Seasonal Forecasts Research Articles

Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast performance from NMME-2 over Indonesia

Abstract Global Subseasonal-to-Seasonal (S2S) precipitation forecast was known to bridge the gap between weather and seasonal forecast, can be utilized as supporting information for decision-making related to hydrometeorological disaster mitigation activities. However, uncertainty in global models caused forecast performance can be different across regions and time periods. Therefore, it is important to evaluate forecast performance before utilizing the prediction result in any region. In this study, performance of probabilistic precipitation forecast from Multi-model Ensemble (MME) of three models in The North American Multi-Model Ensemble phase 2 (NMME-2) project was evaluated in Indonesia region based on two evaluation metrics: continuous ranked probability score (CRPS) and reliability diagram. The evaluation was conducted during the boreal summer (May–October) and boreal winter (November–April) periods, as well as during the active period of subseasonal climate variability phenomenon Madden-Julian Oscillation (MJO). Our result shows that S2S precipitation forecast from MME of CFSv2, CanCM4 and GEOS5 models are sufficiently accurate and reliable during the boreal summer period for Central Sumatra, Southern Sumatra, Southern Kalimantan, Java, Southern Sulawesi, and Southern Papua regions, with a range of CRPS values between 4-16 mm/7 days and a ‘perfect’ reliability category. There is no notable distinction in the performance of S2S precipitation forecast between the active and inactive events of the Madden-Julian Oscillation (MJO). The difference in CRPS values between these two periods is only around 0.8-1.2 mm/7 days, and there is no difference in reliability categories across Indonesia as a whole, nor significant spatial pattern differences.

Statistical Predictability of Euro-Mediterranean Subseasonal Anomalies: The TeWA Approach

Abstract It is widely known from energy balances that global oceans play a fundamental role in atmospheric seasonal anomalies via coupling mechanisms. However, numerical weather prediction models still have limitations in long-term forecasting due to their nonlinear sensitivity to initial deep oceanic conditions. As the Mediterranean climate has highly unpredictable seasonal variability, we designed a complementary method by supposing that 1) delayed teleconnection patterns provide information about ocean–atmosphere coupling on subseasonal time scales through the lens of 2) partially predictable quasi-periodic oscillations since 3) forecast signals can be extracted by smoothing noise in a continuous lead-time horizon. To validate these hypotheses, the subseasonal predictability of temperature and precipitation was analyzed at 11 reference stations in the Mediterranean area in the 1993–2021 period. The novel method, presented here, consists of combining lag-correlated teleconnections (15 indices) with self-predictability techniques of residual quasi-oscillation based on wavelet (cyclic) and autoregressive integrated moving average (ARIMA) (linear) analyses. The prediction skill of this teleconnection–wavelet–ARIMA (TeWA) combination was cross-validated and compared to that of the ECMWF’s Seasonal Forecast System 5 (SEAS5)–ECMWF model (3 months ahead). Results show that the proposed TeWA approach improves the predictability of first-month temperature and precipitation anomalies by 50%–70% compared with the forecast of SEAS5. On a moving-averaged daily scale, the optimum prediction window is 30 days for temperature and 16 days for precipitation. The predictable ranges are consistent with atmospheric bridges in teleconnection patterns [e.g., Upper-Level Mediterranean Oscillation (ULMO)] and are reflected by spatial correlation with sea surface temperature (SST). Our results suggest that combinations of the TeWA approach and numerical models could boost new research lines in subseasonal-to-seasonal forecasting. Significance Statement The Mediterranean climate presents a high natural variability that makes skillful seasonal forecasts very difficult to achieve. We propose to complement the current forecasting methods with a statistical approach that combines two conceptual models: First, climate anomalies (cold/warm or dry/wet periods) are considered as smooth waves (with slow changes); and second, atmospheric and oceanic indices perform the role of atmosphere–ocean interactions, which impact Mediterranean climate variability in a delayed way. The key findings are that combining both sides, a better predictability of climate variability is provided, which is an opportunity to improve natural resource management and planning.

Forecasting hospital outpatient volume using an optimized medical two-stage hybrid grey model

PurposeForecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers.Design/methodology/approachThis paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor.FindingsThis paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models.Originality/valueThe two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.

Hypothesis testing for performance evaluation of probabilistic seasonal rainfall forecasts

Hypothesis testing for performance evaluation of probabilistic seasonal rainfall forecasts

Decadal preference of seasonal ENSO transition through a southern hemisphere climate mode

El Niño Southern Oscillation (ENSO) is the leading interannual coupled climate mode in the tropical Pacific. The seasonal transition of ENSO from boreal winter to the following summer can significantly affect the global climate. One of the major hurdles in understanding the seasonal transition of ENSO is the spring predictability barrier. Here, we show that ENSO’s seasonal transition is modulated by a multidecadal climate mode of boreal spring sea-level pressure (SLP) in the extratropical Southern Hemisphere. This ENSO transition mode (ETM), when characterised by a decrease in SLP and associated clockwise circulation of the surface winds centred over the southeastern subtropical Pacific Ocean, produces westerly anomalies at the equator. These wind anomalies in the equatorial eastern Pacific aid the seasonal warming of Niño3.4 sea surface temperature anomalies (N34SST) from boreal winter to the following summer. The ETM time series shows prominent multidecadal variations at around 50 years. This creates a conducive environment for alternate cold and warm seasonal transitions leading to multidecadal variations in boreal summer N34SST. Thus, ETM provides a physical insight into the seasonal transition of ENSO and leads to a new paradigm for ENSO evolution beyond its peak. This has implications for seasonal ENSO forecasts and decadal climate predictions.

Skill assessment of seasonal forecasts of ocean variables

There is growing demand for seasonal forecast products for marine applications. The availability of consistent and sufficiently long observational records of ocean variables permits the assessment of the spatial distribution of the skill of ocean variables from seasonal forecasts. Here we use state-of-the-art temporal records of sea surface temperature (SST), sea surface height (SSH) and upper 300m ocean heat content (OHC) to quantify the distribution of skill, up to 2 seasons ahead, of two operational seasonal forecasting systems contributing to the seasonal multi-model of the Copernicus Climate Change Services (C3S). This study presents the spatial distribution of the skill of the seasonal forecast ensemble mean in terms of anomaly correlation and root mean square error and compares it to the persistence and climatological benchmarks. The comparative assessment of the skill among variables sheds light on sources/limits of predictability at seasonal time scales, as well as the nature of model errors. Beyond these standard verification metrics, we also evaluate the ability of the models to represent the observed long-term trends. Results show that long-term trends contribute to the skill of seasonal forecasts. Although the forecasts capture the long-term trends in general, some regional aspects remain challenging. Part of these errors can be attributed to specific aspects of the ocean initialization, but others, such as the overestimation of the warming in the Eastern Pacific are also influenced by model error. Skill gains can be obtained by improving the trend representation in future forecasting systems. In the meantime, a forecast calibration procedure that corrects the linear trends can produce substantial skill gains. The results show that calibrated seasonal forecasts beat both the climatological and persistence benchmark almost at every location for all initial dates and lead times. Results demonstrate the value of the seasonal forecasts for marine applications and highlight the importance of representing the decadal variability and trends in ocean heat content and sea level.

Financial markets value skillful forecasts of seasonal climate

Scientific agencies spend substantial sums producing and improving forecasts of seasonal climate, but they do so without much information about these forecasts’ value in practice. Here we show that financial market participants value the production of seasonal forecasts: options traders price the uncertainty generated by upcoming United States National Oceanic and Atmospheric Administration Winter and El Niño Outlooks. Each outlook affects firms throughout the economy, with total market capitalization of $6 and $13 trillion, respectively. A 1% improvement in the skill of the El Niño Outlook reduces firms’ exposure to a one standard deviation shock by $18 billion and induces traders to spend an additional $2 million hedging the outlook’s news. Firms must not be able to undertake ex-ante adaptation that would eliminate their exposure to the forecasted portion of seasonal climate without imposing substantial costs of its own.

Teleconnection of the Quasi-biennial oscillation with boreal winter surface climate in Eurasia and North America

An improved understanding of dynamical coupling from regional to global scales via tropospheric or stratospheric region can be helpful in improving seasonal forecasts for a given region of interest. Here we investigate dynamical coupling between the equatorial stratospheric Quasi-biennial oscillation and the boreal winter surface climate of the Northern Hemisphere mid and high latitudes using 42 years of data (1979–2020). For neutral El Niño Southern Oscillation periods, the Quasi-biennial oscillation westerly phase at 70 hPa favors high sea level pressure in the polar region, colder conditions and deeper snow over Eurasia and North America, and the opposite effects for the easterly phase. When Quasi-biennial oscillation anomalies arrive near the tropopause, it is observed that planetary wave activity is enhanced towards to extratropical region during westerly phase and reduced during easterly phase. This teleconnection pathway via the upper troposphere and lower stratosphere to the high latitude surface is independent of the “stratospheric pathway” (Holton-Tan mechanism). Diagnosis of this pathway can help to improve understanding of sub-seasonal to seasonal variations, and long-range forecasting over Eurasia and North America.

Understanding uptake and interrelationship of climatic agricultural innovations in rural Zambia: Evaluating seasonal climate forecast use and weather shocks

Understanding uptake and interrelationship of climatic agricultural innovations in rural Zambia: Evaluating seasonal climate forecast use and weather shocks

Global ocean surface and subsurface temperature forecast skill over subseasonal to seasonal timescales

Global ocean surface and subsurface temperature forecast skill over subseasonal to seasonal timescales

Novel AI-Powered Dynamic Inventory Management Algorithm in the USA: Machine Learning Dimension

Dynamic inventory management revolves around the practice of progressively modifying inventory degrees to adapt to fluctuations in client demand, production, and supply chain dynamics. At the center, inventory management focuses on upholding enhanced levels of stock to balance consumer service via availability with the costs related to holding excess inventory. This research paper aimed to explore the dynamic inventory management activities employed by organizations in the USA, shedding light on the machine learning strategies that can be deployed and their implications. The performance of the algorithms was empirically evaluated in a Python program experiment utilizing real-world data. To facilitate the data for input into the Neural Network, feature engineering, and selection were imposed to affirm its suitability. This study proposes the Sequence-to-Sequence (Seq2Quant) algorithm, a neural network-powered technique for demand prediction in inventory management. The current experiment compared and contrasted the performance of the Neural Networks against the following baselines, most notably, Naïve Seasonal Forecast, Moving Average Forecast, ARIMA, Naïve Seasonal Forecast with Averaging over four periods, SARIMAX. From the experiment, it was evident that the Seq2Seq had the lowest MAE (17.44) and the lowest SMAPE (66.91), suggesting that it was the best-performing algorithm overall. Besides, SARIMAX and ARIMAX also performed well, with MAE values of 18.33 and 18.09, respectively.

Influence of long-term observed trends on the performance of seasonal hydroclimate forecasts

Skillful forecasts of hydroclimate variables are essential for operational water management, agricultural planning, and food supply. Several studies have attempted to improve the skill of raw forecasts either by post-processing or by incorporating sea surface conditions into raw forecasts. However, to the best of our knowledge, limited to no study has investigated temporal trend, which is present in observed records but is absent from retrospective forecasts (also known as, hindcasts). The current study understands that a temporal trend can be yielded in raw meteorological forecasts by i) updating surface boundary forcings and ii) applying statistical models for either post-processing meteorological forecasts or issuing streamflow forecasting using weather forecasts as predictors. To analytically derive the relationship between temporal trend and forecast performance, this study applies three statistical approaches for post-processing season-ahead hindcasts of the Indian monsoon obtained from three general circulation models (GCM). The findings show that raw hindcasts of the Indian monsoons typically ignore the temporal trend present in the observed records. Furthermore, analytical derivations confirm that the absence of a trend in GCM hindcasts significantly influences post-processing performance. Moreover, a semi-parametric approach could not overcome the limitations of a parametric linear model in yielding a temporal trend in the hindcasts. Potential reasons for the absence of a trend in the hindcast is also discussed.

Understanding winter windstorm predictability over Europe

Abstract. Winter windstorms belong to the most damaging meteorological events in the extra-tropics. Their impact on society makes it essential to understand and improve seasonal forecasts of these extreme events. Skilful predictions on a seasonal timescale have been shown in previous studies by investigating hindcasts from various forecast centres. This study aims to explain storm forecast skill based on relevant dynamical factors. Therefore, a number of factors which are known to influence either windstorms directly or their synoptic relevant systems, mid-latitude cyclones, are investigated. These factors are analysed for their relation to windstorm forecast performance based on a reanalysis (ERA5) and the seasonal hindcast of the UK Met Office (Global Seasonal forecasting system version 5, GloSea5). Within GloSea5, relevant dynamical factors are (1) validated with respect to their physical connections to windstorms, (2) investigated with respect to the seasonal forecast skill of the factors themselves, and (3) assessed on the relevance and influence of their forecast performance to and on windstorm forecast skill. Although not all investigated factors reveal a clear and consistent influence on windstorm forecast skill over Europe, core factors like mean sea level pressure gradient, sea surface temperature, equivalent potential temperature and Eady growth rate show consistent results within these three steps: their physical connection is well represented in the model; these factors are skilfully predicted in storm-relevant regions, and, consequently, this skill leads to increased forecast skill of winter windstorms over Europe. This study thus explains existing forecast skill in winter windstorms but also indicates potential for further model developments to improve seasonal winter windstorm predictions.

Seasonal climate forecast-an important tool in managing the risk of extreme weather events in Australia's wheat industry

This study aims to investigate the benefits of using the Australian Community Climate and Earth-System Simulator-Seasonal Version 2 (ACCESS-S2), a state-of-the-art seasonal climate forecast (SCF) system, to improve contingent decision-making in the Australian wheat industry. Six locations in eastern Australia, three cultivars with varying maturities, and 17 times of sowing (TOS) were considered. Seasonal hindcasts from ACCESS-S2, initialized on the 1st May for the period 1981–2018 were linked with the Agricultural Production System sIMulator (APSIM)-Wheat model (v7.10) to (1) assess the hindcast skill of ACCESS-S2 in predicting the occurrence of extreme weather events and in predicting above/below long-term median wheat yield; (2) identify optimal management strategies in mitigating the impact of extreme weather events; and (3) quantify the yield benefit of using ACCESS-S2 at the key decision-making point of sowing against a baseline management situation (i.e., without the use of SCF). Research results showed that (1) ACCESS-S2 has varying skills in predicting extreme climate indices and wheat yield across the six locations at a seasonal lead time; (2) 68 % of cases had a yield gain using SCF information; (3) across all cases, there was an average yield gain of 281 kg/ha representing an increase of 13 %; (4) benefits of using SCF were seen across predicted wet years, neutral years and dry years accounting for 69 %, 65 % and 72 % respectively, and (5) the identified optimal management options varied from location to location with earlier sowing associated with either suntop or gregory or both being the optimal strategies at most of the locations considered. Overall, there is a demonstrated benefit in utilizing ACCESS-S2 forecasts in the Australian wheat industry to improve farm management decision-making. The benefit can occur in any climate state but with dry years being more likely and significant (with an average yield increase of 97 % across the locations).

Effectiveness of climate information services: an evaluation of the accuracy and socio-economic benefits for smallholder farmers in Niger and Mali

Climate information services are foundational means of building the farmer’s resilience. However, studies are scarce about the accuracy of climate information services in dryland regions such as the West Africa Sahel, like in Mali and Niger. Thus, this study examined the accuracy of climate forecasts and their socio-economic benefits in these two countries. For rainfall forecasts and alerts, we collected the 2022 data from the ‘SMS Sandji’ platform in Mali (Nara) and the national meteorological agency alert database in Niger (Zinder). The socio-economic benefits of climate information were determined using a sample of 900 individuals in Niger and 227 in Mali. The results indicate that both seasonal and daily climate forecasts have high to moderate accuracy from 0.7 to 0.58 for CSI and 0.11 to 0.43 for BS index in Niger, and 0.94 to 0.91 for CSI, and 0.06 to 0.25 for BS in Mali. The results of field survey show that, in general, 87 to 100% of the respondents in Niger and 100% in Mali received the seasonal forecasts. ANOVA also reveals with high significance (p value = 0.0001) that the utilization of climate information plays a crucial role in improving farmers’ average financial incomes with FCFA 24,943 per hectare at season onset to FCFA 15,355 per hectare during the cropping season, and FCFA 6204 per hectare at the end of the season, and time-saving of 36 h per hectare to 8 h per hectare, depending on the period when the information was used. Globally, this work underscores the importance of climate information services and highlights their positive socio-economic impacts to the livelihood of farmers.

Performance‐based evaluation of NMME and C3S models in forecasting the June–August Central African rainfall under the influence of the South Atlantic Ocean Dipole

AbstractIn this study, hindcasts from eight Copernicus Climate Change Service (C3S) and three North American Multi‐Model Ensemble (NMME) operational seasonal forecast systems, based on dynamical climate models, are employed to investigate the influence of the South Atlantic Ocean Dipole (SAOD) on the predictive skill of Central Africa (CA) rainfall. The focus is primarily on the June–July–August season for 1993–2016. The findings reveal that, when regionally averaged, all models exhibit positive skill in predicting CA rainfall, except for the Geophysical Fluid Dynamics Laboratory (GFDL‐SPEAR) model. Notably, there are significant spatial variations in skill across different regions. Model performance is particularly low (high) in the Central African Republic and Congo Basin (Gabon and Chad) and tends to deteriorate with increasing lead‐time. Models that demonstrate a strong connection between SAOD and CA rainfall tend to exhibit better predictive skills in forecasting rainfall, in contrast to models with weaker connections. This leads to a significant in‐phase relationship between the predictive skills of rainfall and the strength of the SAOD–rainfall connection among the models. Furthermore, the atmospheric circulation responding to SST forcing associated with the El Niño–Southern Oscillation exerts a significant influence on the robust atmospheric circulation associated with the climatological mean of SST over the SAO. This suggests that mean state bias in the SAO/equatorial Pacific region plays a role in modulating the strength of the simulated SAOD–CA rainfall connection and, consequently, the prediction skill of CA rainfall. In general, both NMME and C3S models appear to be valuable tools capable of providing essential seasonal information several months in advance. These insights can aid decision‐makers in the region in making informed decisions regarding adaptation and mitigation measures.

Impact of El Niño Southern Oscillation on the first inter-monsoon rainfall over Sri Lanka in the post-El Niño years

Sri Lanka has a tropical monsoon climate. The first intermonsoon (FIM) season is one of four rainfall seasons. Previous investigations have explored the concurrent ramifications of the El Niño Southern Oscillation (ENSO); however, its impact on seasonal rainfall patterns in the country in the post-El Niño years remains unexplored. This study aims to investigate anomalous FIM rainfall in Sri Lanka during the post-El Niño years. Weather station rainfall data from the Department of Meteorology for 114 stations for 1975–2019, Japanese 55-year reanalysis data, and COBE sea surface temperature data were used in this study. A significant negative correlation between rainfall at most stations and the preceding year’s November, December, and January Niño3.4 index (NDJ Niño3.4) was identified for the FIM season. All three super El Niño events and one of the two strong El Niño events agree with the above results, showing a clear negative rainfall anomaly pattern. The Indo-western Pacific Ocean Capacitor effect was identified as the cause of rainfall reduction during the FIM season in Sri Lanka and in contrast, the post-El Niño season in 1988 exhibited a positive rainfall anomaly. A rapid transition from El Niño to La Niña and the associated atmospheric conditions were identified as significant contributors to these features. The findings of this study will help overcome this research gap and improve the accuracy of seasonal forecasts for the country.

Using Calibrated Rainfall Forecasts and Observed Rainfall to Produce Probabilistic Meteorological Drought Forecasts

Most existing drought forecast systems rely only on observed or forecast rainfall, losing valuable context gained from considering both. The lack of a direct link between observed and forecast rainfall reduces the physical consistency of a system, motivating the development of a methodology that can directly link the two. The methodology developed in this study allows the comparison of the calibrated ensemble forecasts of rainfall totals from a dynamical climate model to observed rainfall deficiencies from a gridded rainfall analysis. The methodology is used to create a probabilistic product that forecasts the chance of entering meteorological drought, with lead times of one month (monthly forecast) and three months (seasonal forecast). Existing deficiency areas are included to facilitate analysis of how these areas are forecast to change. The performance of the developed methodology was verified using Percent Correct (PC), Brier Score (BS), and Relative Operating Characteristic (ROC) statistics. Analysis of the forecast plots was also completed visually. Forecast performance for areas with existing deficiencies as well as for non-deficiency areas was promising (PC rates of >79% and >97%, respectively). Although PC rates for observed deficiencies were low across most months, the mean forecast probability for these areas was 36%, indicating the system had value and outperformed climatology. A calibrated, coupled product like the one scoped in this study has not been explored and we note that it could be an invaluable tool for quantifying meteorological drought onset and persistence in Australia.

Forecasting Tropical Annual Maximum Wet‐Bulb Temperatures Months in Advance From the Current State of ENSO

AbstractHumid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet‐bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures by atmospheric convection and wave dynamics. Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TW (TWmax). We develop a multiple linear regression model that explains 80% of variance in tropical mean TWmax and significant regional TWmax variances. The model considers warming trends and El Niño and Southern Oscillation indices. Looking ahead, the strong‐to‐very‐strong El Niño at the end of 2023, with an Oceanic Niño Index of ∼2.0, suggests a 2024 tropical land mean TWmax of 26.2°C (25.9–26.4°C), and a 68% chance (24%–94%) of breaking existing records. This method also predicts regional TWmax in specific areas.

Smallholder farmers’ perceived motivations for the adoption and implementation of climate information services in the Atwima Nwabiagya District, Ghana

This study investigated smallholder farmers’ perceived motivations for the adoption and implementation of climate information services (CIS) in the Atwima Nwabiagya District, Ghana. Specifically, the study answered the following research questions. (i) What is the perception of smallholder farmers on climate change and variability in the Atwima Nwabiagya District?; (ii) What are the CIS accessed and used by the smallholder farmers for farm management decisions?; (iii) What are the perceived motivations for the adoption and implementation of CIS by smallholder farmers?; and; (iv) What are the barriers confronting smallholder farmers in adopting and implementing CIS? Primary data was collected through questionnaire surveys with 200 farming households and four key informant interviews in four selected communities (Kobeng, Seidi, Wioso, and Nkawie Panin) in the study district. Results showed that smallholder farmers perceived climate change and variability through increased windstorms and thunderstorms, variable rainfall, and increased temperature patterns. The surveyed farmers also perceived that the climatic changes affect their agricultural activities adversely. However, the smallholder farmers accessed and used CIS including weather warnings (86%), daily weather forecasts (70%), and seasonal forecasts (60%) to manage climatic risks on the farm. Results also showed that the smallholder farmers were motivated by critical factors including improvement in the acquisition of proactive measures (92%), increased knowledge of climatic patterns that affect food stock (91%), and a reduction in loss of stock produced (88%) to access and use CIS. Despite this, the smallholder farmers were confronted with key barriers in their uptake and utilization of CIS. These were the timeliness of CIS (73%), a lack of weather symbols in the forecasts (65%), and inadequate information on seasonal forecasts for long-term planning (61%). The study concluded that the motivations for smallholder farmers using CIS in the study district may vary depending on their context, goals, and challenges. However, the general motivation is to enhance smallholder farmers’ farm planning and risk management by using weather and climate forecasts to make better decisions on crop selection, planting dates, input use, pest and disease control, irrigation, harvesting, and marketing.