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Journal articles on the topic 'Precipitation prediction'
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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 precipitation predictions over 1–30 days were evaluated in nine major river basins. The daily prediction skill rapidly declines with lead time. In contrast, the correlation coefficient between the cumulative precipitation predictions and corresponding observations increases at first and peaks at 0.7–0.8 after 3–5 days, then gradually decreases and settles at approximately 0.2–0.6. Among the three evaluated models, the ECMWF model demonstrates the best skill, maintaining a correlation coefficient of approximately 0.5 for 2-week cumulative precipitation. Moreover, the correlation coefficient of the model’s prediction is 0.2–0.5 higher than that of the climatological prediction over a large domain for the 30-day cumulative precipitation during the rainy summer. Similarly, the equitable threat score for forecasting below- and above-normal precipitation events presents good results in eastern China but is affected by biases of raw predictions. The variations in the subseasonal prediction skill at different time scales reveal the potential values of cumulative precipitation predictions. The findings of this study can provide practical information for applications that prioritize the long-term aggregation of hydrometeorological variables. Significance Statement The daily and cumulative precipitation prediction skills of three subseasonal prediction products were evaluated over China in this study. Our results reveal the spatiotemporal variations in prediction skill, especially with respect to time scale. Compared to daily precipitation predictions, cumulative precipitation predictions are more skillful, with correlation coefficients peaking at 0.7–0.8 after 3–5 days. These results can provide valuable information for water resource managers who are more concerned with the general conditions over a period than with hydrometeorological events occurring on a particular day. This study can guide end users in applying appropriate time scales to fully exploit numerical weather prediction information and satisfy their specific needs.
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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 the deposition areas and their sizes. This study is an extension of the first part, in which the advanced versions of Peng-Robinson (APR78 EOS) and Soave-Redlich-Kwong (ASRK EOS) cubic equations of state and cubic-plus-association equations of state (CPA EOS) were compared in predicting asphaltene precipitation conditions by using Multiflash software. The prediction was made for live crude oil (API gravity = 24˚ API) from an Iraqi oil field at different temperatures. The required data for modeling are fluid compositional analysis, PVT experiment data, and flow assurance data, which were collected from a fluid analysis report. It was noticed that the agreement in prediction was very high between the ASRK EOS and the CPA EOS for all temperature values and diverged from the APR78 EOS model at low temperatures (T = 50 and 40 ˚C). This study demonstrates the impact of selecting the appropriate model on predicting asphaltene precipitation and its influence on future predictions of the asphaltene deposition problem.
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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 Short-Term Memory (LSTM) network models for predicting the precipitation based on meteorological data from 2008 to 2018 in Jingdezhen City. After identifying the correlation between meteorological variables and the precipitation, nine significant input variables were selected to construct the LSTM model. Then, the selected meteorological variables were refined by the relative importance of input variables to reconstruct the LSTM model. Finally, the LSTM model with final selected input variables is used to predict the precipitation and the performance is compared with other classical statistical algorithms and the machine learning algorithms. The experimental results show that the LSTM is suitable for precipitation prediction. The RNN models, combined with meteorological variables, could predict the precipitation accurately in Jingdezhen City and provide sufficient time to prepare strategies against potential related disasters.
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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 through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.
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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 the prediction of cumulative precipitation, and found to delay evacuation or evacuation orders.
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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 spatial correlation of precipitation is lower. This effect is shown from an empirical experiment carried out with a fully uncorrelated dataset, generated from a space-shake experiment, where the precipitation time series of each grid cell is randomly assigned to another grid cell. The underlying mechanisms of this effect are further highlighted with synthetic predictions simulated using a stochastic spatiotemporal generator. It is shown 1) that the skill increase with spatial aggregation jointly results from the higher and lower values obtained for the resolution and uncertainty terms of the CRPS decomposition, respectively, and 2) that the lower spatial correlation of precipitation is beneficial for both terms. Results obtained for France suggest that the prediction skill indefinitely increases with aggregation. A last experiment is finally proposed to show that this is not expected to be always the case. A prediction skill optimum is, for instance, obtained when the mean areal precipitation is estimated over a region where local precipitations of different grid cells originate from different underlying meteorological processes.
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Šaur, David, and Lukáš Pavlík. "Comparison of accuracy of forecasting methods of convective precipitation." MATEC Web of Conferences 210 (2018): 04035. http://dx.doi.org/10.1051/matecconf/201821004035.
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This article is focused on the comparison of the accuracy of quantitative, numerical, statistical and nowcasting forecasting methods of convective precipitation including three flood events that occurred in the Zlin region in the years 2015 - 2017. Quantitative prediction is applied to the Algorithm of Storm Prediction for outputs “The probability of convective precipitation and The statistical forecast of convective precipitation”. The quantitative prediction of the probability of convective precipitation is primarily compared with the precipitation forecasts calculated by publicly available NWP models; secondary to statistical and nowcasting predictions. The statistical prediction is computed on the historical selection criteria and is intended as a complementary prediction to the first algorithm output. The nowcasting prediction operates with radar precipitation measurements, specifically with X-band meteorological radar outputs of the Zlín Region. Compared forecasting methods are used for the purposes of verification and configuration prediction parameters for accuracy increase of algorithm outputs.
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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 precipitation through its own data at previous time steps while in scenario 2, the central station’s data were imposed into the models, in addition to each station’s data, as exogenous input. Afterwards, the ensemble modeling was generated to improve the performance of the precipitation predictions. To end this aim, two linear and one non-linear ensemble techniques were used and then the obtained outcomes were compared. In terms of efficiency measures, the averaging methods employing scenario 2 and non-linear ensemble method revealed higher prediction efficiency. Also, in terms of Skill score, non-linear neural ensemble method could enhance predicting efficiency up to 44% in the verification step.
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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 Pearson correlation coefficient larger than 0.6. Beyond week 2, predictions generally provide little useful deterministic skill. Sources of extended-range predictability are investigated, focusing on El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO). We found that periods of heavy precipitation associated with ENSO are more predictable at the extended range period. During El Niño years, Southern California tends to receive more precipitation in late winter, and most models show better extended-range prediction skill. On the contrary, during La Niña years Oregon tends to receive more precipitation in winter, with most models showing better extended-range skill. We believe the excessive precipitation and improved extended-range prediction skill are caused by the meridional shift of baroclinic systems as modulated by ENSO. Through examining precipitation anomalies conditioned on the MJO, we verified that active MJO events systematically modulate the area’s precipitation distribution. Our results show that most models do not represent the MJO or its associated teleconnections, especially at phases 3–4. However, some models exhibit enhanced extended-range prediction skills under active MJO conditions.
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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 Precipitation Evaporation Index (SPEI), which represents a complex and difficult time series drought index using climate data for weather phenomena. The Standard Precipitation Evaporation Index is a method of calculating the cumulative precipitation by excluding the cumulative evaporation amount from the cumulative precipitation using precipitation and evapotranspiration data, and the evaporation amount is calculated using the monthly heat index method. The Meteorological Agency evaluated meteorological drought using SPI6, which is a 6-month cumulative precipitation standard, and applied it to machine learning based on monthly data and daily data SPEI6 in this study. As a result, ANN monthly data R2 was 0.488 in Andong and 0.533 in Mungyeong, Gumi 0.594, SVR 0.452, 0.496, 0.564, RF 0.355, 0.467, 0.524, and the daily data are ANN 0.923, 0.919, 0.915, SVR 0.925, 0.923, 0.896, RF 0.915, 0.915, 0.797, and the daily data SPEI at all points. It was confirmed that high prediction was obtained when machine learning was applied to these methods.
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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 field is combined with the Empirical Orthogonal Function decomposition and the causal information flow. Next, the best predictors are screened through Empirical Orthogonal Function decomposition and causal information flow, and a data-driven precipitation prediction model is established using a Back Propagation Neural Network and a Random Forest algorithm to conduct the 1-month, 3-month, and 6-month precipitation predictions. The results show that: The machine learning-based precipitation prediction model has high accuracy and is generally able to predict the precipitation trend in the western region better. The Random Forest algorithm significantly outperforms the Back Propagation Neural Network algorithm in the prediction of the three starting times, and the prediction ability of both models gradually decreases as the starting time increases. Compared with the 2022 flood season prediction scores of the Institute of Atmospheric Sciences of the Chinese Academy of Sciences, the model improves the prediction of 1-month and 3-month precipitation in the western region and provides a new idea for the short-term climate prediction of precipitation in western China.
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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 overcome shortcomings with regard to the structural inadequacy of individual dynamic–statistic predictions, reducing formulation uncertainties and resulting in more stable and accurate predictions. Impact factor analysis indicates that (1) the station-based ensemble prediction, with an anomaly correlation coefficient (ACC) of 0.10–0.11 and a prediction score (PS) score of 69.3–70.2, has shown better skills than the grid-based one as the former produces a probability density distribution of precipitation that is closer to observations than the latter. (2) The use of the spatial average removed anomaly correlation coefficient (SACC) may lower the prediction skill and introduce obvious errors into the estimation of the spatial consistency of prediction anomalies. SACC could be replaced by the revised anomaly correlation coefficient (RACC), which is calculated directly using the precipitation anomalies of each station without subtracting the average precipitation anomaly of all stations. (3) The low dispersal intensity among ensemble samples of the UWE implies that the historically similar errors selected by means of different approaches are quite close to each other, making the correction of the model prediction more reliable. Therefore, the UWE is expected to further improve the accuracy of summer precipitation prediction in China by considering impact factors such as the grid- or station-based ensemble approach, the method of calculating the ACC, and the dispersal intensity of ensemble samples in the application and analysis process of the UWE.
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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 precipitation prediction. M-K test was applied for nearly 30 years of predicting results for time series analysis, the results show that the prediction data with the increase in prediction accuracy tends to gradually increase.
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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. To address these shortcomings, this study introduces an innovative approach for accurately predicting extreme precipitation: the multimodal attention ConvLSTM-GAN for extreme rainfall nowcasting (ER-MACG). This model employs high-resolution Fengyun-4A(FY4A) satellite precipitation products, as well as terrain and atmospheric datasets as inputs. The ER-MACG model enhances the ConvLSTM-GAN framework by optimizing the generator structure with an attention module to improve the focus on critical areas and time steps. This model can alleviate the problem of information loss in the spatial–temporal convolutional long short-term memory network (ConvLSTM) and, compared with the standard ConvLSTM-GAN model, can better handle the detailed changes in time and space in extreme precipitation events to achieve more refined predictions. The main findings include the following: (a) The ER-MACG model demonstrated significantly greater predictive accuracy and overall performance than other existing approaches. (b) The exclusive consideration of DEM and LPW data did not significantly enhance the ability to predict extreme precipitation events in Zhejiang Province. (c) The ER-MACG model significantly improved in identifying and predicting extreme precipitation events of different intensity levels.
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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 resampling the training datasets or lowering the threshold probability for classification cannot improve the predictive skill of the random forest model. The study reveals that the predictive skill of the random forest model shows seasonality, but is only weakly influenced by the geographic diversity of the training dataset. Finally, the study shows that the most important predictor is the precipitation predictions by the radar nowcasts followed by the precipitation predictions by the numerical weather predictions. Although meteorological variables other than precipitation are weaker predictors, the results suggest that they can help to reduce the false alarm ratio and to increase the success ratio of the precipitation prediction.
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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 with expected conditions during an extreme El Niño) while the observed precipitation was below normal. Based on various analysis approaches and using hindcast datasets from multiple seasonal prediction systems, we build up the evidence that low skill in predicting seasonal mean precipitation over California is likely to be due to inherent predictability associated with a low signal-to-noise (SNR) regime. For the same set of seasonal prediction systems, the precipitation variability over California is contrasted with that over the southeast United States where prediction skill, as well as the SNR, is higher. The discussion also notes that building a knowledge base that goes beyond the well-known response to ENSO (based on the linear regression or composite techniques) has proven to be difficult and a systematic approach to reaching resolution to some of the overarching questions is required, and toward that end, a pathway is suggested.
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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 horizontal grid interval of 1 km. The development of multiple linear precipitation systems was predicted in experiments whose initial calculation time was from several hours to immediately before the torrential rain (9:00, 10:00, 11:00, and 12:00 Japan Standard Time on July 5), although there were some displacement errors in the predicted linear precipitation systems. However, the stationary linear precipitation systems were not properly predicted. The predictions showed that the linear precipitation systems formed one after another and moved eastwards. In the relatively accurate prediction whose initial time was 12:00 on July 5, immediately before the torrential rainfall began, the forecast accuracy was evaluated using the 6-h accumulated precipitation (P6h) from 12:00 to 18:00 on July 5, the period of the heaviest rainfall. The average of the P6h in an area 100 km×40 km around the torrential rainfall area was nearly the same for the analysis and the prediction, indicating that the total precipitation amount around the torrential rainfall area was predictable. The result of evaluating the quantitative prediction accuracy using the Fractions Skill Score (FSS) indicated that a difference in location of 25 km (50 km) or greater should be allowed for in the models to produce useful predictions (those defined as having an FSS ≥0.5) for the accumulated rainfall of P6h ≥50 mm (150 mm). The quantitative prediction accuracy examined in this study can be basic information to investigate the usage of predicted precipitation data.
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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 rainfall over the SWUS, at various time scales. Imposing tropical variability improves the representation of the observed North Pacific atmospheric circulation, and the associated SWUS seasonal precipitation. This is also the case at the subseasonal time scale due to the inclusion of the Madden–Julian oscillation (MJO) in the model. When additional nudging is applied in the Arctic, the model skill improves even further, suggesting that improving seasonal predictions in high latitudes may also benefit prediction of SWUS precipitation. An interesting finding of our study is that subseasonal variability represents a source of noise (i.e., limited predictability) for the seasonal time scale. This is because when prescribed in the model, subseasonal variability, mostly the MJO, weakens the El Niño–Southern Oscillation (ENSO) teleconnection with SWUS precipitation. Such knowledge may benefit S2S and seasonal prediction as it shows that depending on the amount of subseasonal activity in the tropics on a given year, better skill may be achieved in predicting subseasonal rather than seasonal rainfall anomalies, and conversely. Significance Statement Subseasonal and seasonal predictability of precipitation over the Southwest United States (SWUS) during the wet season is challenging, and long-range forecasts from climate models still exhibit poor skill over this region. In this study we use numerical experiments with constrained tropical and/or Arctic atmospheric variability to explore how climate processes in these two regions impact the SWUS precipitation. Our results highlight how much forecast skill in SWUS precipitation may be gained from better predictions in tropical and high latitudes, from subseasonal to multiyear time scales.
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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 stations and the daily-improved Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) version 2.0. The North American Multi-Model Ensemble (NMME) monthly precipitation hindcast data during 1982 – 2010 periods from each nine individual global climate models were used to develop seasonal CDD predictions. World Meteorological Organization (WMO) Standard Verification for Long Range Forecast (SVS-LRF) method applied to describe this CDD prediction performance on four different seasons. Investigation of model performance during strong El Niño event in 1997 also conducted in order to get general skill overview regarding extreme climate event. Best performance of CDD prediction generally occurred during JJA and DJF period. MME CDD prediction shows better performance compared to individual model performance for almost all season. Spatial coherence between prediction and observation over rice production centers during 1997 El Niño confirms the skill of CDD predictions. The application of this prediction on agricultural sector will be very useful in order to support rice production sustainability and food security. Further analysis result can be found on full paper.
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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 precipitation in heavy rail steel is ao/as≥0.186, for Ce2S3 precipitation is ao/as≤0.394. The content of cerium dissolved in heavy rail steel is mainly affected by Ce2S3 inclusion. With the precipitations of all inclusions being stable, the curve between the dissolved cerium content and cerium addition becomes linear.
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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 Season3, which represents July to September, the rainy season of North Carolina; the yield model has a significant variable of September, which is the harvest season of sweet potatoes in North Carolina. The precipitation model falls short of predicting the exact value of precipitation, but it catches the trend and seasonality. Despite the insensitivity of the precipitation model, the yield is predicted relatively accurate. The result of this study can be used to design the thresholds of the index insurance. Insurance companies can use thresholds to design insurance plans with different premiums.
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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 the important reference significance for improving the accuracy of precipitation weather forecast. In this research, the two methods are used to predict precipitation in Guilin. According to data of the monthly maximum precipitation, monthly average daily precipitation and monthly total precipitation from 2014 to 2016, this paper establishes the time series model and linear regression analysis model to predict precipitation in 2017 and compare the forecast results. The results show that the monthly average daily precipitation model is best with the accuracy of the time series model, and the residual error of predicted precipitation is 3.08 mm, but the change trend of predicted precipitation is not accord with the actual situation. The residual error is only 0.45 mm through using inter-annual linear regression equation to predict the precipitation, but the predicted summer precipitation is quite different from the actual one. The linear equation established by different seasons is used to predict the precipitation with residual error of 3.25 mm, and it is coincident for the predicted precipitation trend with the actual situation. Furthermore, the predictions fitting errors of spring, summer, autumn and winter are all less than 20%, which are within the scope of the specification prediction error.
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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 comparison with actual observations spanning the period from 2010 to 2022. The obtained results reveal an MSE of 12.84593, an MAE of 2.34407, and an RMSE of 3.584123 for this period. Significantly, the SARIMA model surpasses the predictions of prominent climate models (CMIP6), namely ACCESS_CM2, Earth3_Veg, GISS_E2, and HadGEM3, based on comparative performance assessments. The findings emphasize the robustness of the SARIMA model in capturing the essence of the observations and predicting precipitation patterns, not only through its superior performance against climate models but also through its alignment with actual observations. This study contributes to a deeper understanding of precipitation prediction in the Dead Sea region and underscores the potential of the SARIMA model in enhancing forecasting accuracy for hydrological and climatic investigations
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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 precipitation prediction using recurrent neural networks and long short-term memory. Experiments were carried out using the prediction duration factor, the period as a feature and the amount of data set used, and the optimization model. The results showed that the time-lapse as a shorter feature gives good accuracy. Also, the duration of weekly predictions provides more accuracy than monthly, which is 85.71% compared to 83.33% of the validation data.
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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 prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, −0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme.
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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 fire spots were converted into a continuous density of fire spots. The structure of the prediction model is more realistic, which is conducive to obtaining higher-precision prediction results. Additionally, the support vector machine (SVM) regression model was used to construct a forest fire warning model. When the comprehensive precipitation index was compared with the average precipitation value, the accuracy of the four forest areas in central and northern China in the test set was improved by approximately 10%. The findings are relevant to forest ecologists and managers for future mitigation of forest fires, and also for successful prediction of other fire-prone areas.
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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 different hyperparameters on prediction accuracy. Then, MME predictions based on optimal hyperparameters were constructed for different regions of China. The results showed that all three ML algorithms had an optimal maximum depth less than 2, which means that, based on the current amount of data, the three algorithms could only predict positive or negative precipitation anomalies, and extreme precipitation was hard to predict. The importance of each model in the ML-based MME was quantitatively evaluated. The results showed that NCEP CFSv2 and JMA CPS2 had a higher importance in MME for the eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provided a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME had a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019–2021, exhibiting a significant improvement over the other methods. This shows that ML methods have great potential for improving short-term climate prediction.
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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 than other schemes, according to verification against an independent observation dataset. Predictions of precipitation and snowfall using the Weather Research and Forecasting (WRF) model and the proposed SR equation have shown some skill for a low threshold [1 mm (6 h)−1 and 1 cm (6 h)−1 for precipitation and snowfall depth, respectively]: the 10-case mean threat scores (TSs) are 0.47 and 0.43 for precipitation and snowfall forecasts, respectively. For higher thresholds [5 mm (6 h)−1 and 5 cm (6 h)−1 for precipitation and snowfall depth, respectively], however, TSs for snowfall forecasts tend to be significantly lower than those for the precipitation forecasts. Examination indicates that the poor predictions of relatively heavy snowfall are associated with incorrect prediction(s) of precipitation amount and/or surface air temperature, and the errors of the estimated SRs. The proposed SR equation can be especially useful for snowfall prediction for an area where the spatial variation of precipitation type (e.g., wet or dry snow) is significant.
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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 relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model’s predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area.
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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 accuracy of the predicted results are used Root Mean Square Error (RMSE) as a benchmark. Based on the RMSE value of each method on every location, prediction using GA-Non-Linear Regression is better than Fuzzy Tsukamoto for each location. Compared to Generalized Space-Time Autoregressive-Seemingly Unrelated Regression (GSTAR-SUR), precipitation prediction using GA is better. This has been proved that for 3 locations GA is superior and on 1 location, GA has the least value of deviation level.
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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 collection of the Journal of the Atmospheric Sciences is devoted to articles based on papers presented at the International Workshop on Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models, in Lansdowne, Virginia, in May 2005. This introduction summarizes the findings of the workshop. The special collection includes review articles on satellite observations of clouds and precipitation (Stephens and Kummerow), parameterizations of clouds and precipitation in NWP models (Lopez), radiative transfer in cloudy/precipitating atmospheres (Weng), and assimilation of cloud and precipitation observations (Errico et al.), as well as research papers on these topics.
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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 model training and testing phase. The errors of the model during training and testing compared to the original data are also presented in this study.
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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 performed and evaluated against surface and airborne observations. The simulation with moisture constraints is found to better reproduce precipitation as well as surface PM2.5, whereas the areal extent and amount of precipitation are overpredicted in the simulation without moisture constraints. As a results of overpredicted clouds and precipitation and consequently overpredicted wet scavenging, PM2.5 concentration is generally underpredicted across the model domain in the simulation without moisture constraints. The effects are significant not only in the precipitating region (upwind region, southern China in this study) but also in the downwind region (South Korea) where no precipitation is observed. The difference in upwind precipitation by 77% on average between the two simulations leads to the difference in PM2.5 by ∼39% both in the upwind and downwind regions. The transboundary transport of aerosol precursors, especially nitric acid, has a considerable impact on ammonium-nitrate aerosol formation in the ammonia-rich downwind region. This study highlights that skillful prediction of atmospheric moisture can have ultimate potential to skillful prediction of aerosols across regions.
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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 consolidation of several predictions, accomplished here subjectively by the forecasters, contributes to or detracts from the forecast skill possible from any individual prediction tool. Overall, the skills of the net assessment forecasts for both temperature and precipitation are positive throughout the 1997–2001 period. The skill may have been enhanced during the peak of the 1997/98 El Niño, particularly for tropical precipitation, although widespread positive skill exists even at times of weak forcing from the tropical Pacific. The temporally averaged RPSS for the net assessment temperature forecasts appears lower than that for the AGCMs. Over time, however, the IRI forecast skill is more consistently positive than that of the AGCMs. The IRI precipitation forecasts generally have lower skill than the temperature forecasts, but the forecast probabilities for precipitation are found to be appropriate to the frequency of the observed outcomes, and thus reliable. Over many regions where the precipitation variability is known to be potentially predictable, the net assessment precipitation forecasts exhibit more spatially coherent areas of positive skill than most, if not all, prediction tools. On average, the IRI net assessment forecasts appear to perform better than any of the individual objective prediction tools.
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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 proposed model was trained with precipitation data from the Global Precipitation Climatology Project (GPCP) for the period 1983–2023. It provided monthly drought forecasts, which were validated against observational data and compared with predictions from the North American Multi-Model Ensemble (NMME). Key findings indicate the neural network’s ability to capture complex precipitation patterns and predict drought conditions. The model’s architecture effectively integrates precipitation data, demonstrating superior performance metrics compared to traditional approaches like the NMME. This study reinforces the relevance of using machine learning algorithms as a robust tool for drought prediction, providing critical information that can assist in decision-making for sustainable water resource management.
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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 data of 1971–2009 in Liaoyuan city. Finally, the three models' performances were compared based on models accuracy, models stability and models computational cost. Comparison results showed that for model accuracy, RBFANN performed best, followed by Kriging, and BPANN performed worst; for stability and computational cost, RBFANN and Kriging models performed better than the BPANN model. In conclusion, RBFANN is the best method for precipitation prediction in Liaoyuan city. Therefore, the developed RBFANN model was applied to predict the monthly precipitation for 2010–2019 in the study area.
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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 of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing the time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.
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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, which can better reflect the actual precipitation change. From 2001 to 2010, the maximum deviation of the predicted values never exceeds 4%. The testing results show that the proposed model can increase precipitation forecasting accuracies not only in GPS PWV but also in annual precipitation.
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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 regression using the meteorological drivers of precipitation, sea level pressure, and wind. The prediction of sea levels considering precipitation effects outperformed predictions without precipitation. The benefit of incorporating precipitation was greater at Cox’s Bazar than at Charchanga and Khepupara, reflecting the hilly landscape at Cox’s Bazar. The improved prediction skill was mainly confirmed during the monsoon season, when strong precipitation events occur. It was also revealed that the precipitation over the Bangladesh area is insensitive to the El Niño-Southern Oscillation and Indian Ocean Dipole mode. The precipitation over northern Bangladesh tended to be high in the year of a high sea surface temperature over the Bay of Bengal, which may have contributed to the variation in sea level. The findings suggest that the effect of precipitation plays an essential role in enhancing sea levels during many extreme events. Therefore, incorporating the effect of terrestrial precipitation is essential for the better prediction of extreme sea levels, which helps coastal management and reduction of hazards.
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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 results obtained from RBF, multiple linear regression (MLR), PCA–RBF, and PCA–MLR models included the forecasts of L-ahead (L = 1, 3, 6) hourly accumulated precipitations. The deducted prediction results were compared in terms of four measures [mean absolute error (MAE), RMSE, coefficient of correlation (CC), and coefficient of efficiency (CE)] and four skill scores [percentage error (PE), area-weighted error score (AWES), bias score (BIAS), and equitable threat score (ETS)]. The results showed that predictions obtained using RBF and PCA–RBF were better than those produced by MLR and PCA–MLR. Although both RBF and PCA–RBF can provide good results on average, the network architecture and the learning speed of the PCA–RBF network are superior to those of the simple RBF network. This is because PCA technique could greatly reduce the input parameters and simplify concurrently the network structure. Consequently, the PCA–RBF neural networks can be regarded as a reliable model for predicting precipitation during typhoons.
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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 temperature, dew point, prior precipitation, and air pressure. Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. A prediction-based strategy outperforms equal distribution in managing resources efficiently, as shown in a case study using 2022 Beidahu data. Overall, our research advances precipitation forecasting through deep learning and offers valuable insights for water resource management.
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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 provinces of Aksaray, Ankara, Isparta, Mersin, and Nevşehir using statistical methods, including the Mann-Kendall test, Spearman’s Rho, and the Innovative Şen Test, with the aid of XLSTAT software. The results revealed negative precipitation trends in Aksaray, Ankara, and Nevşehir, while positive trends were observed in Isparta and Mersin. Additionally, complementary data were collected from the Muş Meteorology Provincial Directorate to support the findings.
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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 period. Return levels in the Midwest vary between 54 and 93 mm for 6-h events. Most of the precipitation contributing to these events falls within 1–2 h. Characteristics of the precipitating systems responsible for the extremes are derived from the National Centers for Environmental Prediction stage II and stage IV multisensor precipitation data. The precipitating systems are treated as objects that are identified using an automated procedure. Characteristics considered include object size and the precipitation mean, variance, and maximum within each object. For example, object sizes vary between 96 and 34 480 km2, suggesting that a wide variety of convective precipitating systems can produce subdiurnal extreme precipitation.
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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 be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.
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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 such as nucleation, mixing, collision and coalescence, accretion, riming, secondary ice particle generation, turbulence, and cooling processes. These processes are usually parameterized based on assumed particle size distributions and ice crystal microphysical parameters such as mass, size, and number and mass density. Microphysical algorithms in the numerical models are developed based on their need for applications. Observations of ice-phase precipitation are performed using in situ and remote sensing platforms, including radars and satellite-based systems. Because of the low density of snow particles with small ice water content, their measurements and predictions at the surface can include large uncertainties. Wind and turbulence affecting collection efficiency of the sensors, calibration issues, and sensitivity of ground-based in situ observations of snow are important challenges to assessing the snow precipitation. This chapter’s goals are to provide an overview for accurately measuring and predicting ice-phase precipitation. The processes within and below cloud that affect falling snow, as well as the known sources of error that affect understanding and prediction of these processes, are discussed.
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Kang, Hongwen, Chung-Kyu Park, Saji N. Hameed, and Karumuri Ashok. "Statistical Downscaling of Precipitation in Korea Using Multimodel Output Variables as Predictors." Monthly Weather Review 137, no. 6 (2009): 1928–38. http://dx.doi.org/10.1175/2008mwr2706.1.
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Abstract A pattern projection downscaling method is applied to predict summer precipitation at 60 stations over Korea. The predictors are multiple variables from the output of six operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction was made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of six model downscaled precipitation forecasts using the best predictors and will be referred to as “DMME.” It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse-resolution predictions of general circulation models. Although Korea’s precipitation is strongly influenced by local mountainous terrain, DMME performs well at 59 stations with correlation skill significant at the 95% confidence level. The improvement of the prediction skill is attributed to three steps: coupled pattern selection, optimal predictor selection, and the multimodel downscaled precipitation ensemble. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected, can be used to make skillful predictions of the local precipitation by using appropriate statistical downscaling methods.