Prediction Of Precipitation Research Articles

Role of thermal and dynamical subdaily perturbations over the Tibetan Plateau in 30-day extended-range forecast of East Asian precipitation in early summer

The influence of the thermodynamic forcing of the Tibetan Plateau (TP) on the Asian summer monsoon remains controversial because the role of elevated heating across the TP remains unclear at multiple time scales. At the extended-range scale, the boundary forcing is more important than the initial field in the forecast process. In this study, we investigated the role of subdaily thermodynamic forcing across the TP in generating 30-day predictions of precipitation in East Asia by conducting a series of hindcast experiments. The surface potential vorticity forcing was used to identify typical years when the TP forcings were extremely strong or weak. The results indicated that the subdaily thermal forcing of the TP was very important for improving the East Asian precipitation forecast accuracy, especially for predictions longer than 14 days in June 2022, when diffusion heating is very strong and can develop over the TP. In such a case, the corrected TP heating could not only correct for low-level water vapor transport but also modular uplevel circulation, which could propagate downstream, thus favoring the correct prediction of precipitation over East Asia. However, in the other cases, the individual influences of thermal perturbations across the TP are not the only important factors. These findings reveal ways to improve the extended-range forecast skill over East Asia.

Prediction of microbial-induced calcium carbonate precipitation for self-healing cementitious material

Prediction of microbial-induced calcium carbonate precipitation for self-healing cementitious material

Effect of water on asphaltene-precipitation behavior of CO2-crude oil mixtures during CCUS processes

Water can extensively exist in reservoirs. Since the solubility of CO2 in water is not negligible under reservoir conditions, the aqueous phase should also be considered during the phase-behavior simulations of the CO2 flooding process. With the presence of an asphaltene phase and an aqueous phase, four phases (i.e., a vapor phase, a hydrocarbon phase, an asphaltene phase, and an aqueous phase) can coexist at a given thermodynamic equilibrium. In this study, a robust and efficient four-phase vapor–liquid-liquid-aqueous equilibrium calculation algorithm is applied to conduct four-phase vapor–liquid-asphaltene-aqueous equilibrium calculations by assuming asphaltene is a dense liquid phase. The algorithm is first validated by comparing the calculated asphaltene precipitation data with the presence of water against the experimental asphaltene precipitation data documented in the literature. The validation shows that the calculation results agree well with the experimental data, indicating that such algorithm can make reliable predictions of asphaltene precipitation with the presence of water. The validated algorithm is subsequently used to construct pressure–temperature (PT) and pressure-composition (Px) phase diagrams to study the effect of water on asphaltene precipitation under different pressure/temperature conditions and under the injection of CO2. We can conclude from the calculated results that although adding water to the reservoir fluid can cause obvious changes in the PT phase diagrams, it does not have a noticeable influence on the asphaltene precipitation onsets. However, it can be seen from the calculated Px phase diagrams that the presence of water during the CO2 flooding process can make asphaltene precipitated more easily. The maximum amount of asphaltene precipitation is decreased by the presence of water due to the fact that a considerable amount of CO2 is dissolved in the aqueous phase instead of the liquid hydrocarbon phase.

Advantages of the Multimodel Ensemble Approach for Subseasonal Precipitation Prediction in China and the Driving Factor of the MJO

Advantages of the Multimodel Ensemble Approach for Subseasonal Precipitation Prediction in China and the Driving Factor of the MJO

Contrasting the Intraseasonal Precipitation in the Western and Eastern North Pacific During Boreal Winter

AbstractThe wintertime North Pacific exhibits two types of intraseasonal periodicity in daily precipitation, with a period of 10–20 and 30–90 days in the western and eastern North Pacific. This study aims to identify and compare characteristics and mechanisms of intraseasonal precipitation in these two regions using daily observational data. Our results indicate their notable differences in atmospheric circulations and dynamical processes, despite similarities in moisture sources. A wave train associated with the intraseasonal precipitation in the western North Pacific characterized by anomalous cyclones and anticyclones from West Siberia into the North Pacific is observed, accompanied by anomalous wave activity. By diagnosing local finite‐amplitude wave activity (LWA) budget, we demonstrate that the baroclinic eddy generation initiates the upstream growth of wave activity, whereas its downstream development is primarily attributed to the zonal advection of wave train. The upstream wave train induces barotropic and baroclinic instabilities in Kuroshio‐Oyashio extension (KOE). In contrast, the anomalous atmospheric circulations related to intraseasonal precipitation in the eastern North Pacific feature a tripolar structure, with downstream propagation of wave activity from the eastern North Pacific to the North American continent. Diabatic heating, along with local eddy generation, contributes to the upstream growth of wave activity. Residual term, possibly linked to the wave activity dissipation associated with wave breaking, extends the wave activity downstream. Thermal and baroclinic instabilities, associated with the diabatic heating and local eddy generation, create favorable conditions for the precipitation. This study may contribute to the improvement of intraseasonal precipitation prediction over the pan‐North Pacific region.

Improving Statistical Prediction of Subseasonal CONUS Precipitation Based on ENSO and the MJO by Training With Large Ensemble Climate Simulations

AbstractPrevious studies have highlighted the significant impacts of El Niño–Southern Oscillation (ENSO) and the Madden–Julian Oscillation (MJO) on wintertime precipitation over the contiguous United States (CONUS). Here, we demonstrate skillful statistical prediction of subseasonal precipitation over the CONUS using the information of ENSO and the MJO. Simple statistical tools, such as multiple linear regression, exhibit significant improvement in prediction when trained with large ensemble climate simulations, surpassing those trained solely on observational data. Despite the biases in ENSO and MJO teleconnections in the climate simulations, the abundance of data, exceeding observational records by 100 times, allows more robust statistical relationships to be established, leading to such improvement. The utilization of machine learning tools yields additional gains in prediction skill beyond multiple linear regression. ENSO emerges as a dominant contributor to prediction skill, surpassing the influence of the MJO, whose impact diminishes with increasing forecast lead time.

Temporal, Spatial Characteristics and Prediction of Precipitation in Guangzhou Based on the SARIMA Model

This article obtains nearly 18 years of precipitation data for Guangzhou from the National Centers for Environmental Information (NCEI) under the National Oceanic and Atmospheric Administration (NOAA) and conducts visualization analysis and modeling predictions using R software.Using the classic SARIMA model (the ARIMA model with seasonal components) as well as regression and residual ARIMA models to build and simulate the precipitation series.We also used Basic-bootstrap prediction, which involves resampling the residuals of the fitted model with replacement, using historical values to predict the future and obtain a model parameter, as well as Full-bootstrap prediction (which assumes that the residuals have uncertainty and that the estimated model coefficients themselves also have uncertainty).Therefore, not only are reasonable model residuals obtained through the bootstrap method, but multiple model parameters are also derived from this method to determine more reasonable model parameters, which are then used to forecast the next 12 periods for the fitted model. Comparing the results of the four prediction models, the Full-bootstrap model under the regression model showed the best predictive performance. The study found that there is no significant trend in precipitation changes over time, but short-term fluctuations still exist. Although some extreme values appeared, they remain within a predictable range.Therefore, the relevant city government departments prone to extreme rainfall can make reasonable adjustments to drainage facilities based on local precipitation forecasts

Assessing the Impact of Cumulus Convection and Turbulence Parameterizations on Typhoon Precipitation Forecast

AbstractImproving typhoon precipitation forecast with convection‐permitting models remains challenging. This study investigates the influence of cumulus parameterizations and turbulence models, including the Reconstruction and Nonlinear Anisotropy (RNA) turbulence scheme, on precipitation prediction in multiple typhoon cases. Incorporating the cumulus and RNA schemes increases domain‐averaged precipitation, improves recall scores, and lowers relative error across various precipitation thresholds, which is substantial in three out of four studied typhoon cases. Applying appropriate cumulus parameterization schemes alone also contributes to enhancing heavy precipitation forecasts. In Typhoon Hato, the RNA and Grell‐3 schemes demonstrated a doubled recall rate for extreme rainfall compared to simulations without any cumulus scheme. The improved forecasting ability is attributed to the RNA's capacity to model dissipation and backscatter. The RNA scheme can dynamically reinforce typhoon circulation with upgradient momentum transport in the lower troposphere and enhance the buoyancy by favorable heat flux distribution, which is conducive to developing heavy precipitation.

How Do Model Biases Affect Large-Scale Teleconnections That Control Southwest U.S. Precipitation? Part II: Seasonal Models

Abstract We explore the skill in predicting southwest United States (SWUS) October–March precipitation and associated large-scale teleconnections in an ensemble of hindcasts from seasonal prediction systems. We identify key model biases that degrade the models’ capability to predict SWUS precipitation. The subtropical jet in the Pacific sector is generally too zonal and elongated. This is reflected in the models’ North Pacific ENSO teleconnections that are generally too weak with exaggerated northwest–southeast tilt, compared to observations. Also, the models are too dependent on tropical, El Niño–like, wave train anomalies for producing high seasonal SWUS precipitation, when in observations there is a larger influence of zonal Rossby wave trains such as the one observed in 2016/17. Overall, this is consistent with biases in the basic-flow-inducing errors in the propagation of zonal wave trains in the North Pacific, which affects SWUS precipitation downstream. Although higher skill may be gained from reducing mean flow biases in the models, a case study of the 2016/17 winter illustrates the great challenge behind skillful seasonal prediction of SWUS precipitation. Unsurprisingly, the almost record-breaking precipitation observed that year in the absence of ENSO is not predicted in the hindcasts, and model perturbation experiments suggest that even a perfect prediction of tropical sea surface temperature and tropical atmospheric variability would not have sufficed to produce a reasonable seasonal precipitation prediction. On a more positive note, our perturbation experiments suggest a potential role for Arctic variability that supports findings from prior studies and suggests reexamining high-latitude drivers of SWUS precipitation.

Synergistic effects of El Niño–Southern Oscillation and the Indian Ocean Dipole on Middle Eastern subseasonal precipitation variability and predictability

AbstractThis study investigates the influence of tropical sea‐surface temperature (SST) on subseasonal precipitation variability and predictability in the Middle East. With this aim, we focus on the synergistic effects of the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) during October. We confirmed that these phenomena exhibit a seasonal correlation, with its highest value in October (). Analysis revealed a significant correlation between tropical SST and total precipitation with a minimum two‐month lag (). Notably, the combined effect of a positive ENSO and IOD produces significantly more precipitation than a negative ENSO and IOD, mainly over the Fertile Crescent. This is attributed to increased water‐vapor flux directed towards the Middle East, which is more pronounced during positive ENSO and IOD. Moreover, the study highlights the broader implications of tropical SST on the frequency of rain‐bearing weather types in the eastern Mediterranean, that is, the Red Sea Trough and the Mediterranean low‐pressure systems often termed ‘Cyprus Lows’. Specifically, positive ENSO events coupled with a positive IOD diminish the occurrence of the Red Sea Trough while concurrently increasing the frequency of Cyprus Lows. The increase in Cyprus Low occurrence relates to the relative location and intensity of the subtropical jet, thus influencing the Mediterranean storm track. Despite this apparent effect, the synergistic interaction of a positive ENSO and IOD poses challenges for subseasonal precipitation prediction, both practically and intrinsically. The October 1997 anomalously positive ENSO and IOD case study provided additional support and understanding to the statistical analysis. This research underscores the relationship between tropical SST and precipitation patterns in the Middle East, shedding light on the challenges and complexities of subseasonal to seasonal weather and climate prediction in this vulnerable region.

Impact of Weather Types on Weather Research and Forecasting Model Skill for Temperature and Precipitation Forecasting in Northwest Greece

The accuracy of the Weather Research and Forecasting (WRF) model’s predictions for air temperature and precipitation in northwestern Greece varies under different weather conditions. However, there is a lack of understanding regarding how well the model performs for specific Weather Types (WTs), especially in regions with a complex topography like NW Greece. This study evaluates the WRF model’s ability to predict 2 m air temperature and precipitation for 10 objectively defined WTs. Forecasts are validated against observations from the station network of the National Observatory of Athens, focusing on biases and skill variation across WTs. The results indicate that anticyclonic WTs lead to a significant overestimation of early morning air temperatures, especially for inland stations. The precipitation forecast skill varies depending on the threshold and characteristics of each WT, showing optimal results for WTs where precipitation is associated with a combination of depression activity, and orographic effects. These findings indicate the need for adjustments based on WT in operational forecasting systems for regions with similar topographical complexities.

Skillful Prediction of Indian Monsoon Intraseasonal Precipitation Using Central Indian Ocean Mode and Machine Learning

AbstractMonsoonal precipitation is dominated by intraseasonal variabilities, whose skillful prediction lead time is currently less than 5 days and remains a grand challenge. Here we show that an intrinsic variability in the Indian Ocean, the Central Indian Ocean (CIO) mode, when combined with a machine learning (ML) algorithm, can produce skillful predictions of intraseasonal precipitation over the monsoon region with a lead time of over 15 days, which is close to the theoretical predictability limit. This remarkable skill improvement stems from the fact that the CIO mode is dynamically related to the intraseasonal monsoon rainfall, while the data‐driven ML algorithm suppresses unwanted high‐frequency noise. Using the CIO mode and the ML algorithm, the forecast system hybridizes physical fundamentals and versatility of data‐driven algorithms. The identification of CIO mode and the verification of its significant contribution to intraseasonal predictions advance our understanding of the coupled monsoon system and also underscores the great potential of ML techniques in weather forecasts and climate predictions.

A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

Evaporation and precipitation prediction for future time frames via combined machine learning-climate change models (case study: Quri Gol Wetland, Iran)

Evaporation plays a fundamental role in water resources management. However, due to the interplay of meteorological variables in evaporation calculations, several nonlinear relationships have been proposed. Their effectiveness can be discussed based on the climatic conditions of each region. Therefore, in the present study, the efficiency of machine learning methods, including random forest and gradient boosted tree, generalized linear model, support vector machine, Gaussian process regression, and deep learning under different scenarios resulting from the combination of meteorological variables in forecasting and modeling evaporation in Quri Gol wetland located in East Azarbaijan province of Iran was investigated for a period of thirty years (1991-2020). In the second part, by applying climate change models including LARS-WG and SDSM in three scenarios, the values of meteorological variables were calculated for future periods (2021-2050, 2051-2080 and 2081-2100) and then, in order to estimate evaporation, these variables were used as input in the machine learning model. The output of the machine learning models showed that among the studied meteorological variables, temperature has the most influence in modeling. Also, the accuracy of the performance of all studied models was evaluated as suitable, and among these models the performance of RF and DL models brought the best results. The results of the climate change models also showed that in most scenarios for all three time periods, the cumulative values of evaporation and precipitation have increased, with the difference that the ratio of evaporation increase was higher than precipitation.

Impact of Lunar and Solar Tides on Earth’s Climate: Atmospheric Dynamics and Predictive Modeling

The threat of climate change represents a significant challenge for a considerable proportion of the global population, due to the potential for disasters that it poses. Consequently, it is imperative to conduct comprehensive research on this subject. The objective of this study is to examine the influence of the Moon and Sun on atmospheric tides, the celestial bodies heights and their subsequent impact on temperature, wind distribution and precipitation. A model was developed and verified through satellite observations to assess its predictive capacity for climatic phenomena. The findings indicate that the heights of the moon and sun give riseto tidal phenomena that exert a significant climatic influence, which should not be disregarded. Prioritising the study of these phenomena will facilitate a deeper comprehension of the distribution of atmospheric high tides on Earth, which in turn will enable a more accurate prediction of wind patterns, temperatures and precipitation. This will in turn lead to the prevention of climatic disasters.

Prediction of Summer Precipitation via Machine Learning with Key Climate Variables：A Case Study in Xinjiang, China

Study region: Xinjiang is located in the mid-latitude region of Eurasia in northwestern China. Precipitation is predominantly concentrated in northern Xinjiang, while southern Xinjiang remains comparatively arid. Summer precipitation accounts for 54.4% of the annual total. Study focus: This study aims to develop a machine learning model to predict summer precipitation (June–August) in XJ and explore the key variables contributing to summer precipitation in this region. The SHapley Additive exPlanations method was integrated with an extreme tree model to quantify the contributions of variables towards precipitation. Artificial neural networks, support vector machines, and extreme gradient boosting were considered to predict summer precipitation. To train the ML model, we used precipitation data from 1961 to 2012, whilst the forecast results from 2013 to 2017 were used for validation.New hydrological insights for the regions: The results demonstrated that the ANN model achieved robust performance during both the training and validation periods. For Northern and Southern XJ, the Mean Absolute Error and Root Mean Square Error of the ANN model were 15.34 (20.40) and 23.21 (30.01), respectively. The SHAP analysis showed that in the context of Northern Xinjiang, the Niño B Sea Surface Temperature Anomaly, Western Pacific Subtropical High Intensity, Pacific Subtropical High Intensity, and Multivariate ENSO Index play crucial roles in the prediction of summer precipitation. In Southern Xinjiang, the South China Sea Subtropical High Intensity, South China Sea Subtropical High Area, Western Pacific Warm Pool Strength, and Atlantic multidecadal oscillation have emerged as key variables affecting summer precipitation forecasting.

Bridging the Gap Between Global Weather Prediction and Global Storm‐Resolving Simulation: Introducing the GFDL 6.5‐km SHiELD

AbstractWe introduce a 6.5‐km version of the Geophysical Fluid Dynamics Laboratory (GFDL)'s System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD). This global model is designed to bridge the gap between global medium‐range weather prediction and global storm‐resolving simulation while remaining practical for real‐time forecast. The 6.5‐km SHiELD represents a significant advancement over GFDL's flagship global forecast system, the 13‐km SHiELD. This global model features a holistically‐developed scale‐aware suite of physical parameterizations, stepping into the formidable convective “gray zone” of resolutions below 10 km. Comparative analyses with the 13‐km SHiELD, conducted over a 3‐year hindcast period, highlight noteworthy improvements across global‐scale, regional‐scale, tropical cyclone (TC), and continental convection predictions. In particular, the 6.5‐km SHiELD excels in predicting considerably finer‐scale convective systems associated with large‐scale frontal systems and extratropical cyclones. The predictions of global temperature, wind, cloud, and precipitation are significantly improved in this global model. Regionally, over the contiguous United States and the Maritime Continent, substantial reductions in prediction biases of precipitation, cloud cover, and wind fields are also found. In the mesoscale realm, the model demonstrates prominent improvements in global TC intensity and continental convective precipitation prediction: biases are relieved, and skill is higher. These findings affirm the superiority of the 6.5‐km SHiELD compared to the current 13‐km SHiELD, which will advance weather prediction by successfully addressing both synoptic weather systems and specific storm‐scale phenomena in the same global model.

Time series study of climate variables utilising a seasonal ARIMA technique for the Indian states of Punjab and Haryana

HighlightsTemperature and precipitation time series data from 1950 to 2020 were analyzed for the states of Haryana and Punjab.SARIMA models provided the most accurate temperature predictions for both states, though precipitation predictions in July were sometimes overestimated.SARIMA provided better results than machine learning-based models, with lower RMSE, AIC, and BIC values.Model forecasts can improve flood prediction, urban planning, and water resource management, benefiting scientists and decision-makers.

Precipitation Prediction in Suqian City, Jiangsu Province of China Using LSTM Based on Grey Wolf Optimizer Algorithm

To enhance urban planning and disaster management in the context of global climate change, we focused on precipitation forecasting in Suqian City, Jiangsu Province, and established a Long Short-Term Memory (LSTM) model optimized by the Grey Wolf Optimizer (GWO) algorithm. Firstly, we collected five years of precipitation data to ensure a solid foundation for model training. Secondly, we integrated the GWO algorithm to optimize the LSTM model parameters, thereby improving prediction accuracy. Through data preprocessing, model establishment, and validation, we successfully constructed a precipitation forecasting model tailored for both rainy and non-rainy seasons in Suqian. Lastly, utilizing this model, we predicted the precipitation volume for the upcoming 12 months in Suqian and proposed targeted countermeasures and suggestions based on the forecast results to guide urban planning and disaster management efforts, ensuring that Suqian can effectively address the challenges posed by future precipitation changes. Additionally, by combining actual conditions with predictive data, we refined and optimized Suqian's precipitation management strategies.

Transfer Learning in Weather Prediction: Why, How, and What Should

Transfer learning (TL) is a popular phrase in deep learning (DL) domain. It is one of the latest artificial intelligence (AI) technologies that has a significant impact on big data analysis. Methods of traditional machine learning (ML) require the availability of an adequate quantity of training data as well as similarity of characteristics among the feature spaces corresponding to training and test data while performing supervised learning tasks. However, in real-life analytical problems, data scarcity often arises. In such scenarios, the TL approach has shown effectiveness in transferring knowledge from the source tasks that had large training data to a target task that has less training data. Basically, in TL, a model that has been trained on one task is essentially applied to a second related (but not exact) task. In this way, the issue of distribution mismatch can also be addressed. TL is not like conventional machine learning algorithms that try to learn each task starting from the beginning. Meteorological research is such an example of big data analysis which often faces the data scarcity issue. The current study addresses the contemporary challenges in weather forecasting that can be solved (or better dealt with) using TL methods. It presents a brief review of earlier research with the evolution of various technologies used since 1990s, followed by potential applications of TL algorithms to several key challenges in weather prediction, which includes the prediction of air quality, thunderstorms, precipitation, visibility, and cyclones, among others. Special emphasis is given to high-impact weather (HIW) prediction. These high-impact events are extremely difficult to predict, and they can cause enormous property damage and fatalities around the world. TL techniques have shown advantages in predicting HIW. Various challenging issues in implementing TL technology are then discussed. Finally, we address various prospects associated with TL, propose new research directions, and more importantly mention some concerns for beginners in DL-TL research. An extensive list of references is also provided.