Seasonal Precipitation Forecasts Research Articles

Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

AbstractMonthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.

Seasonal forecast of two-meter temperature and precipitation in Tanzania: A hybrid cluster and point-by-point machine learning approach

Abstract In seasonal weather forecasting, the exact location within a study area determines the relationship between local predicted variables and global predictors. Both dynamic models and machine-learning approaches that define models for single grid points, can determine these relationships with high spatial granularity, but at high computational cost. To avoid the latter, clustering of predicted variables is often used in machine-learning approaches, which however sacrifices geographical resolution. In this paper, we present a machine-learning approach that is a hybrid between grid-point and cluster-based approaches (finres_S2S). This approach preserves geographical resolution, but at low computational cost, and is tested for monthly two-meter temperature and precipitation in Tanzania, and for a lead time of up to 6 months. The finres_S2S approach has a number of advantages over both the cluster and point approaches, including that of perform well compared to a commonly used forecasting dataset from a dynamic model. We find that the dominant predictors for the application area are associated with the El Nino Southern Oscillation, the Madden-Julian-Oscillation and the Indian Ocean Dipole.

Reinterpreting ENSO's Role in Modulating Impactful Precipitation Events in California

AbstractWater years (WY) 2017 and 2023 were anomalously wet for California, each alleviating multiyear drought. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier‐than‐normal winters. We analyze over seven decades of precipitation and snow records along with mid‐tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical seasonal ENSO‐precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO‐independent weather patterns was a major cause of anomalous precipitation in WY2017, record‐setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983, 1998, and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal precipitation forecasts.

Probabilistic seasonal precipitation forecasts using quantiles of ensemble forecasts

Seasonal precipitation forecasting is vital for weather-sensitive sectors. Global Circulation Models (GCM) routinely produce ensemble Seasonal Climate Forecasts (SCFs) but suffer from issues like low forecast resolution and skills. To address these issues in this study, we introduce a post-processing method, Quantile Ensemble Bayesian Model Averaging (QEBMA). It utilises quantiles from a GCM ensemble forecast to create a pseudo-ensemble forecast. Through their reasonable linear relationships with observations, each pseudo-member connects a hurdle distribution with a point mass at zero for dry months and a gamma distribution for wet months. These distributions are mixed to construct a forecast probability distribution with their weights, proportional to the quantiles’ historical forecast performance. QEBMA is applied to three GCMs, including GloSea5 from the United Kingdom, ECMWF from Europe and ACCESS-S1 from Australia, for monthly precipitation forecasts in 32 locations across four climate zones in Australia. Leave-one-month-out cross-validation results illustrate that QEBMA enhances forecast skills compared to raw GCMs and other post-processing techniques, including quantile mapping and Extended Copula Post-Processing (ECPP), for forecast lead time of 0 to 2 months, based on five metrics. The skill improvements achieved by QEBMA are often statistically significant, particularly when compared to raw GCM forecasts across the 32 study locations. Among these post-processing models, only QEBMA consistently outperforms the SCF benchmark climatology, offering a promising alternative for improving seasonal precipitation forecasts.

ML-based regionalization of climate variables to forecast seasonal precipitation for water resources management

Numerous dams and reservoirs have been constructed in South Korea, considering the distribution of seasonal precipitation which highly deviates from the actual one with high precipitation amount in summer and very low amount in other seasons. These water-related structures should be properly managed in order to meet seasonal demands of water resources wherein the forecasting of seasonal precipitation plays a critical role. However, owing to the impact of diverse complex weather systems, seasonal precipitation forecasting has been a challenging task. The current study proposes a novel procedure for forecasting seasonal precipitation by: (1) regionalizing the influential climate variables to the seasonal precipitation with k-means clustering; (2) extracting the features from the regionalized climate variables with machine learning-based algorithms such as principal component analysis (PCA), independent component analysis (ICA), and Autoencoder; and (3) finally regressing the extracted features with one linear model of generalized linear model (GLM) and another nonlinear model of support vector machine (SVM). Two globally gridded climate variables-mean sea level pressure (MSLP) and sea surface temperature (SST)-were teleconnected with the seasonal precipitation of South Korea, denoted as accumulated seasonal precipitation (ASP). Results indicated that k-means clustering successfully regionalized the highly correlated climate variables with the ASP, and all three extraction algorithms-PCA, ICA, and Autoencoder-combined with the GLM and SVM models presented their superiority in different seasons. In particular, the PCA combined with the linear GLM model performed better, and the Autoencoder combined with the nonlinear SVM model did better. It can be concluded that the proposed forecasting procedure of the seasonal precipitation, combined with several ML-based algorithms, can be a good alternative.

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

Increasing the prospective capacity of global crop and rangeland monitoring with phenology tailored seasonal precipitation forecasts

Droughts are more and more often a limiting factor to agricultural production and can have severe negative effects on food security in vulnerable countries. Global agriculture early warning systems monitor agriculture in near real-time by analyzing meteorological data (e.g. precipitation and temperature) and optical remote sensing data as proxy vegetation health to detect possible negative anomalies and trigger warnings. Seasonal climate forecast can add a predictive component and inform about upcoming precipitation deficits, thus allowing anticipation and improved planning of response actions. Here, we propose a scheme to adapt the standard precipitation forecast from the seasonal Copernicus Climate Change Service multi-system to crop and rangeland phenology, making them suitable for agricultural early warning. Precipitation forecasts are first elaborated into tercile maps showing the probability of the most likely tercile (i.e. drier than normal, normal, wetter than normal) and associated skills of all possible monthly periods combinations included in the six months forecasting horizon. Afterwards, agronomically relevant tercile maps are produced for the closest season in time at any location. These maps are obtained by mosaicking the forecasts for the appropriate growing season period at each grid cell. The resulting map shows the tercile probability for the remaining part of the ongoing growing season (if any at time of analysis) or the probability of the next upcoming season (if in between growing season at time of analysis). The proposed methodology offers a precipitation seasonal forecast product ready to use by agricultural analysts and directly ingestible by automatic warning systems.

A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

From California’s Extreme Drought to Major Flooding: Evaluating and Synthesizing Experimental Seasonal and Subseasonal Forecasts of Landfalling Atmospheric Rivers and Extreme Precipitation during Winter 2022/23

Abstract California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.

Short-lead seasonal precipitation forecast in northeastern Brazil using an ensemble of artificial neural networks

This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará’s operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.

Improving Global Subseasonal to Seasonal Precipitation Forecasts Using a Support Vector Machine‐Based Method

AbstractSubseasonal to seasonal (S2s) precipitation forecasts provide great potential for hydrological forecasting at an extended range. The study proposed a support vector machine (SVM) regression‐based method to improve S2s precipitation forecasts from the European Center for Medium‐Range Weather Forecasts (ECMWF) across the globe (60°N to 60°S). Results suggested that the SVM‐based method significantly improved ECMWF daily precipitation forecasts in representing the spatiotemporal variation of precipitation with higher consistency and reduced errors when compared against observations. Furthermore, the SVM‐based method enhanced the probabilistic skill of ECMWF forecasts, providing improved ranked probability skill score (RPSS) for real‐time forecasts in 2020 (e.g., RPSSECMWF = −0.03 and RPSSrg3 = 0.08 for lead week 1). The most substantial improvement from the SVM‐based method is witnessed in regions with complex terrains where ECMWF yielded the worst skill, such as the Andes mountain range, Congo River Basin, and the Tibet Plateau. However, the SVM‐based post‐processing method did not alter the characteristics of precipitation forecasts regarding climate zone and lead time. Both ECMWF and post‐processed forecasts showed higher skill in the temperate and continental climate zones when the lead time is shorter than 2 weeks. In comparison, low‐latitude regions exhibited higher predictability when the lead time is longer than 5 weeks, which is attributed to the slow variation of boundary conditions such as the El Niño‐Southern Oscillation (ENSO).

New Functionalities and Regional/National Use Cases of the Anomaly Hotspots of Agricultural Production (ASAP) Platform

The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning information on agricultural production based on Earth Observation and agro-climatic data in an open and easy to use online platform. Over the last three years, the system has seen several methodological improvements related to the input indicators and to system functionalities. These include: an improved dataset of rainfall estimates for Africa; a new satellite indicator of biomass optimised for near-real-time monitoring; an indicator of crop and rangeland water stress derived from a water balance accounting scheme; the inclusion of seasonal precipitation forecasts; national and sub-national crop calendars adapted to ASAP phenology; and a new interface for the visualisation and analysis of high spatial resolution Sentinel and Landsat data. In parallel to these technical improvements, stakeholders and users uptake was consolidated through the set up of regionally adapted versions of the ASAP system for Eastern Africa in partnership with the Intergovernmental Authority on Development (IGAD) Climate Prediction and Applications Centre (ICPAC), for North Africa with the Observatoire du Sahara et du Sahel (OSS), and through the collaboration with the Angolan National Institute of Meteorology and Geophysics (INAMET), that used the ASAP system to inform about agricultural drought. Finally, ASAP indicators have been used as inputs for quantitative crop yield forecasting with machine learning at the province level for Algeria’s 2021 and 2022 winter crop seasons that were affected by drought.

Seasonal forecast of winter precipitation over China using machine learning models

This study employs two machine learning (ML) models, namely the light gradient boosting machine (LightGBM) and the extreme gradient boosting (XGBoost) model, to perform seasonal forecasts for winter precipitation over China. The seasonal forecast results derived from a dynamic model are used for the purpose of comparison. The predictors employed in the two ML models consist of climate variables from the preceding autumn. The ML models are trained using data during 1979 to 2010 without additional predictor selection process. Consequently, the ML models autonomously extract useful information and then apply it to perform seasonal forecasts of winter precipitation for 2011–2020. Results indicate that the two ML models exhibit reasonable forecast skill and demonstrate superior performance over various regions when compared to the dynamic model. Additionally, the ensemble forecasts of the two ML models and the dynamic model exhibit higher skill in China compared to the dynamic model forecast alone. The forecast skill of the ML models can be attributed to their skillful forecast of the first empirical orthogonal function mode (EOF1) of the winter precipitation. In addition, the split time count method and the Shapley additive explanation (SHAP) values analysis are performed to further understand and explain the ML models forecast results. Results show that the sea surface temperature (SST), especially the SST over the western Pacific plays an important role in the ML model seasonal forecasts of winter precipitation over China, consistent with previous studies. The outcomes of this investigation provide valuable insights that can enhance the practical implementation and understanding of ML models in the field of seasonal forecasting.

Seasonal streamflow forecasting in South America’s largest rivers

Study regionSouth America large basins (>5000 km²) Study focusThis work represents a first assessment of seasonal streamflow forecasts in South America based on a continental-scale application of a large scale hydrologic-hydrodynamic model and ECMWF's seasonal forecasting system precipitation forecasts (SEAS5-SSF) with bias correction. Seasonal streamflow forecasts were evaluated against a reference model run. Forecast skill was estimated relative to the Ensemble Streamflow Prediction (ESP) method. New hydrological insightsWe observed that bias correction was essential to obtain positive skill of SEAS5-SSF over ESP, which remained a hard to beat benchmark, especially in regions with high seasonality, and highly dependent on initial conditions. SEAS5-SSF skill was found to be dependent on initialization month, basin and lead time. Rivers where the skill is higher were Amazon, Araguaia, Tocantins and Paraná.

PyNMME: A python toolkit to retrieve, calibrate and verify seasonal precipitation forecasts

The North American Multi-Model Ensemble (NMME) experiment assembles valuable ensemble forecasts from more than ten global climate models (GCMs). Focusing on environmental applications of NMME forecasts, this paper develops the pyNMME, a Python-based toolkit to implement data retrieval, forecast calibration and forecast verification. Specifically, the toolkit is composed of three modules that streamline the processes of retrieving the NMME data for locations and seasons of interest from high-dimensional datasets, calibrating raw GCM forecasts by the linear scaling, quantile mapping and Bernoulli-Gamma-Gaussian models and verifying predictive performances by twenty verification metrics and six types of diagnostic plots. The results show that the characteristics of bias, reliability and skill of raw forecasts vary across the globe and that the three calibration models in the pyNMME effectively improve raw forecasts to different extents. Overall, the pyNMME can serve as a useful tool to exploit valuable NMME precipitation forecasts for environmental modelling and management.

Stochastic disaggregation of seasonal precipitation forecasts of the West African Regional Climate Outlook Forum

AbstractSeasonal rainfall forecasts from the West African Regional Climate Outlook Forum (RCOF) are essential for adapting to climate variability. However, their temporal aggregated nature is a strong limitation, especially when used with impact models requiring daily resolution, such as hydrological or crop models. To address this issue, this study proposes a temporal disaggregation method for these forecasts using in situ data from two districts (Kandi and Parakou) in northern Benin, spanning from 1971 to 2020. A resampling technique was used to construct a daily historical record that aligns with seasonal rainfall forecasts. Three stochastic disaggregation models for rainfall (SRGs) were developed, including two parametric models (SRG1 and SRG2) and one semiparametric (SRG3). Their parameters were estimated from the resampled record to generate daily synthetic data replicating the forecasts. Evaluation of the SRGs revealed that SRG2, which combined a first‐order Markov chain with a mixed exponential distribution, performs well in simulating various characteristics of the rainy season, including dry spells, wet spells and daily precipitations. Furthermore, SRG2 maintained the trends of the initial forecasts and outperformed SRG1 and SRG3, as confirmed by the chi‐square test. Indeed, a good agreement was observed between the probabilities of the initial prediction and those calculated from the temporal disaggregation with the SRG2 method. Also, for the forecasts expressed by probabilities 15–35–50 and 20–50–30, the cumulative distribution function curves (CDF) of the SRGs exhibited appropriate shifts compared to climatology. These forecasts were specific to the Kandi area in 2008 and 2003, respectively, during the West African RCOFs. Although this study focused specifically on the Kandi and Parakou districts, the temporal disaggregation methodology used can be applied to other locations within West Africa or other RCOFs worldwide. This study offers valuable guidance for generating sector‐specific seasonal forecasts for the West African region.

Catchment‐scale skill assessment of seasonal precipitation forecasts across South Korea

AbstractClimate change is expected to make droughts more frequent and severe all over the world. South Korea is no exception and is already suffering from extreme droughts such as one that prolonged from 2013 to 2015 and caused nation‐wide damage. To mitigate drought damages, better management of existing water infrastructure is essential. A promising opportunity to improve operational decisions is to make use of seasonal weather forecasts that are provided by general circulation models and can be downscaled to the catchment scale. This study hence assesses the skill of seasonal forecasts over 20 catchments in South Korea, where the largest reservoirs are located. Datasets from four weather forecasting centres (ECMWF, UK Met Office, Météo France and DWD) have been evaluated over the period 2011–2020, and their skill quantified using the Continuous Ranked Probability Skill Score (CRPSS). We analyse how skill varies across the seasons and years, and if it can be linked to catchments characteristics. In doing so, we develop a methodology and a Python package to implement it, which is freely available for future applications to other regions. As for the study case, our results showed that among the four forecasting centres, ECMWF's forecasts were the most skilful in South Korea. In particular, seasonal forecasts outperform the climatology for 2 months of lead time and are more skilful during the wet season and in dry years. Linear bias correction is found to be useful to correct systematic seasonal biases, whereas we found no significant correlation between the catchment characteristics and forecast skill. We also investigated the possibility of anticipating dry years from seasonal forecasts and/or ENSO indices but found no significant link.

Seasonal precipitation forecasting for water management in the Kosi Basin, India using large-scale climate predictors

AbstractA novel approach for qualitative seasonal forecast of precipitation at a basin scale is presented as significant enhancement in seasonal forecast at regional and country scales in India. The process utilizes empirical and typically lagged relationships between target variables of interest, namely precipitation at the basin level and various large-scale climate predictors (LSCPs). A total of 14 LSCPs have been considered for the seasonal forecast of precipitation with lead times of 1, 2, and 3 months in the Kosi Basin, India. Random split training and testing were conducted on seven machine-learning (ML) models using a potential predictor dataset for model selection. The Logistic Regression (LR) model was adopted since it had the highest mean accuracy score compared to the remaining six ML models. The LR model has been optimized by testing it on all possible combinations of potential predictors using Leave-One-Out Cross-Validation (CV) scheme. The resulting Seasonal Prediction Model (SPM) provides the probability of each tercile categorized as Above Normal (AN), Normal (N), and Below Normal (BN). The model has been evaluated using various metrics.

Utilization of El Niño–Southern Oscillation projected by climate models in improvement of seasonal precipitation predictability

AbstractImproved seasonal precipitation forecasts can enable more effective water resource management decisions in a number of sectors, including municipal supply, agriculture, hydropower generation, and tourism. This study develops an effective straightforward statistical approach to enhance the quality of seasonal precipitation forecasts through the utilization of El Niño–Southern Oscillation (ENSO) information projected by Coupled General Climate Models (CGCMs). A stochastic weather generation (WG) model is developed to predict seasonal precipitation condition on ENSO condition. The WG model links a nonhomogeneous Markov Chain representing ENSO occurrence model to a bivariate normal distribution for seasonal precipitation conditioned on ENSO phase. Two verification metrics are suggested to measure the degree of predictability of raw, calibrated and climatological seasonal precipitation forecasts over northwest Costa Rica as a case study. Results indicate the potential to narrow the uncertainty of seasonal precipitation forecasts by incorporating CGCMs ENSO cycle information. Precipitation during the late part of the wet season (LS) has more predictability than precipitation in the early part of the wet season (ES). In addition, the degree of predictability decreases with an increase in lead time for a given forecast. A lead time of 1 year maintains a moderate level of predictability likely to support tangible benefits to various decision‐making processes.

The Effect of Remote Teleconnection Patterns on Temperature and Precipitation of the Euphrates-Tigris Basin

The Euphrates-Tigris Basin is the most important water source in the Middle East. The present study examined the relationship between the precipitation and temperature characteristics of the basin using remote teleconnection patterns on a monthly time scale. The effects of the North Atlantic, Arctic Oscillations, North Sea Caspian Pattern, and Western Mediterranean indices were examined. The relationship between teleconnection patterns and precipitation/temperature was investigated by adopting Spearman’s Correlation test. All of the remote teleconnection patterns had significant effects on the temperature and precipitation characteristics of the basin. However, the North Sea Caspian Pattern significantly affected the temperatures of the entire basin. Similarly, the Western Mediterranean index had a significant effect on the average temperatures for four months (February, April, November, and December) in almost the entire basin. Also, the Western Mediterranean Index corralates positively with the precipitation of the basin in January, while the correlation is negative in October, and November. Especially, the Western Mediterranean Index and the North Sea Caspian Pattern showed one-month and two-month delayed relationships in monthly total precipitation in some months. At the extremes of the index values, relationships often became strong and distinct. The study results may be useful for seasonal temperature and precipitation forecasts of the Euphrates-Tigris basin.