Journal articles on the topic 'Flood nowcasting'
To see the other types of publications on this topic, follow the link: Flood nowcasting.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Flood nowcasting.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Vivoni, Enrique R., Dara Entekhabi, Rafael L. Bras, Valeriy Y. Ivanov, Matthew P. Van Horne, Christopher Grassotti, and Ross N. Hoffman. "Extending the Predictability of Hydrometeorological Flood Events Using Radar Rainfall Nowcasting." Journal of Hydrometeorology 7, no. 4 (August 1, 2006): 660–77. http://dx.doi.org/10.1175/jhm514.1.
Full textAbstract:
Abstract The predictability of hydrometeorological flood events is investigated through the combined use of radar nowcasting and distributed hydrologic modeling. Nowcasting of radar-derived rainfall fields can extend the lead time for issuing flood and flash flood forecasts based on a physically based hydrologic model that explicitly accounts for spatial variations in topography, surface characteristics, and meteorological forcing. Through comparisons to discharge observations at multiple gauges (at the basin outlet and interior points), flood predictability is assessed as a function of forecast lead time, catchment scale, and rainfall spatial variability in a simulated real-time operation. The forecast experiments are carried out at temporal and spatial scales relevant for operational hydrologic forecasting. Two modes for temporal coupling of the radar nowcasting and distributed hydrologic models (interpolation and extended-lead forecasting) are proposed and evaluated for flood events within a set of nested basins in Oklahoma. Comparisons of the radar-based forecasts to persistence show the advantages of utilizing radar nowcasting for predicting near-future rainfall during flood event evolution.
2
Silvestro, F., and N. Rebora. "Operational verification of a framework for the probabilistic nowcasting of river discharge in small and medium size basins." Natural Hazards and Earth System Sciences 12, no. 3 (March 23, 2012): 763–76. http://dx.doi.org/10.5194/nhess-12-763-2012.
Full textAbstract:
Abstract. Forecasting river discharge is a very important issue for the prediction and monitoring of ground effects related to severe precipitation events. The meteorological forecast systems are unable to predict precipitation on small spatial (few km) and temporal (hourly) scales. For these reasons the issuing of reliable flood forecasts is not feasible in those regions where the basin's response to rainfall events is very fast and can generate flash floods. This problem can be tackled by using rainfall nowcasting techniques based on radar observations coupled with hydrological modeling. These procedures allow the forecasting of future streamflow with a few hours' notice. However, to account for the short-term uncertainties in the evolution of fine scale precipitation field, a probabilistic approach to rainfall nowcasting is needed. These uncertainties are then propagated from rainfall to runoff through a distributed hydrological model producing a set of equi-probable discharge scenarios to be used for the flood nowcasting with time horizons of a few hours. Such a hydrological nowcasting system is presented here and applied to some case studies. A first evaluation of its applicability in an operational context is provided and the opportunity of using the results quantitatively is discussed.
3
Vivoni, Enrique R., Dara Entekhabi, and Ross N. Hoffman. "Error Propagation of Radar Rainfall Nowcasting Fields through a Fully Distributed Flood Forecasting Model." Journal of Applied Meteorology and Climatology 46, no. 6 (June 1, 2007): 932–40. http://dx.doi.org/10.1175/jam2506.1.
Full textAbstract:
Abstract This study presents a first attempt to address the propagation of radar rainfall nowcasting errors to flood forecasts in the context of distributed hydrological simulations over a range of catchment sizes or scales. Rainfall forecasts with high spatiotemporal resolution generated from observed radar fields are used as forcing to a fully distributed hydrologic model to issue flood forecasts in a set of nested subbasins. Radar nowcasting introduces errors into the rainfall field evolution that result from spatial and temporal changes of storm features that are not captured in the forecast algorithm. The accuracy of radar rainfall and flood forecasts relative to observed radar precipitation fields and calibrated flood simulations is assessed. The study quantifies how increases in nowcasting errors with lead time result in higher flood forecast errors at the basin outlet. For small, interior basins, rainfall forecast errors can be simultaneously amplified or dampened in different flood forecast locations depending on the forecast lead time and storm characteristics. Interior differences in error propagation are shown to be effectively averaged out for larger catchment scales.
4
Moreno, Hernan A., Enrique R. Vivoni, and David J. Gochis. "Limits to Flood Forecasting in the Colorado Front Range for Two Summer Convection Periods Using Radar Nowcasting and a Distributed Hydrologic Model." Journal of Hydrometeorology 14, no. 4 (August 1, 2013): 1075–97. http://dx.doi.org/10.1175/jhm-d-12-0129.1.
Full textAbstract:
Abstract Flood forecasting in mountain basins remains a challenge given the difficulty in accurately predicting rainfall and in representing hydrologic processes in complex terrain. This study identifies flood predictability patterns in mountain areas using quantitative precipitation forecasts for two summer events from radar nowcasting and a distributed hydrologic model. The authors focus on 11 mountain watersheds in the Colorado Front Range for two warm-season convective periods in 2004 and 2006. The effects of rainfall distribution, forecast lead time, and basin area on flood forecasting skill are quantified by means of regional verification of precipitation fields and analyses of the integrated and distributed basin responses. The authors postulate that rainfall and watershed characteristics are responsible for patterns that determine flood predictability at different catchment scales. Coupled simulations reveal that the largest decrease in precipitation forecast skill occurs between 15- and 45-min lead times that coincide with rapid development and movements of convective systems. Consistent with this, flood forecasting skill decreases with nowcasting lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Across the majority of the basins, flood forecasting skill is reduced noticeably for nowcasting lead times greater than 30 min. The authors identified that intermediate basin areas [~(2–20) km2] exhibit the largest flood forecast errors with the largest differences across nowcasting ensemble members. The typical size of summer convective storms is found to coincide well with these maximum errors, while basin properties dictate the shape of the scale dependency of flood predictability for different lead times.
5
Sharif, Hatim O., David Yates, Rita Roberts, and Cynthia Mueller. "The Use of an Automated Nowcasting System to Forecast Flash Floods in an Urban Watershed." Journal of Hydrometeorology 7, no. 1 (February 1, 2006): 190–202. http://dx.doi.org/10.1175/jhm482.1.
Full textAbstract:
Abstract Flash flooding represents a significant hazard to human safety and a threat to property. Simulation and prediction of floods in complex urban settings requires high-resolution precipitation estimates and distributed hydrologic modeling. The need for reliable flash flood forecasting has increased in recent years, especially in urban communities, because of the high costs associated with flood occurrences. Several storm nowcast systems use radar to provide quantitative precipitation forecasts that can potentially afford great benefits to flood warning and short-term forecasting in urban settings. In this paper, the potential benefits of high-resolution weather radar data, physically based distributed hydrologic modeling, and quantitative precipitation nowcasting for urban hydrology and flash flood prediction were demonstrated by forcing a physically based distributed hydrologic model with precipitation forecasts made by a convective storm nowcast system to predict flash floods in a small, highly urbanized catchment in Denver, Colorado. Two rainfall events on 5 and 8 July 2001 in the Harvard Gulch watershed are presented that correspond to times during which the storm nowcast system was operated. Results clearly indicate that high-resolution radar-rainfall estimates and advanced nowcasting can potentially lead to improvements in flood warning and forecasting in urban watersheds, even for short-lived events on small catchments. At lead times of 70 min before the occurrence of peak discharge, forecast accuracies of approximately 17% in peak discharge and 10 min in peak timing were achieved for a 10 km2 highly urbanized catchment.
6
Poméon, Thomas, Niklas Wagner, Carina Furusho, Stefan Kollet, and Ricardo Reinoso-Rondinel. "Performance of a PDE-Based Hydrologic Model in a Flash Flood Modeling Framework in Sparsely-Gauged Catchments." Water 12, no. 8 (July 30, 2020): 2157. http://dx.doi.org/10.3390/w12082157.
Full textAbstract:
Modeling and nowcasting of flash floods remains challenging, mainly due to uncertainty of high-resolution spatial and temporal precipitation estimates, missing discharge observations of affected catchments and limitations of commonly used hydrologic models. In this study, we present a framework for flash flood hind- and nowcasting using the partial differential equation (PDE)-based ParFlow hydrologic model forced with quantitative radar precipitation estimates and nowcasts for a small 18.5 km2 headwater catchment in Germany. In the framework, an uncalibrated probabilistic modeling approach is applied. It accounts for model input uncertainty by forcing the model with precipitation inputs from different sources, and accounts for model parameter uncertainty by perturbing two spatially uniform soil hydraulic parameters. Thus, sources of uncertainty are propagated through the model and represented in the results. To demonstrate the advantages of the proposed framework, a commonly used conceptual model was applied over the same catchment for comparison. Results show the framework to be robust, with the uncalibrated PDE-based model matching streamflow observations reasonably. The model lead time was further improved when forced with precipitation nowcasts. This study successfully demonstrates a parsimonious application of the PDE-based ParFlow model in a flash flood hindcasting and nowcasting framework, which is of interest in applications to poorly or ungauged watersheds.
7
Spyrou, Christos, George Varlas, Aikaterini Pappa, Angeliki Mentzafou, Petros Katsafados, Anastasios Papadopoulos, Marios N. Anagnostou, and John Kalogiros. "Implementation of a Nowcasting Hydrometeorological System for Studying Flash Flood Events: The Case of Mandra, Greece." Remote Sensing 12, no. 17 (August 27, 2020): 2784. http://dx.doi.org/10.3390/rs12172784.
Full textAbstract:
Severe hydrometeorological hazards such as floods, droughts, and thunderstorms are expected to increase in the future due to climate change. Due to the significant impacts of these phenomena, it is essential to develop new and advanced early warning systems for advance preparation of the population and local authorities (civil protection, government agencies, etc.). Therefore, reliable forecasts of extreme events, with high spatial and temporal resolution and a very short time horizon are needed, due to the very fast development and localized nature of these events. In very short time-periods (up to 6 h), small-scale phenomena can be described accurately by adopting a “nowcasting” approach, providing reliable short-term forecasts and warnings. To this end, a novel nowcasting system was developed and presented in this study, combining a data assimilation system (LAPS), a large amount of observed data, including XPOL radar precipitation measurements, the Chemical Hydrological Atmospheric Ocean wave System (CHAOS), and the WRF-Hydro model. The system was evaluated on the catastrophic flash flood event that occurred in the sub-urban area of Mandra in Western Attica, Greece, on 15 November 2017. The event was one of the most catastrophic flash floods with human fatalities (24 people died) and extensive infrastructure damage. The update of the simulations with assimilated radar data improved the initial precipitation description and led to an improved simulation of the evolution of the phenomenon. Statistical evaluation and comparison with flood data from the FloodHub showed that the nowcasting system could have provided reliable early warning of the flood event 1, 2, and even to 3 h in advance, giving vital time to the local authorities to mobilize and even prevent fatalities and injuries to the local population.
8
Yoon, Seong-Sim. "Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting." Remote Sensing 11, no. 6 (March 16, 2019): 642. http://dx.doi.org/10.3390/rs11060642.
Full textAbstract:
Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term numerical weather prediction. As both methods have individual advantages and limitations, in this study we developed a new real-time blending technique to improve the accuracy of rainfall forecasts for hydrological applications. We tested the hydrological applicability of six QPFs used for urban flood forecasting in Seoul, South Korea: the McGill Algorithm for Prediction Nowcasting by Lagrangian Extrapolation (MAPLE), KOrea NOwcasting System (KONOS), Spatial-scale Decomposition method (SCDM), Unified Model Local Data Assimilation and Prediction System (UM LDAPS), and Advanced Storm-scale Analysis and Prediction System (ASAPS), as well as our proposed blended approach based on the assumption that the error of the previously predicted rainfall is similar to that of current predicted rainfall. We used the harmony search algorithm to optimize real-time weights that would minimize the root mean square error between predicted and observed rainfall for a 1 h lead time at 10 min intervals. We tested these models using the Storm Water Management Model (SWMM) and Grid-based Inundation Analysis Model (GIAM) to estimate urban flood discharge and inundation using rainfall from the QPFs as input. Although the blended QPF did not always have the highest correlation coefficient, its accuracy varied less than that of the other QPFs. In addition, its simulated water depth in pipe and spatial extent were most similar to observed inundated areas, demonstrating the value of this new approach for short-term flood forecasting.
9
Lovat, Alexane, Béatrice Vincendon, and Véronique Ducrocq. "Hydrometeorological evaluation of two nowcasting systems for Mediterranean heavy precipitation events with operational considerations." Hydrology and Earth System Sciences 26, no. 10 (May 23, 2022): 2697–714. http://dx.doi.org/10.5194/hess-26-2697-2022.
Full textAbstract:
Abstract. Heavy precipitation events and subsequent flash floods regularly affect the Mediterranean coastal regions. In these situations, forecasting rainfall and river discharges is crucial especially up to 6 h, which is a relevant lead time for emergency services in times of crisis. The present study investigates the hydrometeorological skills of two new nowcasting systems: a numerical weather model AROME-NWC and a nowcasting system blending numerical weather prediction and extrapolation of radar estimation called PIAF. Their performance is assessed for 10 past heavy precipitation events that occurred in southeastern France. Precipitation forecasts are evaluated at a 15- min time resolution and the availability times of forecasts, based on the operational Météo-France suites, are taken into account when performing the evaluation. Rainfall observations and forecasts were first compared using a point-to-point approach. Then the evaluation was conducted from an hydrological point of view, by comparing observed and forecast precipitation over watersheds affected by floods. In general, the results led to the same conclusions for both evaluations. On the very first lead times, up to 1 h 15 min and 1 h 30 min of forecast, the performance of PIAF was higher than AROME-NWC. For longer lead times (up to 3 h) their performances were generally equivalent. An assessment of river discharges simulated with the ISBA-TOP coupled system, which is dedicated to Mediterranean flash flood simulations and driven by AROME-NWC and PIAF rainfall forecasts, was also performed on two exceptional past flash flood events. The results obtained for these two events show that using AROME-NWC or PIAF rainfall forecasts is promising for flash flood forecasting in terms of peak intensity, timing, and first wave of discharge, with an anticipation of these phenomena that can reach several hours.
10
He, Ting, Chao Zhang, and Yi Zhang. "Radar based rainfall nowcasting and its characteristic prediction based on spatially correlated random field, normalized duration line and Kalman filter algorithm." MATEC Web of Conferences 246 (2018): 01028. http://dx.doi.org/10.1051/matecconf/201824601028.
Full textAbstract:
Rainfall is not only one of the most natural processes on the earth, but also an important factor of flood generation. Precise rainfall nowcasting can give an effective warning before hazards occur. This paper presented an ensemble nowcasting methodology which combined two deterministic nowcasting methods: PIV_Semi-Lagrangian and PIV_Lagrangian-Persistence and the spatial correlated random error field. For the deterministic nowcasting methods, the past velocity fields were estimated by Particle Image Velocimetry (PIV) method and the advection fields were extrapolated by Semi-Lagrange and Lagrange-Persistence schemes separately, then the forecasted errors at former time step were simulated by the spatially correlated random error field and were added to the next forecasting steps. Additionally, a predicting method for rain field property was proposed and a Kalman filter algorithm was also implemented for rain field’s centre of mass prediction. The methodology was applied to 8 historical rainfall events occurred in North Rhine Westphalia (NRW), Germany by using high-resolution rainfall data acquired from C-band Essen radar belonging German Weather Service (DWD). Results showed that the promoted ensemble nowcasting methods and the rain field property predicting methods improved the forecasting accuracy obviously which confirmed their effectiveness.
11
Mai, Xiongfa, Haiyan Zhong, and Ling Li. "Combination of XGBoost and PPLK method for improving the precipitation nowcasting." MATEC Web of Conferences 355 (2022): 03039. http://dx.doi.org/10.1051/matecconf/202235503039.
Full textAbstract:
The Precipitation nowcasting can provide high-resolution forecasts of rainfall and hydrometeors in 2 hours and play an important role in risk management for flash flood and debris flow events, but it is a very challenge work. This study proposed a new method which combine of XGBoost method and the PPLK model for precipitation nowcasting (XGB-PPLK). The proposed method was assessed in four different types of storms, and the experimental results show that the XGB-PPLK can improve the probability of detection (POD), as while as reducing the normalized mean square error (NMSE), and maintain the basically equivalent false-alarm ratio (FAR).
12
Šaur, David, and Lukáš Pavlík. "Comparison of accuracy of forecasting methods of convective precipitation." MATEC Web of Conferences 210 (2018): 04035. http://dx.doi.org/10.1051/matecconf/201821004035.
Full textAbstract:
This article is focused on the comparison of the accuracy of quantitative, numerical, statistical and nowcasting forecasting methods of convective precipitation including three flood events that occurred in the Zlin region in the years 2015 - 2017. Quantitative prediction is applied to the Algorithm of Storm Prediction for outputs “The probability of convective precipitation and The statistical forecast of convective precipitation”. The quantitative prediction of the probability of convective precipitation is primarily compared with the precipitation forecasts calculated by publicly available NWP models; secondary to statistical and nowcasting predictions. The statistical prediction is computed on the historical selection criteria and is intended as a complementary prediction to the first algorithm output. The nowcasting prediction operates with radar precipitation measurements, specifically with X-band meteorological radar outputs of the Zlín Region. Compared forecasting methods are used for the purposes of verification and configuration prediction parameters for accuracy increase of algorithm outputs.
13
Raffellini, Martina, Federica Martina, Francesco Silvestro, Francesca Giannoni, and Nicola Rebora. "Performance Evaluation of a Nowcasting Modelling Chain Operatively Employed in Very Small Catchments in the Mediterranean Environment for Civil Protection Purposes." Atmosphere 12, no. 6 (June 18, 2021): 783. http://dx.doi.org/10.3390/atmos12060783.
Full textAbstract:
The Hydro-Meteorological Centre (CMI) of the Environmental Protection Agency of Liguria Region, Italy, is in charge of the hydrometeorological forecast and the in-event monitoring for the region. This region counts numerous small and very small basins, known for their high sensitivity to intense storm events, characterised by low predictability. Therefore, at the CMI, a radar-based nowcasting modelling chain called the Small Basins Model Chain, tailored to such basins, is employed as a monitoring tool for civil protection purposes. The aim of this study is to evaluate the performance of this model chain, in terms of: (1) correct forecast, false alarm and missed alarm rates, based on both observed and simulated discharge threshold exceedances and observed impacts of rainfall events encountered in the region; (2) warning times respect to discharge threshold exceedances. The Small Basins Model Chain is proven to be an effective tool for flood nowcasting and helpful for civil protection operators during the monitoring phase of hydrometeorological events, detecting with good accuracy the location of intense storms, thanks to the radar technology, and the occurrence of flash floods.
14
Poletti, Maria Laura, Francesco Silvestro, Silvio Davolio, Flavio Pignone, and Nicola Rebora. "Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts." Hydrology and Earth System Sciences 23, no. 9 (September 18, 2019): 3823–41. http://dx.doi.org/10.5194/hess-23-3823-2019.
Full textAbstract:
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.
15
Li, Ling, Zhengwei He, Sheng Chen, Xiongfa Mai, Asi Zhang, Baoqing Hu, Zhi Li, and Xinhua Tong. "Subpixel-Based Precipitation Nowcasting with the Pyramid Lucas–Kanade Optical Flow Technique." Atmosphere 9, no. 7 (July 12, 2018): 260. http://dx.doi.org/10.3390/atmos9070260.
Full textAbstract:
Short-term high-resolution quantitative precipitation forecasting (QPF) is very important for flash-flood warning, navigation safety, and other hydrological applications. This paper proposes a subpixel-based QPF algorithm using a pyramid Lucas–Kanade optical flow technique (SPLK) for short-time rainfall forecast. The SPLK tracks the storm on the subpixel level by using the optical flow technique and then extrapolates the precipitation using a linear method through redistribution and interpolation. The SPLK compares with object-based and pixel-based nowcasting algorithms using eight thunderstorm events to assess its performance. The results suggest that the SPLK can perform better nowcasting of precipitation than the object-based and pixel-based algorithms with higher adequacy in tracking and predicting severe storms in 0–2 h lead-time forecasting.
16
Chitwatkulsiri, Detchphol, Hitoshi Miyamoto, Kim Neil Irvine, Sitang Pilailar, and Ho Huu Loc. "Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand." Water 14, no. 10 (May 20, 2022): 1641. http://dx.doi.org/10.3390/w14101641.
Full textAbstract:
In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations.
17
Wang, Cong, Ping Wang, Di Wang, Jinyi Hou, and Bing Xue. "Nowcasting Multicell Short-Term Intense Precipitation Using Graph Models and Random Forests." Monthly Weather Review 148, no. 11 (November 2020): 4453–66. http://dx.doi.org/10.1175/mwr-d-20-0050.1.
Full textAbstract:
AbstractShort-term intense precipitation (SIP; i.e., convective precipitation exceeding 20 mm h−1) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms.
18
Wang, Yadong, and Lin Tang. "A Short-Term Quantitative Precipitation Forecasting Approach Using Radar Data and a RAP Model." Geomatics 1, no. 2 (June 13, 2021): 310–23. http://dx.doi.org/10.3390/geomatics1020017.
Full textAbstract:
Very short-term (0~3 h) radar-based quantitative precipitation forecasting (QPF), also known as nowcasting, plays an essential role in flash flood warning, water resource management, and other hydrological applications. A novel nowcasting method combining radar data and a model wind field was developed and validated with two hurricane precipitation events. Compared with several existing nowcasting approaches, this work attempts to enhance the prediction capabilities from two major aspects. First, instead of using a radar reflectivity field, this work proposes the use of the rainfall rate field estimated from polarimetric radar variables in the motion field derivation. Second, the derived motion field is further corrected by the Rapid Refresh (RAP) model field. With the corrected motion field, the future rainfall rate field is predicted through a linear extrapolation method. The proposed method was validated using two hurricanes: Harvey and Irma. The proposed work shows an enhanced performance according to statistical scores. Compared with the model only and centroid-tracking only approaches, the average probability of detection (POD) increases about 25% and 50%; the average critical success index (CSI) increases about 20% and 37%; and the average false alarm rate (FAR) decreases about 14% and 16%, respectively.
19
Liechti, K., L. Panziera, U. Germann, and M. Zappa. "Flash-flood early warning using weather radar data: from nowcasting to forecasting." Hydrology and Earth System Sciences Discussions 10, no. 1 (January 25, 2013): 1289–331. http://dx.doi.org/10.5194/hessd-10-1289-2013.
Full textAbstract:
Abstract. This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel probabilistic radar-based forecasting chains for flash-flood early warning are investigated in three catchments in the Southern Swiss Alps and set in relation to deterministic discharge forecast for the same catchments. The first probabilistic radar-based forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second probabilistic forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialized with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. We found a clear preference for the probabilistic approach. Discharge forecasts perform better when forced by NORA rather than by a persistent radar QPE for lead times up to eight hours and for all discharge thresholds analysed. The best results were, however, obtained with the REAL-C2 forecasting chain, which was also remarkably skilful even with the highest thresholds. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.
20
Ishihara, Masahito. "Radar Echo Population of Air-Mass Thunderstorms and Nowcasting of Thunderstorm-Induced Local Heavy Rainfalls Part II: A Feasibility Study on Nowcasting." Journal of Disaster Research 8, no. 1 (February 1, 2013): 69–80. http://dx.doi.org/10.20965/jdr.2013.p0069.
Full textAbstract:
Many air-mass thunderstorms were generated in the Tokyo metropolitan area on August 5, 2008, when a severe local rainstorm caused a flash flood in the center of Tokyo. Using three-dimensional radar reflectivity data from the Japan Meteorological Agency (JMA), nowcasting was examined concerning the peak time and peak rainfall intensity of thunderstorms. Four qualitative forecastmethods – precipitation cores aloft, time changes in vertically integrated liquid water, time changes in echo-top height, lightning activity – and three quantitative forecast methods using three parameters were adopted in eight thunderstorms related to heavy-rainfall warnings issued by the JMA on August 5, 2008. While there is much worth further examination in the method using precipitation core aloft, the other methods are not in the stage of operational use in order to forecast time and rainfall intensity at the rainfall peak of each thunderstorm.
21
Panziera, Luca, Marco Gabella, Stefano Zanini, Alessandro Hering, Urs Germann, and Alexis Berne. "A radar-based regional extreme rainfall analysis to derive the thresholds for a novel automatic alert system in Switzerland." Hydrology and Earth System Sciences 20, no. 6 (June 15, 2016): 2317–32. http://dx.doi.org/10.5194/hess-20-2317-2016.
Full textAbstract:
Abstract. This paper presents a regional extreme rainfall analysis based on 10 years of radar data for the 159 regions adopted for official natural hazard warnings in Switzerland. Moreover, a nowcasting tool aimed at issuing heavy precipitation regional alerts is introduced. The two topics are closely related, since the extreme rainfall analysis provides the thresholds used by the nowcasting system for the alerts. Warm and cold seasons' monthly maxima of several statistical quantities describing regional rainfall are fitted to a generalized extreme value distribution in order to derive the precipitation amounts corresponding to sub-annual return periods for durations of 1, 3, 6, 12, 24 and 48 h. It is shown that regional return levels exhibit a large spatial variability in Switzerland, and that their spatial distribution strongly depends on the duration of the aggregation period: for accumulations of 3 h and shorter, the largest return levels are found over the northerly alpine slopes, whereas for longer durations the southern Alps exhibit the largest values. The inner alpine chain shows the lowest values, in agreement with previous rainfall climatologies. The nowcasting system presented here is aimed to issue heavy rainfall alerts for a large variety of end users, who are interested in different precipitation characteristics and regions, such as, for example, small urban areas, remote alpine catchments or administrative districts. The alerts are issued not only if the rainfall measured in the immediate past or forecast in the near future exceeds some predefined thresholds but also as soon as the sum of past and forecast precipitation is larger than threshold values. This precipitation total, in fact, has primary importance in applications for which antecedent rainfall is as important as predicted one, such as urban floods early warning systems. The rainfall fields, the statistical quantity representing regional rainfall and the frequency of alerts issued in case of continuous threshold exceedance are some of the configurable parameters of the tool. The analysis of the urban flood which occurred in the city of Schaffhausen in May 2013 suggests that this alert tool might have complementary skill with respect to radar-based thunderstorm nowcasting systems for storms which do not show a clear convective signature.
22
Silvestro, F., N. Rebora, and G. Cummings. "An attempt to deal with flash floods using a probabilistic hydrological nowcasting chain: a case study." Natural Hazards and Earth System Sciences Discussions 1, no. 6 (December 16, 2013): 7497–515. http://dx.doi.org/10.5194/nhessd-1-7497-2013.
Full textAbstract:
Abstract. The forecast of flash floods is sometimes impossible. In the last two decades, Numerical Weather Prediction Systems have become increasingly reliable with very relevant improvements in terms of quantitative precipitation forecasts. However, some types of events, those that are intense and localized in small areas, are still very difficult to predict. In many cases meteorological models fail to predict the volume of precipitable water at the large scale. Despite the application of modern probabilistic chains that uses precipitation downscaling algorithms in order to forecast the streamflow, some significant flood events remain unpredicted. This was also the case with an event which occurred on 8 and 9 June 2011 in the eastern part of the Liguria Region, Italy. This event affected in particular the Entella basin, which is quite a small watershed that flows into the Mediterranean Sea. The application of a hydrological nowcasting chain as a tool for predicting flash-floods in small and medium size basins with an anticipation time of a few hours (2–5) is here presented. This work investigated the "behaviour" of the chain in the cited event and how it could be exploited for operational purposes. The results in this particular case were encouraging.
23
Liechti, K., L. Panziera, U. Germann, and M. Zappa. "The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps." Hydrology and Earth System Sciences 17, no. 10 (October 10, 2013): 3853–69. http://dx.doi.org/10.5194/hess-17-3853-2013.
Full textAbstract:
Abstract. This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA) initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.
24
Le Bihan, Guillaume, Olivier Payrastre, Eric Gaume, David Moncoulon, and Frédéric Pons. "Regional models for distributed flash-flood nowcasting: towards an estimation of potential impacts and damages." E3S Web of Conferences 7 (2016): 18013. http://dx.doi.org/10.1051/e3sconf/20160718013.
Full text
25
Kann, A., G. Pistotnik, and B. Bica. "INCA-CE: a Central European initiative in nowcasting severe weather and its applications." Advances in Science and Research 8, no. 1 (April 5, 2012): 67–75. http://dx.doi.org/10.5194/asr-8-67-2012.
Full textAbstract:
Abstract. The INCA-CE (Integrated Nowcasting through Comprehensive Analysis – Central Europe) project aims at implementing a transnational weather information system as well as applications for different socio-economic sectors to reduce risks of major economic damage and loss of life caused by severe weather. Civil protection and also stakeholders from economic sectors are in a growing need of accurate and reliable short-term weather forecasts. Within INCA-CE, a state-of-the art nowcasting system (INCA) is implemented at weather services throughout the European Union's CE (Central Europe) Programme Area, providing analyses and short term forecasts to the aforementioned end-users. In a coherent approach, the INCA (Integrated Nowcasting through Comprehensive Analysis) system will be adapted for implementation and use in a number of partner countries. Within transregional working groups, the gap between short-term weather information and its downstream activities in hydrological disaster management, civil protection and road management will be bridged and best practice management and measure plans will be produced. A web-based platform for outreach to related socio-economic sectors will initiate and foster a dialogue between weather services and further stakeholders like tourism or the insurance sector, flood authorities for disaster management, and the construction industry for cost-efficient scheduling and planning. Furthermore, the project will produce a compact guideline for policy makers on how to combine structural development aspects with these new features. In the present paper, an outline of the project implementation, a short overview about the INCA system and two case studies on precipitation nowcasts will be given. Moreover, directions for further developments both within the INCA system and the INCA-CE project will be pointed out.
26
Alfieri, L., D. Velasco, and J. Thielen. "Flash flood detection through a multi-stage probabilistic warning system for heavy precipitation events." Advances in Geosciences 29 (March 1, 2011): 69–75. http://dx.doi.org/10.5194/adgeo-29-69-2011.
Full textAbstract:
Abstract. The deadly combination of short to no warning lead times and the vulnerability of urbanized areas makes flash flood events extremely dangerous for the modern society. This paper contributes to flash flood early warning by proposing a multi-stage warning system for heavy precipitation events based on threshold exceedances within a probabilistic framework. It makes use of meteorological products at different resolutions, namely, numerical weather predictions (NWP), radar-NWP blending, and radar nowcasting. The system is composed by two main modules. First, a European Precipitation Index based on a simulated Climatology (EPIC) and probabilistic weather forecasts is calculated to pinpoint catchments at risk of upcoming heavy precipitation. Then, a Probabilistic Flash Flood Guidance System (PFFGS) is activated at the regional scale and uses more accurate input data to reduce the estimation uncertainty. The system is tested for a high flow event occurred in Catalonia (Spain) in November 2008 and results from the different meteorological input data are compared and discussed. The strength of coupling the two systems is shown in its ability to detect areas potentially at risk of severe meteorological conditions and then monitoring the evolution by providing more accurate information with higher spatial-temporal resolution as the event approaches.
27
Picciotti, E., F. S. Marzano, E. N. Anagnostou, J. Kalogiros, Y. Fessas, A. Volpi, V. Cazac, et al. "Coupling X-band dual-polarized mini-radars and hydro-meteorological forecast models: the HYDRORAD project." Natural Hazards and Earth System Sciences 13, no. 5 (May 16, 2013): 1229–41. http://dx.doi.org/10.5194/nhess-13-1229-2013.
Full textAbstract:
Abstract. Hydro-meteorological hazards like convective outbreaks leading to torrential rain and floods are among the most critical environmental issues world-wide. In that context weather radar observations have proven to be very useful in providing information on the spatial distribution of rainfall that can support early warning of floods. However, quantitative precipitation estimation by radar is subjected to many limitations and uncertainties. The use of dual-polarization at high frequency (i.e. X-band) has proven particularly useful for mitigating some of the limitation of operational systems, by exploiting the benefit of easiness to transport and deploy and the high spatial and temporal resolution achievable at small antenna sizes. New developments on X-band dual-polarization technology in recent years have received the interest of scientific and operational communities in these systems. New enterprises are focusing on the advancement of cost-efficient mini-radar network technology, based on high-frequency (mainly X-band) and low-power weather radar systems for weather monitoring and hydro-meteorological forecasting. Within the above context, the main objective of the HYDRORAD project was the development of an innovative \\mbox{integrated} decision support tool for weather monitoring and hydro-meteorological applications. The integrated system tool is based on a polarimetric X-band mini-radar network which is the core of the decision support tool, a novel radar products generator and a hydro-meteorological forecast modelling system that ingests mini-radar rainfall products to forecast precipitation and floods. The radar products generator includes algorithms for attenuation correction, hydrometeor classification, a vertical profile reflectivity correction, a new polarimetric rainfall estimators developed for mini-radar observations, and short-term nowcasting of convective cells. The hydro-meteorological modelling system includes the Mesoscale Model 5 (MM5) and the Army Corps of Engineers Hydrologic Engineering Center hydrologic and hydraulic modelling chain. The characteristics of this tool make it ideal to support flood monitoring and forecasting within urban environment and small-scale basins. Preliminary results, carried out during a field campaign in Moldova, showed that the mini-radar based hydro-meteorological forecasting system can constitute a suitable solution for local flood warning and civil flood protection applications.
28
Barr, S. L., S. Johnson, X. Ming, M. Peppa, N. Dong, Z. Wen, C. Robson, et al. "FLOOD-PREPARED: A NOWCASTING SYSTEM FOR REAL-TIME IMPACT ADAPTION TO SURFACE WATER FLOODING IN CITIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences VI-4/W2-2020 (September 15, 2020): 9–15. http://dx.doi.org/10.5194/isprs-annals-vi-4-w2-2020-9-2020.
Full textAbstract:
Abstract. Extreme rainfall events pose an ever increasing threat to cities due to the potential for surface water flooding resulting in damage to properties and major disruption of transport systems. Modern sensor networks offer enormous potential for the real-time monitoring of urban systems and potentially allow improved situational awareness of impeding hazards and their impacts such as flooding. However, monitoring in itself is not enough if we are to be able to adapt in in real-time to hazards. Systems are required that allow analytics and models, that feed of real-time observations, to make predictions of impacts and suggest adaption options ahead of the hazard event. The Flood-PREPARED project is developing a system for real-time adaption to surface water flooding. The system comprises of advanced spatiotemporal models of rainfall, surface water flooding and road traffic impacts. These models are linked and orchestrated within into a Big Data workflow that allows events to be simulated using emerging rainfall data recorded by a short range weather radar. This approach allows nowcasting to be undertaken where predictions of surface water inundation and impacts on the road network can be predicted ahead of the rainfall event reaching the city; thus providing the ability for an improved adaptive response to the actual event.
29
Schmid, W., S. Mecklenburg, and J. Joss. "Short-term risk forecasts of heavy rainfall." Water Science and Technology 45, no. 2 (January 1, 2002): 121–25. http://dx.doi.org/10.2166/wst.2002.0036.
Full textAbstract:
Methodologies for risk forecasts of severe weather hardly exist on the scale of nowcasting (0–3 hours). Here we discuss short-term risk forecasts of heavy precipitation associated with local thunderstorms. We use COTREC/RainCast: a procedure to extrapolate radar images into the near future. An error density function is defined using the estimated error of location of the extrapolated radar patterns. The radar forecast is folded (“smeared”) with the density function, leading to a probability distribution of radar intensities. An algorithm to convert the radar intensities into values of precipitation intensity provides the desired probability (or risk) of heavy rainfall at any position within the considered window in space and time. We discuss, as an example, a flood event from summer 2000.
30
Hartke, Samantha H., Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li. "Incorporation of Satellite Precipitation Uncertainty in a Landslide Hazard Nowcasting System." Journal of Hydrometeorology 21, no. 8 (August 1, 2020): 1741–59. http://dx.doi.org/10.1175/jhm-d-19-0295.1.
Full textAbstract:
AbstractMany existing models that predict landslide hazards utilize ground-based sources of precipitation data. In locations where ground-based precipitation observations are limited (i.e., a vast majority of the globe), or for landslide hazard models that assess regional or global domains, satellite multisensor precipitation products offer a promising near-real-time alternative to ground-based data. NASA’s global Landslide Hazard Assessment for Situational Awareness (LHASA) model uses the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) product to issue hazard “nowcasts” in near–real time for areas that are currently at risk for landsliding. Satellite-based precipitation estimates, however, can contain considerable systematic bias and random error, especially over mountainous terrain and during extreme rainfall events. This study combines a precipitation error modeling framework with a probabilistic adaptation of LHASA. Compared with the routine version of LHASA, this probabilistic version correctly predicts more of the observed landslides in the study region with fewer false alarms by high hazard nowcasts. This study demonstrates that improvements in landslide hazard prediction can be achieved regardless of whether the IMERG error model is trained using abundant ground-based precipitation observations or using far fewer and more scattered observations, suggesting that the approach is viable in data-limited regions. Results emphasize the importance of accounting for both random error and systematic satellite precipitation bias. The approach provides an example of how environmental prediction models can incorporate satellite precipitation uncertainty. Other applications such as flood and drought monitoring and forecasting could likely benefit from consideration of precipitation uncertainty.
31
Smith, Brianne K., James Smith, and Mary Lynn Baeck. "Flash Flood–Producing Storm Properties in a Small Urban Watershed." Journal of Hydrometeorology 17, no. 10 (October 1, 2016): 2631–47. http://dx.doi.org/10.1175/jhm-d-16-0070.1.
Full textAbstract:
Abstract The structure and evolution of flash flood–producing storms over a small urban watershed in the mid-Atlantic United States with a prototypical flash flood response is examined. Lagrangian storm properties are investigated through analyses of the 32 storms that produced the largest peak discharges in Moores Run between January 2000 and May 2014. The Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) algorithm is used to track storm characteristics over their life cycle with a focus on storm size, movement, intensity, and location. First, the 13 June 2003 and 1 June 2006 storms, which produced the two largest peak discharges for the study period, are analyzed. Heavy rainfall for the 13 June 2003 and 1 June 2006 storms were caused by a collapsing thunderstorm cell and a slow-moving, low-echo centroid storm. Analyses of the 32 storms show that collapsing storm cells play an important role in peak rainfall rate production and flash flooding. Storm motion is predominantly southwest-to-northeast, and approximately half of the storms exhibited some linear organization. Mean storm total rainfall for the 32 storms displayed an asymmetric distribution around Moores Run, with sharply decreasing gradients southwest of the watershed (upwind and into the city) and increased rainfall to the northeast (downwind and away from the city). Results indicate urban modification of rainfall in flash flood–producing storms. There was no evidence that the storms split around Baltimore. Flood-producing rainfall was highly concentrated in time; on average, approximately 21% of the storm total rainfall fell within 15 min.
32
de Coning, E., and E. R. Poolman. "South African Weather Service operational satellite based precipitation estimation technique: applications and improvements." Hydrology and Earth System Sciences Discussions 7, no. 6 (November 12, 2010): 8837–71. http://dx.doi.org/10.5194/hessd-7-8837-2010.
Full textAbstract:
Abstract. Extreme weather related to heavy or more frequent precipitation events seem to be a likely possibility for the future of our planet. While precipitation measurements can be done by means of rain gauges, the obvious disadvantages of point measurements are driving meteorologists towards remotely sensed precipitation methods. In South Africa more sophisticated and expensive nowcasting technology such as radar and lightning networks are available, supported by a fairly dense rain gauge network of about 1500 gauges. In the rest of southern Africa rainfall measurements are more difficult to obtain. The availability of the local version of the Unified Model and the Meteosat Second Generation satellite data make these products ideal components of precipitation measurement in data sparse regions such as Africa. In this article the local version of the Hydroestimator (originally from NOAA/NESDIS) is discussed as well as its applications for precipitation measurement in this region. Hourly accumulations of the Hydroestimator are currently used as a satellite based precipitation estimator for the South African Flash Flood Guidance system. However, the Hydroestimator is by no means a perfect representation of the real rainfall. In this study the Hydroestimator and the stratiform rainfall field from the Unified Model are both bias corrected and then combined into a new precipitation field which can feed into the South African Flash Flood Guidance system. This new product should provide a more accurate and comprehensive input to the Flash Flood Guidance systems in South Africa as well as southern Africa. In this way the southern African region where data is sparse and very few radars are available can have access to more accurate flash flood guidance.
33
Na and Yoo. "Optimize Short-Term Rainfall Forecast with Combination of Ensemble Precipitation Nowcasts by Lagrangian Extrapolation." Water 11, no. 9 (August 22, 2019): 1752. http://dx.doi.org/10.3390/w11091752.
Full textAbstract:
The rainfall forecasts currently available in Korea are not sufficiently accurate to bedirectly applied to the flash flood warning system or urban flood warning system. As the lead timeincreases, the quality becomes even lower. In order to overcome this problem, this study proposesan ensemble forecasting method. The proposed method considers all available rainfall forecasts asensemble members at the target time. The ensemble members are combined based on the weightedaverage method, where the weights are determined by applying the two conditions of theunbiasedness and minimum error variance. The proposed method is tested with McGill Algorithmfor Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) rainfall forecasts for four stormevents that occurred during the summers of 2016 and 2017 in Korea. In Korea, rainfall forecasts aregenerated every 10 min up to six hours, i.e., there are always a total of 36 sets of rainfall forecasts.As a result, it is found that just six ensemble members is sufficient to make the ensemble forecast.Considering additional ensemble members beyond six does not significantly improve the quality ofthe ensemble forecast. The quality of the ensemble forecast is also found to be better than that of thesingle forecast, and the weighted average method is found to be better than the simple arithmeticaverage method.
34
de Coning, Estelle. "Optimizing Satellite-Based Precipitation Estimation for Nowcasting of Rainfall and Flash Flood Events over the South African Domain." Remote Sensing 5, no. 11 (November 4, 2013): 5702–24. http://dx.doi.org/10.3390/rs5115702.
Full text
35
Belachsen, Idit, Francesco Marra, Nadav Peleg, and Efrat Morin. "Convective rainfall in a dry climate: relations with synoptic systems and flash-flood generation in the Dead Sea region." Hydrology and Earth System Sciences 21, no. 10 (October 12, 2017): 5165–80. http://dx.doi.org/10.5194/hess-21-5165-2017.
Full textAbstract:
Abstract. Spatiotemporal patterns of rainfall are important characteristics that influence runoff generation and flash-flood magnitude and require high-resolution measurements to be adequately represented. This need is further emphasized in arid climates, where rainfall is scarce and highly variable. In this study, 24 years of corrected and gauge-adjusted radar rainfall estimates are used to (i) identify the spatial structure and dynamics of convective rain cells in a dry climate region in the Eastern Mediterranean, (ii) to determine their climatology, and (iii) to understand their relation with the governing synoptic systems and with flash-flood generation. Rain cells are extracted using a segmentation method and a tracking algorithm, and are clustered into three synoptic patterns according to atmospheric variables from the ERA-Interim reanalysis. On average, the cells are about 90 km2 in size, move 13 m s−1 from west to east, and live for 18 min. The Cyprus low accounts for 30 % of the events, the low to the east of the study region for 44 %, and the Active Red Sea Trough for 26 %. The Active Red Sea Trough produces shorter rain events composed of rain cells with higher rain intensities, longer lifetime, smaller area, and lower velocities. The area of rain cells is positively correlated with topographic height. The number of cells is negatively correlated with the distance from the shoreline. Rain-cell intensity is negatively correlated with mean annual precipitation. Flash-flood-related events are dominated by rain cells of large size, low velocity, and long lifetime that move downstream with the main axis of the catchments. These results can be further used for stochastic simulations of convective rain storms and serve as input for hydrological models and for flash-flood nowcasting systems.
36
de Coning, E., and E. Poolman. "South African Weather Service operational satellite based precipitation estimation technique: applications and improvements." Hydrology and Earth System Sciences 15, no. 4 (April 6, 2011): 1131–45. http://dx.doi.org/10.5194/hess-15-1131-2011.
Full textAbstract:
Abstract. Extreme weather related to heavy or more frequent precipitation events seem to be a likely possibility for the future of our planet. While precipitation measurements can be done by means of rain gauges, the obvious disadvantages of point measurements are driving meteorologists towards remotely sensed precipitation methods. In South Africa more sophisticated and expensive nowcasting technology such as radar and lightning networks are available, supported by a fairly dense rain gauge network of about 1500 daily gauges. In the rest of southern Africa rainfall measurements are more difficult to obtain. The local version of the Unified Model and the Meteosat Second Generation satellite data are ideal components of precipitation estimation in data sparse regions such as Africa. In South Africa hourly accumulations of the Hydroestimator (originally from NOAA/NESDIS) are currently used as a satellite based precipitation estimator for the South African Flash Flood Guidance system, especially in regions which are not covered by radar. In this study the Hydroestimator and the stratiform rainfall field from the Unified Model are both bias corrected and then combined into a new precipitation field. The new product was tested over a two year period and provides a more accurate and comprehensive input to the Flash Flood Guidance systems in the data sparse southern Africa. Future work will include updating the period over which bias corrections were calculated.
37
Bouget, Vincent, Dominique Béréziat, Julien Brajard, Anastase Charantonis, and Arthur Filoche. "Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting." Remote Sensing 13, no. 2 (January 13, 2021): 246. http://dx.doi.org/10.3390/rs13020246.
Full textAbstract:
Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risk monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 min. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls.
38
Jurczyk, Anna, Jan Szturc, Irena Otop, Katarzyna Ośródka, and Piotr Struzik. "Quality-Based Combination of Multi-Source Precipitation Data." Remote Sensing 12, no. 11 (May 27, 2020): 1709. http://dx.doi.org/10.3390/rs12111709.
Full textAbstract:
A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.
39
Rotach, M. W., M. Arpagaus, M. Dorninger, C. Hegg, A. Montani, and R. Ranzi. "Uncertainty propagation for flood forecasting in the Alps: different views and impacts from MAP D-PHASE." Natural Hazards and Earth System Sciences 12, no. 8 (August 3, 2012): 2439–48. http://dx.doi.org/10.5194/nhess-12-2439-2012.
Full textAbstract:
Abstract. D-PHASE was a Forecast Demonstration Project of the World Weather Research Programme (WWRP) related to the Mesoscale Alpine Programme (MAP). Its goal was to demonstrate the reliability and quality of operational forecasting of orographically influenced (determined) precipitation in the Alps and its consequences on the distribution of run-off characteristics. A special focus was, of course, on heavy-precipitation events. The D-PHASE Operations Period (DOP) ran from June to November~2007, during which an end-to-end forecasting system was operated covering many individual catchments in the Alps, with their water authorities, civil protection organizations or other end users. The forecasting system's core piece was a Visualization Platform where precipitation and flood warnings from some 30 atmospheric and 7 hydrological models (both deterministic and probabilistic) and corresponding model fields were displayed in uniform and comparable formats. Also, meteograms, nowcasting information and end user communication was made available to all the forecasters, users and end users. D-PHASE information was assessed and used by some 50 different groups ranging from atmospheric forecasters to civil protection authorities or water management bodies. In the present contribution, D-PHASE is briefly presented along with its outstanding scientific results and, in particular, the lessons learnt with respect to uncertainty propagation. A focus is thereby on the transfer of ensemble prediction information into the hydrological community and its use with respect to other aspects of societal impact. Objective verification of forecast quality is contrasted to subjective quality assessments during the project (end user workshops, questionnaires) and some general conclusions concerning forecast demonstration projects are drawn.
40
Bacelar, Luiz Carlos Salgueiro Donato. "Avaliação da chuva prevista em curto prazo por radar meteorológico." Ciência e Natura 40 (May 11, 2018): 42. http://dx.doi.org/10.5902/2179460x29995.
Full textAbstract:
This article presents the first evaluation of the method used by the Brazilian Centre for Monitoring and Early Warning of NaturalDisasters for the short term forecast (nowcasting) of precipitation by weather radar. In this evaluation, four cases of intenserainfall in the radius of Pico do Couto radar were studied, giving the flood risk alerts sent for the municipality of Nova Friburgo,in the state of Rio de Janeiro. In relation to rain gauge data, a median under rate of 43% for the radar precipitation wasobserved when using the SRI (Surface Rainfall Intensity) method. After the rainfall correction, the cross-correlation extrapolationmethod was evaluated for the 30, 60, 90 and 120 minute forecast horizons, with accumulated precipitation every 30 minutes. Theprobabilities of detection (POD) obtained higher values for lower rainfall thresholds (at least 1 or 5 mm of accumulated rainfall)when compared to larger amount accumulated (20 and 30 mm). The spatial and temporal series of rainfall presented higher errors(MAE and RMSE) for 90 and 120 minutes of forecast. For all the events, an overestimate (PBIAS positive) of the predictions wasobserved in relation to the observed radar rain field itself.
41
Wu, Dali, Li Wu, Tao Zhang, Wenxuan Zhang, Jianqiang Huang, and Xiaoying Wang. "Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model." Atmosphere 13, no. 12 (November 24, 2022): 1963. http://dx.doi.org/10.3390/atmos13121963.
Full textAbstract:
Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced PredRNN-V2. We introduce the self-attention mechanism and the long-term memory state into the model and design a new set of gating mechanisms. To better capture different intensities of precipitation, the loss function with weights was designed. We further train the model using a combination of reverse scheduled sampling and scheduled sampling to learn the long-term dynamics from the radar echo sequences. Experimental results show that the new model (ISA-PredRNN) can effectively extract the spatiotemporal features of radar echo maps and obtain radar echo prediction results with a small gap from the ground truths. From the comparison with the other six models, the new ISA-PredRNN model has the most accurate prediction results with a critical success index (CSI) of 0.7001, 0.5812 and 0.3052 under the radar echo thresholds of 10 dBZ, 20 dBZ and 30 dBZ, respectively.
42
Antonetti, Manuel, Christoph Horat, Ioannis V. Sideris, and Massimiliano Zappa. "Ensemble flood forecasting considering dominant runoff processes – Part 1: Set-up and application to nested basins (Emme, Switzerland)." Natural Hazards and Earth System Sciences 19, no. 1 (January 7, 2019): 19–40. http://dx.doi.org/10.5194/nhess-19-19-2019.
Full textAbstract:
Abstract. Flash floods evolve rapidly during and after heavy precipitation events and represent a potential risk for society. To predict the timing and magnitude of a peak runoff, it is common to couple meteorological and hydrological models in a forecasting chain. However, hydrological models rely on strong simplifying assumptions and hence need to be calibrated. This makes their application difficult in catchments where no direct observation of runoff is available. To address this gap, a flash-flood forecasting chain is presented based on (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip); (ii) meteorological data from state-of-the-art numerical weather prediction models (COSMO-1, COSMO-E); (iii) operationally available soil moisture estimations from the PREVAH hydrological model; and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes from the so-called maps of runoff types, which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is event-based and parametrised a priori based on the results of sprinkling experiments. This prediction chain has been evaluated using data from April to September 2016 in the Emme catchment, a medium-sized flash-flood-prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of runoff types, which allowed sensitivity of the forecast performance to the mapping approaches to be analysed. Furthermore, special emphasis was placed on the predictive power of the new forecasting chains in nested subcatchments when compared with a prediction chain including an original version of the runoff generation module of PREVAH calibrated for one event. Results showed a low sensitivity of the predictive power to the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of runoff types with high involvement of expert knowledge did not guarantee more skill. In the larger basins of the Emme region, process-based forecasting chains revealed comparable skill to a prediction system including a conventional hydrological model. In the small nested subcatchments, although the process-based forecasting chains outperformed the original runoff generation module, no forecasting chain showed satisfying skill in the sense that it could be useful for decision makers. Despite the short period available for evaluation, preliminary outcomes of this study show that operational flash-flood predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.
43
Yussouf, Nusrat, John S. Kain, and Adam J. Clark. "Short-Term Probabilistic Forecasts of the 31 May 2013 Oklahoma Tornado and Flash Flood Event Using a Continuous-Update-Cycle Storm-Scale Ensemble System." Weather and Forecasting 31, no. 3 (June 1, 2016): 957–83. http://dx.doi.org/10.1175/waf-d-15-0160.1.
Full textAbstract:
Abstract A continuous-update-cycle storm-scale ensemble data assimilation (DA) and prediction system using the ARW model and DART software is used to generate retrospective 0–6-h ensemble forecasts of the 31 May 2013 tornado and flash flood event over central Oklahoma, with a focus on the prediction of heavy rainfall. Results indicate that the model-predicted probabilities of strong low-level mesocyclones correspond well with the locations of observed mesocyclones and with the observed damage track. The ensemble-mean quantitative precipitation forecast (QPF) from the radar DA experiments match NCEP’s stage IV analyses reasonably well in terms of location and amount of rainfall, particularly during the 0–3-h forecast period. In contrast, significant displacement errors and lower rainfall totals are evident in a control experiment that withholds radar data during the DA. The ensemble-derived probabilistic QPF (PQPF) from the radar DA experiment is more skillful than the PQPF from the no_radar experiment, based on visual inspection and probabilistic verification metrics. A novel object-based storm-tracking algorithm provides additional insight, suggesting that explicit assimilation and 1–2-h prediction of the dominant supercell is remarkably skillful in the radar experiment. The skill in both experiments is substantially higher during the 0–3-h forecast period than in the 3–6-h period. Furthermore, the difference in skill between the two forecasts decreases sharply during the latter period, indicating that the impact of radar DA is greatest during early forecast hours. Overall, the results demonstrate the potential for a frequently updated, high-resolution ensemble system to extend probabilistic low-level mesocyclone and flash flood forecast lead times and improve accuracy of convective precipitation nowcasting.
44
Li, Dawei, Yudi Liu, and Chaohui Chen. "MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data." Geoscientific Model Development 14, no. 6 (June 29, 2021): 4019–34. http://dx.doi.org/10.5194/gmd-14-4019-2021.
Full textAbstract:
Abstract. Eastern China is one of the most economically developed and densely populated areas in the world. Due to its special geographical location and climate, eastern China is affected by different weather systems, such as monsoons, shear lines, typhoons, and extratropical cyclones. In the near future, the rainfall rate becomes difficult to predict precisely due to these systems. Traditional physics-based methods such as numerical weather prediction (NWP) tend to perform poorly on nowcasting problems due to the spin-up issue. Moreover, various meteorological stations are distributed in this region, generating a large amount of observation data every day, which have great potential for application to data-driven methods. Thus, it is important to train a data-driven model from scratch that is suitable for the specific weather situation of eastern China. However, due to the high degrees of freedom and nonlinearity of machine learning algorithms, it is difficult to add physical constraints. Therefore, with the intention of using various kinds of data as a proxy for physical constraints, we collected three kinds of data (radar, satellite, and precipitation data) in the flood season from 2017 to 2018 in this area and preprocessed them into tensors (256×256) that cover eastern China with a domain of 12.8×12.8∘. The developed multisource data model (MSDM) combines the optical flow, random forest, and convolutional neural network (CNN) algorithms. It treats the precipitation nowcasting task as an image-to-image problem, which takes radar and satellite data with an interval of 30 min as inputs and predicts radar echo intensity with a lead time of 30 min. To reduce the smoothing caused by convolutions, we use the optical flow algorithm to predict satellite data in the following 120 min. The predicted radar echoes from the MSDM together with satellite data from the optical flow algorithm are recursively implemented in the MSDM to achieve a 120 min lead time. The MSDM predictions are comparable to those of other baseline models with a high temporal resolution of 6 min. To solve blurry image problems, we applied a modified structural similarity (SSIM) index as a loss function. Furthermore, we use the random forest algorithm with predicted radar and satellite data to estimate the rainfall rate, and the results outperform those of the traditional, nonlinear radar reflectivity factor and rainfall rate (Z–R) relationships that use logarithmic functions. The experiments confirm that machine learning with multisource data provides more reasonable predictions and reveals a better nonlinear relationship between radar echo and precipitation rate. Apart from developing complicated machine learning algorithms, exploiting the potential of multisource data will yield more improvements.
45
Sokol, Zbyněk, Jan Szturc, Johanna Orellana-Alvear, Jana Popová, Anna Jurczyk, and Rolando Célleri. "The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review." Remote Sensing 13, no. 3 (January 20, 2021): 351. http://dx.doi.org/10.3390/rs13030351.
Full textAbstract:
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.
46
Guardiola-Albert, Carolina, Carlos Rivero-Honegger, Robert Monjo, Andrés Díez-Herrero, Carlos Yagüe, Jose María Bodoque, and Francisco J. Tapiador. "Automated convective and stratiform precipitation estimation in a small mountainous catchment using X-band radar data in Central Spain." Journal of Hydroinformatics 19, no. 2 (November 29, 2016): 315–30. http://dx.doi.org/10.2166/hydro.2016.225.
Full textAbstract:
For the purposes of weather nowcasting, flood risk monitoring and water resources assessment, it is often difficult to achieve a reliable spatio-temporal representation of rainfall due to a low rain gauge network density. However, quantitative precipitation estimation (QPE) has acquired new prospects with the introduction of weather radars, thanks to their higher spatio-temporal resolution. Although a wide number of QPE algorithms are available for using C-band radar data, only a few studies have employed X-band radar. In this study the microscale rainfall variability in a small catchment is automatically measured using short-range X-band radar variograms and classifying precipitation into convective and stratiform types with a recently published index. The aim is to apply a straightforward geostatistical algorithm, named ordinary kriging of radar errors (OKRE), to integrate X-band radar and rain gauge measurements in a mountainous catchment (15 km2) in central Spain. As expected, convective events presented higher estimation errors due to their complex spatial and temporal variability. Despite this fact, errors are sufficiently small and results are reliable rainfall estimations. The two main contributions of this work are the adaptation of the OKRE method to small spatial scales and its application automatically differentiating between convective and stratiform events.
47
Sierra-Lorenzo, Maibys, Jose Medina, Juana Sille, Adrián Fuentes-Barrios, Shallys Alfonso-Águila, and Tania Gascon. "Verification by Multiple Methods of Precipitation Forecast from HDRFFGS and SisPI Tools during the Impact of the Tropical Storm Isaias over the Dominican Republic." Atmosphere 13, no. 3 (March 19, 2022): 495. http://dx.doi.org/10.3390/atmos13030495.
Full textAbstract:
During 2020, the Dominican Republic received the impact of several tropical organisms. Among those that generated the greatest losses in the country, tropical storm Isaias stands out because of the significant precipitation (327.6 mm at Sabana del Mar during 29–31 July 2020) and flooding it caused. The study analyzes the behavior of the products of the Flash Flood Guidance System (FFGS) and the Nowcasting and Very Short Range Prediction System (Spanish acronym SisPI) for the quantitative precipitation forecast (QPF) of the precipitation generated by Isaias on 30 July 2020 over the Dominican Republic. Traditional categorical verification and featured-based spatial verification methods are used in the study, taking as observation the quantitative precipitation estimation of GPM. The results show that both numerical weather prediction systems are powerful tools for QPF and also to contribute to the prevention and mitigation of disasters caused by the extreme hydro-meteorological event analyzed. For the forecast of rain occurrence, the HIRESW-NMMB product of FFGS presented the highest ability with a CSI greater than 0.4. The HIRESW-ARW and SisPI products not only presented high rates of false alarms but also performed better in forecasting heavy rain values. The results of the verification based on objects with the MODE are consistent with those obtained in the verification by categories. The HIRESW-NMMB product underestimated the intense rainfall values by approximately 60 mm, while HIRESW-ARW and SisPI tools presented minor differences, the latter being the one with the greatest skill.
48
Herrnegger, M., H. P. Nachtnebel, and K. Schulz. "From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution." Hydrology and Earth System Sciences 19, no. 11 (November 23, 2015): 4619–39. http://dx.doi.org/10.5194/hess-19-4619-2015.
Full textAbstract:
Abstract. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. In contrast, the most reliable hydrological information available refers to runoff, which in the presented work is used as input for an inverted HBV-type rainfall–runoff model that is embedded in a root finding algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value closely matching the observed runoff. The inverse model is applied and tested to the Schliefau and Krems catchments, situated in the northern Austrian Alpine foothills. The correlations between inferred rainfall and station observations in the proximity of the catchments are of similar magnitude compared to the correlations between station observations and independent INCA (Integrated Nowcasting through Comprehensive Analysis) rainfall analyses provided by the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). The cumulative precipitation sums also show similar dynamics. The application of the inverse model is a promising approach to obtain additional information on mean areal rainfall. This additional information is not solely limited to the simulated hourly data but also includes the aggregated daily rainfall rates, which show a significantly higher correlation to the observed values. Potential applications of the inverse model include gaining additional information on catchment rainfall for interpolation purposes, flood forecasting or the estimation of snowmelt contribution. The application is limited to (smaller) catchments, which can be represented with a lumped model setup, and to the estimation of liquid rainfall.
49
Berenguer, Marc, Carles Corral, Rafael Sánchez-Diezma, and Daniel Sempere-Torres. "Hydrological Validation of a Radar-Based Nowcasting Technique." Journal of Hydrometeorology 6, no. 4 (August 1, 2005): 532–49. http://dx.doi.org/10.1175/jhm433.1.
Full textAbstract:
Abstract Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs generated with the model using the entire series of radar measurements. In both cases, results obtained by a simpler nowcasting technique are used as a reference to evaluate improvements. Validation showed that precipitation fields forecasted with S-PROG seem to be better than fields forecasted using simpler techniques. Additionally, hydrological validation led the authors to point out that the use of radar-based nowcasting techniques allows the anticipation window in which flow estimates are forecasted with enough quality to be sensibly extended.
50
Choi, Yeji, Keumgang Cha, Minyoung Back, Hyunguk Choi, and Taegyun Jeon. "RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations." Remote Sensing 13, no. 18 (September 11, 2021): 3627. http://dx.doi.org/10.3390/rs13183627.
Full textAbstract:
Quantitative precipitation prediction is essential for managing water-related disasters, including floods, landslides, tsunamis, and droughts. Recent advances in data-driven approaches using deep learning techniques provide improved precipitation nowcasting performance. Moreover, it has been known that multi-modal information from various sources could improve deep learning performance. This study introduces the RAIN-F+ dataset, which is the fusion dataset for rainfall prediction, and proposes the benchmark models for precipitation prediction using the RAIN-F+ dataset. The RAIN-F+ dataset is an integrated weather observation dataset including radar, surface station, and satellite observations covering the land area over the Korean Peninsula. The benchmark model is developed based on the U-Net architecture with residual upsampling and downsampling blocks. We examine the results depending on the number of the integrated dataset for training. Overall, the results show that the fusion dataset outperforms the radar-only dataset over time. Moreover, the results with the radar-only dataset show the limitations in predicting heavy rainfall over 10 mm/h. This suggests that the various information from multi-modality is crucial for precipitation nowcasting when applying the deep learning method.