Relevant bibliographies by topics / Precipitation prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Precipitation prediction'

Author: Grafiati
Published: 3 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Precipitation prediction"

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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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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) models, the importance of horizontal resolution for forecast accuracy, the effect of radar data assimilation and the advantage of performing ensemble forecasts rather than less costly, deterministic forecasts. The investigation is carried out through the analysis of statistical measures, skill scores and time-longitude diagrams of precipitation fields. An interesting finding of this study is that, during the study period, the diurnal variability of precipitation was influenced by a combination of weak thermal forcing and strong synoptic forcing, resulting in large scale precipitation systems consistent in terms of initiation timing and propagation characteristics. In addition, the radar observations showed much more consistency than the model forecasts. It is likely that timing and positioning errors led to larger spread of forecasted precipitation coverage and intensity throughout the time-longitude domain. None of the analysis presented here proved the superiority of the 4-km resolution models over the 15-km resolution GEM. It was however determined that radar data assimilation and ensemble prediction added value to the forecasts, mainly through the reduction of positional errors.<br>L'objectif de cette étude est l'évaluation des prévisions de précipitations aux États-Unis effectuées par trois systèmes de prévision numérique par rapport aux observations radars pour 24 jours entre le 16 avril et le 6 juin 2008. L'intérêt principal est de déterminer l'habileté des systèmes de prévision de reproduire le cycle diurne de précipitations. Les différences entre ces systèmes nous permettent d'apprécier l'importance de la résolution horizontale du modèle, de la prévision probabiliste et de l'assimilation des données radar. L'étude est effectuée par l'analyse des mesures statistiques et des diagrammes temps-longitude des champs de précipitations. Un résultat intéressant de ce travail est que, lors de la période d'étude, la variabilité diurne de précipitations a été influencée par une certaine combinaison des forçages synoptiques et thermiques. Donc, la plupart des systèmes observés ont les mêmes propriétés au niveau de l'initiation et de la propagation. D'ailleurs, les prévisions numériques n'ont pas réussi à reproduire ces propriétés. La variabilité observée dans les systèmes de précipitations générés par les modèles est possiblement causée par des erreurs de phase.L'analyse présentée dans ce mémoire ne démontre pas la supériorité des prévisions de précipitations à petite échelle. Cependant, l'assimilation des données radars et la prévision d'ensemble contribuent à l'amélioration des prévisions de précipitations.
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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 accurately represent the catchment rainfall. As a result, this study aimed to improve the estimation of catchment precipitation and evaluate how improving estimation catchment precipitation affects the prediction of streamflows.
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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 in this area. Investigative studies were performed to quantify the value of available tools and data, which indicated the promise and the pitfalls of emerging ideas. Our studies suggested that an intelligent combination of NWP model outputs and remotely sensed radar measurements, that uses process physics and data dictated tools, could improve distributed QPF. Radar measurements have distributed structure, while NWP-QPF incorporate large scale physics. Localized precipitation processes are not well handled by NWP models, and grid average NWP-QPF are not too useful for distributed QPF owing to the spatial variability of rainfall. However, forecasts for atmospheric variables from NWP have information relevant for modeling localized processes and improving distributed QPF, especially in the Summer. Certain precipitation processes like advection and large scale processes could be modeled using physically based algorithms. The physics for other processes like localized convection or residual structures are not too well understood, and data dictated tools like traditional statistical models or Artificial Neural Networks (ANN) are often more applicable.<br>(cont.) A new strategy for distributed QPF has been proposed that utilizes information from radar and NWP. This strategy decomposes the QPF problem into component processes, and models these processes using precipitation physics and data dictated tools, as appropriate and applicable. The proposed strategy improves distributed QPF over existing techniques like radar extrapolation alone, NWP-QPF with or without statistical error correction, hybrid models that combine radar extrapolation with NWP-QPF, parameterized physically based methods, and data dictated tools alone. New insights are obtained on the component processes of distributed precipitation, the information content in radar and NWP, and the achievable precipitation predictability.<br>by Auroop R. Ganguly.<br>Ph.D.
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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 global precipitation ground-truth for evaluating alternative millimeter-wave satellite designs and for developing methods for millimeter-wave precipitation retrieval and assimilation. The model's predicted millimeter-wave atmospheric radiances were found to statistically agree with those observed by satellite instruments [Advanced Microwave Sounding Unit-A/B (AMSU-A/B)] on the United States National Ocean and Atmospheric Administration NOAA-15, -16, and -17 satellites over 122 global representative storms. Whereas such radiance agreement was found to be sensitive to assumptions in MM5 and the radiative transfer model, precipitation retrieval accuracies predicted using the MM5/TBSCAT/F([lambda]) model were found to be robust to the assumptions.<br>(cont.) Appropriate specifications for geostationary microwave sounders and their precipitation retrieval accuracies were studied. It was found that a 1.2-m micro-scanned filled-aperture antenna operating at 118/166/183/380/425 GHz, which is relatively inexpensive, simple to build, technologically mature, and readily installed on a geostationary satellite, could provide useful observation of important global precipitation with ~20-km resolution every 15 minutes. AMSU global precipitation retrieval algorithms for retrieving surface precipitation rate, peak vertical wind, and water-paths for rainwater, snow, graupel, cloud water, cloud ice, and the sum of rainwater, snow, and graupel, over non-icy surfaces were developed separately using a statistical ensemble of global precipitation predicted by the MM5/TBSCAT/F([lambda]) model. Different algorithms were used for land and sea, where principal component analysis was used to attenuate unwanted noises, such as surface effects and angle dependence. The algorithms were found to perform reasonably well for all types of precipitation as evaluated against MM5 ground-truth. The algorithms also work over land with snow and sea ice, but with a strong risk of false detections. AMSU surface precipitation rates retrieved using the algorithm developed in this thesis reasonably agree with those retrieved for the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) aboard the Aqua satellite over both land and sea.<br>(cont.) Surface precipitation rates retrieved using the Advanced Microwave Sounding Unit (AMSU) aboard NOAA-15 and -16 satellites were further compared with four similar products derived from other systems that also observed the United States Great Plains (USGP) during the summer of 2004. These systems include AMSR-E aboard the Aqua satellite, the Special Sensor Microwave/Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites, the passive Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) aboard the TRMM satellite, and a surface precipitation rate product (NOWRAD), produced and marketed by Weather Services International Corporation (WSI) using observations from the Weather Surveillance Radar-1988 Doppler (WSR-88D) systems of the Next-Generation Weather Radar (NEXRAD) program. The results show the reasonable agreement among these surface precipitation rate products where the difference is mostly in the retrieval resolution, which depends on instruments' characteristics. A technique for assimilating precipitation information from observed millimeter-wave radiances to MM5 model was proposed. Preliminary study shows that wind and other correction techniques could help align observations at different times so that information from observed radiances is used at appropriate locations.<br>by Chinnawat Surussavadee.<br>Ph.D.
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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"

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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"

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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 Seasonal (S2S) precipitation forecast to ascertain its usefulness as a climate change adaptation tool over Nigeria. Observed daily and monthly CHIRPS reanalysis precipitation amount and the ECMWF subseasonal weekly precipitation forecast data for the period 1995–2015 was used. The forecast and observed precipitation were analyzed from May to September while El Nino and La Nina years were identified using the Oceanic Nino Index. Skill of the forecast was determined from standard metrics: Bias, Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC).The Bias, RMSE, and ACC scores reveal that the ECMWF model is capable of predicting precipitation over Southern Nigeria, with the best skill at one week lead time and poorest skills at lead time of 4 weeks. Results also show that the model is more reliable during El Nino years than La-Nina. However, some improvement in the model by ECMWF can give better results and make this tool a more dependable tool for disaster risk preparedness, reduction and prevention of possible damages and losses from extreme rainfall during the wet season, thus enhancing climate change adaptation.
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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"

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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 metastability, which can vary from SR=aCa2+aCO32−Ksp(CaCO3)=7.0, 2.9 and 2.7 at 80, 100 and 120°C, respectively, with no sand in the water, and 4.8-4.9, 2.7 and 2.6 with sand present. An even more drastic effect was observed when calcite scale was added to the water. At 100-120°C the precipitation at SR = 1.6 was observed after a few seconds. This is reasonable since the calcite crystals suspended in the solution act as crystallization sites for the dissolved carbonate. It was also observed that Mg2+ and SO42- ions in the solution increased the induction time and retarded the CaCO3 crystallization. This implies that compounds that block active sites for calcite crystallization may inhibit CaCO3 precipitation. With Mg2+ and SO42- ions in the solution aragonite was formed. At constant SR the activity ratio aMg2+/aca2+ was found to have a different effect on the induction time as the Ca2+ concentration was varied. The induction time for precipitation increased linearly with the probability that a magnesium ion was next to a calcium ion at constant SR. This probability is proportional to the concentration product mMg2+ * mca2+.
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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 a reliable FeS solubility prediction model. The experimental results show that mackinawite is the predominant precipitated scale and could be stable for a week at pH higher than 6.0. Iron sulfide precipitation is under diffusion control, accelerated by high temperature and ionic strength. At pH 6 – 7, the aqueous phase neutral species, such as FeSaq0, plays an important role in the solubility and precipitation kinetic. Based on this study, a new solubility model that combines Pitzer theory and ion-complexes (speciation of ferrous ion) has been developed for iron sulfide solubility calculation and scale prediction.
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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 evaporation and contain also precipitates. A preliminary quantitative model considering the relationships between different variables is described. To calculate water chemistry, thermodynamic equilibrium and precipitation of solids in concentrated solutions, Debye-Hückel/Davies type activity coefficients model is used.
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Reports on the topic "Precipitation prediction"

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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 method will become more expensive. A number of chemicals are reported to reduce or prevent salt deposition. Among them are ferrocyanide and some organic molecules such as nitrilotriacetic acid and nitrilotriacetamide (NTAm). These inhibitors are thought to prevent salt precipitation by crystal modification or by interfering with crystal growth. Their effectiveness, however, varies with their concentration and the chemistry of the brines. For example, ferrocyanide is a very effective salt inhibitor; however, at low pH or in the presence of large amounts of iron it decomposes rendering it ineffective. As shown in Figs. 1 and 2 where supersaturated solutions of NaCl are cooled to room temperature, the performance of both chemicals is reduced as the reservoir water increases in calcium and/or magnesium, eventually becoming ineffective. But even when precipitate is formed, both inhibitors affect the properties of the precipitate so that there is no caking with no tendency to form large crystals associated with sodium chloride scale. The questions concerning the environmental issues associated with ferrocyanide that arose during Phase I are addressed in this report.
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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 (CMA) is an hourly gridded NetCDF dataset which provides atmospheric forcing for the synthesis model. CMA includes a variety of parameters from multiple sources. The Real-Time Mesoscale Analysis (RTMA; De Pondeca et al. 2011) provides the surface momentum and the thermodynamic atmospheric data. The radiation parameters and total cloud cover percentage are from the North American Mesoscale (NAM) Forecast System fields. The hourly precipitation is extracted from the Next Generation Weather Radar (NEXRAD) Level-III. Gridded sea surface temperature fields (SST) are computed daily using a 10-day running mean of the Advanced Very High-Resolution Radiometer (AVHRR) SST product. The Coupled Ocean-Atmosphere Response Experiment flux (COARE) algorithm calculates sensible heat flux and surface momentum stresses (Fairall et al. 2003). CMA’s spatial domain’s lowest west grid point is at 90.13°W, 29°N, and the highest east grid point is at 87.05°W, 30.94°N. The grid spacing is 0.01 degree, and the grid dimension is 309 by 195.
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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 tests in moist sand to confirm minimal difference in horizontal soil restraint between moist and dry sand. 2.Performed tests to gauge the variation in horizontal load reduction with separation between the pipe and an inclined trench wall lined with two layers of geotextile. 3.Performed tests in compacted 19 mm (0.75 in) minus sand and crushed limestone (referred to locally in British Columbia as road mulch) to attempt to provide larger difference between horizontal forces developed with and without lining a trench wall with geotextile. 4.Performed tests to attempt to confirm oblique horizontal-axial soil restraint behavior reported in small-scale tests and centrifuge tests. Benefit: Rather than undertake further physical testing to better understand how the presence of single or dual layers of geotextile fabric changes the mechanisms by which soil restraint develops for horizontal ground displacement, future efforts should focus on numerical simulation preferably using discrete element methods. Until full-scale test data are available to confirm consistent prediction of oblique horizontal-axial soil restraint, the practice of treating horizontal and axial soil springs independently in the analysis of buried pipeline response to ground displacement, as is the current practice, should be maintained.
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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 (GLOFs), is becoming more pronounced in this region under the exacerbating effects of climate change. For instance, in the last forty years or so more than 70% of the flood events in the region took place during the summer monsoon season. Against this backdrop, the HKH Monsoon Outlook 2025 serves as a preliminary frame of reference into the summer monsoon conditions likely to prevail in the region during June – September 2025, based on seasonal forecasts for South Asia at large. The seasonal estimates are collated from the APEC1 Climate Centre (APCC), Copernicus Climate Service (C3S), International Research Institute for Climate and Society (IRI), 31st Session of South Asian Climate Outlook Forum (SASCOF -31) and several national agencies for meteorological assessments. With the forecasters unanimously predicting oceanic and atmospheric phenomena that usually affect (read disrupt) monsoon patterns in South Asia – such as, the El Nino Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Madden-Jullian Oscillation (MJO) activities – to be neutral and /or weak during JuneJuly-August 2025, the likelihood of summer monsoon precipitations is potent this year. However, based on the incidence of below-normal snow cover in the Northern Hemisphere, especially between January and March 2025, along with an estimated mean summer temperature anomaly in South Asia ranging from 0.5°C to 2°C above normal, they also predict high probability of above-normal precipitations for most of South Asia, including HKH swathes. Looking at this possibility, we surmise that the HKH region is likely to be exposed to intensifying risks of rain-induced hazards like flash floods, landslides, and GLOFs if precipitations are intense or prolonged.
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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 gage records. This research expands on these initial findings, evaluating the NWM’s performance across the contiguous United States (CONUS) at gage locations indexed to the NHDPlus Version 2.1 stream network. The results suggest that the NWM provides adequate performance for assessing streamflow normalcy where USGS gages are not present, with accuracy ranging from 40% to 60% in the western half of CONUS and 60% to 80% in the eastern half.
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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 finite difference method (FDM) and finite element method (FEM) are conventional in approximating solutions to Richards’ equation. But such numerical solvers are time-consuming when used in real-time. Physics-informed neural networks (PINNs) are neural networks relying on physical equations in approximating solutions. Once trained, these networks can output approximations in a speedy manner. Thus, PINNs have attracted massive attention in the numerical PDE community. This project aims to apply PINNs to the Richards’ equation to predict underground soil moisture content under known precipitation data.
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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 Forecasting (WRF) meteorological model to approximate the meteorological conditions associated with a series of historical, small-scale explosive test events that occurred at the Big Black Test Site in Bovina, Mississippi. These meteorological conditions were then incorporated into a full-wave acoustic model to generate meteorology-informed predictions of infrasound propagation. A series of WRF simulations was conducted with varying degrees of horizontal resolution—1, 3, and 15 km—to investigate the spatial sensitivity of these infrasound predictions. The results illustrate that convective precipitation events demonstrate potentially observable effects on local infrasound propagation due to strong, heterogeneous gradients in temperature and wind associated with the convective events themselves. Therefore, to accurately predict infrasound propagation on local scales, it may be necessary to use convection-permitting meteorological models with a horizontal resolution ≤4 km at locations and times that support mesoscale convective activity.
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