Rainfall Forecasts Research Articles

Improving Afternoon Thunderstorm Prediction over Complex Terrain with the Assimilation of Dense Ground-Based Observations: Four Cases in the Taipei Basin

Abstract In this study, we investigate the impact of assimilating densely distributed Global Navigation Satellite System (GNSS) zenith total delay (ZTD) and surface station (SFC) data on the prediction of very short-term heavy rainfall associated with afternoon thunderstorm (AT) events in the Taipei basin. Under weak synoptic-scale conditions, four cases characterized by different rainfall features are chosen for investigation. Experiments are conducted with a 3-h assimilation period, followed by 3-h forecasts. Also, various experiments are performed to explore the sensitivity of AT initialization. Data assimilation experiments are conducted with a convective-scale Weather Research and Forecasting–local ensemble transform Kalman filter (WRF-LETKF) system. The results show that ZTD assimilation can provide effective moisture corrections. Assimilating SFC wind and temperature data could additionally improve the near-surface convergence and cold bias, further increasing the impact of ZTD assimilation. Frequently assimilating SFC data every 10 min provides the best forecast performance especially for rainfall intensity predictions. Such a benefit could still be identified in the earlier forecast initialized 2 h before the start of the event. Detailed analysis of a case on 22 July 2019 reveals that frequent assimilation provides initial conditions that can lead to fast vertical expansion of the convection and trigger an intense AT. This study proposes a new metric using the fraction skill score to construct an informative diagram to evaluate the location and intensity of heavy rainfall forecast and display a clear characteristic of different cases. Issues of how assimilation strategies affect the impact of ground-based observations in a convective ensemble data assimilation system and AT development are also discussed. Significance Statement In this study, we investigate the impact of frequently assimilating densely distributed ground-based observations on predicting four afternoon thunderstorm events in the Taipei basin. While assimilating GNSS-ZTD data can improve the moisture fields for initializing convection, assimilating surface station data improves the prediction of rainfall location and intensity, particularly when surface data are assimilated at a very high frequency of 10 min.

Real-time flood forecasting using an integrated hydrologic and hydraulic model for the Vamsadhara and Nagavali basins, Eastern India

AbstractDue to recent rainfall extremes and tropical cyclones that form over the Bay of Bengal during the pre- and post-monsoon seasons, the Nagavali and Vamsadhara basins in India experience frequent floods, causing significant loss of human life and damage to agricultural lands and infrastructure. This study provides an integrated hydrologic and hydraulic modeling system that is based on the Soil and Water Assessment Tool model and the 2-Dimensional Hydrological Engineering Centre-River Analysis System, which simulates floods using Global Forecasting System rainfall forecasts with a 48-h lead time. The integrated model was used to simulate the streamflow, flood area extent, and depth for the historical flood events (i.e., 1991–2018) with peak discharges of 1200 m3/s in the Nagavali basin and 1360 m3/s in the Vamsadhara basin. The integrated model predicted flood inundation depths that were in good agreement with observed inundation depths provided by the Central Water Commission. The inundation maps generated by the integrated modeling system with a 48-h lead time for tropical cyclone Titli demonstrated an accuracy of more than 75%. The insights gained from this study will help the public and government agencies make better decisions and deal with floods.

Forecasting bathing water quality in the UK: A critical review

AbstractClimate change is altering rainfall patterns resulting in increasing variability and intensity of rainfall events worldwide. Increases to short duration, intense rainfall (i.e., convective rainfall), will lead to increases in sewage overflow and run‐off from agricultural land. Such events generate spikes in micro‐organisms from feces and manure, especially Escherichia coli and intestinal enterococci, that temporarily end up in bathing waters posing serious health risks to bathers. Forecasting of bathing water quality associated with convective rainfall presents a distinctive forecasting challenge due to high uncertainties associated with predicting the timing, location, and impact of such events. In this article, we review examples of bathing water quality forecasting practices, with a focus on the United Kingdom where convective rainfall in the summer bathing water season is a particular concern, and question whether the current approach is robust in a changing climate. We discuss potential upgrades in bathing water forecasting and identify the main challenges that must be addressed before an improved framework for bathing water forecasting can be achieved. Although developments in meteorological and hydrological short‐range forecasting capabilities are promising, convective rainfall forecasting has significant predictability limits. We suggest taking full advantage of short‐range forecasts to provide sub‐daily bathing water forecasts, focusing on targeted bathing water monitoring regimes to improve model accuracy with the ultimate goal of providing improved information and guidance for beach users.This article is categorized under: Science of Water > Water and Environmental Change Human Water > Water Governance Science of Water > Water Quality

Type - 2 mamdani fuzzy inference system based model for rainfall forecasting

Weather Forecasting is very essential as it is helpful in saving lives and materials by predicting disasters such as cyclonic storms, tsunamis, extreme rainfall, etc. Within the defined range of rainfall rate approximation, this study investigates the application of Fuzzy Logic (FL) to forecast rainfall using the Interval Type –2 Fuzzy Inference System (IT2FIS). Environmental parameters which influence rainfall have been applied in this analysis and every implementation is carried out using MATLAB 9.13. The performance of IT2FIS model is compared with the actual data. Correlation coefficient (R2) and Root Mean-Squared Error (RMSE) have been used to evaluate the performance metrics of the proposed model. The results suggest that the IT2FIS model can capture the dynamic behavior of rainfall data and generate reasonable results, implying that it might be beneficial in long-term rainfall prediction.

Extended-Range Forecast of Winter Rainfall in the Yangtze River Delta Based on Intra-Seasonal Oscillation of Atmospheric Circulations

The Yangtze River Delta (YRD) is an important economic region in China. Heavy winter rainfall may pose serious threats to city operations. To ensure the safe operation of the city, meteorological departments need to provide forecast results for the Spring Festival travel rush weather service. Therefore, the extended-range forecast of winter rainfall is of considerable importance. To solve this problem, based on the analysis of low-frequency rainfall and the intra-seasonal oscillation of atmospheric circulation, an extended-range forecast model for winter rainfall is developed using spatiotemporal projection methods and is applied to a case study from 2020. The results show that: (1) The precipitation in the YRD during the winter has a significant intra-seasonal oscillation (ISO) with a periodicity of 10–30 d. (2) The atmospheric circulations associated with winter rainfall in the YRD have a significant characteristic of low-frequency oscillation. From a 30-day to a 0-day lead, large modifications appear in the low-frequency atmospheric circulations at low, mid, and high latitudes. At low latitudes, strong wet convective activity characterized by a negative OLR combined with a positive RH700 correlation coefficient moves northwestward and covers the entire YRD. Meanwhile, the Western Pacific subtropical high (WPSH) characterized by a positive Z500 anomaly enhances and lifts northward. At mid and high latitudes, the signal of negatively correlated Z500 northwest of Lake Balkhash propagates southeastward, indicating the cold is air moving southward. Multiple circulation factors combine together and lead to the precipitation process in the YRD. (3) Taking the intra-seasonal dynamical evolution process of the atmospheric circulation as the prediction factor, the spatiotemporal method is used to build the model for winter mean extended-range precipitation anomaly tendency in the YRD. The hindcast for the recent 10 years shows that the ensemble model has a higher skill that can reach up to 20 days. In particular, the skill of the eastern part of the YRD can reach 25 days. (4) The rainfall in the 2019/2020 winter has a significant ISO. The ensemble model could forecast the most extreme precipitation for 20 days ahead.

Forecasting monthly rainfall using hybrid time-series models and Monte Carlo simulation amidst security challenges: a case study of five districts from northern Nigeria

AbstractNigeria’s agricultural sector relies heavily on rainfall, but insecurity in various regions poses significant challenges. This study aims to address this issue by identifying secure, rain-rich areas in northern Nigeria to support sustainable agriculture. Two models, one integrating classical statistical methods (polynomial and Fourier series fittings) and another using a hybrid approach (artificial neural networks, polynomial, and Fourier series fittings), were employed to analyze historical rainfall data from 1981 to 2021 in the selected districts (Kano, Zaria, Bida, Nguru, and Yelwa) known for their rainfall levels and security stability. The study demonstrates that the machine learning-classical hybrid model outperforms existing models, including the classical-classical hybrid and benchmark models like Iwok’s (2016) model, Fourier series, and SARIMA models. Multi-step ahead forecasting with this hybrid model reveals potential changes in rainfall patterns. Notably, Kano, Zaria, Bida, and Yelwa are expected to experience increased rainfall from 2022 to 2026, while Nguru may initially witness decreased rainfall, with improvement in the final year (2026). In conclusion, this study introduces an effective approach for rainfall modeling and forecasting, facilitating the identification of secure agricultural regions in northern Nigeria. These findings carry implications for crop production and agricultural development, contributing to climate resilience efforts and assisting stakeholders in strategic decision-making for regional agricultural investments.

TOWARDS BUILDING AN INTELLIGENT MOBILE APP FOR RAINFALL PREDICTION USING WEIGHTED AVERAGE ENSEMBLE DEEP SUPERVISED LEARNING

Predicting rainfall accurately and on time is essential for a variety of tasks, including electricity generation, building, flood warnings, aviation operations, and the sustainable use of water resources. Traditionally, rainfall forecasts were based on physical models of the atmosphere, which are imprecise over extended periods of time due to their instability to perturbations and thus, are inaccurate for large periods. Artificial Intelligence (AI) techniques as a means of predicting rainfall have been applied in most developed countries, these have been found to offer many possibilities. AI techniques are more robust to perturbations. Over the past 20 years, rainfall forecasting using AI approaches has demonstrated astounding precision. Rainfall forecasting accuracy could be increased by AI algorithms that use historical weather data elements to reveal hidden trends. This study thereafter proposed the development of an AI mobile app for predicting rainfall that is based on weighted average ensemble deep supervised learning that combines three machine learning algorithms namely; Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). . In order to prevent future outbreaks of rainfall hazards, the proposed AI mobile app for rainfall prediction will aid in raising awareness among the public, government officials, planning agencies, disaster management agencies, and related organizations.

Hydroclimatic projection: statistical learning and downscaling model for rainfall and runoff forecasting

Abstract The study is carried out to investigate the surface runoff depth with changing precipitation due to climate change in the study area where sandy loam and loamy soil are dominant. In this study, future rainfall is projected by a statistical downscaling model (SDSM) using a set of predictors derived from a Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) [the Norwegian Earth System Model (NorESM)] with updated scenarios SSP 4.5 and SSP 8.5. Daily rainfall values for the observed period (1981 to 2014) are validated using statistical learning and evaluated with matrices, namely, root mean square error (RMSE), coefficient of correlation, and Nash–Sutcliffe efficiency (NSE), which are found to be valid for further predictions. Rainfall projections show a decrease in rainfall trend of 50% from 2030 to 2040 for scenario SSP 4.5 and an increase of 7% from 2040 to 2050. Predicted rainfall for scenario SSP 8.5 shows a similar trend of decreasing rainfall of 24% for the period 2030–2040 and an increase of 19% in the period 2040–2050. Furthermore, these rainfall values are spatially modelled in a geographic information system (GIS) and rainfall maps are obtained. The obtained rainfall map, land-use map, and soil map are overlaid to compute curve numbers and runoff depths. A similar trend of decrease in runoff is observed for the period 2030–2050. The overall trend of climate change shows a water-stressed scenario.

Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network

AbstractPrecipitation exerts far‐reaching impacts on both socio‐economic fabric and individual well‐being, necessitating concerted efforts in accurate forecasting. Deep learning (DL) models have increasingly demonstrated their prowess in forecasting meteorological elements. However, traditional DL prediction models often grapple with heavy rainfall forecasting. In this study, we propose physics‐informed localized graph neural network models called ω‐GNN and ω‐EGNN, constrained by the coupling of physical variables and climatological background to predict precipitation in China. These models exhibit notable and robust improvements in identifying heavy rainfall while maintaining excellent performance in forecasting light rain by comparing to numerical weather prediction (NWP) and other DL models with multiple perturbation experiments in different data sets. Surprisingly, within a certain range, even when a DL model utilizes more input variables, GNN can still maintain its advantage. The methods to fuse physics into DL model demonstrated in this study may be promising and call for future studies.

Artificial intelligence predicts normal summer monsoon rainfall for India in 2023

Inaccuracy in the All Indian Summer Monsoon Rainfall (AISMR) forecast has major repercussions for India's economy and people's daily lives. Improving the accuracy of AISMR forecasts remains a challenge. An attempt is made here to address this problem by taking advantage of recent advances in machine learning techniques. The data-driven models trained with historical AISMR data, the Niño3.4 index, and categorical Indian Ocean Dipole values outperform the traditional physical models, and the best-performing model predicts that the 2023 AISMR will be roughly 790 mm, which is typical of a normal monsoon year.

Interpretable spatial-temporal attention convolutional network for rainfall forecasting

The interpretability of rainfall forecasting models is a major challenge in the field of artificial intelligence. Its importance is equal to the evaluation of model accuracy. Owing to the uncertainty and nonlinearity of the rainfall forecasting process, existing hydrological rainfall forecasting models often have low prediction robustness, and the machine learning method ignores the influence of physical factors in the surrounding areas; thus, the physical interpretability of the model is inferior. This study proposes an interpretable spatial-temporal attention convolution network for rainfall interval forecasting (IDSTA-TCN). To analyze and mine the uncertainty of input data more effectively, we enhanced a variational mode decomposition (EBO-VMD) algorithm based on Bayesian optimization to decompose the input data. To improve the performance of the original temporal convolution network (TCN) unit in rainfall forecasting and to effectively use the spatial-temporal information of rainfall forecasting data, we improved the spatial-temporal attention mechanism, dynamically combined the geographical information of the meteorological stations in the surrounding area to assist the network unit in better mining the nonlinear impact of historical input information on current rainfall forecasting, and improved the prediction performance of the IDSTA-TCN model. Finally, we designed a more reasonable error factor adjustment interval output strategy to better quantify the impact of prediction errors on the rainfall interval prediction results. Moreover, we conducted a series of experiments to compare and analyze the accuracy of each model according to the most commonly used evaluation indicators in the field of rainfall forecasting. The results demonstrate that the proposed IDSTA-TCN model is superior to the baseline models in most cases. We preliminarily analyzed the spatial-temporal attention weights, which explained the physical rationality of our model.

Time series analysis and development of simulation model for monthly rainfall using ARIMA model

Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregre ssive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) model for forecasting monthly rainfall in Junagadh Station, Gujarat. Utilizing 50 years of historical data (1968 to 2016), the SARIMA model predicts weekly rainfall for the subsequent five years (2018 to 2022). Through comprehensive evaluation using ACF and PACF plots, AIC, SBC, MAPE, and MAE values, the study identifies SARIMA (1,0,0)(3,1,1)12 as the optimal model, offering the most accurate prediction. The robust results affirm that the SARIMA model provides reliable and satisfactory weekly rainfall predictions. This research contributes valuable insights into the precision and efficacy of SARIMA models for rainfall forecasting, aiding in strategic water resource management in the Junagadh region.

A Comparative Analysis of ARIMA and other Statistical Techniques in Rainfall Forecasting: A Case Study in Kolkata (KMC), West Bengal

Rainfall forecasting in urban areas is a significant consideration for city planners due to its connection with urban water management. In this study, the ARIMA (auto-regressive integrated moving average) model, as well as several regression approaches such as simple linear and second to sixth-degree polynomial regression equations, have been used to forecast the annual rainfall based on 120 years of monthly and annual rainfall from 1901 to 2020 in Kolkata Municipal Corporation (KMC), West Bengal. This study compares the performance of ARIMA and other regression techniques in forecasting rainfall using the metrics of R-squared and root mean square error (RMSE). The ARIMA model has been implemented using machine learning techniques in the Python programming language, while additional regression equations have been computed and analyzed using Microsoft Excel 2019. In order to employ the ARIMA model, all assumptions were assessed, and the optimal model order was established using the import auto-Arima package from the pmdarima.arima library. The stepwise model.aic function yielded 0,1,1 as the most suitable order for the model. The findings indicate that, out of all the regression methods employed for rainfall prediction, the fifth-degree polynomial equation exhibits the lowest root mean square error (RMSE), establishing it as the most effective model for rainfall forecasting in this study.

Long-term and short-term rainfall forecasting using deep neural network optimized with flamingo search optimization algorithm

Rainfall forecasting is essential because heavy and irregular rainfall creates many impacts like destruction of crops and farms. Here, the occurrence of rainfall is highly related to atmospheric parameters. Thus, a better forecasting model is essential for an early warning that can minimize risks and manage the agricultural farms in a better way. In this manuscript, Deep Neural Network (DNN) optimized with Flamingo Search Optimization Algorithm (FSOA) is proposed for Long-term and Short-term Rainfall forecasting. Here, the rainfall data is obtained from the standard dataset as Sudheerachary India Rainfall Analysis (IRA). Moreover, the Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) approach is utilized for pre-processing process. Also, the deep neural network is utilized for performing rainfall prediction and classification. Additionally, the parameters of the DNN model is optimizing by Flamingo Search Optimization Algorithm. Finally, the proposed MFEEWT-DNN- FSOA approach has effectively predict the rainfall in different locations around India. The proposed model is implemented in Python tool and the performance metrics are calculated. The proposed MFEEWT-DNN- FSOA approach has achieved 25%, 26%, 25.5% high accuracy and 35.8%, 24.7%, 15.9% lower error rate for forecasting rainfall in Cannur at Kerala than the existing Map-Reduce based Exponential Smoothing Technology for rainfall prediction (MR-EST-RP), modular artificial neural networks with support vector regression for rainfall prediction (MANN-SVR-RP), and biogeography-based extreme learning machine (BBO-ELM) (BBO-ELM-RP) methods respectively.

PRAKIRAAN INDEKS KEKERINGAN MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) BERDASARKAN DATA STANDARDIZED PRECIPITATION INDEX (SPI) KOTA BANJARBARU

Drought is a disaster that has a bad impact, especially in the city of Banjarbaru. There are various ways to reduce the impact of drought in the future, one of which is by looking for information regarding the predicted drought index for the following year. The data used in this research to find the drought index value is Banjarbaru City rainfall data for 2007-2022. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is a calculated method for predicting rainfall data and the data obtained is a forecast of rainfall in the city of Banjarbaru for the next 12 periods, namely SARIMA (0,2,3) (0,1,1)12. This model is a model that is suitable for use because it has fulfilled several tests, namely stationarity of variance and mean, significance test, white noise test and normality test with an AIC value of 1022,60 and an equation model obtained from SARIMA (0,2,3) (0,1,1)12 is (1-B)2 (1-B12) Zt=(1+1,77B-0,54B2+ 0,23B3 )(1-0,96B12 )εt. After obtaining forecast rainfall data for the next 12 periods. Rainfall data for 2007-2022 and forecast results for 2023 were used to find the drought index value using the Standardized Precipitation Index (SPI) method. It was found that the highest negative drought index value occurred in January, namely -1,774, including the dry category and the drought index had a positive value The highest occurred in June, namely 0,582, including the normal category. The calculation results of this drought index forecast are used to provide additional information to anticipate drought disasters in the future. Keywords: Drought Index, Rainfall, SPI Method, SARIMA Method, AIC

Process-Informed Subsampling Improves Subseasonal Rainfall Forecasts in Central America.

Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process-informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly-changing processes. Process-informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases.

Short-term rainfall prédiction using prédictive analytics: A case study in Telangana

Rainfall forecasting is critical because heavy rains can bring a full of tragedies. This prediction aids people in requiring preventive steps, and it should be correct. There are two types of rainfall forecasting: short-term forecasting and long-term forecasting. Predictions are typically correct for short-term, but creating a model for future rainfall prediction is the most difficult task. Because it is strongly tied to economy and human lives, heavy precipitation forecast could be a severe disadvantage for natural science departments. It’s the cause of natural disasters like floods and droughts that affect people all around the world every year. For countries like India, where agriculture is the primary source of income, the accuracy of rainfall estimates is critical. Regression may be used in the prediction of precipitation utilizing machine learning approaches. The goal of this work is to provide non-experts with easy access to the techniques and approaches used in the field of precipitation prediction, as well as a comparison of the various machine learning algorithms.

Monthly Rainfall Forecasting Using High Order Singh's Fuzzy Time Series Based on Interval Ratio Methods: Case Study Semarang City, Indonesia

Monthly Rainfall Forecasting Using High Order Singh's Fuzzy Time Series Based on Interval Ratio Methods: Case Study Semarang City, Indonesia

Prediction of Streamflow in River Basin-Using ANN

In addition to the flood level predictions, our system provides valuable insights into future rainfall patterns. With the data set we have gathered, we can determine the expected amount of rainfall in the upcoming months. By combining the flood level predictions with the rainfall data, we can better understand the overall flood risk and take proactive measures to mitigate its impact. Our system equips us with the necessary information to make informed decisions and enhance flood preparedness strategies. The main difference is that we focus on predicting flood levels using a combination of current water level data and real-time weather data. This allows us to have a more accurate understanding of potential flood events. Additionally, our paper also incorporates rainfall data to assess the risk of flooding in the coming months. By considering multiple factors, we aim to provide a more holistic understanding of flood risks and enhance preparedness strategies. By combining the flood level predictions with rainfall forecasts, we can assess the flood risk in the coming months and take preventive actions, such as implementing early warning systems or strengthening infrastructure, to minimize the impact of potential flood events.

Long-range forecasting of daily rainfall using machine learning techniques

Long-range forecasting of daily rainfall using machine learning techniques