Nowcasting of 1-h precipitation using radar and NWP data

Sokol, Zbyněk

doi:10.1016/j.jhydrol.2005.12.023

Cited by 27 publications

(13 citation statements)

References 8 publications

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“…Among all the levels reported in Table II, the 700 hPa isobar presents the highest overall correlation. This pressure level was already found to be the most representative of the rain field advection for different general circulation models as those in [28,29]. Lower levels (850, 775 hPa) underestimate the rain advection speed and the proximity to the ground perturbs the direction estimation, whereas, on the contrary, the advection velocity is overestimated at higher layers (600, 500 hPa).…”

Section: Comparison Between Radar-derived Wind Speed and Direction Ansupporting

confidence: 54%

Weather radar data for site diversity predictions and evaluation of the impact of rain field advection

Luini

Jeannin²,

Capsoni

et al. 2010

Satell Commun Network

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This paper presents the analysis on two weather radar datasets, collected at Spino d’Adda (Italy) and Bordeaux (France), for the simulation and the performance evaluation of a site diversity system. Results from the two locations are compared and the impact of different factors such as the baseline and link orientation is assessed and related to the local climatologic characteristics. The results obtained are then compared with the model currently recommended by the ITU-R for the estimation of the site diversity performance. A linkage is then established between the preferable baseline orientation and the predominant direction of the rain field advection. The rain field displacement is finally shown to be well approximated by the wind speed and direction relative to the 700 hPa isobar extracted from the ECMWF ERA-40 meteorological database

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Section: Comparison Between Radar-derived Wind Speed and Direction Ansupporting

confidence: 54%

Weather radar data for site diversity predictions and evaluation of the impact of rain field advection

Luini

Jeannin²,

Capsoni

et al. 2010

Satell Commun Network

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“…Additionally, reliable rainfall forecasts with adequate lead time and accuracy are essential for flash flood forecasting. In general, the gauged rainfall, radar data (Sinclair and Pegram, 2005;Mazzetti and Todini, 2009) and satellite data (Sorooshian et al, 2000;Kubota et al, 2007) have been used for quantitative precipitation estimates, and some studies have used multiple precipitation sources (Sokol, 2006;Chiang et al, 2007). Therefore, this method is necessary for assessing the applicability of using rainfall data obtained from various sources.…”

Section: Utilization Of a P -A Curve For Flash Flood Forecastingmentioning

confidence: 99%

Development of a precipitation–area curve for warning criteria of short-duration flash flood

Bae

Lee

Moon

2018

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents quantitative criteria for flash flood warning that can be used to rapidly assess flash flood occurrence based on only rainfall estimates. This study was conducted for 200 small mountainous sub-catchments of the Han River basin in South Korea because South Korea has recently suffered many flash flood events. The quantitative criteria are calculated based on flash flood guidance (FFG), which is defined as the depth of rainfall of a given duration required to cause frequent flooding (1-2-year return period) at the outlet of a small stream basin and is estimated using threshold runoff (TR) and antecedent soil moisture conditions in all sub-basins. The soil moisture conditions were estimated during the flooding season, i.e., July, August and September, over 7 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)) using the Sejong University Rainfall Runoff (SURR) model. A ROC (receiver operating characteristic) analysis was used to obtain optimum rainfall values and a generalized precipitation-area (P -A) curve was developed for flash flood warning thresholds. The threshold function was derived as a P -A curve because the precipitation threshold with a short duration is more closely related to basin area than any other variables. For a brief description of the P -A curve, generalized thresholds for flash flood warnings can be suggested for rainfall rates of 42, 32 and 20 mm h −1 in sub-basins with areas of 22-40, 40-100 and > 100 km 2 , respectively. The proposed P -A curve was validated based on observed flash flood events in different sub-basins. Flash flood occurrences were captured for 9 out of 12 events. This result can be used instead of FFG to identify brief flash flood (less than 1 h), and it can provide warning information to decision-makers or citizens that is relatively simple, clear and immediate.

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“…Before applying the predictor set of NFS model for further application, it is necessary to scale the time of the NFS outputs in accordance with that of rain gauges. To achieve forecasts on an hourly scale, most of the previous works (Sokol, 2006) use the linear interpolation technique. Accordingly, linear interpolation was introduced to obtain the required hourly series.…”

Section: Nwp Model Outputsmentioning

confidence: 99%

Integrating hydrometeorological information for rainfall‐runoff modelling by artificial neural networks

Chiang

Chang

2009

Hydrological Processes

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Abstract:The major purpose of this study is to effectively construct artificial neural networks-based multistep ahead flood forecasting by using hydrometeorological and numerical weather prediction (NWP) information. To achieve this goal, we first compare three mean areal precipitation forecasts: radar/NWP multisource-derived forecasts (P r ), NWP precipitation forecasts (P n ), and improved precipitation forecasts (P m ) by merging P r and P n . The analysis shows that the accuracy of P m is higher than that of P r and P n . The analysis also indicates that the NWP precipitation forecasts do provide relative effectiveness to the merging procedure, particularly for forecast lead time of 4-6 h. In sum, the merged products performed well and captured the main tendency of rainfall pattern. Subsequently, a recurrent neural network (RNN)-based multistep ahead flood forecasting techniques is produced by feeding in the merged precipitation. The evaluation of 1-6-h flood forecasting schemes strongly shows that the proposed hydrological model provides accurate and stable flood forecasts in comparison with a conventional case, and significantly improves the peak flow forecasts and the time-lag problem. An important finding is the hydrologic model responses which do not seem to be sensitive to precipitation predictions in lead times of 1-3 h, whereas the runoff forecasts are highly dependent on predicted precipitation information for longer lead times (4-6 h). Overall, the results demonstrate that accurate and consistent multistep ahead flood forecasting can be obtained by integrating predicted precipitation information into ANNs modelling.

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Nowcasting of 1-h precipitation using radar and NWP data

Cited by 27 publications

References 8 publications

Weather radar data for site diversity predictions and evaluation of the impact of rain field advection

Weather radar data for site diversity predictions and evaluation of the impact of rain field advection

Development of a precipitation–area curve for warning criteria of short-duration flash flood

Integrating hydrometeorological information for rainfall‐runoff modelling by artificial neural networks

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