A Comparison of Multiscale GSI-Based EnKF and 3DVar Data Assimilation Using Radar and Conventional Observations for Midlatitude Convective-Scale Precipitation Forecasts

Johnson, Aaron; Wang, Xuguang; Carley, Jacob R.; Wicker, Louis J.; Karstens, Christopher D.

doi:10.1175/mwr-d-14-00345.1

Cited by 106 publications

(104 citation statements)

References 73 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…Convective-scale precipitation forecasting is an inherently multiscale challenge because of the broad range of spatial scales impacting the initiation and evolution of convective systems (Lorenz 1969;Perkey and Maddox 1985;Zhang et al 2007; Rotunno and Snyder 2008;Johnson et al 2014;. Small-scale errors in the initial state can grow upscale and contaminate even the larger scales of the forecast (e.g., Zhang et al 2006Zhang et al , 2007.…”

Section: Introductionmentioning

confidence: 99%

“…Small-scale IC perturbations can have significant impacts on SSEF spread as a result of rapid propagation and upscale growth (Hohenegger et al 2006;Hohenegger and Schär 2007a,b;Zhang et al 2003Zhang et al , 2006Leoncini et al 2010;Johnson et al 2014). However, initial studies have suggested that the added benefit of small-scale IC perturbations may be very limited when larger-scale perturbations are already present (Johnson et al 2014;Kong et al 2007).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Study of Multiscale Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasts

Johnson

Wang

2016

Monthly Weather Review

Self Cite

View full text Add to dashboard Cite

The impacts of multiscale flow-dependent initial condition (IC) perturbations for storm-scale ensemble forecasts of midlatitude convection are investigated using perfect-model observing system simulation experiments. Several diverse cases are used to quantitatively and qualitatively understand the impacts of different IC perturbations on ensemble forecast skill. Scale dependence of the results is assessed by evaluating 2-h storm-scale reflectivity forecasts separately from hourly accumulated mesoscale precipitation forecasts.Forecasts are initialized with different IC ensembles, including an ensemble of multiscale perturbations produced by a multiscale data assimilation system, mesoscale perturbations produced at a coarser resolution, and filtered multiscale perturbations. Mesoscale precipitation forecasts initialized with the multiscale perturbations are more skillful than the forecasts initialized with the mesoscale perturbations at several lead times. This multiscale advantage is due to greater consistency between the IC perturbations and IC uncertainty. This advantage also affects the short-term, smaller-scale forecasts. Reflectivity forecasts on very small scales and very short lead times are more skillful with the multiscale perturbations as a direct result of the smaller-scale IC perturbation energy. The small-scale IC perturbations also contribute to some improvements to the mesoscale precipitation forecasts after the ;5-h lead time. Altogether, these results suggest that the multiscale IC perturbations provided by ensemble data assimilation on the convection-permitting grid can improve storm-scale ensemble forecasts by improving the sampling of IC uncertainty, compared to downscaling of IC perturbations from a coarser-resolution ensemble.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study of Multiscale Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasts

Johnson

Wang

2016

Monthly Weather Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…9) and forecasts initialized from Hyb_SR_20km, were better than all 3DVAR-initialized forecast sets. That downscaled 20-km hybrid analyses (i.e., Hyb_SR_ 20km) initialized better 4-km precipitation forecasts than true 4-km 3DVAR analyses echoes Johnson et al (2015)'s findings and indicates that incorporation of flow-dependent BECs was more important than analysis resolution.…”

Section: Results For 4-km Forecasts Initialized From Downscaled 20mentioning

confidence: 54%

“…Moreover, Ancell (2012) found that 4-km EnKF analyses initialized better 12-h surface wind and temperature forecasts over the U.S. Pacific Northwest than 36-km EnKF analyses, even though the 4-km EnKF had 40 members and the 36-km EnKF had 80 members. Similarly, Johnson et al (2015) noted that downscaled 12-km EnKF analyses initialized worse 4-km precipitation forecasts than true 4-km EnKF analyses through ;6 h for 10 cases over the central United States, although their 4-km analyses assimilated radar observations whereas their 12-km analyses did not.…”

Section: Introductionmentioning

confidence: 91%

“…Numerous studies have assimilated radar observations with convection-allowing EnKFs for selected severe weather cases (e.g., Snyder and Zhang 2003;Zhang et al 2004;Dowell at al. 2004;Yussouf et al 2013 and references therein), and convection-allowing EnKF analyses can initialize better forecasts than convection-allowing 3DVAR analyses (Zhang et al 2009a;Gao et al 2013;Johnson et al 2015). Moreover, Ancell (2012) found that 4-km EnKF analyses initialized better 12-h surface wind and temperature forecasts over the U.S. Pacific Northwest than 36-km EnKF analyses, even though the 4-km EnKF had 40 members and the 36-km EnKF had 80 members.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Improving Large-Domain Convection-Allowing Forecasts with High-Resolution Analyses and Ensemble Data Assimilation

Schwartz

2016

Mon. Wea. Rev.

View full text Add to dashboard Cite

Analyses with 20-km horizontal grid spacing were produced from continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and ''hybrid'' variational-ensemble data assimilation (DA) systems over a domain spanning the conterminous United States. These analyses initialized 36-h Weather Research and Forecasting Model forecasts containing a large convection-allowing 4-km nested domain, where downscaled 20-km 3DVAR, EnSRF, and hybrid analyses initialized the 4-km forecasts. Overall, hybrid analyses initialized the best 4-km precipitation forecasts.Furthermore, whether 4-km precipitation forecasts could be improved by initializing them with true 4-km analyses was assessed. As it was computationally infeasible to produce 4-km continuously cycling ensembles over the large 4-km domain, several ''dual-resolution'' hybrid DA configurations were adopted where 4-km backgrounds were combined with 20-km ensembles to produce 4-km hybrid analyses. Additionally, 4-km 3DVAR analyses were produced.In both hybrid and 3DVAR frameworks, initializing 4-km forecasts with true 4-km analyses, rather than downscaled 20-km analyses, yielded superior precipitation forecasts over the first 12 h. Differences between forecasts initialized from 4-km and downscaled 20-km hybrid analyses were smaller for 18-36-h forecasts, but there were occasionally meaningful differences. Continuously cycling the 4-km backgrounds and using static background error covariances with larger horizontal length scales in the hybrid led to better forecasts. All hybrid-initialized forecasts, including those initialized from downscaled 20-km analyses, were more skillful than forecasts initialized from 4-km 3DVAR analyses, suggesting the analysis method was more important than analysis resolution.

show abstract

Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF‐RTFDDA

et al. 2017

View full text Add to dashboard Cite

In this study, a lightning data assimilation (LDA) scheme was developed and implemented in the National Center for Atmospheric Research Weather Research and Forecasting‐Real‐Time Four‐Dimensional Data Assimilation system. In this LDA method, graupel mixing ratio (qg) is retrieved from observed total lightning. To retrieve qg on model grid boxes, column‐integrated graupel mass is first calculated using an observation‐based linear formula between graupel mass and total lightning rate. Then the graupel mass is distributed vertically according to the empirical qg vertical profiles constructed from model simulations. Finally, a horizontal spread method is utilized to consider the existence of graupel in the adjacent regions of the lightning initiation locations. Based on the retrieved qg fields, latent heat is adjusted to account for the latent heat releases associated with the formation of the retrieved graupel and to promote convection at the observed lightning locations, which is conceptually similar to the method developed by Fierro et al. Three severe convection cases were studied to evaluate the LDA scheme for short‐term (0–6 h) lightning and precipitation forecasts. The simulation results demonstrated that the LDA was effective in improving the short‐term lightning and precipitation forecasts by improving the model simulation of the qg fields, updrafts, cold pool, and front locations. The improvements were most notable in the first 2 h, indicating a highly desired benefit of the LDA in lightning and convective precipitation nowcasting (0–2 h) applications.

show abstract

A Comparison of Multiscale GSI-Based EnKF and 3DVar Data Assimilation Using Radar and Conventional Observations for Midlatitude Convective-Scale Precipitation Forecasts

Cited by 106 publications

References 73 publications

A Study of Multiscale Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasts

A Study of Multiscale Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasts

Improving Large-Domain Convection-Allowing Forecasts with High-Resolution Analyses and Ensemble Data Assimilation

Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF‐RTFDDA

Contact Info

Product

Resources

About