Comparison of NMC and Ensemble-Based Climatological Background-Error Covariances in an Operational Limited-Area Data Assimilation System

Stanešić, Antonio; Horvath, Kristian; Keresturi, Endi

doi:10.3390/atmos10100570

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…EXP_CV5 and EXP_CV7 assimilate radar observations (both reflectivity and radial velocity) every 3 h from 0600 UTC to 1200 UTC, taking the analysis field assimilated at 1200 UTC as the initial field and integrating forward for 6 h. The difference is that the former uses the BE matrix of the CV5 scheme for assimilation, while the latter uses the BE matrix of the CV7 scheme for assimilation. Current studies generally agree that the BE matrix obtained statistically using the NMC method overestimates the characteristic length scale (Liu et al, 2005;Li et al, 2012;Sun et al, 2016;Stanesic et al, 2019;Thiruvengadam et al, 2020). Therefore, in the two assimilation experiments, the length scales are reduced to half of the default value, which are set to 0.5.…”

Section: Model Configuration and Experimental Designmentioning

confidence: 86%

Comparison of different momentum control variables on assimilating radar observations for the forecasts of a dispersive convective event

et al. 2023

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In this study, the effects of background error covariance (BE) using the stream function ψ and unbalanced velocity potential χu as momentum control variables (CV5 scheme) and BE using the velocity U and V as momentum control variables (CV7 scheme) on assimilating radar radial velocity and reflectivity data for short-term forecasts of dispersive convection in a weak environmental field are explored based on the weather research and forecasting model (WRF) model and its 3DVAR assimilation system. The 4 km resolution forecast samples are generated to formulate the CV5 and CV7 BE by the National Meteorological Center (NMC) method. The single-observation experiments reveal that the differences between the two BE statistics are mainly reflected on the momentum control variables. The increment of wind field from CV7 shows more small-scale local characteristics. Comparing with control experiment, real radar observation assimilation tests of CV5 and CV7 both improve the reflectivity and precipitation forecasts. But the CV7 scheme improves the forecasting of strong convective systems in weak environmental fields better than CV5. First, the CV7 scheme improves both reflectivity and dispersive precipitation forecasts and significantly suppresses the spurious precipitation forecasts when compared with the CV5 scheme. In addition, CV7 also significantly reduces the forecast errors of surface variables and the wind analysis from CV7 is more local. Further analysis shows that the CV7 improves the water vapor convergence conditions compared to the CV5 scheme, which may be the reason for its better performance in the subsequent forecasts.

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Section: Model Configuration and Experimental Designmentioning

confidence: 86%

Comparison of different momentum control variables on assimilating radar observations for the forecasts of a dispersive convective event

et al. 2023

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“…Numerous validation and verification methods, in both an operational and in a research context, applied over the years confirmed that the ALADIN model also provides very good representation of the vertical state of the atmosphere (e.g., Horvath et al, 2009;Ivančan-Picek et al, 2016;Stanešić et al, 2019). For ALADIN-HR44, the vertical grid is stretched with 73 hybrid sigma-pressure levels, with the lowest vertical level at approximately 10 m above ground level, while dynamic adaptation products have 15 vertical levels (with 8 levels in the first 1000 m).…”

Section: Numerical Modelmentioning

confidence: 97%

The 2017 Split wildfire in Croatia: evolution and the role of meteorological conditions

Tomašević

Cheung²,

Vučetić³

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. The Split wildfire in July 2017, which was one of the most severe wildfires in the history of this Croatian World Heritage Site, is the focus in this study. The Split fire is a good example of a wildfire–urban interface, with unexpected fire behavior including rapid downslope spread to the coastal populated area. This study clarifies the meteorological conditions behind the fire event, those that have limited the effectiveness of firefighting operations, and the rapid escalation and expansion of the fire zones within 30 h. The Split fire propagation was first reconstructed using radio logs, interviews with firefighters and pilots involved in the intervention, eyewitness statements, digital photographs from fire detection cameras, media, and the monthly firefighting journal. Four phases of fire development have been identified. Then, weather observations and numerical simulations using an enhanced-resolution operational model are utilized to analyze the dynamics in each phase of the fire runs. The synoptic background of the event includes large surface pressure gradient between the Azores anticyclone accompanied by a cold front and a cyclone over the southeastern Balkan Peninsula. At the upper level, there was a deep shortwave trough extending from the Baltic Sea to the Adriatic Sea, which developed into a cut-off low. Such synoptic conditions have resulted in the maximum fire weather index in 2017. Combined with topography, they also locally provoke the formation of the strong northeasterly bura wind along the Adriatic coast, which has been accompanied by a low-level jet (LLJ). The bura (downslope wind), with mid- to low-level gravity-wave breaking and turbulence mixing (as in the hydraulic jump theory), also facilitated the subsidence of dry air from the upper troposphere and rapid drying at the surface. This study demonstrates that numerical guidance that indicates the spatial and temporal occurrence of a LLJ is highly capable of explaining the Split fire evolution from the ignition potential to its extinguishment stage. Thus, in addition to the conventional fire weather indices, such products are able to improve fire weather behavior forecasting and in general more effective decision-making in fire management.

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“…An alternative technique to calculate the static error statistics is the ensemble-based method (ENS) [45], where the forecast differences are the differences between two ensemble members, i.e., member i and member i + 1, where i denotes the sequence index of an ensemble member. Compared to the NMC method, the static error statistics from the ENS method have been demonstrated to improve the forecast quality [44,46]. Therefore, the ENS method was adopted to calculate the B-matrix in this study.…”

Section: Design Of Feature-dependent Bmentioning

confidence: 99%

“…Remote Sens. 2022, 14, 4537 6 of 22 method, the static error statistics from the ENS method have been demonstrated to improve the forecast quality [44,46]. Therefore, the ENS method was adopted to calculate the B-matrix in this study.…”

Section: Design Of Feature-dependent Bmentioning

confidence: 99%

Impact of Feature-Dependent Static Background Error Covariances for Satellite-Derived Humidity Assimilation on Analyses and Forecasts of Multiple Sea Fog Cases over the Yellow Sea

et al. 2022

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Assimilation of satellite-derived humidity with a homogenous static background error covariance (B) matrix computed over the entire computational domain (Full-B) tends to overpredict sea fog coverage. A feature-dependent B (Fog-B) is proposed to address this issue. In Fog-B, the static error statistics for clear air and foggy areas are calculated separately using a feature-dependent binning method. The resultant error statistics are used simultaneously at appropriate locations guided by the satellite-derived sea fog. Diagnostics show that Full-B generally has broader horizontal and vertical length scales and larger error variances than Fog-B below ~300 m except for the vertical length scale near the surface. Experiments on three sea fog cases over the Yellow Sea are conducted to understand and examine the impact of Fog-B on sea fog analyses and forecasts. Results show that using Full-B produces greater and broader water vapor mixing ratio increments and thus predicts larger sea fog coverage than using Fog-B. Further evaluations suggest that using Fog-B has greater forecast skills in sea fog coverage and more accurate moisture conditions than using Full-B.

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Comparison of NMC and Ensemble-Based Climatological Background-Error Covariances in an Operational Limited-Area Data Assimilation System

Cited by 8 publications

References 33 publications

Comparison of different momentum control variables on assimilating radar observations for the forecasts of a dispersive convective event

Comparison of different momentum control variables on assimilating radar observations for the forecasts of a dispersive convective event

The 2017 Split wildfire in Croatia: evolution and the role of meteorological conditions

Impact of Feature-Dependent Static Background Error Covariances for Satellite-Derived Humidity Assimilation on Analyses and Forecasts of Multiple Sea Fog Cases over the Yellow Sea

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