S2S Forecast Research Articles

Subseasonal-to-seasonal (S2S) prediction of atmospheric rivers in the Northern Winter

Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.

Listening to Stakeholders III: Potential Users Evaluate Product Content and Design for Subseasonal Extreme Precipitation Forecasts

Abstract Extreme precipitation over a 2-week period can cause significant impacts to life and property. Trustworthy and easy-to-understand forecasts of these extreme periods on the subseasonal-to-seasonal time frame may provide additional time for planning. The Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES2iP) project team conducted three workshops over 6 years to engage with stakeholders to learn what is needed for decision-making for subseasonal precipitation. In this study, experimental subseasonal-to-seasonal (S2S) forecast products were designed, using knowledge gained from previous stakeholder workshops, and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Our stakeholders preferred a combination of products that covered the spatial extent, regional daily values, with associated uncertainty, and text narratives with anticipated impacts for planning within the S2S time frame. When targeting longer extremes, having information regarding timing of expected impacts was seen as crucial for planning. We found that there is increased uncertainty tolerance with stakeholders when using products at longer lead times that typical skill metrics, such as critical success index or anomaly correlation coefficient, do not capture. Therefore, the use of object-oriented verification that allows for more flexibility in spatial uncertainty might be beneficial for evaluating S2S forecasts. These results help to create a foundation for the design, verification, and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers.

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

AbstractSub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region.

Spatial and Temporal Variation of Subseasonal-to-Seasonal (S2S) Precipitation Reforecast Skill across the CONUS

Abstract Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resource management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash–Sutcliffe efficiency (NSE) coefficient, has been analyzed toward understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components—correlation, conditional bias, and unconditional bias—by four seasons, three lead times (1–12, 1–22, and 1–32 days), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 in. per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists, and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during the fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Postprocessing using simple model output statistics could reduce both unconditional and conditional biases to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show that efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.

Stratospheric impact on subseasonal forecast uncertainty in the Northern extratropics

Extreme states of the polar stratospheric circulation during winter tend to be followed by anomalies in the near-surface circulation for several weeks, especially over the North Atlantic/Eurasia (NAE) sector. Previous research has highlighted an associated robust increase in subseasonal to seasonal (S2S) forecast skill related to forecast ensemble mean anomalies. Here we explore the additional impact of polar stratospheric circulation extremes on ensemble spread, a key measure of forecast uncertainty and the associated predictability. We find that over the Norwegian Sea and around Scandinavia S2S ensemble spread in near-surface geopotential height is significantly reduced following weak polar vortex states (enhanced predictability), whereas it is increased following strong polar vortex states (reduced predictability), with anomaly magnitudes reaching as high as 20%. Notably, modulations of forecast uncertainty are found even over regions of near-zero ensemble mean anomaly. We find decreased forecast uncertainty to be linked to decreased synoptic-scale storm activity and vice versa for increased forecast uncertainty. Our results furthermore suggest that modulation of S2S forecast uncertainty by the stratosphere dominates over NAE, whereas over the North Pacific modulations due to El Niño/ La Niña are more important.

Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere

AbstractSubseasonal‐to‐seasonal (S2S) prediction has gained momentum in the recent past as a need for predictions between the weather forecasting timescale and seasonal timescale exists. The availability of S2S databases makes prediction and predictability studies possible over all the regions of the globe. Most S2S studies are, however, relevant to the northern hemisphere. In this review, the S2S literature relevant to the southern hemisphere (SH) are presented. Predictive skill, sources of predictability, and the application of S2S predictions are discussed. Indications from the subseasonal predictability studies for the SH regions suggest that predictive skill is limited to 2 weeks in general, particularly for temperature and rainfall, which are the variables most frequently investigated. However, temperature has enhanced skill compared to rainfall. More S2S prediction studies that include the quantification of the sources of predictability and the identification of windows of opportunity need to be conducted for the SH, particularly for the southern African region. The African continent is vulnerable to weather‐ and climate‐related disasters, and S2S forecasts can assist in alleviating the risk of such disasters.

Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1–4 over South Africa

AbstractThe probabilistic forecast skill level of statistically downscaled European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal‐to‐seasonal (S2S) forecasts is determined in predicting maximum and minimum temperatures for weeks 1–4 lead times during 20‐year December–January–February (DJF) seasons from 2001 to 2020 over South Africa. Skilful S2S forecasts are vital in assisting decision‐makers in the development of contingency planning for any eventualities that may arise because of weather and climate phenomena. Extreme high‐ and low‐temperature events over a prolonged period can lead to hyperthermia and hypothermia, respectively, and can lead to loss of life. The results from the relative operating characteristic (ROC) and reliability diagrams indicate that the ECMWF S2S model has skill in predicting maximum temperature up to week 3 ahead, particularly over the central and eastern parts of South Africa. The ROC scores indicate that the model has skill in predicting minimum temperature up to week 4 ahead for the above‐normal category, particularly over the central and eastern parts of South Africa. Reliability diagrams indicate that the model has a tendency of overestimating the below‐normal category when predicting both maximum and minimum temperatures for weeks 1–4 lead times over South Africa. Furthermore, canonical correlation analysis (CCA) pattern analysis suggests that when there are anomalously positive and negative predicted 850‐hPa geopotential heights located over South Africa, there are anomalously hot and cold conditions during the DJF seasons over most parts of South Africa, respectively. These results suggests that statistical downscaling of model forecasts can improve forecast skill. Moreover, the results suggest that there is potential for S2S predictions in South Africa, and as such, S2S prediction system for maximum and minimum temperatures can be developed.

Skilful sub‐seasonal forecasts of aggregated temperature over Europe

AbstractSubseasonal‐to‐seasonal (S2S) forecasts span the prediction range of weeks to 2–3 months ahead, bridging the gap between medium‐range and seasonal weather forecasts. There has been growing interest in S2S forecasts in recent years, largely because of the many potential uses of forecasts spanning these timescales. However, the skill of S2S forecasts beyond the first 2 weeks or so is poor, potentially limiting the usability of these forecasts. We show in this study that when considering accumulated temperatures, there is in fact good forecasting skill over Europe for accumulation periods up to 30 days ahead. Using a set of S2S hindcasts, we show using both a deterministic and a probabilistic measure of skill that the accumulated 2‐metre temperature forecasts out to 30 days are skilful over most of Europe. In summer, South West Europe has highest skill, while in winter North East Europe has highest skill. As an example application of such forecasts, we also evaluate the skill for summer cooling degree‐days (CDD) and winter heating degree‐days (HDD). For 30‐day winter HDD, there is good skill in all four European regions; for 30‐day summer CDD, the skill is limited in North West Europe, but still good in other regions.

Assessment of subseasonal-to-seasonal (S2S) ensemble extreme precipitation forecast skill over Europe

Abstract. Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. Of particular interest is the subseasonal-to-seasonal (S2S) prediction timescale. The S2S prediction timescale has received increasing attention in the research community because of its importance for many sectors. However, very few forecast skill assessments of precipitation extremes in S2S forecast data have been conducted. The goal of this article is to assess the forecast skill of rare events, here extreme precipitation, in S2S forecasts, using a metric specifically designed for extremes. We verify extreme precipitation events over Europe in the S2S forecast model from the European Centre for Medium-Range Weather Forecasts. The verification is conducted against ERA5 reanalysis precipitation. Extreme precipitation is defined as daily precipitation accumulations exceeding the seasonal 95th percentile. In addition to the classical Brier score, we use a binary loss index to assess skill. The binary loss index is tailored to assess the skill of rare events. We analyze daily events that are locally and spatially aggregated, as well as 7 d extreme-event counts. Results consistently show a higher skill in winter compared to summer. The regions showing the highest skill are Norway, Portugal and the south of the Alps. Skill increases when aggregating the extremes spatially or temporally. The verification methodology can be adapted and applied to other variables, e.g., temperature extremes or river discharge.

Prediction Skill and Practical Predictability Depending on the Initial Atmospheric States in S2S Forecasts

Abstract The hypothesis that predictability depends on the atmospheric state in the planetary-scale low-frequency variability in boreal winter was examined. We first computed six typical weather patterns from 500-hPa geopotential height anomalies in the Northern Hemisphere using self-organizing map (SOM) and k-clustering analysis. Next, using 11 models from the subseasonal-to-seasonal (S2S) operational and reforecast archive, we computed each model’s climatology as a function of lead time to evaluate model bias. Although the forecast bias depends on the model, it is consistently the largest when the forecast begins from the atmospheric state with a blocking-like pattern in the eastern North Pacific. Moreover, the ensemble-forecast spread based on S2S multimodel forecast data was compared with empirically estimated Fokker–Planck equation (FPE) parameters based on reanalysis data. The multimodel mean ensemble-forecast spread was correlated with the diffusion tensor norm; they are large for the cases when the atmospheric state started from a cluster with a blocking-like pattern. As the multimodel mean is expected to substantially reduce model biases and may approximate the predictability inherent in nature, we can summarize that the atmospheric state corresponding to the cluster was less predictable than others. Significance Statement The purpose of this study is to examine the performance of week-to-month forecasts by analyzing multimodel forecast results. We established the hypothesis proposed by the previous studies that the accuracy of forecasts depended on the atmospheric state. Together with the data-based method on predictability, an atmospheric state with the anticyclone anomaly in the eastern North Pacific exhibited low predictability. Our results provide a method to foresee the ability of week-to-month forecasts.

Subseasonal Ensemble Prediction of Flash Droughts over China

Abstract Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowledge about their forecast skills due to the lack of appropriate identification and assessment procedures. Here, we investigate the forecast skill of flash droughts over China with lead times up to 3 weeks by using hindcast datasets from the Subseasonal-to-Seasonal Prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1 week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead 1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1 week. In terms of probabilistic forecast, ECMWF has a higher Brier skill score than NCEP, especially over eastern China, which is consistent with higher temperature and precipitation forecast skill. The multimodel ensemble has the highest Brier skill score. This study suggests the importance of multimodel ensemble flash drought forecasting. Significance Statement Flash droughts have raised considerable concern, but whether they can be predicted at subseasonal time scales remains unclear. This study evaluates forecast skill of flash droughts over China based on ECMWF and NCEP hindcast data. Focusing on the historical flash drought events identified by the onset speed and duration, it is found that the ECMWF model outperformed the NCEP model with higher hit rates, lower false alarm ratios, and higher equitable threat scores, especially during the first week. However, less than 30% of the drought events can be captured in most regions by both models. An ensemble of the two models showed skill improvement against the ECMWF model for both deterministic and probabilistic forecasts.

The Impact of Tropical Pacific SST Biases on the S2S Forecast Skill over North America in the UFS Global Coupled Model

Abstract The impact of tropical Pacific sea surface temperature (SST) biases on the deterministic skill of the Unified Forecast System (UFS) coupled model Prototype 5 is evaluated during weeks 1–4 of the forecast. The evaluation is limited to the contiguous United States (CONUS) and two seasons: boreal summer (June–September) and winter (December–March). The tropical SST in the UFS model is warmer than in observations and bias patterns show seasonal dependence especially in the central and western Pacific. During boreal summer, the bias is located north of the equator whereas in winter, the bias is located in the Southern Hemisphere. A regression analysis indicates a significant relationship between these SST biases and the biases in the surface temperature and precipitation over the CONUS along with midtroposphere large-scale circulation and North Pacific storm-track activity. The SST biases affect the biases in other fields from week 1 of the forecast and the impact becomes stronger as the lead time increases to week 4. The impact of SST biases on the biases in other fields show a qualitative relationship to the patterns of forecast errors of the fields.

Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence

Abstract There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications.

Improving Subseasonal-to-Seasonal forecasts in predicting the occurrence of extreme precipitation events over the contiguous U.S. using machine learning models

The precipitation forecasts at the Subseasonal-to-Seasonal (S2S) scale are valuable information to assist water resources planning and decision-making at an extended range. But the raw S2S precipitation forecasts are rather limited regarding their predictive skills, which hinders further hydrological applications. Many previous studies were carried out to validate the S2S precipitation forecasts from the perspective of the ensemble “mean”, while existing study, which evaluates and improves the ability of S2S forecasts in predicting extreme precipitation events with their ensemble spreads, is rarely reported. This study aims to improve the S2S forecasts in predicting the occurrence of weekly extreme precipitation events above 99% over the contiguous United States (CONUS). The Random Forest Classifiers (RF) were employed and additional forecasts variables (i.e., surface air temperature, geopotential heights at 500 hPa and 850 hPa) were included to post-process the raw S2S forecasts from the NASA's Goddard Earth Observation System model version five (GEOS5). Different RF training inputs and RF hyperparameter sensitivity analysis are examined. We found that (1) using S2S precipitation forecast as the only inputs to RF, the forecast quality is improved significantly only at week 1; (2) When additional forecasts variables are included in RF training, the forecast skill tend to improve slightly at longer lead times after weeks 2; (3) The tunning of the maximum tree depth of RFs combine with the inclusion of additional forecasts variables as inputs to RF can improve the forecasts skill at all lead times over CONUS. In short, this study demonstrated the effectiveness of the application of RF as well as the effectiveness of additional forecast variables in improving the S2S extreme precipitation forecasts, which could be potentially useful for flood and river ensemble forecasting. Multiple statistical metrics, including the ensemble probability of detections (EPOD), ensemble false alarm ratios (EFAR), ensemble critical success index (ECSI), Brier Sill Score (BSS), and Area Under the Receiver Operating Characteristics Curves (AUROC) are employed for a comprehensive evaluation of the predictive performances of S2S extreme precipitation forecasts under different experiment scenarios.

Improved sub-seasonal forecasts to support preparedness action for meningitis outbreak in Africa

West African countries are hit annually by meningitis outbreaks which occur during the dry season and are linked to atmospheric variability. This paper describes an innovative co-production process between the African Centre of Meteorological Applications for Development (ACMAD; forecast producer) and the World Health Organisation Regional Office for Africa (WHO AFRO; forecast user) to support awareness, preparedness and response actions for meningitis outbreaks. Using sub-seasonal to seasonal (S2S) forecasts, this co-production enables ACMAD and WHO AFRO to build initiative that increases the production of useful climate services in the health sector. Temperature and relative humidity forecasts are combined with dust forecasts to operationalize a meningitis early warning system (MEWS) across the African meningitis belt with a two-week lead time. To prevent and control meningitis, the MEWS is produced from week 1 to 26 of the year. This study demonstrates that S2S forecasts have good skill at predicting dry and warm atmospheric conditions precede meningitis outbreaks. Vigilance levels objectively defined within the MEWS are consistent with reported cases of meningitis. Alongside developing a MEWS, the co-production process provided a framework for analysis of climate and environmental risks based on reanalysis data, meningitis burden, and health service assessment, to support the development of a qualitative roadmap of country prioritization for defeating meningitis by 2030 across the WHO African region. The roadmap has enabled the identification of countries most vulnerable to meningitis epidemics, and in the context of climate change, supports plans for preventing, preparing, and responding to meningitis outbreaks.

Evaluating prospects for subseasonal-to-seasonal forecast-based anticipatory action from a global perspective

Globally, the direct cost of natural disasters stands in the hundreds of billions of USD per year, at a time when water resources are under increasing stress and variability. Much of this burden rests on low- and middle-income countries that, despite their relative lack of wealth, exhibit considerable vulnerability such that losses measurably impact GDP. Within these countries, a growing middle class retains much of its wealth in property that may be increasingly exposed, while the few assets the poor may possess are often highly exposed. Vulnerability to extreme events is thus heterogeneous at both the global and subnational level. Moreover, the distribution and predictability of extreme events is also heterogeneous. Disaster managers and relief organizations are increasingly consulting operational climate information services as a way to mitigate the risks of extreme events, but appropriately targeting vulnerable communities remains a challenge. The advent of forecast-based anticipatory action has added to the suite of opportunities—and complexity—of operationalizing such services given varying prediction skill. Forecasts, including those at the subseasonal-to-seasonal (S2S) scale, may allow disaster managers to shift effort and therefore some risk from post-disaster response to pre-disaster preparedness; however, given the recent emergence of such programs, only a few, specific case studies have been evaluated. We therefore conduct a country-scale analysis pairing S2S forecast skill for monthly and seasonal lead times with flood and drought disaster risk to explore the potential for forecast-based anticipatory action programs broadly. To investigate subnational heterogeneity in risk and predictability, we also evaluate focused outcomes for the Greater Horn of Africa and Peru. Results suggest that forecast skill plays a large part in determining suitability for early action, and that skill itself varies considerably by disaster type, lead time, and location. Moreover, the physical and socioeconomic factors of risk can vary greatly between national and subnational levels, such that finer scale evaluations may considerably improve the effectiveness of early action protocols. By considering vulnerability at multiple spatial scales and forecast skill at multiple temporal scales, this analysis provides a first identification of promising locations for anticipatory action protocol development.

Application of real time S2S forecasts over Eastern Africa in the co-production of climate services

Application of real time S2S forecasts over Eastern Africa in the co-production of climate services

Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions

Abstract The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

Advances in the Prediction of MJO Teleconnections in the S2S Forecast Systems

Abstract This study evaluates the ability of state-of-the-art subseasonal-to-seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden–Julian oscillation and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision-makers with a better understanding of the potential of S2S predictions related to MJO teleconnections.

A seven-parameter Bernoulli-Gamma-Gaussian model to calibrate subseasonal to seasonal precipitation forecasts

Subseasonal to seasonal (S2S) precipitation forecasts can fill the gap between weather and seasonal forecasts. While raw forecasts from S2S models are informative, calibration is necessary to correct systematic biases and quantify forecast uncertainty to facilitate applications of S2S forecasts. This paper develops a seven-parameter Bernoulli-Gamma-Gaussian model to calibrate S2S precipitation forecasts. The Bernoulli distribution characterizes the occurrence of zero versus non-zero values of precipitation, the Gamma distribution accounts for the distribution of non-zero precipitation amounts and the bivariate Gaussian distribution formulates the relationship between raw forecasts and observations. A case study of the East River catchment in South China is devised for the S2S forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECWMF). The Bernoulli-Gamma-Gaussian model is used to calibrate raw forecasts at the daily time step and the Schaake Shuffle is used to instill the temporal patterns to form ensemble time series forecasts. The results show that raw S2S forecasts generally suffer from considerable forecast bias and unreliable ensemble spread and can exhibit negative forecast skill. Compared to the quantile mapping, the Bernoulli-Gamma-Gaussian model with the Schaake Shuffle is effective in correcting biases, improving reliability and yielding forecast skill for daily and accumulated precipitation. Furthermore, a sliding window of 15 days tends to be suitable to pool samples to deal with sampling variability and precipitation seasonality for the purpose of calibration of S2S forecasts. Overall, the Bernoulli-Gamma-Gaussian model can serve as an effective tool to calibrate S2S precipitation forecasts for hydrological modeling and water resources management.