple framework of objective CI detection for the study of CI climatology in China. The framework was tested using radar mosaic maps in South China that were accessible online. The identified CI events were validated in both direct and indirect ways. On the basis of a direct manual check, nearly all of the identified CI cells had an organized motion. The precipitation echoes of the cells had a median duration of approximately 2.5 h. The CI occurrences were further compared with rainfall estimates to ensure physical consistency. The diurnal cycle of CI occurrence exhibits three major modes: a late-night-to-morning peak at the windward coasts and offshore, a noon-to-late-afternoon peak on the coastal land, and an evening-to-early-morning peak over the northwestern highland. These spatial modes agree well with those of rainfall, indirectly suggesting the reliability of the CI statistics. By processing radar mosaic maps, such a framework could be applied for studying CI climatology over China and other regions.

scholarly journals Impact of a Stochastic Kinetic Energy Backscatter Scheme on Warm Season Convection-Allowing Ensemble Forecasts

2016 ◽  
Vol 144 (5) ◽  
pp. 1887-1908 ◽  
Author(s):  
Jeffrey D. Duda ◽  
Xuguang Wang ◽  
Fanyou Kong ◽  
Ming Xue ◽  
Judith Berner
Keyword(s):  
Kinetic Energy ◽  
Wrf Model ◽  
Warm Season ◽  
Weather Research And Forecasting ◽  
Potential Utility ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Central United States ◽  
Surface Observations

The efficacy of a stochastic kinetic energy backscatter (SKEB) scheme to improve convection-allowing probabilistic forecasts was studied. While SKEB has been explored for coarse, convection-parameterizing models, studies of SKEB for convective scales are limited. Three ensembles were compared. The SKMP ensemble used mixed physics with the SKEB scheme, whereas the MP ensemble was configured identically but without using the SKEB scheme. The SK ensemble used the SKEB scheme with no physics diversity. The experiment covered May 2013 over the central United States on a 4-km Weather Research and Forecasting (WRF) Model domain. The SKEB scheme was successful in increasing the spread in all fields verified, especially mid- and upper-tropospheric fields. Additionally, the rmse of the ensemble mean was maintained or reduced, in some cases significantly. Rank histograms in the SKMP ensemble were flatter than those in the MP ensemble, indicating the SKEB scheme produces a less underdispersive forecast distribution. Some improvement was seen in probabilistic precipitation forecasts, particularly when examining Brier scores. Verification against surface observations agree with verification against Rapid Refresh (RAP) model analyses, showing that probabilistic forecasts for 2-m temperature, 2-m dewpoint, and 10-m winds were also improved using the SKEB scheme. The SK ensemble gave competitive forecasts for some fields. The SK ensemble had reduced spread compared to the MP ensemble at the surface due to the lack of physics diversity. These results suggest the potential utility of mixed physics plus the SKEB scheme in the design of convection-allowing ensemble forecasts.

scholarly journals Evaluation and Bias Correction in WRF Model Forecasting of Precipitation and Potential Evapotranspiration

2019 ◽  
Vol 20 (5) ◽  
pp. 965-983 ◽  
Author(s):  
Theodor Bughici ◽  
Naftali Lazarovitch ◽  
Erick Fredj ◽  
Eran Tas
Keyword(s):  
Bias Correction ◽  
Potential Evapotranspiration ◽  
Irrigation Management ◽  
Hybrid Approach ◽  
Wrf Model ◽  
Irrigation Scheduling ◽  
Weather Research And Forecasting ◽  
Forecast Bias ◽  
Synoptic Conditions ◽  
High Bias

Abstract A reliable forecast of potential evapotranspiration (ET0) is key to precise irrigation scheduling toward reducing water and agrochemical use while optimizing crop yield. In this study, we examine the benefits of using the Weather Research and Forecasting (WRF) Model for ET0 and precipitation forecasts with simulations at a 3-km grid spatial resolution and an hourly temporal resolution output over Israel. The simulated parameters needed to calculate ET0 using the Penman–Monteith (PM) approach, as well as calculated ET0 and precipitation, were compared to observations from a network of meteorological stations. WRF forecasts of all PM meteorological parameters, except wind speed Ws, were significantly sensitive to seasonality and synoptic conditions, whereas forecasts of Ws consistently showed high bias associated with strong local effects, leading to high bias in the evaluated PM–ET0. Local Ws bias correction using observations on days preceding the forecast and interpolation of the resulting PM–ET0 to other locations led to significant improvement in ET0 forecasts over the investigated area. By using this hybrid forecast approach (WRFBC) that combines WRF numerical simulations with statistical bias corrections, daily ET0 forecast bias was reduced from an annual mean of 13% with WRF to 3% with WRFBC, while maintaining a high model–observation correlation. WRF was successful in predicting precipitation events on a daily event basis for all four forecast lead days. Considering the benefit of the hybrid approach for forecasting ET0, the WRF Model was found to be a high-potential tool for improving crop irrigation management.

scholarly journals The GEFS-Based Daily Reference Evapotranspiration (ETo) Forecast and Its Implication for Water Management in the Southeastern United States

2014 ◽  
Vol 15 (3) ◽  
pp. 1152-1165 ◽  
Author(s):  
Di Tian ◽  
Christopher J. Martinez
Keyword(s):  
Water Management ◽  
United States ◽  
Reference Evapotranspiration ◽  
Southeastern United States ◽  
Demand Forecasting ◽  
Skill Score ◽  
Irrigation Scheduling ◽  
Forecast Skill ◽  
Ensemble Forecasts ◽  
Relative Operating Characteristic

Abstract NOAA’s second-generation retrospective forecast (reforecast) dataset was created using the currently operational Global Ensemble Forecast System (GEFS). It has the potential to accurately forecast daily reference evapotranspiration ETo and can be useful for water management. This study was conducted to evaluate daily ETo forecasts using the GEFS reforecasts in the southeastern United States (SEUS) and to incorporate the ETo forecasts into irrigation scheduling to explore the usefulness of the forecasts for water management. ETo was estimated using the Penman–Monteith equation, and ensemble forecasts were downscaled and bias corrected using a forecast analog approach. The overall forecast skill was evaluated using the linear error in probability space skill score, and the forecast in five categories (terciles and 10th and 90th percentiles) was evaluated using the Brier skill score, relative operating characteristic, and reliability diagrams. Irrigation scheduling was evaluated by water deficit WD forecasts, which were determined based on the agricultural reference index for drought (ARID) model driven by the GEFS-based ETo forecasts. All forecast skill was generally positive up to lead day 7 throughout the year, with higher skill in cooler months compared to warmer months. The GEFS reforecast improved ETo forecast skill for all lead days over the SEUS compared to the first-generation reforecast. The WD forecasts driven by the ETo forecasts showed higher accuracy and less uncertainty than the forecasts driven by climatology, indicating their usefulness for irrigation scheduling, hydrological forecasting, and water demand forecasting in the SEUS.

Systematic Evaluation of the Impact of Assimilating a Network of Ground-Based Remote Sensing Profilers for Forecasts of Nocturnal Convection Initiation during PECAN

2020 ◽  
Vol 148 (12) ◽  
pp. 4703-4728
Author(s):  
Samuel K. Degelia ◽  
Xuguang Wang ◽  
David J. Stensrud ◽  
David D. Turner
Keyword(s):  
Remote Sensing ◽  
High Frequency ◽  
Skill Score ◽  
Forecast Skill ◽  
Systematic Evaluation ◽  
Large Variability ◽  
Synoptic Forcing ◽  
Convection Initiation ◽  
The Impact ◽  
Forecast Quality

AbstractNocturnal convection is often initiated by mechanisms that cannot be easily observed within the large gaps between rawinsondes or by conventional surface networks. To improve forecasts of such events, we evaluate the systematic impact of assimilating a collocated network of high-frequency, ground-based thermodynamic and kinematic profilers collected as part of the 2015 Plains Elevated Convection At Night (PECAN) experiment. For 13 nocturnal convection initiation (CI) events, we find small but consistent improvements when assimilating thermodynamic observations collected by Atmospheric Emitted Radiance Interferometers (AERIs). Through midlevel cooling and moistening, assimilating the AERIs increases the fractions skill score (FSS) for both nocturnal CI and precipitation forecasts. The AERIs also improve various contingency metrics for CI forecasts. Assimilating composite kinematic datasets collected by Doppler lidars and radar wind profilers (RWPs) results in slight degradations to the forecast quality, including decreases in the FSS and traditional contingency metrics. The impacts from assimilating thermodynamic and kinematic profilers often counteract each other, such that we find little impact on the detection of CI when both are assimilated. However, assimilating both datasets improves various properties of the CI events that are successfully detected (timing, distance, shape, etc.). We also find large variability in the impact of assimilating these remote sensing profilers, likely due to the number of observing sites and the strength of the synoptic forcing for each case. We hypothesize that the lack of flow-dependent methods to diagnose observation errors likely contributes to degradations in forecast skill for many cases, especially when assimilating kinematic profilers.

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