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Journal articles on the topic 'Precipitation forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 March 2023

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1

Collier, Chris G., and Roman Kzyzysztofowicz. "Quantitative precipitation forecasting." Journal of Hydrology 239, no. 1-4 (December 2000): 1–2. http://dx.doi.org/10.1016/s0022-1694(00)00389-9.

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2

Browning, K. A. "Quantitative Precipitation Forecasting." Weather 58, no. 3 (March 1, 2003): 126–27. http://dx.doi.org/10.1256/wea.245.02.

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3

Asgari-Motlagh, Xaniyar, Mehdi Ketabchy, and Ali Daghighi. "Probabilistic Quantitative Precipitation Forecasting Using Machine Learning Methods and Probable Maximum Precipitation." International Academic Journal of Science and Engineering 06, no. 01 (June 4, 2019): 1–14. http://dx.doi.org/10.9756/iajse/v6i1/1910001.

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4

Krzysztofowicz, Roman, and Chris G. Collier. "Quantitative Precipitation Forecasting II." Journal of Hydrology 288, no. 1-2 (March 2004): 1. http://dx.doi.org/10.1016/j.jhydrol.2003.11.007.

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5

Tao, P., S. Tie-Yuan, Y. Zhi-Yuan, and W. Jun-Chao. "Application of quantitative precipitation forecasting and precipitation ensemble prediction for hydrological forecasting." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 96–101. http://dx.doi.org/10.5194/piahs-368-96-2015.

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Abstract. The precipitation in the forecast period influences flood forecasting precision, due to the uncertainty of the input to the hydrological model. Taking the ZhangHe basin as the example, the research adopts the precipitation forecast and ensemble precipitation forecast product of the AREM model, uses the Xin Anjiang hydrological model, and tests the flood forecasts. The results show that the flood forecast result can be clearly improved when considering precipitation during the forecast period. Hydrological forecast based on Ensemble Precipitation prediction gives better hydrological forecast information, better satisfying the need for risk information for flood prevention and disaster reduction, and has broad development opportunities.
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6

Adams, Neil. "Precipitation Forecasting at High Latitudes." Weather and Forecasting 19, no. 2 (April 2004): 456–72. http://dx.doi.org/10.1175/1520-0434(2004)019<0456:pfahl>2.0.co;2.

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7

Morelli, S., and R. Santangelo. "Statistical forecasting of daily precipitation." Il Nuovo Cimento C 12, no. 2 (March 1989): 139–49. http://dx.doi.org/10.1007/bf02523787.

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8

Tsanis, Ioannis K., Paulin Coulibaly, and Ioannis N. Daliakopoulos. "Improving groundwater level forecasting with a feedforward neural network and linearly regressed projected precipitation." Journal of Hydroinformatics 10, no. 4 (October 1, 2008): 317–30. http://dx.doi.org/10.2166/hydro.2008.006.

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A module that uses neural networks was developed for forecasting the groundwater changes in an aquifer. A modified standard Feedforward Neural Network (FNN), trained with the Levenberg–Marquardt (LM) algorithm with five input variables (precipitation, temperature, runoff, groundwater level and specific yield) with a deterministic component, is used. The deterministic component links precipitation with the seasonal recharge of the aquifer and projects the seasonal average precipitations. A new algorithm is applied to forecast the groundwater level changes in Messara Valley, Crete, Greece, where groundwater level has been steadily decreasing due to overexploitation during the last 20 years. Results from the new algorithm show that the introduction of specific yield improved the groundwater level forecasting marginally but the linearly projected precipitation component drastically increased the window of forecasting up to 30 months, equivalent to five biannual time-steps.
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9

Krzysztofowicz, Roman, and Thomas A. Pomroy. "Disaggregative Invariance of Daily Precipitation." Journal of Applied Meteorology 36, no. 6 (June 1, 1997): 721–34. http://dx.doi.org/10.1175/1520-0450-36.6.721.

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Abstract Disaggregative invariance refers to stochastic independence between the total precipitation amount and its temporal disaggregation. This property is investigated herein for areal average and point precipitation amounts accumulated over a 24-h period and disaggregated into four 6-h subperiods. Statistical analyses of precipitation records from 1948 to 1993 offer convincing empirical evidence against the disaggregative invariance and in favor of the conditional disaggregative invariance, which arises when the total amount and its temporal disaggregation are conditioned on the timing of precipitation within the diurnal cycle. The property of conditional disaggregative invariance allows the modeler or the forecaster to decompose the problem of quantitative precipitation forecasting into three tasks: (i) forecasting the precipitation timing; (ii) forecasting the total amount, conditional on timing; and (iii) forecasting the temporal disaggregation, conditional on timing. Tasks (ii) and (iii) can be performed independently of one another, and this offers a formidable advantage for applications.
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10

Silva, Renato Ramos da, Adilson Wagner Gandú, Julia Clarinda Cohen, Paulo Kuhn, and Maria Aurora Mota. "Weather forecasting for Eastern Amazon with OLAM model." Revista Brasileira de Meteorologia 29, spe (December 2014): 11–22. http://dx.doi.org/10.1590/0102-778620130026.

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The OLAM model has as its characteristics the advantage to represent simultaneously the global and regional meteorological phenomena using the application of a grid refinement scheme. During the REMAM project the model was applied for a few case studies to evaluate its performance on numerical weather prediction for the eastern Amazon region. Case studies were performed for the twelve months of the year of 2009. The model results for those numerical experiments were compared with the observed data for the region of study. Precipitation data analysis showed that OLAM is able to represent the average mean accumulated precipitation and the seasonal features of the events occurrence, but can't predict the local total amount of precipitation. However, individual evaluation for a few cases had shown that OLAM was able to represent the dynamics and forecast a few days in advance the development of coastal meteorological systems such as the squall lines that are one of the most important precipitating systems of the Amazon.
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11

Xu, Lei, Nengcheng Chen, Chao Yang, Hongchu Yu, and Zeqiang Chen. "Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning." Hydrology and Earth System Sciences 26, no. 11 (June 14, 2022): 2923–38. http://dx.doi.org/10.5194/hess-26-2923-2022.

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Abstract. Precipitation forecasting is an important mission in weather science. In recent years, data-driven precipitation forecasting techniques could complement numerical prediction, such as precipitation nowcasting, monthly precipitation projection and extreme precipitation event identification. In data-driven precipitation forecasting, the predictive uncertainty arises mainly from data and model uncertainties. Current deep learning forecasting methods could model the parametric uncertainty by random sampling from the parameters. However, the data uncertainty is usually ignored in the forecasting process and the derivation of predictive uncertainty is incomplete. In this study, the input data uncertainty, target data uncertainty and model uncertainty are jointly modeled in a deep learning precipitation forecasting framework to estimate the predictive uncertainty. Specifically, the data uncertainty is estimated a priori and the input uncertainty is propagated forward through model weights according to the law of error propagation. The model uncertainty is considered by sampling from the parameters and is coupled with input and target data uncertainties in the objective function during the training process. Finally, the predictive uncertainty is produced by propagating the input uncertainty in the testing process. The experimental results indicate that the proposed joint uncertainty modeling framework for precipitation forecasting exhibits better forecasting accuracy (improving RMSE by 1 %–2 % and R2 by 1 %–7 % on average) relative to several existing methods, and could reduce the predictive uncertainty by ∼28 % relative to the approach of Loquercio et al. (2020). The incorporation of data uncertainty in the objective function changes the distributions of model weights of the forecasting model and the proposed method can slightly smooth the model weights, leading to the reduction of predictive uncertainty relative to the method of Loquercio et al. (2020). The predictive accuracy is improved in the proposed method by incorporating the target data uncertainty and reducing the forecasting error of extreme precipitation. The developed joint uncertainty modeling method can be regarded as a general uncertainty modeling approach to estimate predictive uncertainty from data and model in forecasting applications.
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12

Tian, Di, Eric F. Wood, and Xing Yuan. "CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States." Hydrology and Earth System Sciences 21, no. 3 (March 9, 2017): 1477–90. http://dx.doi.org/10.5194/hess-21-1477-2017.

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Abstract. This paper explored the potential of a global climate model for sub-seasonal forecasting of precipitation and 2 m air temperature. The categorical forecast skill of 10 precipitation and temperature indices was investigated using the 28-year sub-seasonal hindcasts from the Climate Forecast System version 2 (CFSv2) over the contiguous United States (CONUS). The forecast skill for mean precipitation and temperature as well as for the frequency and duration of extremes was highly dependent on the forecasting indices, regions, seasons, and leads. Forecasts for 7- and 14-day temperature indices showed skill even at weeks 3 and 4, and generally were more skillful than precipitation indices. Overall, temperature indices showed higher skill than precipitation indices over the entire CONUS region at sub-seasonal scale. While the forecast skill related to mean precipitations was low in summer over the CONUS, the number of rainy days, number of consecutive rainy days, and number of consecutive dry days showed considerably high skill for the western coastal region. The presence of active Madden–Julian Oscillation (MJO) events improved CFSv2 weekly mean precipitation forecast skill over most parts of the CONUS, but it did not necessarily improve the weekly mean temperature forecasts. The 30-day forecasts of precipitation and temperature indices calculated from the downscaled monthly CFSv2 forecasts were less skillful than those calculated directly from CFSv2 daily forecasts, suggesting the usefulness of CFSv2 for sub-seasonal hydrological forecasting.
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13

Kumar, K. Krishna, and M. K. Soman. "Quantitative precipitation forecasting over Narmada Catchment." Journal of Earth System Science 102, no. 2 (June 1993): 313–28. http://dx.doi.org/10.1007/bf02861506.

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14

Hall, Tony, Harold E. Brooks, and Charles A. Doswell. "Precipitation Forecasting Using a Neural Network." Weather and Forecasting 14, no. 3 (June 1999): 338–45. http://dx.doi.org/10.1175/1520-0434(1999)014<0338:pfuann>2.0.co;2.

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15

Golding, B. W. "Quantitative precipitation forecasting in the UK." Journal of Hydrology 239, no. 1-4 (December 2000): 286–305. http://dx.doi.org/10.1016/s0022-1694(00)00354-1.

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16

Kim, Tae-Woong. "Monthly precipitation forecasting using rescaling errors." KSCE Journal of Civil Engineering 10, no. 2 (March 2006): 137–43. http://dx.doi.org/10.1007/bf02823932.

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17

Kumar, Deepak, Anshuman Singh, Pijush Samui, and Rishi Kumar Jha. "Forecasting monthly precipitation using sequential modelling." Hydrological Sciences Journal 64, no. 6 (April 17, 2019): 690–700. http://dx.doi.org/10.1080/02626667.2019.1595624.

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18

Fox, Neil I., and James W. Wilson. "Very short period quantitative precipitation forecasting." Atmospheric Science Letters 6, no. 1 (January 2005): 7–11. http://dx.doi.org/10.1002/asl.83.

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19

Yucel, I., and A. Onen. "Evaluating the extreme precipitation events using a mesoscale atmosphere model and satellite based precipitation product." Natural Hazards and Earth System Sciences Discussions 1, no. 6 (December 4, 2013): 6979–7014. http://dx.doi.org/10.5194/nhessd-1-6979-2013.

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Abstract. Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance of the Weather Research and Forecasting (WRF) model and the Multi Precipitation Estimates (MPE) algorithm in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in the West Black Sea Region of Turkey. Precipitations derived from WRF model with and without three-dimensional variational (3-DVAR) data assimilation scheme and MPE algorithm at high spatial resolution (4 km) are compared with gauge precipitation. WRF-derived precipitation showed capabilities in capturing the timing of precipitation extremes and in some extent the spatial distribution and magnitude of the heavy rainfall events wheras MPE showed relatively weak skills in these aspects. WRF skills in estimating such precipitation characteristics are enhanced with the application of 3-DVAR scheme. Direct impact of data assimilation on WRF precipitation reached to 12% and at some points there exists quantitative match for heavy rainfall events, which are critical for hydrological forecast.
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20

Mwangi, E., F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger. "Forecasting droughts in East Africa." Hydrology and Earth System Sciences 18, no. 2 (February 18, 2014): 611–20. http://dx.doi.org/10.5194/hess-18-611-2014.

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Abstract. The humanitarian crises caused by the recent droughts (2008–2009 and 2010–2011) in East Africa have illustrated that the ability to make accurate drought forecasts with sufficient lead time is essential. The use of dynamical model precipitation forecasts in combination with drought indices, such as the Standardized Precipitation Index (SPI), can potentially lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for March–May and October–December rain seasons when evaluated against measurements from the available in situ stations from East Africa. The forecast for October–December rain season has higher skill than for the March–May season. ECMWF forecasts add value to the consensus forecasts produced during the Greater Horn of Africa Climate Outlook Forum (GHACOF), which is the present operational product for precipitation forecast over East Africa. Complementing the original ECMWF precipitation forecasts with SPI provides additional information on the spatial extent and intensity of the drought event.
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21

Vincendon, B., V. Ducrocq, O. Nuissier, and B. Vié. "Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting." Natural Hazards and Earth System Sciences 11, no. 5 (May 23, 2011): 1529–44. http://dx.doi.org/10.5194/nhess-11-1529-2011.

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Abstract. Mediterranean intense weather events often lead to devastating flash-floods. Extending the forecasting lead times further than the watershed response times, implies the use of numerical weather prediction (NWP) to drive hydrological models. However, the nature of the precipitating events and the temporal and spatial scales of the watershed response make them difficult to forecast, even using a high-resolution convection-permitting NWP deterministic forecasting. This study proposes a new method to sample the uncertainties of high-resolution NWP precipitation forecasts in order to quantify the predictability of the streamflow forecasts. We have developed a perturbation method based on convection-permitting NWP-model error statistics. It produces short-term precipitation ensemble forecasts from single-value meteorological forecasts. These rainfall ensemble forecasts are then fed into a hydrological model dedicated to flash-flood forecasting to produce ensemble streamflow forecasts. The verification on two flash-flood events shows that this forecasting ensemble performs better than the deterministic forecast. The performance of the precipitation perturbation method has also been found to be broadly as good as that obtained using a state-of-the-art research convection-permitting NWP ensemble, while requiring less computing time.
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22

Gascón, Estíbaliz, Tim Hewson, and Thomas Haiden. "Improving Predictions of Precipitation Type at the Surface: Description and Verification of Two New Products from the ECMWF Ensemble." Weather and Forecasting 33, no. 1 (January 4, 2018): 89–108. http://dx.doi.org/10.1175/waf-d-17-0114.1.

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Abstract The medium-range ensemble (ENS) from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) is used to create two new products intended to face the challenges of winter precipitation-type forecasting. The products themselves are a map product that represents which precipitation type is most likely whenever the probability of precipitation is &gt;50% (also including information on lower probability outcomes) and a meteogram product, showing the temporal evolution of the instantaneous precipitation-type probabilities for a specific location, classified into three categories of precipitation rate. A minimum precipitation rate is also used to distinguish dry from precipitating conditions, setting this value according to type, in order to try to enforce a zero frequency bias for all precipitation types. The new products differ from other ECMWF products in three important respects: first, the input variable is discretized, rather than continuous; second, the postprocessing increases the output information content; and, third, the map-based product condenses information into a more accessible format. The verification of both products was developed using four months’ worth of 3-hourly observations of present weather from manual surface synoptic observation (SYNOPs) in Europe during the 2016/17 winter period. This verification shows that the IFS is highly skillful when forecasting rain and snow, but only moderately skillful for freezing rain and rain and snow mixed, while the ability to predict the occurrence of ice pellets is negligible. Typical outputs are also illustrated via a freezing-rain case study, showing interesting changes with lead time.
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23

Liu, Yan Ping, Yong Wang, and Zhen Wang. "RBF Prediction Model Based on EMD for Forecasting GPS Precipitable Water Vapor and Annual Precipitation." Advanced Materials Research 765-767 (September 2013): 2830–34. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2830.

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The forecast of precipitations is important in meteorology and atmospheric sciences. A new model is proposed based on empirical mode decomposition and the RBF neural network. Firstly, GPS PWV time series is broken down into series of different scales intrinsic mode function. Secondly, the phase space reconstruction is done. Thirdly, each component is predicted by RBF. Finally, the final prediction value is reconstructed. Next, the model is tested on annual precipitation sequence from 2001 to 2010 in northeast China. The result shows that predictive value is close to the actual precipitation, which can better reflect the actual precipitation change. From 2001 to 2010, the maximum deviation of the predicted values never exceeds 4%. The testing results show that the proposed model can increase precipitation forecasting accuracies not only in GPS PWV but also in annual precipitation.
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Sun, Xiao, Shi Fan Qiao, and Ji Ren Xie. "The Study of Precipitation Forecast Model on EMD-RBF Neural Network - A Case Study on Northeast China." Applied Mechanics and Materials 641-642 (September 2014): 119–22. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.119.

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Based on the principal of forecast of Artificial Neural Network, Radial Basis Function neural network and Radial Basis Function neural network based on EMD were introduced into the field of precipitation forecasting in this article. With the precipitation data of 27 sites from1950-2010, EMD-RBF network was set up, and the difference between the predictive value and the actual precipitation data was discussed. The results showed that the correlation Of EMD-RBF forecast precipitation and actual precipitation is more than 0.9. Of all sites, the maximum relative prediction error of 17 sites is less than 10%, the maximum relative error does not exceed 15%.The EMD-RBF model had good quality on forecasting precision, which provided a new method for precipitation forecasting.
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Du, Jinglin, Yayun Liu, and Zhijun Liu. "Study of Precipitation Forecast Based on Deep Belief Networks." Algorithms 11, no. 9 (September 4, 2018): 132. http://dx.doi.org/10.3390/a11090132.

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Due to the impact of weather forecasting on global human life, and to better reflect the current trend of weather changes, it is necessary to conduct research about the prediction of precipitation and provide timely and complete precipitation information for climate prediction and early warning decisions to avoid serious meteorological disasters. For the precipitation prediction problem in the era of climate big data, we propose a new method based on deep learning. In this paper, we will apply deep belief networks in weather precipitation forecasting. Deep belief networks transform the feature representation of data in the original space into a new feature space, with semantic features to improve the predictive performance. The experimental results show, compared with other forecasting methods, the feasibility of deep belief networks in the field of weather forecasting.
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Schroeder de Witt, Christian, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Alfredo Kalaitzis, Matthew Chantry, Duncan Watson-Parris, and Piotr Bilinski. "RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 14902–10. http://dx.doi.org/10.1609/aaai.v35i17.17749.

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Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce RainBench, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of relevant meteorological data from the ERA5 reanalysis product, and IMERG precipitation data. We also release PyRain, a library to process large precipitation datasets efficiently. We present an extensive analysis of our novel dataset and establish baseline results for two benchmark medium-range precipitation forecasting tasks. Finally, we discuss existing data-driven weather forecasting methodologies and suggest future research avenues.
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27

He, J. Y., and X. G. Zhu. "Precipitation distribution and forecasting in hurricane ‘Hermine’." IOP Conference Series: Earth and Environmental Science 626 (January 7, 2021): 012013. http://dx.doi.org/10.1088/1755-1315/626/1/012013.

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28

Gahrs, Gregory E., Scott Applequist, Richard L. Pfeffer, and Xu-Feng Niu. "Improved Results for Probabilistic Quantitative Precipitation Forecasting*." Weather and Forecasting 18, no. 5 (October 2003): 879–90. http://dx.doi.org/10.1175/1520-0434(2003)018<0879:irfpqp>2.0.co;2.

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Du, Jun, Steven L. Mullen, and Frederick Sanders. "Short-Range Ensemble Forecasting of Quantitative Precipitation." Monthly Weather Review 125, no. 10 (October 1997): 2427–59. http://dx.doi.org/10.1175/1520-0493(1997)125<2427:srefoq>2.0.co;2.

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30

Iriany, A., D. Rosyida, A. D. Sulistyono, and B. N. Ruchjana. "Precipitation forecasting using neural network model approach." IOP Conference Series: Earth and Environmental Science 458 (April 4, 2020): 012020. http://dx.doi.org/10.1088/1755-1315/458/1/012020.

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31

Reeves, Heather Dawn, Kimberly L. Elmore, Alexander Ryzhkov, Terry Schuur, and John Krause. "Sources of Uncertainty in Precipitation-Type Forecasting." Weather and Forecasting 29, no. 4 (July 22, 2014): 936–53. http://dx.doi.org/10.1175/waf-d-14-00007.1.

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Abstract Five implicit precipitation-type algorithms are assessed using observed and model-forecast sounding data in order to measure their accuracy and to gauge the effects of model uncertainty on algorithm performance. When applied to observed soundings, all algorithms provide very reliable guidance on snow and rain (SN and RA). However, their skills for ice pellets and freezing rain (IP and FZRA) are comparatively low. Most misclassifications of IP are for FZRA and vice versa. Deeper investigation reveals that no method used in any of the algorithms to differentiate between IP and FZRA allows for clear discrimination between the two forms. The effects of model uncertainty are also considered. For SN and RA, these effects are minimal and each algorithm performs reliably. Conversely, IP and FZRA are strongly impacted. When the range of uncertainty is fully accounted for, their resulting wet-bulb temperature profiles are nearly indistinguishable, leading to very poor skill for all algorithms. Although currently available data do not allow for a thorough investigation, comparison of the statistics from only those soundings that are associated with long-duration, horizontally uniform regions of FZRA shows there are significant differences between these profiles and those that are from more transient, highly variable environments. Hence, a five-category (SN, RA, IP, FZRA, and IP–FZRA mix) approach is advocated to differentiate between sustained regions of horizontally uniform FZRA (or IP) from more mixed environments.
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Solgi, Abazar, Vahid Nourani, and Mohammad Bagherian Marzouni. "Evaluation of nonlinear models for precipitation forecasting." Hydrological Sciences Journal 62, no. 16 (November 29, 2017): 2695–704. http://dx.doi.org/10.1080/02626667.2017.1392529.

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Wu, Yajing, Xuebing Yang, Wensheng Zhang, and Qiuming Kuang. "Mixture probabilistic model for precipitation ensemble forecasting." Quarterly Journal of the Royal Meteorological Society 145, no. 725 (September 13, 2019): 3516–34. http://dx.doi.org/10.1002/qj.3637.

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Wu, Xiaodan, Cao Hongxing, Andrew Flitman, Wei Fengying, and Feng Guolin. "Forecasting Monsoon Precipitation Using Artificial Neural Networks." Advances in Atmospheric Sciences 18, no. 5 (September 2001): 950–58. http://dx.doi.org/10.1007/bf03403515.

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35

Cancelliere, A., G. Di Mauro, B. Bonaccorso, and G. Rossi. "Drought forecasting using the Standardized Precipitation Index." Water Resources Management 21, no. 5 (December 12, 2006): 801–19. http://dx.doi.org/10.1007/s11269-006-9062-y.

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Li, Ji, Yangbo Chen, Huanyu Wang, Jianming Qin, Jie Li, and Sen Chiao. "Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model." Hydrology and Earth System Sciences 21, no. 2 (March 2, 2017): 1279–94. http://dx.doi.org/10.5194/hess-21-1279-2017.

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Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1–15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.
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Dong, Ningpeng, Mingxiang Yang, Jianqiu Li, and Shaokui Hao. "Flood Forecasting with Merged Satellite Precipitation and Hydrologic Model." E3S Web of Conferences 350 (2022): 01029. http://dx.doi.org/10.1051/e3sconf/202235001029.

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Flood forecasting has been an effective way to reduce the potential flood hazards for a sustainable socio-economy. However, the lack of in-situ precipitation records has limited the applicability of flood forecasting with hydrologic models in poorly gauged basins. To address this problem, we aim to develop a flood forecasting framework based on the merged satellite precipitation and a hydrologic model. The framework was then applied to a small basin in the upper Lequan River Basin, Hainan, China for flood forecasting experiments. Results indicate that the combination of merged satellite precipitation and hydrologic model can generally well reproduce the past major flood events occurred in the basin. Our approaches are expected to provide new insights into the flood forecasting in small and poorly gauged basins and can be used to support the sustainable development of the socio-economy.
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Wang, Hua, Wenchuan Wang, Yujin Du, and Dongmei Xu. "Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction." Water 13, no. 15 (July 21, 2021): 1997. http://dx.doi.org/10.3390/w13151997.

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Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.
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39

MOHAMMED SALISU, ALFA. "FORECASTING DROUGHT WITH ARIMA MODEL AND STANDARDIZED PRECIPITATION INDEX (SPI)." Science Proceedings Series 1, no. 2 (April 24, 2019): 32–34. http://dx.doi.org/10.31580/sps.v1i2.616.

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Drought forecasting is an important forecasting procedure for preparing and managing water resources for all creatures. Natural disasters across the regions such as flooding, earthquakes, droughts etc. have caused damages to life as a result of which numerous researches have been conducted to assist in reducing the phenomenon. Consequently, therefore, this study considered using Auto-Regressive Integrated Moving Average (ARIMA) model in forecasting drought using Standardized Precipitation Index (SPI) as a forecasting tool which was used to measure and classify drought. The models are developed to forecast the SPI series. Results indicated the forecasting ability of the ARIMA models which increases as the timescales. The study is aimed at using ARIMA method for modeling SPI data series. The studies used data set made up of 624 months, obtained from 1954 to 2008. In the analysis only SPI3 series was non-seasonal while others have seasonality and Seasonal ARIMA was carried out, SPI12 was significant compared with the forecasting accuracy alongside the diagnostic checking having a minimum error of RMSE and MAE in both testing and training phases. The research contributes to the discovering of feasible forecasting of drought and demonstrates that the established model is good and appropriate for forecasting drought.
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40

Mwangi, E., F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger. "Forecasting droughts in East Africa." Hydrology and Earth System Sciences Discussions 10, no. 8 (August 8, 2013): 10209–30. http://dx.doi.org/10.5194/hessd-10-10209-2013.

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Abstract. The humanitarian crisis caused by the recent droughts (2008–2009 and 2010–2011) in the East African region have illustrated that the ability to make accurate drought predictions with adequate lead time is essential. The use of dynamical model forecasts and drought indices, such as Standardized Precipitation Index (SPI), promises to lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for both rain seasons when evaluated against measurements from the available in-situ stations from East Africa. The October–December rain season has higher skill that the March–May season. ECMWF forecasts add value to the statistical forecasts produced during the Greater Horn of Africa Climate Outlook Forums (GHACOF) which is the present operational product. Complementing the raw precipitation forecasts with SPI provides additional information on the spatial extend and intensity of the drought event.
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41

SEN, P. N. "A mathematical model for QPF for flood Forecasting purposes." MAUSAM 42, no. 2 (February 28, 2022): 201–4. http://dx.doi.org/10.54302/mausam.v42i2.3152.

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A mathematical, model for Quantitative Precipitation Forecasting (QPF) has been developed on the basis of physical and dynamical laws. The surface and upper air meteorological observations have been used as inputs in the model. The output is the rate of precipitation from which the amount of precipitation can be computed time integration. The model can be used operationally for rainfall forecasting.
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42

Valdez, Emixi Sthefany, François Anctil, and Maria-Helena Ramos. "Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems." Hydrology and Earth System Sciences 26, no. 1 (January 14, 2022): 197–220. http://dx.doi.org/10.5194/hess-26-197-2022.

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Abstract. This study aims to decipher the interactions of a precipitation post-processor and several other tools for uncertainty quantification implemented in a hydrometeorological forecasting chain. We make use of four hydrometeorological forecasting systems that differ by how uncertainties are estimated and propagated. They consider the following sources of uncertainty: system A, forcing, system B, forcing and initial conditions, system C, forcing and model structure, and system D, forcing, initial conditions, and model structure. For each system's configuration, we investigate the reliability and accuracy of post-processed precipitation forecasts in order to evaluate their ability to improve streamflow forecasts for up to 7 d of forecast horizon. The evaluation is carried out across 30 catchments in the province of Quebec (Canada) and over the 2011–2016 period. Results are compared using a multicriteria approach, and the analysis is performed as a function of lead time and catchment size. The results indicate that the precipitation post-processor resulted in large improvements in the quality of forecasts with regard to the raw precipitation forecasts. This was especially the case when evaluating relative bias and reliability. However, its effectiveness in terms of improving the quality of hydrological forecasts varied according to the configuration of the forecasting system, the forecast attribute, the forecast lead time, and the catchment size. The combination of the precipitation post-processor and the quantification of uncertainty from initial conditions showed the best results. When all sources of uncertainty were quantified, the contribution of the precipitation post-processor to provide better streamflow forecasts was not remarkable, and in some cases, it even deteriorated the overall performance of the hydrometeorological forecasting system. Our study provides an in-depth investigation of how improvements brought by a precipitation post-processor to the quality of the inputs to a hydrological forecasting model can be cancelled along the forecasting chain, depending on how the hydrometeorological forecasting system is configured and on how the other sources of hydrological forecasting uncertainty (initial conditions and model structure) are considered and accounted for. This has implications for the choices users might make when designing new or enhancing existing hydrometeorological ensemble forecasting systems.
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43

Rosyida, Diana, Atiek Iiriany, and Nurjannah Nurjannah. "GSTAR-X-SUR Model with Neural Network Approach on Residuals." CAUCHY 5, no. 4 (June 17, 2019): 203. http://dx.doi.org/10.18860/ca.v5i4.5647.

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<p class="Abstract">One of the models that combine time and inter-location elements is Generalized Space Time Autoregressive (GSTAR) model. GSTAR model involving exogenous variables is GSTARX model. The exogenous variables which are used in GSTAR model can be both metrical and non-metrical data. Exogenous variable that can be applied into the forecasting of precipitation is non-metrical data which is in a form of precipitation intensity of a certain location. Currently, precipitation possesses patterns and characteristics difficult to identify, and thus can be interpreted as non-linear phenomenon. Non-linear model which is much developed now is neural network. Parameter estimation method employed is Seemingly Unrelated Regression (SUR) model approach, which can solve the correlation between residual models. This current research employed GSTARX-SUR modelling with neural network approach on residuals. The data used in this research were the records of 10-day precipitations in four regions in West Java, namely Cisondari, Lembang, Cianjur, and Gunung Mas, from 2005 to 2015. The GSTARX-SUR NN modelling resulted in precipitation deviation average of the forecast and the actual data at 4.1385 mm. This means that this model can be used as an alternative in forecasting precipitation.<strong> </strong></p>
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44

Buzzi, A., S. Davolio, P. Malguzzi, O. Drofa, and D. Mastrangelo. "Heavy rainfall episodes over Liguria in autumn 2011: numerical forecasting experiments." Natural Hazards and Earth System Sciences 14, no. 5 (May 26, 2014): 1325–40. http://dx.doi.org/10.5194/nhess-14-1325-2014.

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Abstract. The Liguria coastal region in Italy was affected by two heavy rainfall episodes and subsequent severe flooding that occurred at the end of October and the beginning of November 2011. In both cases, the very large accumulated precipitation maxima were associated with intense and quasi-stationary convective systems that developed near the coast, both related to orographic lift and similar low-level mesoscale flow patterns over the Ligurian Sea, giving rise to pronounced convergence lines. This study aims at analysing the main dynamical processes responsible for the onset, lifecycle, intensity and localisation/propagation of the precipitating systems, using the ISAC convection-permitting model MOLOCH applied at different spatial resolutions and comparing model output fields with available observations. The ability of the model in quantitative precipitation forecasting (QPF) is tested with respect to initial conditions and model horizontal resolution. Although precipitation maxima remain underestimated in the model experiments, it is shown that errors in QPF in both amount and position tend to decrease with increasing grid resolution. It is shown that model accuracy in forecasting rainfall amounts and localisation of the precipitating systems critically depends on the ability to represent the cold air outflow from the Po Valley to the Ligurian Sea, which determines the position and intensity of the mesoscale convergence lines over the sea. Such convergence lines controls, together with the lifting produced by the Apennines chain surrounding the coast, the onset of the severe convection.
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45

Buzzi, A., S. Davolio, P. Malguzzi, O. Drofa, and D. Mastrangelo. "Heavy rainfall episodes over Liguria of autumn 2011: numerical forecasting experiments." Natural Hazards and Earth System Sciences Discussions 1, no. 6 (December 5, 2013): 7093–135. http://dx.doi.org/10.5194/nhessd-1-7093-2013.

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Abstract. The Liguria coastal region in Italy was affected by two heavy rainfall and consequent severe flood episodes that occurred at the end of October and beginning of November 2011. The very large accumulated precipitation maxima were associated, in both cases, with intense and quasi-stationary convective systems developed near the coast, both related to orographic lift and similar low-level mesoscale flow patterns over the Ligurian Sea, giving rise to pronounced convergence lines. This study aims at analyzing the main dynamical processes responsible for the onset, lifecycle, intensity and localization/propagation of the precipitating systems, using the ISAC convection-permitting model MOLOCH applied at different spatial resolutions and comparing model output fields with available observations. The ability of the model in forecasting quantitative precipitation (QPF) is tested with respect to initial analysis and model horizontal resolution. Although precipitation maxima remain underestimated in the model experiments, it is shown that forecast errors of QPF in both amount and position tend to decrease with increasing grid resolution. It is shown that model accuracy in forecasting rainfall amounts and localization of the precipitating systems critically depends, in both episodes, on the ability in representing the cold air outflow from the Po Valley to the Ligurian Sea, which determines the position and intensity of the mesoscale convergence lines over the sea. Such convergence lines controls, together with the lifting produced by the Apennines chain surrounding the coast, the onset of the severe convection.
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46

Han, Ji-Young, and Song-You Hong. "Precipitation Forecast Experiments Using the Weather Research and Forecasting (WRF) Model at Gray-Zone Resolutions." Weather and Forecasting 33, no. 6 (November 14, 2018): 1605–16. http://dx.doi.org/10.1175/waf-d-18-0026.1.

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Abstract In the Weather Research and Forecasting (WRF) community, a standard model setup at a grid size smaller than 5 km excludes cumulus parameterization (CP), although it is unclear how to determine a cutoff grid size where convection permitting can be assumed adequate. Also, efforts to improve high-resolution precipitation forecasts in the range of 1–10 km (the so-called gray zone for parameterized precipitation physics) have recently been made. In this study, we attempt to statistically evaluate the skill of a gray-zone CP with a focus on the quantitative precipitation forecast (QPF) in the summertime. A WRF Model simulation with the gray-zone simplified Arakawa–Schubert (GSAS) CP at 3-km spatial resolution over East Asia is evaluated for the summer of 2013 and compared with the results from a conventional setup without CP. A statistical evaluation of the 3-month simulations shows that the GSAS demonstrates a typical distribution of the QPF skill, with high (low) scores and bias in the light (heavy) precipitation category. The WRF without CP seriously suppresses light precipitation events, but its skill for heavier categories is better. Meanwhile, a new set of precipitation data, which is simply averaged precipitation from the two simulations, demonstrates the best skill in all precipitation categories. Bearing in mind that high-resolution QPF requires essential challenges in model components, along with complexity in precipitating convection mechanisms over geographically different regions, this proposed method can serve as an alternative for improving the QPF for practical usage.
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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.

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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.
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48

Milléo, Carla, and Ricardo Carvalho de Almeida. "Application of RBF artificial neural networks to precipitation and temperature forecasting in Paraná, Brazil." Ciência e Natura 43 (March 1, 2021): e40. http://dx.doi.org/10.5902/2179460x43258.

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Precipitation and temperature have an impact on various sectors of society, such as agriculture, power generation, water availability, so it is essential to develop accurate monthly forecasts. The objective of this study is to develop an artificial neural network (ANN) model for monthly temperature and precipitation forecasts for the state of Paraná, Brazil. An important step in the ANN model is the selection of input variables, for which the forward stepwise regression method was used. After identifying the predictor variables for the forecasting model, the Radial Basis Function (RBF) ANN was developed with 50 neurons in the hidden layer and one neuron in the output layer. For the precipitation forecasting models, better performances were obtained for forecasting the data smoothed by the three-month moving average, since noisy data, such as monthly precipitation, are more difficult to be simulated by the neural network. For the temperature forecasts, the ANN model performed well both in the monthly temperature forecast and in the 3-month moving average forecast. This study showed the suitability of forecasting precipitation and temperature with the use of RBF ANNs, especially in the forecast of the monthly temperature.
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49

Lavers, David A., Shaun Harrigan, and Christel Prudhomme. "Precipitation Biases in the ECMWF Integrated Forecasting System." Journal of Hydrometeorology 22, no. 5 (May 2021): 1187–98. http://dx.doi.org/10.1175/jhm-d-20-0308.1.

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AbstractPrecipitation is a key component of the global water cycle and plays a crucial role in flooding, droughts, and water supply. One way to manage its socioeconomic effects is based on precipitation forecasts from numerical weather prediction (NWP) models, and an important step to improve precipitation forecasts is by diagnosing NWP biases. In this study, we investigate the biases in precipitation forecasts from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS). Using the IFS control forecast from 12 June 2019 to 11 June 2020 at 5219 stations globally, we show that in each of the boreal winter and summer half years, the IFS 1) has an average global wet bias and 2) displays similar bias patterns for forecasts starting at 0000 and 1200 UTC and across forecast days 1–5. These biases are dependent on observed (climatological) precipitation; stations with low observed precipitation have an IFS wet bias, while stations with high observed precipitation have an IFS dry bias. Southeast Asia has a wet bias of 1.61 mm day−1 (in boreal summer) and over the study period the precipitation is overestimated by 31.0% on forecast day 3. This is the hydrological signature of several hypothesized processes including issues specifying the IFS snowpack over the Tibetan Plateau, which may affect the mei-yu front. These biases have implications for IFS land–atmosphere feedbacks, river discharge, and for ocean circulation in the Southeast Asia region. Reducing these biases could lead to more accurate forecasts of the global water cycle.
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Sloughter, J. Mc Lean, Adrian E. Raftery, Tilmann Gneiting, and Chris Fraley. "Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging." Monthly Weather Review 135, no. 9 (September 1, 2007): 3209–20. http://dx.doi.org/10.1175/mwr3441.1.

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Abstract Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are posterior probabilities of the models generating the forecasts and reflect the forecasts’ relative contributions to predictive skill over a training period. It was developed initially for quantities whose PDFs can be approximated by normal distributions, such as temperature and sea level pressure. BMA does not apply in its original form to precipitation, because the predictive PDF of precipitation is nonnormal in two major ways: it has a positive probability of being equal to zero, and it is skewed. In this study BMA is extended to probabilistic quantitative precipitation forecasting. The predictive PDF corresponding to one ensemble member is a mixture of a discrete component at zero and a gamma distribution. Unlike methods that predict the probability of exceeding a threshold, BMA gives a full probability distribution for future precipitation. The method was applied to daily 48-h forecasts of 24-h accumulated precipitation in the North American Pacific Northwest in 2003–04 using the University of Washington mesoscale ensemble. It yielded predictive distributions that were calibrated and sharp. It also gave probability of precipitation forecasts that were much better calibrated than those based on consensus voting of the ensemble members. It gave better estimates of the probability of high-precipitation events than logistic regression on the cube root of the ensemble mean.
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