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Relevant bibliographies by topics / Precipitation prediction

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Academic literature on the topic 'Precipitation prediction'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Precipitation prediction"

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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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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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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Dissertations / Theses on the topic "Precipitation prediction"

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Tiwari, Pushp Raj. "Dynamical downscaling for wintertime seasonal prediction of precipitation over northwest India." Thesis, IIT Delhi, 2016. http://localhost:8080/xmlui/handle/12345678/7091.

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Wahl, Sabrina [Verfasser]. "Uncertainty in mesoscale numerical weather prediction: probabilistic forecasting of precipitation / Sabrina Wahl." Bonn : Universitäts- und Landesbibliothek Bonn, 2015. http://d-nb.info/1080561099/34.

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Surcel, Madalina. "A comparison study of precipitation forecasts from three numerical weather prediction systems." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66848.

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Precipitation forecasts over the Continental US from three numerical weather prediction systems, the 4-km resolution, Storm Scale Ensemble Prediction System (SSEF), the 15-km resolution Global Environmental Multiscale (GEM) model and the 28-km resolution Weather Research and Forecasting (WRF) model, are compared and validated against radar observations for 24 precipitation cases between 16 April and 06 June 2008. The diversity of these systems allows the discussion of several issues: the representation of the diurnal cycle of precipitation in Numerical Weather Prediction (NWP

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Hossain, Md Monowar. "CMIP5 Decadal Precipitation at Catchment Level and Its Implication to Future Prediction." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/89149.

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This study assesses the monthly precipitation of CMIP5 decadal experiment over Brisbane River catchment for a spatial resolution of 0.050 and then predicts the monthly precipitation for decadal timescale through a Bidirectional LSTM and Machine Learning Algorithms using GCMs and observed data. To use GCM data in this future prediction, investigations were carried out for a suitable spatial interpolation method, a better simulation period, model drifts, and drift correction alternatives based on different skill tests.

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Gnanasekar, Nithyakumaran. "Temperature and Hourly Precipitation Prediction System for Road Bridge using Artificial Neural Networks." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448873819.

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Binder, Peter. "Aspects of precipitation simulation in numerical weather prediction : towards an operational mesoscale NWP model /." Zürich : Schweizerische Meteorologische Anstalt, 1992. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=9908.

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Mbali, Siphumelelo. "Improving estimation of precipitation and prediction of river flows in the Jonkershoek mountain catchment." University of the Western Cape, 2016. http://hdl.handle.net/11394/5878.

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Magister Scientiae - MSc (Earth Science)<br>Rainfall is the main input into the land phase of the hydrological cycle which greatly determines the available water resources. Accurate precipitation information is critical for mountain catchments as they are the main suppliers of usable water to the human population. Rainfall received in mountain catchments usually varies with altitude due to the orographic influence on the formation of rainfall. The Langrivier mountain catchment, a sub-catchment of the Jonkershoek research catchment, was found to have a network of rain gauges that does not accur

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Ganguly, Auroop Ratan. "Distributed quantitative precipitation forecasts combining information from radar and numerical weather prediction model outputs." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8374.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.<br>Includes bibliographical references (p. 205-218).<br>Applications of distributed Quantitative Precipitation Forecasts (QPF) range from flood forecasting to transportation. Obtaining QPF is acknowledged to be one of the most challenging areas in hydrology and meteorology. Recent advances in precipitation physics, Numerical Weather Prediction (NWP) models, availability of high quality remotely sensed measurements, and data dictated forecasting tools, offer the opportunity of improvements

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Surussavadee, Chinnawat. "Passive millimeter-wave retrieval of global precipitation utilizing satellites and a numerical weather prediction model." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38537.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2007.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (p. 229-234).<br>This thesis develops and validates the MM5/TBSCAT/F([lambda]) model, composed of a mesoscale numerical weather prediction (NWP) model (MM5), a two-stream radiative transfer model (TBSCAT), and electromagnetic models for icy hydrometeors (F([lambda])), to be used as a g

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Pieper, Patrick [Verfasser]. "Meteorological Drought - Universal Monitoring and reliable seasonal Prediction with the Standardized Precipitation Index / Patrick Pieper." Hamburg : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2020. http://d-nb.info/1227582404/34.

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Books on the topic "Precipitation prediction"

1

Dohring, Henry. Precipitation: Prediction, formation, and environmental impact. Nova Science Publishers, 2011.

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Michaelides, Silas, ed. Precipitation: Advances in Measurement, Estimation and Prediction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77655-0.

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Hayes, Pamela Speers. Prediction of precipitation in Western Washington State. Washington State Dept. of Transportation, Planning, Research and Public Transportation Division, 1991.

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Silas, Michaelides, ed. Precipitation: Advances in measurement, estimation, and prediction. Springer, 2008.

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Hayes, Pamela Speers. Diagnosis and prediction of precipitation in regions of complex terrain. Washington State Dept. of Transportation, Planning, Research and Public Transportation Division, 1986.

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J-W, Bao, and Environmental Technology Laboratory (Oceanic and Atmospheric Research Laboratories), eds. A case study of the impact of off-shore P-3 observations on the prediction of coastal wind and precipitation. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Oceanic and Atmospheric Research Laboratories, Environmental Technology Laboratory, 2000.

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J-W, Bao, and Environmental Technology Laboratory (Oceanic and Atmospheric Research Laboratories), eds. A case study of the impact of off-shore P-3 observations on the prediction of coastal wind and precipitation. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Oceanic and Atmospheric Research Laboratories, Environmental Technology Laboratory, 2000.

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Cox, Jonathan Peter. Hydrometeorological aspects of drought management. University of Salford, 1993.

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J-W, Bao, and Environmental Technology Laboratory (Oceanic and Atmospheric Research Laboratories), eds. A case study of the impact of off-shore P-3 observations on the prediction of coastal wind and precipitation. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Oceanic and Atmospheric Research Laboratories, Environmental Technology Laboratory, 2000.

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Alberto, Montanari, Bárdossy András, Reeves A. D, Duck R. W, and European Geophysical Society, eds. I. Predicting and estimating extremes of precipitation. Pergamon, 2001.

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Book chapters on the topic "Precipitation prediction"

1

Harvey, Harold H., and Douglas M. Whelpdale. "On the prediction of acid precipitation events and their effects on fishes." In Acidic Precipitation. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-3385-9_57.

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Satirakul, Yuparwadee, Tanawat Butngam, and Surapol Phunyapinuant. "Discrepant ESD-CDM Test System and Failure Yield Prediction between ESD Association and JEDEC Standards." In Electrostatic Precipitation. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89251-9_155.

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Janowiak, John. "Validation of Rainfall Algorithms at the NOAA Climate Prediction Center." In Measuring Precipitation From Space. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_31.

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Anagnostou, Emmanouil N. "Assessment of Satellite Rain Retrieval Error Propagation in the Prediction of Land Surface Hydrologi." In Measuring Precipitation From Space. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_28.

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Wang, Chung-Chieh, Shin-Hau Chen, Pi-Yu Chuang, and Chih-Sheng Chang. "Quantitative Precipitation Forecasts Using Numerical Models: The Example of Taiwan." In Numerical Weather Prediction: East Asian Perspectives. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40567-9_15.

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Muruganandam, Niveditha, and Ramsundram Narayanan. "Aerosol Optical Depth vs. PM2.5: Adaptation of Hybrid Optimization Algorithms for Temporal Prediction." In Aerosol Optical Depth and Precipitation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55836-8_12.

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Akeh, Ugbah Paul, Steve Woolnough, and Olumide A. Olaniyan. "ECMWF Subseasonal to Seasonal Precipitation Forecast for Use as a Climate Adaptation Tool Over Nigeria." In African Handbook of Climate Change Adaptation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_97.

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AbstractFarmers in most parts of Africa and Asia still practice subsistence farming which relies minly on seasonal rainfall for Agricultural production. A timely and accurate prediction of the rainfall onset, cessation, expected rainfall amount, and its intra-seasonal variability is very likely to reduce losses and risk of extreme weather as well as maximize agricultural output to ensure food security.Based on this, a study was carried out to evaluate the performance of the European Centre for Medium-range Weather Forecast (ECMWF) numerical Weather Prediction Model and its Subseasonal to Seaso

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Min, Ki-Hong, Miranti Indri Hastuti, Ji-Won Lee, Jeong-Ho Bae, Jae-Geun Lee, and Yushin Kim. "Assimilation of Multiscale Remote Sensing Data to Improve Mesoscale Precipitation Forecasting." In Numerical Weather Prediction: East Asian Perspectives. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40567-9_10.

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Sato, Ryoma, Hisashi Kashima, and Takehiro Yamamoto. "Short-Term Precipitation Prediction with Skip-Connected PredNet." In Artificial Neural Networks and Machine Learning – ICANN 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_37.

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Yakubu, Abdulaziz Tunde, Abdultaofeek Abayomi, and Naven Chetty. "Machine Learning-Based Precipitation Prediction Using Cloud Properties." In Hybrid Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_23.

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Conference papers on the topic "Precipitation prediction"

1

Binti Mahmud, Husniyah, and Takahiro Osawa. "Enhanced precipitation prediction through the integration of gauge observations with satellite-based precipitation prediction models utilizing the Bayesian model averaging (BMA) technique in Kelantan, Malaysia." In Remote Sensing of the Atmosphere, Clouds, and Precipitation VIII, edited by Cheng-Yung Huang, Eastwood Im, and Song Yang. SPIE, 2025. https://doi.org/10.1117/12.3038111.

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Huang, Jiuxi, Cong Liang, Shuo Li, Hongmei Zhang, and Juntao Li. "Prediction of Intense Convective Precipitation via EMD-NARX-DBN." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606909.

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Şentop, Mehmet Selahaddin, Meriç Yücel, and Burak Berk Üstündağ. "AI-Based Short-Term Precipitation Prediction in Precision Agriculture." In 2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2024. http://dx.doi.org/10.1109/agro-geoinformatics262780.2024.10661053.

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Li, Chong, Chuandong Xia, Shuang Xu, Weilong Ban, Jinglin Cui, and Hua Bai. "Accurate Precipitation Prediction Model Based on Deep Learning Algorithm." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10859700.

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Wang, Jinfu, Chuchen Zhang, and Xinye Ge. "Precipitation Prediction and Drought Condition Analysis in Hulunbuir Based on the Standardized Precipitation Index and LSTM Model." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020315.

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Østvold, Terje, and Preben Randhol. "Prediction and Kinetics of Carbonate Scaling from Oil Field Waters." In CORROSION 2002. NACE International, 2002. https://doi.org/10.5006/c2002-02317.

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Abstract A laboratory study of CaCO3 scaling kinetics has been undertaken to obtain information useful for prediction of CaCO3 scale formation. The induction time for precipitation has been determined in a series of experiments where SR, T and the ionic composition of the water from which precipitation occurs, have been varied. The effect of sand from a North Sea oilfield reservoir on the rate of CaCO3 precipitation has also been investigated. Our data shows that sand enhances the rate and reduces the induction time for calcite precipitation. The formation of solid CaCO3 has a window of metast

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Fang, Yong, Menchita Dumlao, and Joey Aviles. "Research on Monthly Precipitation Prediction Model Based on WOA-CEEMDAN-BiLSTM." In 2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI). IEEE, 2024. http://dx.doi.org/10.1109/iotaai62601.2024.10692875.

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Wang, Xin, Saebom Ko, Alex Yi-Tsung Lu, et al. "New Approach to Iron Sulfide Scale Modeling and Prediction at pH 4-7." In CORROSION 2020. NACE International, 2020. https://doi.org/10.5006/c2020-14532.

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Abstract In this study, a plug flow reactor was built to investigate iron sulfide scale precipitation at various temperatures, pH and ionic strength conditions and two pieces of carbon steel C1018 coupons were put inside as reaction surfaces. The ferrous ion and total sulfide in collected effluent samples were measured to determine precipitation kinetics and solubility. The solid that formed on the steel surfaces were analyzed by Scanning Electron Microscopy (SEM/EDS) and X-ray Diffraction (XRD). The solubility data from this study and literature were collected and fitted by Matlab to build up

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Jiang, Mingbo, Xiaoyu Sun, Zengliang Zang, Dan Niu, Hongbin Wang, and Jinjin Liu. "ConvMFP-A Convolution-based Multi-source Fusion Prediction Network for Precipitation Nowcasting." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865191.

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Carpén, Leena, Petri Kinnunen, Tero Hakkarainen, et al. "Prediction of Corrosion Risk of Stainless Steel in Concentrated Solutions." In CORROSION 2006. NACE International, 2006. https://doi.org/10.5006/c2006-06410.

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Abstract Precipitation of sulfate in concentrated solutions may enhance the corrosion risk of stainless steels. The aim of this study is to develop methods and procedures to clarify the risk of stainless steels to localized corrosion in concentrated solutions. In this part of the study the dependence of pitting corrosion susceptibility of stainless steel UNS S30400 (AISI 304, EN 1.4301) on chloride concentration, sulfate concentration and temperature is studied experimentally using potentiodynamic measurements. The special attention is in concentrated solutions which can form due to extensive

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Reports on the topic "Precipitation prediction"

1

Feldman, Daniel, V. Chandrasekar, P. Dennedy-Frank, et al. Reliable modeling and prediction of precipitation & radiation for mountainous hydrology. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769771.

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Shield, Stephen Allan, and Zhenxue Dai. Comparison of Uncertainty of Two Precipitation Prediction Models at Los Alamos National Lab Technical Area 54. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1211603.

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Weiss. PR-318-06701-R01 Predicting and Mitigating Salt Precipitation. Pipeline Research Council International, Inc. (PRCI), 2009. http://dx.doi.org/10.55274/r0010976.

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Brine solutions are often produced during gas storage operations, and when these solutions encounter changing temperature or pressure, salt can precipitate. This salt (NaCl) can impair productivity and may even result in abandonment of wells. Dilution with fresh water is the preferred method of mitigating the salt buildup. Existing salt deposits are dissolved with fresh water. Additionally, fresh water is used as a produced water diluent to reduce supersaturation with respect to NaCl. However, this can be expensive depending on the method of application, and as fresh water becomes scarcer, the

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Fitzpatrick, Patrick, and Yee Lau. CONCORDE Meteorological Analysis (CMA) - Data Guide. The University of Southern Mississippi, 2023. http://dx.doi.org/10.18785/sose.003.

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CONCORDE is the CONsortium for oil spill exposure pathways in COastal River-Dominated Ecosystems (CONCORDE), and is an interdisciplinary research program funded by the Gulf of Mexico Research Initiative (GoMRI) to conduct scientific studies of the impacts of oil, dispersed oil and dispersant on the Gulf’s ecosystem (Greer et al. 2018). A CONCORDE goal is to implement a synthesis model containing circulation and biogeochemistry components of the Northern Gulf of Mexico shelf system which can ultimately aid in prediction of oil spill transport and impacts. The CONCORDE Meteorological Analysis (C

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Honegger, Wijewickreme, and Monroy. L52325 Assessment of Geosynthetic Fabrics to Reduce Soil Loads on Buried Pipelines - Phase I and II. Pipeline Research Council International, Inc. (PRCI), 2011. http://dx.doi.org/10.55274/r0010398.

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High soil loads on buried pipelines can lead to unacceptably high pipeline strains developed in response to permanent ground displacement. Common causes of permanent ground displacement are related to slope instability as a result of heavy precipitation or ground subsidence. In addition, several permanent ground displacement hazards are related to earthquakes including surface fault displacement, triggered landslide movement, surface ground settlement related to liquefaction, and lateral spread displacement. Result: Four specific areas of investigation were completed: 1.Performed baseline test

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Shrestha, Sarthak, and Manish Shrestha. Hindu Kush Himalaya (HKH) monsoon outlook 2025. International Centre for Integrated Mountain Development (ICIMOD), 2025. https://doi.org/10.53055/icimod.1091.

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The Hindu Kush Himalaya (HKH) region is highly susceptible to the influence of monsoon, a periodic wind system, especially in the Indian Ocean and southern Asia. The summer monsoon, between June and September, is the major source of precipitation in the region with significant impacts on the hydrology of its rivers, which form the lifeline of nearly two billion people in the region. While a good monsoon is essential for replenishing these river systems, malevolence of water-related disasters such as floods, landslides, storms, heat waves, wildfires, droughts, glacial lake outburst floods (GLOF

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Sparrow, Kent, Stephen Brown, Christopher French, Mark Wahl, Joseph Gutenson, and Kyle Gordon. Integrating NOAA’s National Water Model (NWM) into the Antecedent Precipitation Tool (APT) to support Clean Water Act decision-making. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49187.

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This study examines the effectiveness of the National Water Model (NWM) in assessing streamflow normalcy under the Clean Water Act, compared to the commonly used Antecedent Precipitation Tool (APT). The APT, used by the Environmental Protection Agency, US Army Corps of Engineers, and environmental consultants, evaluates waterbody conditions based on precipitation data. However, it was found to be less accurate in predicting streamflow normalcy compared to USGS gage data. The NWM, on the other hand, showed promising results in preliminary analyses, outperforming the APT when compared to USGS ga

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Guan, Jiajing, Sophia Bragdon, and Jay Clausen. Predicting soil moisture content using Physics-Informed Neural Networks (PINNs). Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48794.

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Environmental conditions such as the near-surface soil moisture content are valuable information in object detection problems. However, such information is generally unobtainable at the necessary scale without active sensing. Richards’ equation is a partial differential equation (PDE) that describes the infiltration process of unsaturated soil. Solving the Richards’ equation yields information about the volumetric soil moisture content, hydraulic conductivity, and capillary pressure head. However, Richards’ equation is difficult to approximate due to its nonlinearity. Numerical solvers such as

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Nyhan, J., R. Beckman, and B. Bowen. An analysis of precipitation occurrences in Los Alamos, New Mexico, for long-term predictions of waste repository behavior. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6432475.

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Alter, Ross, Michelle Swearingen, and Mihan McKenna. The influence of mesoscale atmospheric convection on local infrasound propagation. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48157.

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Infrasound—that is, acoustic waves with frequencies below the threshold of human hearing—has historically been used to detect and locate distant explosive events over global ranges (≥1,000 km). Simulations over these ranges have traditionally relied on large-scale, synoptic meteorological information. However, infrasound propagation over shorter, local ranges (0–100 km) may be affected by smaller, mesoscale meteorological features. To identify the effects of these mesoscale meteorological features on local infrasound propagation, simulations were conducted using the Weather Research and Foreca

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