Dynamics and Structure of Mesoscale Error Covariance of a Winter Cyclone Estimated through Short-Range Ensemble Forecasts

2005 ◽  
Vol 133 (10) ◽  
pp. 2876-2893 ◽  
Author(s):  
Fuqing Zhang
Keyword(s):  
Short Range ◽  
Large Scale ◽  
Small Scale ◽  
Horizontal Distance ◽  
Moist Convection ◽  
Error Growth ◽  
State Variables ◽  
Ensemble Forecasts ◽  
Error Covariance ◽  
Cross Covariance

Abstract Several sets of short-range mesoscale ensemble forecasts generated with different types of initial perturbations are used in this study to investigate the dynamics and structure of mesoscale error covariance in an intensive extratropical cyclogenesis event that occurred on 24–25 January 2000. Consistent with past predictability studies of this event, it is demonstrated that the characteristics and structure of the error growth are determined by the underlying balanced dynamics and the attendant moist convection. The initially uncorrelated errors can grow from small-scale, largely unbalanced perturbations to large-scale, quasi-balanced structured disturbances within 12–24 h. Maximum error growth occurred in the vicinity of upper-level and surface zones with the strongest potential vorticity (PV) gradient over the area of active moist convection. The structure of mesoscale error covariance estimated from these short-term ensemble forecasts is subsequently flow dependent and highly anisotropic, which is also ultimately determined by the underlying governing dynamics and associated error growth. Significant spatial and cross covariance (correlation) exists between different state variables with a horizontal distance as large as 1000 km and across all vertical layers. Qualitatively similar error covariance structure is estimated from different ensemble forecasts initialized with different perturbations.

Error Growth Dynamics within Convection-Allowing Ensemble Forecasts over Central U.S. Regions for Days of Active Convection

2021 ◽  
Author(s):  
Xiaoran Zhuang ◽  
Ming Xue ◽  
Jinzhong Min ◽  
Zhiming Kang ◽  
Naigeng Wu ◽  
...  
Keyword(s):  
Diurnal Cycle ◽  
Large Scale ◽  
Response Times ◽  
Forecast Error ◽  
Small Scale ◽  
Forecast Errors ◽  
Error Growth ◽  
Ensemble Forecasts ◽  
Scale Error ◽  
Active Convection

AbstractError growth is investigated based on convection-allowing ensemble forecasts starting from 0000 UTC for 14 active convection events over central to eastern U.S. regions from spring 2018. The analysis domain is divided into the NW, NE, SE and SW quadrants (subregions). Total difference energy and its decompositions are used to measure and analyze error growth at and across scales. Special attention is paid to the dominant types of convection with respect to their forcing mechanisms in the four subregions and the associated difference in precipitation diurnal cycles. The discussions on the average behaviors of error growth in each region are supplemented by 4 representative cases. Results show that the meso-γ-scale error growth is directly linked to precipitation diurnal cycle while meso-α-scale error growth has strong link to large scale forcing. Upscale error growth is evident in all regions/cases but up-amplitude growth within own scale plays different roles in different regions/cases.When large-scale flow is important (as in the NE region), precipitation is strongly modulated by the large-scale forcing and becomes more organized with time, and upscale transfer of forecast error is stronger. On the other hand, when local instability plays more dominant roles (as in the SE region), precipitation is overall least organized and has the weakest diurnal variations. Its associated errors at the γ– and β-scale can reach their peaks sooner and meso-α-scale error tends to rely more on growth of error with its own scale. Small-scale forecast errors are directly impacted by convective activities and have short response time to convection while increasingly larger scale errors have longer response times and delayed phase within the diurnal cycle.

scholarly journals Mesoscale Predictability of Moist Baroclinic Waves: Convection-Permitting Experiments and Multistage Error Growth Dynamics

2007 ◽  
Vol 64 (10) ◽  
pp. 3579-3594 ◽  
Author(s):  
Fuqing Zhang ◽  
Naifang Bei ◽  
Richard Rotunno ◽  
Chris Snyder ◽  
Craig C. Epifanio
Keyword(s):  
Large Scale ◽  
Random Noise ◽  
Baroclinic Instability ◽  
Growth Dynamics ◽  
Small Scale ◽  
Moist Convection ◽  
Error Growth ◽  
Baroclinic Waves ◽  
Convective Scale ◽  
Shear Production

Abstract A recent study examined the predictability of an idealized baroclinic wave amplifying in a conditionally unstable atmosphere through numerical simulations with parameterized moist convection. It was demonstrated that with the effect of moisture included, the error starting from small random noise is characterized by upscale growth in the short-term (0–36 h) forecast of a growing synoptic-scale disturbance. The current study seeks to explore further the mesoscale error-growth dynamics in idealized moist baroclinic waves through convection-permitting experiments with model grid increments down to 3.3 km. These experiments suggest the following three-stage error-growth model: in the initial stage, the errors grow from small-scale convective instability and then quickly [O(1 h)] saturate at the convective scales. In the second stage, the character of the errors changes from that of convective-scale unbalanced motions to one more closely related to large-scale balanced motions. That is, some of the error from convective scales is retained in the balanced motions, while the rest is radiated away in the form of gravity waves. In the final stage, the large-scale (balanced) components of the errors grow with the background baroclinic instability. Through examination of the error-energy budget, it is found that buoyancy production due mostly to moist convection is comparable to shear production (nonlinear velocity advection). It is found that turning off latent heating not only dramatically decreases buoyancy production, but also reduces shear production to less than 20% of its original amplitude.

scholarly journals Estimating sediment thickness by using horizontal distance to outcrop as secondary information

2017 ◽  
Author(s):  
Nils-Otto Kitterød
Keyword(s):  
Cross Validation ◽  
Estimation Error ◽  
Small Scale ◽  
Horizontal Distance ◽  
Estimation Variance ◽  
Sediment Thickness ◽  
Preferential Sampling ◽  
Cross Covariance ◽  
The Cross ◽  
Secondary Information

Abstract. Sediment thickness (D) was estimated utilizing a publically available well database from Norway, GRANADA. General challenges associated with such databases typically involve clustering and bias of the data material due to preferential sampling. However, if information about horizontal distance to the nearest outcrop (L) is included, does the spatial estimation of D improve? This idea was tested comparing two cross-validation results: ordinary kriging (OK) where L was disregarded; and co-kriging (CK) where cross-covariance between D and L was included. The analysis resulted in only minor differences between OK and CK in terms of absolute estimation error, however CK produced more precise results than OK. All observations were declustered and transformed to standard normal probability density functions before estimation and back transformed for the cross-validation analysis. The semivariogram analysis gave correlation lengths for D and L of approx. 10 km and 6 km. These correlations reduce the estimation variance in the cross-validation analysis because more than 50 % of the data material had two or more observations within a radius of 5 km. The small-scale variance of D, however, was about 50 % of the total variance, which gave an accuracy of less than 60 % for most of the cross-validation cases. Despite of the noisy character in the data material, the analysis demonstrates that L can be used as a secondary information to reduce the estimation variance of D.

scholarly journals Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI EnKF for the Analysis and Short-Term Forecast of a Mesoscale Convective System

2020 ◽  
Vol 148 (5) ◽  
pp. 2111-2133 ◽  
Author(s):  
Rong Kong ◽  
Ming Xue ◽  
Alexandre O. Fierro ◽  
Youngsun Jung ◽  
Chengsi Liu ◽  
...  
Keyword(s):  
Mesoscale Convective System ◽  
Convective System ◽  
Lightning Flash ◽  
Moist Convection ◽  
State Variables ◽  
Short Term ◽  
Pixel Resolution ◽  
Cross Covariance ◽  
Mesoscale Convective ◽  
Total Lightning

Abstract The recently launched Geostationary Operational Environmental Satellite “R-series” (GOES-R) satellites carry the Geostationary Lightning Mapper (GLM) that measures from space the total lightning rate in convective storms at high spatial and temporal frequencies. This study assimilates, for the first time, real GLM total lightning data in an ensemble Kalman filter (EnKF) framework. The lightning flash extent density (FED) products at 10-km pixel resolution are assimilated. The capabilities to assimilate GLM FED data are first implemented into the GSI-based EnKF data assimilation (DA) system and tested with a mesoscale convective system (MCS). FED observation operators based on graupel mass or graupel volume are used. The operators are first tuned through sensitivity experiments to determine an optimal multiplying factor to the operator, before being used in FED DA experiments FEDM and FEDV that use the graupel-mass or graupel-volume-based operator, respectively. Their results are compared to a control experiment (CTRL) that does not assimilate any FED data. Overall, both DA experiments outperform CTRL in terms of the analyses and short-term forecasts of FED and composite/3D reflectivity. The assimilation of FED is primarily effective in regions of deep moist convection, which helps improve short-term forecasts of convective threats, including heavy precipitation and lightning. Direct adjustments to graupel mass via observation operator as well as adjustments to other model state variables through flow-dependent ensemble cross covariance within EnKF are shown to work together to generate model-consistent analyses and overall improved forecasts.

scholarly journals Understanding the Predictability within Convection-Allowing Ensemble Forecasts in East China: Meteorological Sensitivity, Forecast Error Growth and Associated Precipitation Uncertainties Across Spatial Scales

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 234 ◽  
Author(s):  
Xiaoran Zhuang ◽  
Naigeng Wu ◽  
Jinzhong Min ◽  
Yuan Xu
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Forecast Error ◽  
Strong Relationship ◽  
Ensemble Forecast ◽  
Observation System ◽  
Dabie Mountains ◽  
Moist Convection ◽  
Error Growth ◽  
Error Energy

This study investigates the practical predictability of two simulated mesoscale convective systems (MCS1 and MCS2) within a state-of-the-art convection-allowing ensemble forecast system. The two MCSs are both controlled by the synoptic Meiyu-front but differ in mesoscale orographic forcing. An observation system simulation experiment (OSSE) setup is first built, which includes flow-dependent multiple-scale initial and lateral boundary perturbations and a 12 h 30-member ensemble forecast is thereby created. In combination with the difference total energy, the decorrelation scale and the ensemble sensitivity analysis, both forecast error evolution, precipitation uncertainties and meteorological sensitivity that describe the practical predictability are assessed. The results show large variabilities of precipitation forecasts among ensemble members, indicative of the practical predictability limit. The study of forecast error evolution shows that the error energy in the MCS1 region in which the convection is blocked by the Dabie Mountains exhibits a simultaneous peak pattern for all spatial scales at around 6 h due to strong moist convection. On the other hand, when large-scale flow plays a more important role, the forecast error energy in the MCS2 region exhibits a stepwise increase with increasing spatial scale. As a result of error energy growth, the precipitation uncertainties evolve from small scales and gradually transfer to larger scales, implying a strong relationship between error growth and precipitation across spatial scales, thus explaining the great precipitation variability within ensemble members. These results suggest the additional forcing brought by the Dabie Mountains could regulate the predictability of Meiyu-frontal convection, which calls for a targeted perturbation design in convection-allowing ensemble forecast systems with respect to different forcing mechanisms.

scholarly journals Sensitivity of Forecast Uncertainty to Different Microphysics Schemes within a Convection-Allowing Ensemble during SoWMEX-IOP8

2021 ◽  
Author(s):  
Chin-Hung Chen ◽  
Kao-Shen Chung ◽  
Shu-Chih Yang ◽  
Li-Hsin Chen ◽  
Pay-Liam Lin ◽  
...  
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Forecast Error ◽  
Convective System ◽  
State Variables ◽  
Forecast Uncertainty ◽  
Ensemble Forecasts ◽  
Weather System ◽  
Microphysics Scheme ◽  
Double Moment

AbstractA mesoscale convective system that occurred in southwestern Taiwan on 15 June 2008 is simulated using convection-allowing ensemble forecasts to investigate the forecast uncertainty associated with four microphysics schemes—the Goddard Cumulus Ensemble (GCE), Morrison (MOR), WRF single-moment 6-class (WSM6), and WRF double-moment 6-class (WDM6) schemes. First, the essential features of the convective structure, hydrometeor distribution, and microphysical tendencies for the different microphysics schemes are presented through deterministic forecasts. Second, ensemble forecasts with the same initial conditions are employed to estimate the forecast uncertainty produced by the different ensembles with the fixed microphysics scheme. GCE has the largest spread in most state variables due to its most efficient phase conversion between water species. By contrast, MOR results in the least spread. WSM6 and WDM6 have similar vertical spread structures due to their similar ice-phase formulae. However, WDM6 produces more ensemble spread than WSM6 does below the melting layer, resulting from its double-moment treatment of warm rain processes. The model simulations with the four microphysics schemes demonstrate upscale error growth through spectrum analysis of the root-mean difference total energy (RMDTE). The RMDTE results reveal that the GCE and WDM6 schemes are more sensitive to initial condition uncertainty, whereas the MOR and WSM6 schemes are relatively less sensitive to that for this event. Overall, the diabatic heating–cooling processes connect the convective-scale cloud microphysical processes to the large-scale dynamical and thermodynamical fields, and they significantly affect the forecast error signatures in the multiscale weather system.

Intrinsic versus Practical Limits of Atmospheric Predictability and the Significance of the Butterfly Effect

2016 ◽  
Vol 73 (3) ◽  
pp. 1419-1438 ◽  
Author(s):  
Y. Qiang Sun ◽  
Fuqing Zhang
Keyword(s):  
Large Scale ◽  
Initial Perturbation ◽  
Forecast Accuracy ◽  
Initial Condition ◽  
Moist Convection ◽  
Error Growth ◽  
Initial Error ◽  
Baroclinic Waves ◽  
Atmospheric Predictability ◽  
Butterfly Effect

Abstract Limits of intrinsic versus practical predictability are studied through examining multiscale error growth dynamics in idealized baroclinic waves with varying degrees of convective instabilities. In the dry experiment free of moist convection, error growth is controlled primarily by baroclinic instability under which forecast accuracy is inversely proportional to the amplitude of the baroclinically unstable initial-condition error (thus the prediction can be continuously improved without limit through reducing the initial error). Under the moist environment with strong convective instability, rapid upscale growth from moist convection leads to the forecast error being increasingly less sensitive to the scale and amplitude of the initial perturbations when the initial-error amplitude is getting smaller; these diminishing returns may ultimately impose a finite-time barrier to the forecast accuracy (limit of intrinsic predictability and the so-called “butterfly effect”). However, if the initial perturbation is sufficiently large in scale and amplitude (as for most current-day operational models), the baroclinic growth of large-scale finite-amplitude initial error will control the forecast accuracy for both dry and moist baroclinic waves; forecast accuracy can be improved (thus the limit of practical predictability can be extended) through the reduction of initial-condition errors, especially those at larger scales. Regardless of the initial-perturbation scales and amplitude, the error spectrum will adjust toward the slope of the background flow. Inclusion of strong moist convection changes the mesoscale kinetic energy spectrum slope from −3 to ~−5/3. This change further highlights the importance of convection and the relevance of the butterfly effect to both the intrinsic and practical limits of atmospheric predictability, especially at meso- and convective scales.

scholarly journals Using Convection-Allowing Ensembles to Understand the Predictability of an Extreme Rainfall Event

2016 ◽  
Vol 144 (10) ◽  
pp. 3651-3676 ◽  
Author(s):  
Erik R. Nielsen ◽  
Russ S. Schumacher
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Regional Scale ◽  
Extreme Rainfall ◽  
Rainfall Event ◽  
Small Scale ◽  
Extreme Rainfall Event ◽  
Error Growth ◽  
Ensemble Spread ◽  
South Central

This research uses convection-allowing ensemble forecasts to address aspects of the predictability of an extreme rainfall event that occurred in south-central Texas on 25 May 2013, which was poorly predicted by operational and experimental numerical models and caused a flash flood in San Antonio that resulted in three fatalities. Most members of the ensemble had large errors in the location and magnitude of the heavy rainfall, but one member approximately reproduced the observed rainfall distribution. On a regional scale a flow-dependent diurnal cycle in ensemble spread growth is observed with large growth associated with afternoon convection, but the growth rate then reduced after convection dissipates the next morning rather than continuing to grow. Experiments that vary the magnitude of the perturbations to the initial and lateral boundary conditions reveal flow dependencies on the scales responsible for the ensemble growth and the degree to which practical (i.e., deficiencies in observing systems and numerical models) and intrinsic predictability limits (i.e., moist convective dynamic error growth) affect a particular convective event. Specifically, it was found that large-scale atmospheric forcing tends to dominate the ensemble spread evolution, but small-scale error growth can be of near-equal importance in certain convective scenarios where interaction across scales is prevalent and essential to the local precipitation processes. In a similar manner, aspects of the “upscale error growth” and “downscale error cascade” conceptual models are seen in the experiments, but neither completely explains the spread characteristics seen in the simulations.

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.

scholarly journals Moist convection drives an upscale energy transfer at Jovian high latitudes

2022 ◽  
Author(s):  
Lia Siegelman ◽  
Patrice Klein ◽  
Andrew P. Ingersoll ◽  
Shawn P. Ewald ◽  
William R. Young ◽  
...  
Keyword(s):  
Energy Source ◽  
Large Scale ◽  
Small Scale ◽  
Dynamical Regime ◽  
High Latitudes ◽  
Moist Convection ◽  
Zonal Jets ◽  
Theoretical Support ◽  
Rayleigh Benard Convection ◽  
Rayleigh Benard

AbstractJupiter’s atmosphere is one of the most turbulent places in the solar system. Whereas observations of lightning and thunderstorms point to moist convection as a small-scale energy source for Jupiter’s large-scale vortices and zonal jets, this has never been demonstrated due to the coarse resolution of pre-Juno measurements. The Juno spacecraft discovered that Jovian high latitudes host a cluster of large cyclones with diameter of around 5,000 km, each associated with intermediate- (roughly between 500 and 1,600 km) and smaller-scale vortices and filaments of around 100 km. Here, we analyse infrared images from Juno with a high resolution of 10 km. We unveil a dynamical regime associated with a significant energy source of convective origin that peaks at 100 km scales and in which energy gets subsequently transferred upscale to the large circumpolar and polar cyclones. Although this energy route has never been observed on another planet, it is surprisingly consistent with idealized studies of rapidly rotating Rayleigh–Bénard convection, lending theoretical support to our analyses. This energy route is expected to enhance the heat transfer from Jupiter’s hot interior to its troposphere and may also be relevant to the Earth’s atmosphere, helping us better understand the dynamics of our own planet.

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