Assimilation of extrapolated radar reflectivity into a NWP model and its impact on a precipitation forecast at high resolution

Sokol, Zbyněk

doi:10.1016/j.atmosres.2010.09.008

Cited by 44 publications

(32 citation statements)

References 20 publications

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“…This article extends the cited work (Sokol, 2011) and shows a more complex evaluation of the assimilation technique combining the extrapolation and numerical modelling. The newer version of the COSMO model is used and applied to a significantly larger dataset.…”

Section: Introductionmentioning

confidence: 69%

“…Therefore, the nudging method is still used to assimilate radar reflectivity data among other observations (e.g. Sokol andRezacova, 2006, 2009;Stephan et al, 2008;Dixon et al, 2009). The application of the nudging was also supported by our focus on the very short-range precipitation forecast because the time consumption of the 4D-Var and PF methods restricts their operational application for very short lead times.…”

Section: Introductionmentioning

confidence: 83%

“…The assimilation utilises a water vapour correction (WVC) method (Falkovich et al, 2000;Sokol and Rezacova, 2009;Sokol, 2011). This method increases/decreases model water vapour depending on whether the model underestimates/overestimates observed precipitation.…”

Section: Assimilation Methodsmentioning

confidence: 99%

“…The assimilation method presented utilizes extrapolated radar reflectivity data for the first 60 minutes in the assimilation based on nudging as if they were observed. This method was used by Sokol (2011) and has been proven to improve precipitation forecasts for the first two or three hours on several occasions.…”

Section: Introductionmentioning

confidence: 99%

“…The newer version of the COSMO model is used and applied to a significantly larger dataset. In addition to the Fractions Skill Score (Roberts and Lean, 2008), used by Sokol (2011), the evaluation employs the SAL technique (Wernli et al, 2008) and a new evaluation method, which stems from the definition of the categorical measures Probability of Detection and False Alarm Rate (Wilks, 2006).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity

Sokol

Zacharov

2011

Quart J Royal Meteoro Soc

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Two methods for assimilating radar reflectivity into the COSMO numerical weather prediction (NWP) model were compared to precipitation forecasts. The first method assimilated observed radar reflectivity, and the second one assimilated observed and extrapolated radar reflectivity. The assimilation technique was based on the correction of the model's water vapour mixing ratio. The extrapolation was performed by the COTREC method and was 1 hour long. The model's horizontal resolution was 2.8 km.The comparison of methods was based on verification of the observed and forecast hourly precipitation. The comparison was performed for the 1 st , 2 nd and 3 rd hours of each forecast. On the whole, 45 forecasts from nine days of convective precipitation were evaluated for each hour. The evaluation included subjective verification and the following objective skill scores: Fractions Skill Scores, SAL and a measure based on a categorical-probabilistic approach.The results confirmed that assimilation complemented by the extrapolated data improves the accuracy of precipitation forecasts. The improvement was obvious in a majority of the single forecasts studied, and it is confirmed by all evaluation techniques. COSMO forecasts that used the extrapolation showed reasonable competence in forecasting for the first and the second hours.

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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 83%

Section: Assimilation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity

Sokol

Zacharov

2011

Quart J Royal Meteoro Soc

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Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events

Jia

Bray

Han

2012

Hydrological Processes

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The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high-resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real-time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper-air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three-dimensional variational data-assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km 2 ) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one-dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two-dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases.

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Detecting the Vertical Structure of Extreme Precipitation in the Headwater Area of Yellow River Using the Dual‐Frequency Precipitation Radar Onboard the Global Precipitation Measurement Mission

Song,

Qi,

Lyu

et al. 2024

Intl Journal of Climatology

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In the context of global warming, the rise in extreme precipitation events in high‐altitude headwater areas has introduced greater hydrological uncertainty. However, the limited understanding of the physical mechanisms driving extreme precipitation in these areas hinders efforts to mitigate the potential rise in future precipitation risks. This study analysed the extreme precipitation events in the headwater area of the Yellow River (HAYR) from May to September each year from 2015 to 2020 using satellite‐based data from Dual‐frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory and Integrated Multi‐satellite Retrievals for GPM (IMERG). The results show that stratiform precipitation (SP) determines the spatial extent of extreme precipitation events, while convective precipitation (CP) largely affects the rainfall intensity. Statistical analysis from different extreme precipitation events indicates that the rain rate of CP is 2 to 3 times higher than that of SP, thus zones of intense precipitation in the study area are normally dominated by CP. Vertically, the topographic lifting in complex mountainous regions exerts opposite effects on the precipitation rates of SP and CP, weakening the precipitation intensity of SP while enhancing that of CP. The peak precipitation rate in the midstream and downstream regions is observed at approximately 5 km, whereas the upstream region displays a distinctive double‐peaked distribution, with one peak at 8.5 km and another near the surface. This study provides a better understanding of the interior structure evolution process of plateau precipitation, as well as the associated microphysical properties, and highlights some insights to improve microphysical parameterization in the future model developments.

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Assimilation of extrapolated radar reflectivity into a NWP model and its impact on a precipitation forecast at high resolution

Cited by 44 publications

References 20 publications

Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity

Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity

Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events

Detecting the Vertical Structure of Extreme Precipitation in the Headwater Area of Yellow River Using the Dual‐Frequency Precipitation Radar Onboard the Global Precipitation Measurement Mission

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