Journal articles: 'Rain forecasting'

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Hutchins, David A. "Forecasting the rain ratio." Nature 476, no. 7358 (August 2011): 41–42. http://dx.doi.org/10.1038/476041a.

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Bridger, Nguyen, and Chiao. "Developing Spatially Accurate Rainfall Predictions for the San Francisco Bay Area through Case Studies of Atmospheric River and other Synoptic Events." Atmosphere 10, no. 9 (September 12, 2019): 541. http://dx.doi.org/10.3390/atmos10090541.

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Rainfall patterns in the San Francisco Bay Area (SFBA) are highly influenced by local topography. It has been a forecasting challenge for the main US forecast models. This study investigates the ability of the Weather Research and Forecasting (WRF) model to improve upon forecasts, with particular emphasis on the rain shadow common to the southern end of the SFBA. Three rain events were evaluated: a mid-season atmospheric river (AR) event with copious rains; a typical non-AR frontal passage rain event; and an area-wide rain event in which zero rain was recorded in the southern SFBA. The results show that, with suitable choices of parameterizations, the WRF model with a resolution around 1 km can forecast the observed rainfall patterns with good accuracy, and would be suitable for operational use, especially to water and emergency managers. Additionally, the three synoptic situations were investigated for further insight into the common ingredients for either flooding rains or strong rain shadow events.

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Adhikary, Sajal Kumar, Nitin Muttil, and Abdullah Gokhan Yilmaz. "Improving streamflow forecast using optimal rain gauge network-based input to artificial neural network models." Hydrology Research 49, no. 5 (December 5, 2017): 1559–77. http://dx.doi.org/10.2166/nh.2017.108.

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Abstract Accurate streamflow forecasting is of great importance for the effective management of water resources systems. In this study, an improved streamflow forecasting approach using the optimal rain gauge network-based input to artificial neural network (ANN) models is proposed and demonstrated through a case study (the Middle Yarra River catchment in Victoria, Australia). First, the optimal rain gauge network is established based on the current rain gauge network in the catchment. Rainfall data from the optimal and current rain gauge networks together with streamflow observations are used as the input to train the ANN. Then, the best subset of significant input variables relating to streamflow at the catchment outlet is identified by the trained ANN. Finally, one-day-ahead streamflow forecasting is carried out using ANN models formulated based on the selected input variables for each rain gauge network. The results indicate that the optimal rain gauge network-based input to ANN models gives the best streamflow forecasting results for the training, validation and testing phases in terms of various performance evaluation measures. Overall, the study concludes that the proposed approach is highly effective to achieve the enhanced streamflow forecasting and could be a viable option for streamflow forecasting in other catchments.

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Costa-Patry, Etienne, Sven Holzendorf, and Matthias Pätsch. "Car Rain Sensors as Mobile Measuring Stations in Heavy-rain Forecasting." ATZ worldwide 123, no. 5-6 (May 2021): 70–75. http://dx.doi.org/10.1007/s38311-021-0655-0.

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KAWASAKI, Shoji, Masaaki KOYAMA, Shunsuke FUKAMI, and Chisa KOBAYASHI. "Forecasting Occurrence of Localized Heavy Rain and Tornado." Journal of The Institute of Electrical Engineers of Japan 134, no. 9 (2014): 604–7. http://dx.doi.org/10.1541/ieejjournal.134.604.

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Mazzeffi, Michael A., and Jacob T. Gutsche. "Forecasting Extracorporeal Membrane Oxygenation Outcomes: Rain or Shine?" Journal of Cardiothoracic and Vascular Anesthesia 33, no. 4 (April 2019): 918–19. http://dx.doi.org/10.1053/j.jvca.2018.09.021.

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Migon, Helio S., and Ana Beatriz S. Monteiro. "Rain-fall modeling: An Application of Bayesian forecasting." Stochastic Hydrology and Hydraulics 11, no. 2 (April 1997): 115–27. http://dx.doi.org/10.1007/bf02427911.

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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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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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Shu, Zhangkang, Jianyun Zhang, Junliang Jin, Lin Wang, Guoqing Wang, Jie Wang, Zhouliang Sun, et al. "Evaluation and Application of Quantitative Precipitation Forecast Products for Mainland China Based on TIGGE Multimodel Data." Journal of Hydrometeorology 22, no. 5 (May 2021): 1199–219. http://dx.doi.org/10.1175/jhm-d-20-0004.1.

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AbstractWe evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

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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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Song, Kun, Xichuan Liu, and Taichang Gao. "Real-Time Rainfall Estimation Using Microwave Links: A Case Study in East China during the Plum Rain Season in 2020." Sensors 21, no. 3 (January 28, 2021): 858. http://dx.doi.org/10.3390/s21030858.

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Accurate and real-time rainfall estimation is a pressing need for forecasting the flood disaster and reducing the loss. In this study, we exploit the potential of estimating the rainfall by microwave links in East China. Eight microwave links at 15 GHz and 23 GHz, operated by China Mobile, are used for estimating the rain rate in real-time in Jiangyin, China from June to July 2020. First, we analyze the correlation between the rain-induced attenuation of microwave links and the rain rate measured by rain gauges. The correlation coefficient values are higher than 0.77 with the highest one over 0.9, showing a strong positive correlation. The real-time results indicate that microwave links estimate the rainfall with a higher temporal resolution than the rain gauges. Meanwhile, the rain rate that was estimated by microwave links also correlates well with the actual rain rate, and most of the values of the mean absolute error are less than 1.50 mm/h. Besides, the total rainfall’s relative deviation values are less than 5% with the smallest one reaching 1%. The quantitative results also indicate that microwave links could lead to better forecasting of water levels and, hence, better warnings for flood disasters.

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Silva, Elton John Robaina da, Camila Nascimento Alves, Priscila Celebrini de Oliveira Campos, Raquel Aparecida Abrahão Costa e. Oliveira, Maria Esther Soares Marques, José Carlos Cesar Amorim, and Igor Paz. "Comparison of Rain Gauge Network and Weather Radar Data: Case Study in Angra dos Reis, Brazil." Water 14, no. 23 (December 4, 2022): 3944. http://dx.doi.org/10.3390/w14233944.

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This paper presents a comparison between rain gauge network and weather radar data in Angra dos Reis city, located in the State of Rio de Janeiro (RJ), Brazil. The city has a high incidence of natural disasters, especially associated with heavy rains in densely populated areas. In this work, weather radar data with a spatial resolution of 1 km were obtained from dual-polarimetric S-band radar operated by the Environmental State Institute of Rio de Janeiro (INEA), located in the Guaratiba neighborhood in Rio de Janeiro city, Brazil; the rain gauge measurements were provided by the National Center for Monitoring and Warning of Natural Disasters (CEMADEN), which is composed of a network with 30 rain gauges covering the studied area. The comparison between the two datasets enables the analysis of which radar products better fit the rain gauge network’s accumulated rainfall by quantifying the uncertainties in precipitation estimates at radar pixels where rain gauges are located. The results indicated that radar products generated with the help of regression techniques obtained from the relation between radar reflectivities and rain gauge measurements were a better fit, constituting essential information while dealing with efficient regulation for rainfall monitoring and forecasting to minimize the risks associated with extreme events.

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Bagtasa, Gerry. "Assessment of Tropical Cyclone Rainfall from GSMaP and GPM Products and Their Application to Analog Forecasting in the Philippines." Atmosphere 13, no. 9 (August 30, 2022): 1398. http://dx.doi.org/10.3390/atmos13091398.

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Tropical cyclone (TC) rainfall is both a resource and a hazard in the Philippines. Observation of its spatiotemporal distribution is necessary for water and disaster mitigation management. This study evaluated the performance of two high-resolution satellite precipitation datasets—the GSMaP and GPM-IMERG—in estimating accumulated TC rainfall in the Philippines from 2000 to 2021. TC rain is defined as rainfall within 5° of a TC center. Several estimation algorithms were included in the assessment. The uncalibrated near-real-time GSMaP_NRT and early version GPM_ER, the reanalysis GSMaP_RNL, and the gauge-calibrated GSMaP_G and GPM_G. The assessment shows the worst scores for the uncalibrated GSMaP_NRT and GSMaP_RNL, followed by GPM_ER with station correlation coefficient (CC) values of 0.63, 0.67, and 0.73, respectively, compared to GSMaP_G CC of 0.79 and GPM_G CC of 0.77. GSMaP_G and GPM_G also gave the least bias and error, with a consistently high (>0.6) probability of detection (POD) and Pierce skill score (PSS) up to rainfall of 300 mm. In addition to the evaluation, the GSMaP_G and GPM_G were used in the analog forecasting of TC rain. Analog forecasting is based on the principle that past weather conditions can occur again. In TC rain analog forecasting, past TCs with similar intensities and tracks are assumed to bring similar rainfall amounts and distribution as a current TC. Composite mean TC rainfall from historical satellite precipitation estimations was produced to create TC rain forecasts. Results show the analog TC rain forecasts generally captured the spatial distribution of TC rain and performed better than the uncalibrated GSMaP_NRT, with a mean station correlation of 0.62–0.67, POD greater than 0.7, and positive PSS indicating good skills. However, forecasts have a false alarm ratio greater than 0.8 for 150 mm rain and have difficulty producing extreme rainfall (>250 mm).

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TANAKA, Kohji, Hiroki TSUJIKURA, Yutaka OOYAGI, and Masayuki SUGIURA. "DEVELOPMENT OF FLOOD FORECASTING SISTEM INCLUDING RAIN FORCAST ERRORS." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 69, no. 4 (2013): I_1591—I_1596. http://dx.doi.org/10.2208/jscejhe.69.i_1591.

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Cristani, Matteo, Francesco Domenichini, Francesco Olivieri, Claudio Tomazzoli, and Margherita Zorzi. "It could rain: weather forecasting as a reasoning process." Procedia Computer Science 126 (2018): 850–59. http://dx.doi.org/10.1016/j.procs.2018.08.019.

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Löwe, Roland, Peter Steen Mikkelsen, Michael R. Rasmussen, and Henrik Madsen. "State-space adjustment of radar rainfall and skill score evaluation of stochastic volume forecasts in urban drainage systems." Water Science and Technology 68, no. 3 (August 1, 2013): 584–90. http://dx.doi.org/10.2166/wst.2013.284.

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Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input for forecasting outflow from two catchments in the Copenhagen area. Stochastic grey-box models are applied to create the runoff forecasts, providing us with not only a point forecast but also a quantification of the forecast uncertainty. Evaluating the results, we can show that using the adjusted radar data improves runoff forecasts compared with using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short-term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time control.

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Yan, Rong Ge, Yu Long Jia, Li Hua Zhu, and Qing Xin Yang. "Giant Magnetostrictive Freezing Rain Sensor." Advanced Materials Research 902 (February 2014): 163–66. http://dx.doi.org/10.4028/www.scientific.net/amr.902.163.

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As a disastrous weather, hazards of freezing rain can not be ignored. The important thing to be solved at present is using advanced technology and material to correctly detect and improve the forecasting ability of freezing rain. Based on the damage of freezing rain and excellent properties of the giant magnetostrictive materials, this paper gives a giant magnetostrictive freezing rain sensor. When there is different thickness of freezing rain, natural frequency of the sensor will change. Resonance is regained by adjusting the frequency of the power. From natural frequency change, the thickness of the freezing rain can be known. Using COMSOL software, modal analysis of different thickness freezing rain for the giant magnetostrictive freezing rain sensor is studied. The results show that there is big difference in natural frequency with difference thickness of freezing rain, which is easy to achieve automatic frequency tracking and monitor.

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Fernández-Alvarez, José C., Albenis Pérez-Alarcon, Alfo J. Batista-Leyva, and Oscar Díaz-Rodríguez. "Evaluation of Precipitation Forecast of System: Numerical Tools for Hurricane Forecast." Advances in Meteorology 2020 (August 5, 2020): 1–16. http://dx.doi.org/10.1155/2020/8815949.

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Heavy rainfall events, typically associated with tropical cyclones (TCs), provoke intense flooding, consequently causing severe losses to life and property. Therefore, the amount and distribution of rain associated with TCs must be forecasted precisely within a reasonable time to guarantee the protection of lives and goods. In this study, the skill of the Numerical Tool for Hurricane Forecast (NTHF) for determining rainfall pattern, average rainfall, rainfall volume, and extreme amounts of rain observed during TCs is evaluated against Tropical Rainfall Measuring Mission (TRMM) data. A sample comprising nine systems formed in the North Atlantic basin from 2016 to 2018 is used, where the analysis begins 24 h before landfall. Several statistical indices characterising the abilities of the NTHF and climatology and persistence model for rainfalls (R-CLIPER) for forecasting rain as measured by the TRMM are calculated at 24, 48, and 72 h forecasts for each TC and averaged. The model under consideration presents better forecasting skills than the R-CLIPER for all the attributes evaluated and demonstrates similar performances compared with models reported in the literature. The proposed model predicts the average rainfall well and presents a good description of the rain pattern. However, its forecast of extreme rain is only applicable for 24 h.

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Faure, Ghislain, Philippe Chambon, and Pierre Brousseau. "Operational Implementation of the AROME Model in the Tropics: Multiscale Validation of Rainfall Forecasts." Weather and Forecasting 35, no. 2 (March 16, 2020): 691–710. http://dx.doi.org/10.1175/waf-d-19-0204.1.

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Abstract Météo-France runs operationally, for the needs of several overseas regions in the tropical belt, five numerical weather prediction configurations, based on the convection-permitting model AROME and called the AROME-OM system. These configurations use the high-resolution model [Integrated Forecasting System (IFS)] from the European Centre for Medium-Range Weather Forecasts (ECMWF) for both initialization and lateral forcing. In this study, the performance of the AROME-OM system for rainfall forecasting is compared to the one of ECMWF IFS. The validation uses spatialized rainfall estimates over a 24-h time period at two time scales (daily and annual), from both satellite and ground-based instruments. It has been performed over a 10-month period and across five tropical domains. The intercomparison demonstrates consistent signals across domains and scales. The added value of the AROME-OM system compared to ECMWF IFS is shown for rain/no-rain discrimination and for rain accumulations larger than 10 mm day−1. The AROME-OM system also shows a better ability to forecast realistic rain patterns over these tropical regions. The main weakness found is for an intermediate range of rain accumulations, from 1 to 10 mm day−1, for which the ECMWF IFS forecasts slightly outperform the AROME-OM system forecasts.

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Dung, Dang Quoc, Nguyen The Hao, and Nguyen Minh Giam. "Rain Forecasting for Ho Chi Minh City Using Doppler Weather Radar Dwsr-2500C." GeoScience Engineering 62, no. 1 (March 1, 2016): 1–10. http://dx.doi.org/10.1515/gse-2016-0002.

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Abstract Rainfall amounts vary randomly over time and space. Rainfall monitoring and forecasting is a difficult task, especially for a short-term period from 30 minutes to 3 hours. Recently Doppler weather radars have been used as one of the new solutions in the short-term forecasting of extreme rain or storm. This research presents some results of forecasting the wind direction, velocity, and rainfall of a typical rainy day, 14 September 2010, based on CAPPI images of a DWSR-2500C radar in the Nha Be district, Ho Chi Minh City (HCMC). The results showed that the Doppler radar, in a scanning radius of 30 km, is very effective in forecasting extreme rainfall for each region and district when reflected radar signals from clouds moving towards the city are detected. This research provides useful information in the forecast of extreme rainfall for flood prevention works in the HCM City.

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Kanavos, Andreas, Maria Trigka, Elias Dritsas, Gerasimos Vonitsanos, and Phivos Mylonas. "A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data." Electronics 10, no. 16 (August 4, 2021): 1872. http://dx.doi.org/10.3390/electronics10161872.

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In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives storage and processes data in real-time, in order to extract useful knowledge from different sensors related to weather data. In following, the numerical weather prediction model aims at forecasting the weather type given three precipitation classes namely rain, freezing rain, and snow as recorded in the Automated Surface Observing System (ASOS) network. For depicting the effectiveness of our proposed schema, a regularization technique for feature selection so as to avoid overfitting is implemented. Several classification models covering three different categorization methods namely the Bayesian, decision trees, and meta/ensemble methods, have been investigated in a real dataset. The experimental analysis illustrates that the utilization of the regularization technique could offer a significant boost in forecasting performance.

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Armon, Moshe, Francesco Marra, Yehouda Enzel, Dorita Rostkier-Edelstein, and Efrat Morin. "Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model." Hydrology and Earth System Sciences 24, no. 3 (March 16, 2020): 1227–49. http://dx.doi.org/10.5194/hess-24-1227-2020.

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Abstract. Heavy precipitation events (HPEs) can lead to natural hazards (e.g. floods and debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological, and societal effects of HPEs. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with these events. Information from rain gauges is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterised by the highest rain intensities; however, for short durations, the highest rain intensities were found for the inland desert. During the rainy season, the rain field's centre of mass progresses from the sea inland. Rainfall during HPEs is highly localised in both space (less than a 10 km decorrelation distance) and time (less than 5 min). WRF model simulations were accurate in generating the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors.

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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 >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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Ivković, Marija, Andrijana Todorović, and Jasna Plavšić. "Improved input to distributed hydrologic model in areas with sparse subdaily rainfall data using multivariate daily rainfall disaggregation." Journal of Hydroinformatics 20, no. 4 (March 6, 2018): 784–97. http://dx.doi.org/10.2166/hydro.2018.053.

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Abstract Flood forecasting relies on good quality of observed and forecasted rainfall. In Serbia, the recording rain gauge network is sparse and rainfall data mainly come from dense non-recording rain gauges. This is not beneficial for flood forecasting in smaller catchments and short-duration events, when hydrologic models operating on subdaily scale are applied. Moreover, differences in rainfall amounts from two types of gauges can be considerable, which is common in operational hydrological practice. This paper examines the possibility of including daily rainfall data from dense observation networks in flood forecasting based on subdaily data, using the extreme flood event in the Kolubara catchment in May 2014 as a case study. Daily rainfall from a dense observation network is disaggregated to hourly scale using the MuDRain multivariate disaggregation software. The disaggregation procedure results in well-reproduced rainfall dynamics and adjusts rainfall volume to the values from the non-recording gauges. The fully distributed wflow_hbv model, which is under development as a forecasting tool for the Kolubara catchment, is used for flood simulations with two alternative hourly rainfall data. The results show an improvement when the disaggregated rainfall from denser network is used, thus indicating the significance of better representation of rainfall temporal and spatial variability for flood forecasting.

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KANEKO, Ryo, Shiho ONOMURA, and Makoto NAKAYOSHI. "INVESTIGATION OF A REAL-TIME RAIN FORECASTING USING U-NET." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 76, no. 2 (2020): I_403—I_408. http://dx.doi.org/10.2208/jscejhe.76.2_i_403.

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Brémaud, P. J., and Y. B. Pointin. "Forecasting heavy rainfall from rain cell motion using radar data." Journal of Hydrology 142, no. 1-4 (February 1993): 373–89. http://dx.doi.org/10.1016/0022-1694(93)90019-6.

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Merkle, Edgar C., and Robert Hartman. "Weighted Brier score decompositions for topically heterogenous forecasting tournaments." Judgment and Decision Making 13, no. 2 (March 2018): 185–201. http://dx.doi.org/10.1017/s1930297500007099.

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AbstractBrier score decompositions, including those attributed to Murphy and to Yates, provide popular metrics for estimating forecast performance attributes like calibration and discrimination. However, the decompositions are generally limited to situations where forecasters make successive forecast judgments against the same class of substantive event (e.g., rain vs. no rain). They do not readily translate to common situations where: forecasts are weighted unequally; forecasts can be made against a range of heterogeneous topics and events over varying time horizons; forecasts can be updated over time until an event occurs or an event deadline is reached; or outcome alternatives can vary in number and nature (e.g., ordered vs. unordered outcomes) across forecast questions. In this paper, we propose extensions of the Murphy and Yates decompositions to address these features. The extensions involve new analytic expressions for the decompositions of weighted Brier scores, along with proposed resampling methods. We use data from a recent forecasting tournament to illustrate the methods.

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Dominguez, David, Luis de Juan del Villar, Odette Pantoja, and Mario González-Rodríguez. "Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model." Sustainability 14, no. 2 (January 9, 2022): 691. http://dx.doi.org/10.3390/su14020691.

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The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest.

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Abdulah, Yunus Ashari, and Maryanto. "Rencana Produksi Pengangkutan Overburden Berdasarkan Pola Hujan di PT X Site Asam-Asam, Desa Riam Andungan, Kecamatan Kintap, Kabupaten Tanah Laut, Provinsi Kalimantan Selatan." Jurnal Riset Teknik Pertambangan 1, no. 1 (July 7, 2021): 8–21. http://dx.doi.org/10.29313/jrtp.v1i1.28.

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Abstract. PT X is a subsidiary of PT Pama Persada which is engaged in coal mining by using an open pit system, so that its mining activities are very much needed in the weather. Based on production output data, monthly production targets are not achieved, displayed in 2018 and 4 months of which production targets are not achieved in May, August, September, and December. This causes erratic rain that can be canceled due to mining activities. That way it needs to be reviewed in determining production targets based on rain predictions. In this research an analysis is performed first, then data processing is performed using rain classification, traffic classification, and productivity calculation. The results of this study on the correlation analysis of the relationship between rain hours to rainfall, slippery, and rainfall intensity are all three influential, it's just that the effect is very strong based on the correlation test that is the rainfall intensity with a significant value <0.05 and the correlation value 0.704 is at intervals 0.61 - 0.80, including the strong correlation category. Then the results of rain hour forecasting in January 2019 are 71.25 hours, February 2019 is 53.92 hours, and in March 42.06 hours. For forecasting slippery time in January 2019 is 23.97, in February 2019 that is 18.18, and in March 2019 14.30 hours. Calculation of monthly production targets is based on forecasting rain hours, the results of forecasting production targets for 2019 in January are 1,205,331.64 BCM, February 1,106,561.46 BCM, in March 1,406,595.41 BCM. Keywords: Rain, Slippery, Rain Prediction, Productivity, Production Target. Abstrak. PT X merupakan anak perusahaan dari PT Pama Persada yang bergerak di bidang penambangan batubara dengan menerapkan sistem tambang terbuka (Surface Mining), sehingga dalam kegiatan penambangannya sangat bergantung pada keadaan cuaca. Berdasarkan data hasil produksi, tidak tercapainya target produksi perbulan, tercatat pada tahun 2018 terdapat 4 bulan yg tidak tercapainya target produksi yaitu pada bulan Mei, Agustus, September, dan Desember. Hal tersebut disebabkan keadaan hujan yang tidak menentu yang dapat menghentikan aktivitas kegiatan tambang. Dengan begitu perlu dilakukan pengkajian ulang dalam menentukan target produksi berdasarkan prediksi hujan. Dalam penelitian ini dilakukan analisis korelasi terlebih dahulu, keudian dilakukan pengolahan data dengan menggunakan klasifikasi intensitas hujan, tahap peramalan, dan perhitungan produktivitas. Hasil dari penelitian ini pada analisis korelasi hubungan antara jam hujan terhadap curah hujan, slippery, dan intensitas hujan ketiganya berpengaruh, hanya saja nilai yang pengaruhnya sangat kuat berdasarkan uji korelasi yaitu intensitas hujan dengan nilai signifikan < 0,05 dan nilai korelasi 0,704 berada pada interval 0,61 − 0,80 termasuk kategori korelasi kuat. Kemudian hasil dari peramalan jam hujan pada bulan januari 2019 yaitu 71,25 jam, bulan februari 2019 yaitu 53,92 jam, dan pada bulan maret 42,06 jam. Untuk peramalan waktu slippery pada bulan januari 2019 yaitu 23,97, pada bulan februari 2019 yaitu 18,18, dan pada bulan maret 2019 14,30 jam. Perhitungan target produksi bulanan berdasarkan peramalan jam hujan, hasil peramalan target produksi tahun 2019 pada bulan Januari yaitu 1.205.331,64 BCM, bulan Februari 1.106.561,46 BCM, pada bulan Maret 1.406.595,41 BCM.

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Islam, Mohammad Shohidul, Sultana Easmin Siddika, and S. M. Injamamul Haque Masum. "Rainfall Prediction in South-Eastern Part of Bangladesh by Linear Regression Method." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 119. http://dx.doi.org/10.23956/ijermt.v6i6.255.

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Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The model prediction of rainfall was compared with true rainfall which was collected from rain gauge of different stations and it was found that the model rainfall prediction has given good results.

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Wandishin, Matthew S., Michael E. Baldwin, Steven L. Mullen, and John V. Cortinas. "Short-Range Ensemble Forecasts of Precipitation Type." Weather and Forecasting 20, no. 4 (August 1, 2005): 609–26. http://dx.doi.org/10.1175/waf871.1.

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Abstract Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall.

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Scheuerer, Michael, Scott Gregory, Thomas M. Hamill, and Phillip E. Shafer. "Probabilistic Precipitation-Type Forecasting Based on GEFS Ensemble Forecasts of Vertical Temperature Profiles." Monthly Weather Review 145, no. 4 (March 21, 2017): 1401–12. http://dx.doi.org/10.1175/mwr-d-16-0321.1.

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Abstract A Bayesian classification method for probabilistic forecasts of precipitation type is presented. The method considers the vertical wet-bulb temperature profiles associated with each precipitation type, transforms them into their principal components, and models each of these principal components by a skew normal distribution. A variance inflation technique is used to de-emphasize the impact of principal components corresponding to smaller eigenvalues, and Bayes’s theorem finally yields probability forecasts for each precipitation type based on predicted wet-bulb temperature profiles. This approach is demonstrated with reforecast data from the Global Ensemble Forecast System (GEFS) and observations at 551 METAR sites, using either the full ensemble or the control run only. In both cases, reliable probability forecasts for precipitation type being either rain, snow, ice pellets, freezing rain, or freezing drizzle are obtained. Compared to the model output statistics (MOS) approach presently used by the National Weather Service, the skill of the proposed method is comparable for rain and snow and significantly better for the freezing precipitation types.

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Dolciné, L., H. Andrieu, and M. N. French. "Evaluation of a conceptual rainfall forecasting model from observed and simulated rain events." Hydrology and Earth System Sciences 2, no. 2/3 (September 30, 1998): 173–82. http://dx.doi.org/10.5194/hess-2-173-1998.

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Abstract. Very short-term rainfall forecasting models designed for runoff analysis of catchments, particularly those subject to flash-floods, typically include one or more variables deduced from weather radars. Useful variables for defining the state and evolution of a rain system include rainfall rate, vertically integrated rainwater content and advection velocity. The forecast model proposed in this work complements recent dynamical formulations by focusing on a formulation incorporating these variables using volumetric radar data to define the model state variables, determining the rainfall source term directly from multi-scan radar data, explicitly accounting for orographic enhancement, and explicitly incorporating the dynamical model components in an advection-diffusion scheme. An evaluation of this model is presented for four rain events collected in the South of France and in the North-East of Italy. Model forecasts are compared with two simple methods: persistence and extrapolation. An additional analysis is performed using an existing mono-dimensional microphysical meteorological model to produce simulated rain events and provide initialization data. Forecasted rainfall produced by the proposed model and the extrapolation method are compared to the simulated events. The results show that the forecast model performance is influenced by rainfall temporal variability and performance is better for less variable rain events. The comparison with the extrapolation method shows that the proposed model performs better than extrapolation in the initial period of the forecast lead-time. It is shown that the performance of the proposed model over the extrapolation method depends essentially on the additional vertical information available from voluminal radar.

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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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Reyes-García, Victoria, Álvaro Fernández-Llamazares, Maximilien Guèze, and Sandrine Gallois. "Does Weather Forecasting Relate to Foraging Productivity? An Empirical Test among Three Hunter-Gatherer Societies." Weather, Climate, and Society 10, no. 1 (January 1, 2018): 163–77. http://dx.doi.org/10.1175/wcas-d-17-0064.1.

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Abstract Previous research has studied the association between ethnoclimatological knowledge and decision-making in agriculture and pastoral activities but has paid scant attention to how ethnoclimatological knowledge might affect hunting and gathering, an important economic activity for many rural populations. The work presented here tests whether people who can forecast temperature and rain display higher hunting and gathering returns (measured as kilograms per hour for hunting and cash equivalent for gathering). Data were collected among three indigenous, small-scale, subsistence-based societies largely dependent on hunting and gathering for their livelihoods: the Tsimane’ (Amazonia, n = 107), the Baka (Congo basin, n = 164), and the Punan Tubu (Borneo, n = 103).The ability to forecast rainfall and temperature varied from one society to another, but the average consistency between people’s 1-day rainfall and temperature forecasts and instrumental measurements was low. This study found a statistically significant positive association between consistency in forecasting rain and the probability that a person engaged in hunting. Conversely, neither consistency in forecasting rain nor consistency in forecasting temperature were associated in a statistically significant way with actual returns to hunting or gathering activities. The authors discuss methodological limitations of the approach, suggesting improvements for future work. This study concludes that, other than methodological issues, the lack of strong associations might be partly explained by the fact that an important characteristic of local knowledge systems, including ethnoclimatological knowledge, is that they are widely socialized and shared.

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Ruhiat, Yayat. "Forecasting rainfall and potential for repeated events to predict flood areas in Banten province, Indonesia." Journal of Measurements in Engineering 10, no. 2 (June 27, 2022): 68–80. http://dx.doi.org/10.21595/jme.2022.22363.

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In a period of ten years, from 2011-2020 rainfall in Indonesia is relatively high, with annual rainfall between 460.5-4,627.4 mm. The high rainfall has implications for flooding in several provinces. During this period, almost every year several areas in Banten Province experienced floods. To predict areas of Banten Province that have the potential for flooding, forecasts of rainfall and the potential for repeated occurrences of high rainfall are carried out. In making the forecast, observations were made at the Serang Meteorological Station, the Budiarto Curug Meteorological Station, the South Tangerang Climatology Station, and the Tangerang Geophysics Station. Rainfall data from the four stations were analyzed by Fourier transform, Gumbel method and Mononobe method. Distribution analysis results obtained rainfall in Banten Province between 0.0-607.9 mm with the length of rainy days per month between 0-26 days. Then, the results of the Fourier transform analysis; Banten Province included a monsoon rain pattern with unimodial rainfall. Furthermore, the results of the analysis of the Gumbel method and the Mononobe method, Banten Province included the category of moderate rain and tended to be heavy, even extreme. Based on the results of the analysis using these two methods, in 2025 in Banten Province, it is predicted that 11 % heavy rain, 3 % very heavy rain and 1 % extreme rain are predicted. In that year, it is predicted that there will be 65 sub-districts in Banten Province that have the potential for flooding. The sub-districts that have the potential for flooding are mostly located in Serang Regency, Serang City, Tangerang City, and South Tangerang City. This potential flood is predicted to occur in: January, February, March, April, May, October and November.

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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.

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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.

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Shastri, Niket, and Kamlesh Kamlesh Pathak. "New Cloud Detection Index (CDI) for Forecasting the Extreme Rain Events." Advances in Remote Sensing 08, no. 01 (2019): 30–39. http://dx.doi.org/10.4236/ars.2019.81002.

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Tiesi, Alessandro, Mario Marcello Miglietta, Dario Conte, Oxana Drofa, Silvio Davolio, Piero Malguzzi, and Andrea Buzzi. "Heavy Rain Forecasting by Model Initialization With LAPS: A Case Study." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 6 (June 2016): 2619–27. http://dx.doi.org/10.1109/jstars.2016.2520018.

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Marsan, David, Daniel Schertzer, and Shaun Lovejoy. "Causal space-time multifractal processes: Predictability and forecasting of rain fields." Journal of Geophysical Research: Atmospheres 101, no. D21 (November 1, 1996): 26333–46. http://dx.doi.org/10.1029/96jd01840.

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Guo, S. J. "Computer-Aided Project Duration Forecasting Subjected to the Impact of Rain." Computer-Aided Civil and Infrastructure Engineering 15, no. 1 (January 2000): 67–74. http://dx.doi.org/10.1111/0885-9507.00172.

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Shah, Chirag, Chathra Hendahewa, and Roberto González-Ibáñez. "Rain or shine? Forecasting search process performance in exploratory search tasks." Journal of the Association for Information Science and Technology 67, no. 7 (May 13, 2015): 1607–23. http://dx.doi.org/10.1002/asi.23484.

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Macalalad, Rhonalyn V., Roy A. Badilla, Olivia C. Cabrera, and Gerry Bagtasa. "Hydrological Response of the Pampanga River Basin in the Philippines to Intense Tropical Cyclone Rainfall." Journal of Hydrometeorology 22, no. 4 (April 2021): 781–94. http://dx.doi.org/10.1175/jhm-d-20-0184.1.

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AbstractThe Philippines is frequently affected by tropical cyclones (TCs), and understanding the flood response of the Pampanga River basin (PRB) from TC-induced rain is needed in effective disaster risk management. As large uncertainties remain in TC rain forecasting, we propose a simple checklist method for flood forecasting of the PRB that depends on the general TC track, season, and accumulated rainfall. To this end, flood events were selected based on the alert, alarm, and critical river height levels established by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). Results show that all flood events in the PRB were induced by TCs. All intense TCs that directly traversed the PRB resulted in critical-level floods. These TCs also had the shortest flood onset of 7–27 h from alert to critical level. Flooding from distant landfalling TCs, on the other hand, are dependent on season. TCs traversing north (south) of the PRB induced flooding only during the southwest (northeast) monsoon season. These TCs can raise water levels from alert to critical in 11–48 h. Remote precipitation from non-landfalling TCs can also induce critical-level flooding but with a longer onset time of 59 h. These results indicate that a simple checklist method can serve as a useful tool for flood forecasting in regions with limited data and forecasting resources.

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Yang, Lu, Mingxuan Chen, Xiaoli Wang, Linye Song, Meilin Yang, Rui Qin, Conglan Cheng, and Siteng Li. "Classification of Precipitation Type in North China Using Model-Based Explicit Fields of Hydrometeors with Modified Thermodynamic Conditions." Weather and Forecasting 36, no. 1 (February 2021): 91–107. http://dx.doi.org/10.1175/waf-d-20-0005.1.

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AbstractThe ability to forecast thermodynamic conditions aloft and near the surface is critical to the accurate forecasting of precipitation type at the surface. This paper presents an experimental version of a new scheme for diagnosing precipitation type. The method considers the optimum surface temperature threshold associated with each precipitation type and combines model-based explicit fields of hydrometeors with higher-resolution modified thermodynamic and topographic information to determine precipitation types in North China. Based on over 60 years of precipitation-type samples from North China, this study explores the climatological characteristics of the five precipitation types—snow, rain, ice pellets (IP), rain/snow mix (R/S MIX), and freezing rain (FZ)—as well as the suitable air temperature Ta and wet-bulb temperature Tw thresholds for distinguishing different precipitation types. Direct output from numerical weather prediction (NWP) models, such as temperature and humidity, was modified by downscaling and bias correction, as well as by incorporating the latest surface observational data and high-resolution topographic data. Validation of the precipitation-type forecasts from this scheme was performed against observations from the 2016 to 2019 winter seasons and two case studies were also analyzed. Compared with the similar diagnostic routine in the High-Resolution Rapid Refresh (HRRR) forecasting system used to predict precipitation type over North China, the skill of the method proposed here is similar for rain and better for snow, R/S MIX, and FZ. Furthermore, depiction of the diagnosed boundary between R/S MIX and snow is good in most areas. However, the number of misclassifications for R/S MIX is significantly larger than for rain and snow.

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Kuligowski, Robert J., Yaping Li, and Yu Zhang. "Impact of TRMM Data on a Low-Latency, High-Resolution Precipitation Algorithm for Flash-Flood Forecasting." Journal of Applied Meteorology and Climatology 52, no. 6 (June 2013): 1379–93. http://dx.doi.org/10.1175/jamc-d-12-0107.1.

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AbstractData from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been challenging because of the data refresh and latency issues inherent in an instrument in low Earth orbit (LEO). To evaluate their potential impact on low-latency satellite rainfall estimates, rain rates from both the TRMM Microwave Imager (TMI) and precipitation radar (PR) were ingested into a multisensor framework that calibrates high-refresh, low-latency IR brightness temperature data from geostationary platforms against the more accurate but low-refresh, higher-latency rainfall rates available from microwave (MW) instruments on board LEO platforms. The TRMM data were used in two ways: to bias adjust the other MW data sources to match the distribution of the TMI rain rates, and directly alongside the MW rain rates in the calibration dataset. The results showed a significant reduction in false alarms and also a significant reduction in bias for those pixels for which rainfall was correctly detected. The MW bias adjustment was found to have much greater impact than the direct use of the TMI and PR rain rates in the calibration data, but this is not surprising since the latter represented perhaps only 10% of the calibration dataset.

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Orellana-Alvear, Johanna, Rolando Célleri, Rütger Rollenbeck, Paul Muñoz, Pablo Contreras, and Jörg Bendix. "Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model." Remote Sensing 12, no. 12 (June 20, 2020): 1986. http://dx.doi.org/10.3390/rs12121986.

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Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 < NSE < 0.75; 0.72 < KGE < 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available.

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Ling, Hongjie, Zhidong Wang, Shuai An, Guohuai Sun, and Yangyue Yan. "Research on Calculation Method of Rain Load on Structures Based on Discrete Particle Model." Advances in Civil Engineering 2022 (November 2, 2022): 1–16. http://dx.doi.org/10.1155/2022/2107987.

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With the frequent occurrence of extreme weather, the rain load of structures under high wind speeds accompanied by heavy rainfall conditions has become a hot issue for research. In this paper, based on the discrete particle model, the raindrop impact load correction formula is proposed. A rapid calculation method for the rain load of structures is formed through experimental verification. And the forecasting program is developed independently to complete the calculation of rain load per unit flat plate under different wind speed and rainfall intensity combination states. The rain load correction coefficient Δ C w is defined as the dimensionless coefficient of the rain load per unit area of the flat plate in a fully developed rain field driven by wind speed. The rain load is factored into the wind load calculation formula by way of the correction coefficient Δ C w to form the calculation formula for the rain load of structures. The results show that Δ C w has little correlation with wind speed but is closely related to rainfall intensity R. The calculation formula for Δ C w about rainfall intensity is fitted. When rainfall intensity R = 709.2 mm/h, Δ C w is approximately 22.3%. The research results in this paper provide theoretical and technical support for guiding the rapid calculation of rain load on structures.

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Kaufmann, P., F. Schubiger, and P. Binder. "Precipitation forecasting by a mesoscale numerical weather prediction (NWP) model: eight years of experience." Hydrology and Earth System Sciences 7, no. 6 (December 31, 2003): 812–32. http://dx.doi.org/10.5194/hess-7-812-2003.

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Abstract. The Swiss Model, a hydrostatic numerical weather prediction model, has been used at MeteoSwiss for operational forecasting at the meso-beta scale (mesh-size 14 km) from 1994 until 2001. The quality of the quantitative precipitation forecasts is evaluated for the eight years of operation. The seasonal precipitation over Switzerland and its dependence on altitude is examined for both model forecasts and observations using the Swiss rain gauge network sampling daily precipitation at over 400 stations for verification. The mean diurnal cycle of precipitation is verified against the automatic surface observation network on the basis of hourly recordings. In winter, there is no diurnal forcing of precipitation and the modelled precipitation agrees with the observed values. In summer, the convection in the model starts too early, overestimates the amount of precipitation and is too short-lived. Skill scores calculated for six-hourly precipitation sums show a constant level of performance over the model life cycle. Dry and wet seasons influence the model performance more than the model changes during its operational period. The comprehensive verification of the model precipitation is complemented by the discussion of a number of heavy rain events investigated during the RAPHAEL project. The sensitivities to a number of model components are illustrated, namely the driving boundary fields, the internal partitioning of parameterised and grid-scale precipitation, the advection scheme and the vertical resolution. While a small impact of the advection scheme had to be expected, the increasing overprediction of rain with increasing vertical resolution in the RAPHAEL case studies was larger than previously thought. The frequent update of the boundary conditions enhances the positioning of the rain in the model. Keywords: numerical weather prediction, quantitative precipitation forecast, model verification

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Radhitya, Made Leo, and Gede Iwan Sudipa. "PENDEKATAN Z-SCORE DAN FUZZY DALAM PENGUJIAN AKURASI PERAMALAN CURAH HUJAN." SINTECH (Science and Information Technology) Journal 3, no. 2 (October 28, 2020): 149–56. http://dx.doi.org/10.31598/sintechjournal.v3i2.567.

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Abstract:

Determination of rainfall is important to determine the intensity of rain that occurs in an area. Rain intensity that is too high will certainly have a bad impact. Forecasting or prediction techniques are used to determine the likelihood of intensity occurring in the following year. However, rainfall data are continuous numerical data. Measurement of accuracy becomes more difficult if the data type is like that. So, this study tests the accuracy of rainfall forecasting in the city of Denpasar from a different perspective. This test combines the Z-score method and the Fuzzy set theory to normalize and classify rainfall data. This combination determines the degree of rainfall membership divided into Upper, Middle, and Lower levels. Based on the results of rainfall accuracy testing starting in 2012-2016 obtained an average value of accuracy of 85% with training data that is data in 2007-2015. The normalization process greatly affects the value of the training data.

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Journal articles on the topic 'Rain forecasting'

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