Representing radar rainfall uncertainty with ensembles based on a time-variant geostatistical error modelling approach

Cecinati, Francesca; Rico‐Ramirez, Miguel A.; Heuvelink, G.B.M.; Han, Dawei

doi:10.1016/j.jhydrol.2017.02.053

Cited by 44 publications

(29 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess bias effects at distances < 10 km we advocate installation of a well-designed network of rain gauges with stations located at preselected locations that would allow sound geostatistical analysis of small-scale rainfall variability and spatial correlation analysis. We refer to Ciach and Krajewski (2006), who present such analysis for a dense experimental network of 53 stations. The interstation distance of the rain gauges in this study is too large to capture the effect of distance to large-scale open water bodies on CMORPH rainfall error.…”

Section: Discussionmentioning

confidence: 99%

“…For distances < 10 km errors by CMORPH increased, but the small sample size of stations and the weak signal require further study. To assess how bias is affected at short distances to a large-scale water body, a specifically designed and dense gauging network is advocated (see Ciach and Krajewski, 2006) that allows evaluation of small-scale rainfall variability. A detailed analysis of small spatial variability and spatial correlation analysis of rain-gauged observations presumably is a prerequisite before satellite rainfall effects at short distances to a large-scale water body can be assessed.…”

Section: Analysis Of Gauge and Cmorph Rainfall Estimatesmentioning

confidence: 99%

“…Correction schemes for rainfall estimates are developed for climate models (Maraun, 2016;Grillakis et al, 2017;Switanek et al, 2017), for radar approaches (Cecinati et al, 2017;Yoo et al, 2014), and for satellite-based, multi-sensor approaches (Najmaddin et al, 2017;Valdés-Pineda et al, 2016). In this study the focus is on satellite rainfall estimates (SREs) to improve reliability in spatio-temporal rainfall representation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin

Gumindoga

Rientjes

Haile

et al. 2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Satellite rainfall estimates (SREs) are prone to bias as they are indirect derivatives of the visible, infrared, and/or microwave cloud properties, and hence SREs need correction. We evaluate the influence of elevation and distance from large-scale open water bodies on bias for Climate Prediction Center-MORPHing (CMORPH) rainfall estimates in the Zambezi basin. The effectiveness of five linear/non-linear and time–space-variant/-invariant bias-correction schemes was evaluated for daily rainfall estimates and climatic seasonality. The schemes used are spatio-temporal bias (STB), elevation zone bias (EZ), power transform (PT), distribution transformation (DT), and quantile mapping based on an empirical distribution (QME). We used daily time series (1998–2013) from 60 gauge stations and CMORPH SREs for the Zambezi basin. To evaluate the effectiveness of the bias-correction schemes spatial and temporal cross-validation was applied based on eight stations and on the 1998–1999 CMORPH time series, respectively. For correction, STB and EZ schemes proved to be more effective in removing bias. STB improved the correlation coefficient and Nash–Sutcliffe efficiency by 50 % and 53 %, respectively, and reduced the root mean squared difference and relative bias by 25 % and 33 %, respectively. Paired t tests showed that there is no significant difference (p < 0.05) in the daily means of CMORPH against gauge rainfall after bias correction. ANOVA post hoc tests revealed that the STB and EZ bias-correction schemes are preferable. Bias is highest for very light rainfall (< 2.5 mm d−1), for which most effective bias reduction is shown, in particular for the wet season. Similar findings are shown through quantile–quantile (q–q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by > 28 %. The temporal cross-validation approach showed effectiveness of the bias-correction schemes. Taylor diagrams show that station elevation has an influence on CMORPH performance. Effects of distance > 10 km from large-scale open water bodies are minimal, whereas effects at shorter distances are indicated but are not conclusive for a lack of rain gauges. Findings of this study show the importance of applying bias correction to SREs.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Analysis Of Gauge and Cmorph Rainfall Estimatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin

Gumindoga

Rientjes

Haile

et al. 2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The residual radar measurement uncertainty can be effectively modelled by the non-stationary generator to obtain QPE ensembles, which reproduce the local statistical characteristics and anisotropy of the observed rainfall fields (Jordan et al, 2003;Ciach et al, 2007;Villarini et al, 2009;Germann et al, 2009;Cecinati et al, 2017). In addition, we believe that current radar rain gauge merging and adjustment techniques (e.g.…”

Section: Future Perspectivesmentioning

confidence: 99%

A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform

Nerini

Bešič

Sideris

et al. 2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. In this paper we present a non-stationary stochastic generator for radar rainfall fields based on the short-space Fourier transform (SSFT). The statistical properties of rainfall fields often exhibit significant spatial heterogeneity due to variability in the involved physical processes and influence of orographic forcing. The traditional approach to simulate stochastic rainfall fields based on the Fourier filtering of white noise is only able to reproduce the global power spectrum and spatial autocorrelation of the precipitation fields. Conceptually similar to wavelet analysis, the SSFT is a simple and effective extension of the Fourier transform developed for space-frequency localisation, which allows for using windows to better capture the local statistical structure of rainfall. The SSFT is used to generate stochastic noise and precipitation fields that replicate the local spatial correlation structure, i.e. anisotropy and correlation range, of the observed radar rainfall fields. The potential of the stochastic generator is demonstrated using four precipitation cases observed by the fourth generation of Swiss weather radars that display significant non-stationarity due to the coexistence of stratiform and convective precipitation, differential rotation of the weather system and locally varying anisotropy. The generator is verified in its ability to reproduce both the global and the local Fourier power spectra of the precipitation field. The SSFT-based stochastic generator can be applied and extended to improve the probabilistic nowcasting of precipitation, design storm simulation, stochastic numerical weather prediction (NWP) downscaling, and also for other geophysical applications involving the simulation of complex nonstationary fields.

show abstract

“…Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.Atmosphere 2019, 10, 458 2 of 21 models that are used to measure and predict rainfall motion and evolution [15,16]. A number of radar rainfall nowcasting models have attempted to quantify this uncertainty and to express the output statistics in probabilistic ways [17,18]. The uncertainty can be modeled using specific all error sources associated with the radar rainfall nowcasting procedure [19,20] or by a functional-statistical scheme that quantifies the relationship between the radar rainfall nowcasts and the corresponding true reference rainfall collected using methods such as gauge rainfall measurements [11,17].…”

mentioning

confidence: 99%

Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models

2019

View full text Add to dashboard Cite

Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.

show abstract

Representing radar rainfall uncertainty with ensembles based on a time-variant geostatistical error modelling approach

Cited by 44 publications

References 67 publications

Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin

Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin

A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform

Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models

Contact Info

Product

Resources

About