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Journal articles on the topic 'Precipitation prediction'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Liu, Shiyuan, Wentao Li, and Qingyun Duan. "Spatiotemporal Variations in Precipitation Forecasting Skill of Three Global Subseasonal Prediction Products over China." Journal of Hydrometeorology 24, no. 11 (2023): 2075–90. http://dx.doi.org/10.1175/jhm-d-23-0071.1.

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Abstract Subseasonal to seasonal (S2S) predictions, which bridge the gap between weather forecasts and climate outlooks, have the great societal benefits of improving water resource management and food security. However, there are tremendous disparities in the forecasting skills of subseasonal precipitation prediction products. This study investigates the spatiotemporal variations in the precipitation forecasting skill of three subseasonal prediction products from the CMA, ECMWF, and NCEP over China. Daily precipitation predictions with lead times ranging from 1 to 30 days and cumulative preci

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Ali, Ali A., and Ghassan H. Abdul-Majeed. "Modeling Asphaltene Precipitation-Part II: Comparative Study for Asphaltene Precipitation Curve Prediction Methods." Journal of Engineering 31, no. 1 (2025): 38–53. https://doi.org/10.31026/j.eng.2025.01.03.

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Asphaltenes' solubility in crude oils is frequently affected by temperature, pressure, and oil composition changes. This could lead to the precipitation and deposition of asphaltene in various parts of the total production system, which would cause a significant economic impact. Predicting the conditions of asphaltene precipitation will be very useful in two cases. In the first case, without the problem, it will be useful in specifying the optimum operating conditions of oil production operations. In the second case, with the problem occurring, the prediction model will be useful in knowing th

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Kang, Jinle, Huimin Wang, Feifei Yuan, Zhiqiang Wang, Jing Huang, and Tian Qiu. "Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China." Atmosphere 11, no. 3 (2020): 246. http://dx.doi.org/10.3390/atmos11030246.

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Precipitation is a critical input for hydrologic simulation and prediction, and is widely used for agriculture, water resources management, and prediction of flood and drought, among other activities. Traditional precipitation prediction researches often established one or more probability models of historical data based on the statistical prediction methods and machine learning techniques. However, few studies have been attempted deep learning methods such as the state-of-the-art for Recurrent Neural Networks (RNNs) networks in meteorological sequence time series predictions. We deployed Long

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Zhang, Ying, Semu Moges, and Paul Block. "Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia." Hydrology and Earth System Sciences 22, no. 1 (2018): 143–57. http://dx.doi.org/10.5194/hess-22-143-2018.

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Abstract. Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined thr

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Murata, Atsuo, Toshihisa Doi, Rin Hasegawa, and Waldemar Karwowski. "Delayed Evacuation after a Disaster Because of Irrational Prediction of the Future Cumulative Precipitation Time Series under Asymmetry of Information." Symmetry 14, no. 1 (2021): 6. http://dx.doi.org/10.3390/sym14010006.

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This study investigated biased prediction of cumulative precipitation, using a variety of patterns of histories of cumulative precipitation, to explore how such biased prediction could delay evacuation or evacuation orders. The irrationality in predicting the future of cumulative precipitation was examined to obtain insights into the causes of delayed evacuation or evacuation orders using a simulated prediction of future cumulative precipitation based on the cumulative precipitation history. Anchoring and adjustment, or availability bias stemming from asymmetry of information, was observed in

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Li, Zhe, Zhongyuan Xia, and Jiaying Ke. "Evaluation of a BCC-CPSv3-S2Sv2 Model for the Monthly Prediction of Summer Extreme Precipitation in the Yellow River Basin." Atmosphere 16, no. 7 (2025): 830. https://doi.org/10.3390/atmos16070830.

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The performance of monthly prediction of extreme precipitation from the BCC-CPSv3-S2Sv2 model over the Yellow River Basin (YRB) using historical hindcast data from 2008 to 2022 was evaluated in this study, mainly from three aspects: overall performance in predicting daily precipitation rates, systematic biases, and monthly prediction of extreme precipitation metrics. The results showed that the BCC-CPSv3-S2Sv2 model demonstrates approximately 10-day predictive skill for summer daily precipitation over the YRB. Relatively higher skill regions concentrate in the central basin, while skill degrad

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Chardon, Jérémy, Anne-Catherine Favre, and Benoît Hingray. "Effects of Spatial Aggregation on the Accuracy of Statistically Downscaled Precipitation Predictions." Journal of Hydrometeorology 17, no. 5 (2016): 1561–78. http://dx.doi.org/10.1175/jhm-d-15-0031.1.

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Abstract The effects of spatial aggregation on the skill of downscaled precipitation predictions obtained over an 8 × 8 km2 grid from circulation analogs for metropolitan France are explored. The Safran precipitation reanalysis and an analog approach are used to downscale the precipitation where the predictors are taken from the 40-yr ECMWF Re-Analysis (ERA-40). Prediction skill—characterized by the continuous ranked probability score (CRPS), its skill score, and its decomposition—is generally found to continuously increase with spatial aggregation. The increase is also greater when the spatia

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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

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9

Nourani, Vahid, Selin Uzelaltinbulat, Fahreddin Sadikoglu, and Nazanin Behfar. "Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation." Atmosphere 10, no. 2 (2019): 80. http://dx.doi.org/10.3390/atmos10020080.

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The aim of ensemble precipitation prediction in this paper was to achieve the best performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based modeling was proposed for prediction of monthly precipitation with three different AI models (feed forward neural network-FFNN, adaptive neural fuzzy inference system-ANFIS and least square support vector machine-LSSVM) for the seven stations located in the Turkish Republic of Northern Cyprus (TRNC). Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station’s pre

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10

Pan, Baoxiang, Kuolin Hsu, Amir AghaKouchak, Soroosh Sorooshian, and Wayne Higgins. "Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range." Journal of Climate 32, no. 1 (2018): 161–82. http://dx.doi.org/10.1175/jcli-d-18-0355.1.

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Abstract Precipitation variability significantly influences the heavily populated West Coast of the United States, raising the need for reliable predictions. We investigate the region’s short- to extended-range precipitation prediction skill using the hindcast database of the Subseasonal-to-Seasonal Prediction Project (S2S). The prediction skill–lead time relationship is evaluated, using both deterministic and probabilistic skill scores. Results show that the S2S models display advantageous deterministic skill at week 1. For week 2, prediction is useful for the best-performing model, with a Pe

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Kim, Kyosik, Byunghyun Kim, and Kun-Yeun Han. "Performance Evaluation of Effective Drought Prediction Using Machine Learning." Journal of the Korean Society of Hazard Mitigation 21, no. 2 (2021): 195–204. http://dx.doi.org/10.9798/kosham.2021.21.2.195.

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There has been much research recently to improve the prediction of drought, but the frequency and pattern of drought displays an irregular time series that limits its predictability, making it difficult to predict with only a single model, and high-level predictions cannot be made even when many models are applied. Therefore, many studies have been conducted to improve predictions by using explanatory variables such as precipitation, temperature, sunshine duration, and air volume as input data. The purpose of this study is to devise a method for predicting drought using the Standard Precipitat

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Li, Hongchen, and Ming Li. "Modeling of Precipitation Prediction Based on Causal Analysis and Machine Learning." Atmosphere 14, no. 9 (2023): 1396. http://dx.doi.org/10.3390/atmos14091396.

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The factors influencing precipitation in western China are quite complex, which increases the difficulty in determining accurate predictors. Hence, this paper models the monthly measured precipitation data from 240 meteorological stations in mainland China and the precipitation data from the European Centre for Medium-Range Weather Forecasts and the National Climate Centre and employs 88 atmospheric circulation indices to develop a precipitation prediction scheme. Specifically, a high-quality grid-point field is created by fusing and revising the precipitation data from multiple sources. This

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Wang, Xiaojuan, Zihan Yang, Shuai Li, Qingquan Li, and Guolin Feng. "Dynamic–statistic combined ensemble prediction and impact factors of China's summer precipitation." Nonlinear Processes in Geophysics 32, no. 2 (2025): 117–30. https://doi.org/10.5194/npg-32-117-2025.

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Abstract. The dynamic–statistic prediction shows excellent performance with regard to monthly and seasonal precipitation prediction in China and has been applied to several dynamical models. In order to further improve the prediction skill of summer precipitation in China, the unequal-weighted ensemble prediction (UWE) using outputs of the dynamic–statistic prediction is presented, and its possible impact factors are also analysed. Results indicate that the UWE has shown promise in improving the prediction skill of summer precipitation in China on account of the fact that the UWE can ov

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Ma, Zhan Qing, Yong Mei Xie, and Shu Yao Wen. "Markov Chain for Predicting of Annual Precipitation Based on Entropy Weight." Advanced Materials Research 518-523 (May 2012): 4034–38. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.4034.

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Aimed at the feature of annual precipitation,this paper puts forward a predicting Markov chain method based on entropy weight. Data of precipitation in Hangzhou,from 1956 to 2009,was used as an example. The precipitation can be predicted year by year using the Markov chain models based on entropy weight. Hangzhou past 5 years the results of precipitation yearly basis,respectively：the absolute error of 73mm,27mm,-22mm,-17mm and 20mm;the relative error was 5.66%, 2.03%,-1.59%,-1.08% and 1.30%.The error value of smaller than ±5％ and ±10％ was 36.67% and 60.00% respectively in the 30 years of preci

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15

Lu, Mingyue, Jingke Zhang, Manzhu Yu, et al. "ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data." Remote Sensing 16, no. 20 (2024): 3911. http://dx.doi.org/10.3390/rs16203911.

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Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for the combined terrain and atmospheric effects, resulting in shortcomings in extreme precipitation forecasting accuracy. Additionally, the satellite data resolution used in prior studies fails to precisely capture nuanced details of abrupt changes in extreme precipitation. T

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Mao, Yiwen, and Asgeir Sorteberg. "Improving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Forest." Weather and Forecasting 35, no. 6 (2020): 2461–78. http://dx.doi.org/10.1175/waf-d-20-0080.1.

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AbstractA binary classification model is trained by random forest using data from 41 stations in Norway to predict the precipitation in a given hour. The predictors consist of results from radar nowcasts and numerical weather predictions. The results demonstrate that the random forest model can improve the precipitation predictions by the radar nowcasts and the numerical weather predictions. This study clarifies whether certain potential factors related to model training can influence the predictive skill of the random forest method. The results indicate that enforcing a balanced prediction by

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17

Kumar, Arun, and Mingyue Chen. "Understanding Skill of Seasonal Mean Precipitation Prediction over California during Boreal Winter and Role of Predictability Limits." Journal of Climate 33, no. 14 (2020): 6141–63. http://dx.doi.org/10.1175/jcli-d-19-0275.1.

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AbstractUsing extensive hindcasts from seasonal prediction systems participating in the North American Multi-Model Ensemble (NMME), possible causes for low skill in predicting seasonal mean precipitation over California during December–February (DJF) are investigated. The analysis focuses on investigating two possibilities for low prediction skill: role model biases or inherent predictability limits. The motivation for the analysis was the seasonal prediction during DJF 2015/16 that called for enhanced probability for above normal precipitation over southern California (which was consistent wi

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18

Kato, Ryohei, Ken-ichi Shimose, and Shingo Shimizu. "Predictability of Precipitation Caused by Linear Precipitation Systems During the July 2017 Northern Kyushu Heavy Rainfall Event Using a Cloud-Resolving Numerical Weather Prediction Model." Journal of Disaster Research 13, no. 5 (2018): 846–59. http://dx.doi.org/10.20965/jdr.2018.p0846.

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Torrential rainfall associated with linear precipitation systems occurred in Northern Kyushu, Japan, during July 5–6, 2017, causing severe damage in Fukuoka and Oita Prefectures. According to our statistical survey using ground rain gauges, the torrential rainfall was among the heaviest in recorded history for 6- and 12-h accumulated rainfall, and was unusual because heavy rain continued locally for nine hours. The predictability of precipitation associated with linear precipitation systems for this event was investigated using a cloud-resolving numerical weather prediction model with a horizo

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19

Peings, Y., Y. Lim, and G. Magnusdottir. "Potential Predictability of Southwest U.S. Rainfall: Role of Tropical and High-Latitude Variability." Journal of Climate 35, no. 6 (2022): 1697–717. http://dx.doi.org/10.1175/jcli-d-21-0775.1.

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Abstract This study explores the potential predictability of Southwest U.S. (SWUS) precipitation for the November–March season in a set of numerical experiments performed with the Whole Atmospheric Community Climate Model. In addition to the prescription of observed sea surface temperature and sea ice concentration, observed variability from the MERRA-2 reanalysis is prescribed in the tropics and/or the Arctic through nudging of wind and temperature. These experiments reveal how a perfect prediction of tropical and/or Arctic variability in the model would impact the prediction of seasonal rain

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20

Setiawan, A. M., Y. Koesmaryono, A. Faqih, and D. Gunawan. "Application of Consecutive Dry Days (CDD) Multi-Model Ensemble (MME) Prediction to Support Agricultural Sector in South Sulawesi Rice Production Centers." IOP Conference Series: Earth and Environmental Science 893, no. 1 (2021): 012081. http://dx.doi.org/10.1088/1755-1315/893/1/012081.

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Abstract Sufficient water availability during the vegetative, reproductive, and early ripening phases of the rice plants is essential. Information on drought, such as Consecutive Dry Days (CDD) predictions in this period, became very crucial and had an important role in maintaining rice production stability. The aim of this study is to investigate the performance of CDD Multi-Model Ensemble prediction, which is applied to South Sulawesi rice production centers. CDD observation was calculated using high resolution gridded precipitation blending data, obtained from BMKG precipitation network sta

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Liu, Cheng Jun, Hong Liang Liu, and Mao Fa Jiang. "Model Prediction on the Behavior of Cerium in Heavy Rail Steel." Advanced Materials Research 255-260 (May 2011): 3984–87. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.3984.

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The thermodynamic model which quantificationally described the behavior of cerium in heavy rail steel was proposed. From the model, the effects of cerium on the composition, sequence and transformation condition of inclusions and the content of cerium dissolved in heavy rail steel were studied principally. When the cleanliness of heavy rail steel is low, the sequence of inclusions precipitation in heavy rail steel is Ce2O3, Ce2O2S, Ce2S3 and CeS. With increasing the cleanliness of steel, the sequence of inclusions precipitation is Ce2O2S and CeS. At 1783 K, the necessary condition of Ce2O3 pre

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Li, Zijun. "Sweet Potato Yield Prediction for Index Insurance in North Carolina." Advances in Economics, Management and Political Sciences 15, no. 1 (2023): 13–22. http://dx.doi.org/10.54254/2754-1169/15/20230858.

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Agriculture index insurance is an innovative topic that has not been well studied in the United States. North Carolina produces 1.7 billion pounds of sweet potatoes in 2020, but currently, there is no insurance to reduce the financial risk of farmers. As a result, index insurance focusing on North Carolina sweet potato farmers can be profitable. In this study, the precipitation is forecasted by the linear model using the first lag and seasonal factors. The predicted precipitations from May to September are then used to predict the yield. The precipitation model has significant factors for Seas

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Mo, X. Q., G. W. Lan, Y. L. Du, and Z. X. Chen. "THE COMPARISON OF TWO PRECIPITATION PREDICTION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1025–32. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1025-2020.

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Abstract. Precipitation forecasts play the role in flood control and drought relief. At present, the time series analysis and the linear regression analysis are two of most commonly used methods. The time series analysis is relatively simple as it only requires historical precipitation data. The model of the linear regression analysis can ensure high accuracy for causality analysis and short, medium and long-term prediction. Guilin is the region of the heavy rain center in Guangxi, which frequently suffers serious losses from rainstorms. Selecting a better model to predict precipitation has th

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Ottom, Mohammad Ashraf, Fayha Al-Shibli, and Mohammed S. Atoum. "The Future of Data Storytelling for Precipitation Prediction in the Dead- Sea-Jordan Using SARIMA Model." International Journal of Membrane Science and Technology 10, no. 1 (2023): 1159–69. http://dx.doi.org/10.15379/ijmst.v10i1.2794.

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This research presents a comprehensive study focused on precipitation prediction for the Dead Sea region utilizing the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The investigation seeks to interpret the accuracy and reliability of the SARIMA model's predictions by comparing them with predictions derived from climate modeling techniques. The evaluation is based on key performance metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Additionally, the paper examines the SARIMA model's predictive capabilities through a c

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Mishka, Alditya Priatna, and C. Djamal Esmeralda. "Precipitation prediction using recurrent neural networks and long short-term memory." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 5 (2020): 2525~2532. https://doi.org/10.12928/TELKOMNIKA.v18i5.14887.

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Prediction of meteorological variables such as precipitation, temperature, wind speed, and solar radiation is beneficial for human life. The variable observations data is available from time to time for more than thirty years, scattered each observation station makes the opportunity to map patterns into predictions. However, the complexity of weather variables is very high, one of which is influenced by Decadal phenomena such as El-Nino Southern Oscillation and IOD. Weather predictions can be reviewed for the duration, prediction variables, and observation stations. This research proposed prec

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Wang, Huijun, and Ke Fan. "A New Scheme for Improving the Seasonal Prediction of Summer Precipitation Anomalies." Weather and Forecasting 24, no. 2 (2009): 548–54. http://dx.doi.org/10.1175/2008waf2222171.1.

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Abstract A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the p

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Chen, Jiajun, Xiaoqing Wang, Ying Yu, Xinzhe Yuan, Xiangyin Quan, and Haifeng Huang. "Improved Prediction of Forest Fire Risk in Central and Northern China by a Time-Decaying Precipitation Model." Forests 13, no. 3 (2022): 480. http://dx.doi.org/10.3390/f13030480.

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With the increase in extreme climate events, forest fires burn in much larger areas. Therefore, it is important to accurately predict forest fire frequencies. Precipitation is an important factor that affects the probability of future forest fires. Previous models used average precipitation values, but the attenuation of precipitation was not considered. In this study, a time-decaying precipitation algorithm was used to calculate the comprehensive precipitation index. This method can better represent the effect of precipitation in predicting the occurrence of forest fires. Moreover, observed f

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Chen, Tianyu. "U-Net-based Precipitation Predict by Cloud Map." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 633–38. http://dx.doi.org/10.54097/hset.v39i.6615.

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Many traditional precipitation prediction methods in meteorology nowadays require many types of data to be input as parameters. This research is to investigate ways to use deep learning techniques for precipitation prediction using only input cloud maps. This paper establishes a technical route for predicting rainfall through cloud map data using U-Net, and experiments. Rainfall models were successfully trained using U-Net and predicted.

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Bodri, L. "Precipitation prediction with neural networks." Acta Geodaetica et Geophysica Hungarica 36, no. 2 (2001): 207–16. http://dx.doi.org/10.1556/ageod.36.2001.2.7.

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Chirigati, Fernando. "Accurate short-term precipitation prediction." Nature Computational Science 1, no. 11 (2021): 709. http://dx.doi.org/10.1038/s43588-021-00161-5.

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Rodriguez, C., A. Hernandez, M. R. Fidalgo, and J. Garmendia. "Statistical method of precipitation prediction." Atmospheric Research 28, no. 3-4 (1992): 299–309. http://dx.doi.org/10.1016/0169-8095(92)90014-2.

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Yang, Jie, Ying Xiang, Jiali Sun, and Xiazhen Xu. "Multi-Model Ensemble Prediction of Summer Precipitation in China Based on Machine Learning Algorithms." Atmosphere 13, no. 9 (2022): 1424. http://dx.doi.org/10.3390/atmos13091424.

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The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS2 model data, we conducted a multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms: the decision tree (DT), random forest (RF), and adaptive boosting (AB) algorithms. On this basis, we explored the applicability of ML algorithms for ensemble prediction of seasonal precipitation in China, as well as the impact of diff

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Byun, Kun-Young, Jun Yang, and Tae-Young Lee. "A Snow-Ratio Equation and Its Application to Numerical Snowfall Prediction." Weather and Forecasting 23, no. 4 (2008): 644–58. http://dx.doi.org/10.1175/2007waf2006080.1.

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Abstract This study 1) presents a logistic regression equation of the snow ratio (SR) for use in a conversion of numerically predicted precipitation amounts into snowfall depths and 2) examines the quality of snowfall-depth forecasts using the proposed SR equation. A logistic regression equation of SR has been derived with surface air temperature as the predictor, using observed 3-h snow ratio and surface air temperature. It is obtained for each of several ranges of the precipitation rate to reduce the large variability of SR. The proposed scheme is found to reproduce the observed SRs better t

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Zhang, Tianpeng, Donghai Wang, Lindong Huang, Yihao Chen, and Enguang Li. "Residual Spatiotemporal Convolutional Neural Network Based on Multisource Fusion Data for Approaching Precipitation Forecasting." Atmosphere 15, no. 6 (2024): 628. http://dx.doi.org/10.3390/atmos15060628.

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Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relations

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Asyrofa, Rahmi, and Firdaus Mahmudy Wayan. "Regression Modelling for Precipitation Prediction Using Genetic Algorithms." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 3 (2017): 1290–300. https://doi.org/10.12928/TELKOMNIKA.v15i3.4028.

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This paper discusses the formation of an appropriate regression model in precipitation prediction. Precipitation prediction has a major influence to multiply the agricultural production of potatoes in Tengger, East Java, Indonesia. Periodically, the precipitation has non-linear patterns. By using a non-linear approach, the prediction of precipitation produces more accurate results. Genetic algorithm (GA) functioning chooses precipitation period which forms the best model. To prevent early convergence, testing the best combination value of crossover rate and mutation rate is done. To test the a

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Errico, Ronald M., George Ohring, Fuzhong Weng, et al. "Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models: Introduction to the JAS Special Collection." Journal of the Atmospheric Sciences 64, no. 11 (2007): 3737–41. http://dx.doi.org/10.1175/2007jas2622.1.

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Abstract To date, the assimilation of satellite measurements in numerical weather prediction (NWP) models has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and precipitation. This special collection describes how to use this information to initialize clouds and precipitation in models. Since clouds and precipitation often occur in sensitive regions for forecast impacts, such improvements are likely necessary for continuing to acquire significant gains in weather forecasting. This special collect

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Ryu, Young-Hee, Seung-Ki Min, and Christoph Knote. "Role of Upwind Precipitation in Transboundary Pollution and Secondary Aerosol Formation: A Case Study during the KORUS-AQ Field Campaign." Journal of Applied Meteorology and Climatology 61, no. 2 (2022): 159–74. http://dx.doi.org/10.1175/jamc-d-21-0162.1.

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Abstract Clouds and precipitation play critical roles in wet removal of aerosols and soluble gases in the atmosphere, and hence their accurate prediction largely influences accurate prediction of air pollutants. In this study, the impacts of clouds and precipitation on wet scavenging and long-range transboundary transport of pollutants are examined during the 2016 Korea–United States Air Quality (KORUS-AQ) field campaign using the Weather Research and Forecasting Model coupled with chemistry. Two simulations—one in which atmospheric moisture is constrained and one in which it is not—are perfor

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Goddard, L., A. G. Barnston, and S. J. Mason. "Evaluation of the IRI'S “Net Assessment” Seasonal Climate Forecasts: 1997–2001." Bulletin of the American Meteorological Society 84, no. 12 (2003): 1761–82. http://dx.doi.org/10.1175/bams-84-12-1761.

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The International Research Institute for Climate Prediction (IRI) net assessment seasonal temperature and precipitation forecasts are evaluated for the 4-yr period from October–December 1997 to October–December 2001. These probabilistic forecasts represent the human distillation of seasonal climate predictions from various sources. The ranked probability skill score (RPSS) serves as the verification measure. The evaluation is offered as time-averaged spatial maps of the RPSS as well as area-averaged time series. A key element of this evaluation is the examination of the extent to which the con

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RAKOTOARIMANANA, Rija Santaniaina, Tiana Razefania RAMAHEFY, and Solofo Randrianja. "Prediction of Monthly Precipitation by Recurrent Neural Network." International Journal of Progressive Sciences and Technologies 42, no. 1 (2023): 265. http://dx.doi.org/10.52155/ijpsat.v42.1.5850.

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This article aims to make a “Monthly precipitation prediction by Recurrent Neural Network”. Faced with climate change and the harmful effects that it is currently causing, in particular the climate problem, this experience could help everyone to make decisions or measures for certain more developed studies or actions to be undertaken to deal with this situation. The method used for prediction is the Recurrent Neural Network, more precisely Long Short-Term Memory (LTSM) models. This method is suitable for processing temporal data. After the operation, we obtained the predictions during the mode

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Anochi, Juliana Aparecida, and Marilia Harumi Shimizu. "Precipitation Forecasting and Drought Monitoring in South America Using a Machine Learning Approach." Meteorology 4, no. 1 (2024): 1. https://doi.org/10.3390/meteorology4010001.

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Climate forecasting is essential for energy production, agricultural activities, transportation, and civil defense sectors, serving as a foundation for decision-making and risk management. This study addresses the challenge of accurately predicting extreme droughts in South America, a region highly vulnerable to climate variability. By employing a supervised neural network (NN) within a machine learning framework, we developed a methodology to forecast precipitation and subsequently calculate the Standardized Precipitation Index (SPI) for predicting drought conditions across the continent. The

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Luo, Jiannan, Wenxi Lu, Yefei Ji, and Dajun Ye. "A comparison of three prediction models for predicting monthly precipitation in Liaoyuan city, China." Water Supply 16, no. 3 (2016): 845–54. http://dx.doi.org/10.2166/ws.2016.006.

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Accurate prediction of precipitation is of great importance for irrigation management and disaster prevention. In this study, back propagation artificial neural network (BPANN), radial basis function artificial neural network (RBFANN) and Kriging methods were applied and compared to predict the monthly precipitation of Liaoyuan city, China. An autocorrelation analysis method was used to determine model input variables first, and then BPANN, RBFANN and Kriging methods were applied to recognize the relationship between previous precipitation and later precipitation with the monthly precipitation

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Lotfirad, Morteza, Hassan Esmaeili-Gisavandani, and Arash Adib. "Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran." Journal of Water and Climate Change 13, no. 2 (2021): 383–406. http://dx.doi.org/10.2166/wcc.2021.287.

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Abstract The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficients of SPI and SPEI were 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest

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Islam, Md Anowarul, and Tomonori Sato. "Influence of Terrestrial Precipitation on the Variability of Extreme Sea Levels along the Coast of Bangladesh." Water 13, no. 20 (2021): 2915. http://dx.doi.org/10.3390/w13202915.

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The coastal area of Bangladesh is highly vulnerable to extreme sea levels because of high population exposure in the low-lying deltaic coast. Since the area lies in the monsoon region, abundant precipitation and the resultant increase in river discharge have raised a flood risk for the coastal area. Although the effects of atmospheric forces have been investigated intensively, the influence of precipitation on extreme sea levels in this area remains unknown. In this study, the influence of precipitation on extreme sea levels for three different stations were investigated by multivariate regres

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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, w

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Wei, Chih-Chiang. "RBF Neural Networks Combined with Principal Component Analysis Applied to Quantitative Precipitation Forecast for a Reservoir Watershed during Typhoon Periods." Journal of Hydrometeorology 13, no. 2 (2012): 722–34. http://dx.doi.org/10.1175/jhm-d-11-03.1.

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Abstract The forecast of precipitations during typhoons has received much attention in recent years. It is important in meteorology and atmospheric sciences. Hence, the study on precipitation nowcast during typhoons is of great significance to operators of a reservoir system. This study developed an improved neural network that combines the principal component analysis (PCA) technique and the radial basis function (RBF) network. The developed methodology was employed to establish the quantitative precipitation forecast model for the watershed of the Shihmen Reservoir in northern Taiwan. The re

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Wang, Maofa, Bingcheng Yan, Yibo Zhang, et al. "Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model." Atmosphere 15, no. 11 (2024): 1308. http://dx.doi.org/10.3390/atmos15111308.

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Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include tempera

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Hitchens, Nathan M., Robert J. Trapp, Michael E. Baldwin, and Alexander Gluhovsky. "Characterizing Subdiurnal Extreme Precipitation in the Midwestern United States." Journal of Hydrometeorology 11, no. 1 (2010): 211–18. http://dx.doi.org/10.1175/2009jhm1129.1.

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Abstract This research establishes a methodology to quantify the characteristics of convective cloud systems that produce subdiurnal extreme precipitation. Subdiurnal extreme precipitation events are identified by examining hourly precipitation data from 48 rain gauges in the midwestern United States during the period 1956–2005. Time series of precipitation accumulations for 6-h periods are fitted to the generalized Pareto distribution to determine the 10-yr return levels for the stations. An extreme precipitation event is one in which precipitation exceeds the 10-yr return level over a 6-h pe

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Şevgin, Fatih. "The Statistical Methods for Precipitation Prediction with Trend Analysis." Fırat Üniversitesi Mühendislik Bilimleri Dergisi 37, no. 1 (2025): 433–41. https://doi.org/10.35234/fumbd.1604593.

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This study investigates the condensation of water vapor in the atmosphere, precipitating to the ground in either solid or liquid form. This meteorological variable exhibits temporal and spatial variations influenced by climate change and other factors. To better analyze the effects of climate change on precipitation, the Konya Closed Basin was selected as the research area. Key parameters and datasets critical for various sectors and activities—from hydraulic structure design to irrigation planning—were identified. Seasonal and annual precipitation trend analyses were conducted for the provinc

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Jiang, Xianan, Duane E. Waliser, Peter B. Gibson, Gang Chen, and Weina Guan. "Why Seasonal Prediction of California Winter Precipitation Is Challenging." Bulletin of the American Meteorological Society 103, no. 12 (2022): E2688—E2700. http://dx.doi.org/10.1175/bams-d-21-0252.1.

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Abstract Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can

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Gultepe, I., A. J. Heymsfield, P. R. Field, and D. Axisa. "Ice-Phase Precipitation." Meteorological Monographs 58 (January 1, 2017): 6.1–6.36. http://dx.doi.org/10.1175/amsmonographs-d-16-0013.1.

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AbstractIce-phase precipitation occurs at Earth’s surface and may include various types of pristine crystals, rimed crystals, freezing droplets, secondary crystals, aggregates, graupel, hail, or combinations of any of these. Formation of ice-phase precipitation is directly related to environmental and cloud meteorological parameters that include available moisture, temperature, and three-dimensional wind speed and turbulence, as well as processes related to nucleation, cooling rate, and microphysics. Cloud microphysical parameters in the numerical models are resolved based on various processes

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