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

Sokol, Zbyněk; Zacharov, Petr

doi:10.1002/qj.970

Cited by 31 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extrapolation methods, discussed above, calculate the prediction for a given place and a given time by transferring the existing precipitation field along Lagrangian trajectories. Frequently, these methods assume that precipitation does not change over the time along the trajectories e.g., [91,[98][99][100][101][102][103][104]. This assumption is acceptable in case of stratiform precipitation for forecasts with lead times of several hours; however, in case of convective precipitation, the precipitation can change (even significantly) along the trajectories.…”

Section: Quantitative Precipitation Forecastmentioning

confidence: 99%

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

et al. 2021

View full text Add to dashboard Cite

Radar-based rainfall information has been widely used in hydrological and meteorological applications, as it provides data with a high spatial and temporal resolution that improve rainfall representation. However, the broad diversity of studies makes it difficult to gather a condensed overview of the usefulness and limitations of radar technology and its application in particular situations. In this paper, a comprehensive review through a categorization of radar-related topics aims to provide a general picture of the current state of radar research. First, the importance and impact of the high temporal resolution of weather radar is discussed, followed by the description of quantitative precipitation estimation strategies. Afterwards, the use of radar data in rainfall nowcasting as well as its role in preparation of initial conditions for numerical weather predictions by assimilation is reviewed. Furthermore, the value of radar data in rainfall-runoff models with a focus on flash flood forecasting is documented. Finally, based on this review, conclusions of the most relevant challenges that need to be addressed and recommendations for further research are presented. This review paper supports the exploitation of radar data in its full capacity by providing key insights regarding the possibilities of including radar data in hydrological and meteorological applications.

show abstract

Section: Quantitative Precipitation Forecastmentioning

confidence: 99%

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Defining the initial state of the atmosphere is critical to the skill of weather forecasts for time scales anywhere between nowcasting to the medium‐range (up to 10 days; Rabier, , Sokol & Zacharov, ). To initialize global numerical weather prediction (NWP) models, such as the Integrated Forecast System at the European Centre for Medium‐range Weather forecasting (ECMWF), data assimilation techniques are employed to produce an analysis using a vast quantity of observations from a diverse range of instruments.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Representativity Error of Cloud Profiling Observations for Data Assimilation

Fielding

Stiller

2019

JGR Atmospheres

View full text Add to dashboard Cite

Active observations from profiling instruments such as cloud radar or lidar have proved invaluable in improving our understanding of clouds and their interaction with the atmosphere. However, while they undoubtedly contain a wealth of information about the atmospheric state, the assimilation of such observations into NWP models has so far had mixed success. One reason is the mismatch of scales between the relatively coarse model resolution and the narrow beam‐width of the instruments. To obtain an optimal analysis, this representativity error due to this mismatch in scales must be included within the observation error covariance matrix. In this paper, we demonstrate how a relatively straightforward approach that uses the local variance of the measurement combined with a global estimate of correlation provides a useful estimate of the representativity error. We also show how the method can be used to estimate the correlation in representativity error between observations in both the horizontal and vertical, potentially removing the need to thin the observations, or inflate their observation error. The method is evaluated using 2‐D and 3‐D fields of simulated and observed radar reflectivity before an example application to profiling observations is shown using Cloudsat data.

show abstract

“…The reduced continuous rank probability score (RCRPS; Eq. 7; Trinh et al, 2013) is computed as the CRPS (Brown, 1974;Matheson and Winkler, 1976;Unger, 1985;Stanki et al, 1989;Hersbach, 2000), reduced with the standard deviation of the observed discharge over the analysed time period (hereafter σ 2 ):…”

Section: Methodology Of Analysismentioning

confidence: 99%

Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

Poletti

Silvestro

Davolio

et al. 2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Forecasting flash floods some hours in advance is still a challenge, especially in environments made up of many small catchments. Hydrometeorological forecasting systems generally allow for predicting the possibility of having very intense rainfall events on quite large areas with good performances, even with 12–24 h of anticipation. However, they are not able to predict the exact rainfall location if we consider portions of a territory of 10 to 1000 km2 as the order of magnitude. The scope of this work is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with a lead time of 2–8 h. The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

show abstract

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

Cited by 31 publications

References 19 publications

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

Characterizing the Representativity Error of Cloud Profiling Observations for Data Assimilation

Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

Contact Info

Product

Resources

About