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Reliable medium-range weather forecasts are crucial for both science and society. Although weather predictions primarily rely on numerical weather models, the artificial intelligence (AI) weather big models have shown potential for accurate weather forecasts with reduced computational costs. However, prediction skills of big models remain uncertain, particularly in East Asia, which limits further application of weather AI models. To systematically evaluate predictive capabilities of Pangu, FuXi, and GraphCast models over East Asia, their prediction results are focusing on 500 hPa geopotential height, 2 m air temperature, 10 m wind speed, precipitation, and track of tropical cyclones.ECMWF reanalysis V5 (ERA5) datasets are utilized to provide the initial conditions for big models, and to assess their predictive skill. Additionally, precipitation observations and China Meteorological Administration tropical cyclone datasets are utilized to access big models as well. FuXi shows the highest forecasting skills among 3 big models for 500 hPa geopotential height. The forecast from FuXi is reliable for up to 9.75 days, while the forecasts from Pangu and GraphCast are reliable for 8.75 days and 8.5 days, respectively. For 2 m air temperature forecasting, FuXi presents higher skills with an averaged temporal anomaly correlation coefficient (TCC) ranging from 0.48 to 0.91, while TCCs of Pangu and GraphCast are 0.43-0.91 and 0.38-0.83, respectively. Among 3 models, only FuXi and GraphCast provide precipitation forecasts. FuXi shows higher prediction skill compared to GraphCast in forecasting precipitation, light rain, and moderate rain; however, GraphCast has advantage in heavy rain forecast. As the lead time increases, the threat scores (TSs) of FuXi for rainfall, light rainfall and moderate rainfall are 0.22-0.41, 0.15-0.24 and 0.06-0.22, respectively. The model demonstrates higher skill in the northern and southeastern regions of China. For predicting the track of cyclones, Pangu model demonstrates superior predictive skill. As the lead time increases from 6 hours to 240 hours, biases of Pangu's prediction track increase from 17.5 km to 1850 km.The study focuses on the prediction skill of various AI big models through TCC, spatial anomaly correlation coefficient, and TS. Generally, the performance of FuXi is superior for most elements. And reasonable evaluation of AI model is helpful for the development of AI models.

The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe. The year‐to‐year contributions to multi‐model seasonal forecast skill in the Copernicus C3S ensemble of seven prediction systems are assessed for the wintertime NAO and EA, and well‐forecast and poorly‐forecast years are identified. Years with high NAO predictability are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Well‐forecast EA winters also generally occurred when there was substantial tropical forcing, although the relationship was less robust than for the NAO. These results support previous findings of the impacts of tropical forcing on the North Atlantic and show this is important from a multi‐model seasonal forecasting perspective. Plain Language Summary: The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are two important indicators of atmospheric variability in the North Atlantic. They can have a substantial impact on European winter weather. The ability of seasonal forecast models to forecast the NAO and EA varies from year to year. This intermittency of forecast skill is investigated in seven different seasonal forecast systems from the Copernicus C3S database, by focusing on the most well‐forecast and poorly‐forecast years. Years where the NAO is well‐forecast are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Similar but weaker results hold for the EA. These results are valuable for increasing the usability of seasonal forecasts by identifying conditions under which forecasts are more likely to be skillful. Key Points: Seasonal forecast skill of the wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) is intermittentWell‐forecast NAO/EA winters generally occur when there is substantial tropical forcing [ABSTRACT FROM AUTHOR]

Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System (NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves (MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean (TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Niño-Southern Oscillation (ENSO). The forecast skills for the MHWs over the tropical Indian Ocean (TIO), tropical Atlantic Ocean (TAO), and tropical Northwest Pacific (NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window (less than 17 months) occurs for the TAO and NWP. Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer. [ABSTRACT FROM AUTHOR]

Convective‐scale ensembles are used routinely in operational centres around the world to produce probabilistic precipitation forecasts, but a lack of spread between members is providing forecasts that are frequently overconfident. This deficiency can be corrected by increasing spread, increasing forecast accuracy, or both. A recent development in the Met Office forecasting system is the inclusion of large‐scale blending (LSB) in the convective‐scale data assimilation scheme. This method aims to reduce the synoptic‐scale forecast error in the analysis by reducing the influence of the convective‐scale data assimilation at scales that are too large to be constrained by the limited domain. These scales are instead initialised using output from the global data assimilation scheme, which we expect to reduce the forecast error and thus improve the spread–skill relationship. In this study, we quantify the impact of LSB on the spread–skill relationship of hourly precipitation accumulations by comparing forecast ensembles with and without LSB over a 17‐day summer trial period. This trial found modest but significant improvements to the spread–skill relationship as calculated using metrics based on the Fractions Skill Score. Skill is improved for a lower precipitation centile by an average of 0.56% at the largest scales, but a corresponding degradation of spread limits the overall correction. The spread–skill disparity is reduced the most in the higher centiles due to a more muted spread response, with significant reductions of up to 0.40% obtained at larger scales. Case‐study analysis using a novel extension of the Localised Fractions Skill Score demonstrates how spread–skill improvements transfer to smaller‐scale features, not just the scales that have been blended. There are promising signs that further spread–skill improvements can be made by implementing LSB more fully within the ensemble, and we encourage the Met Office to continue developing this technique. [ABSTRACT FROM AUTHOR]

Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based forecasts which predict multiple ecosystem variables. To explore how forecast performance varies for water temperature and dissolved oxygen, two freshwater variables important for lake ecosystem functioning, we produced probabilistic forecasts at multiple depths over two open-water seasons in Lake Sunapee, NH, USA. Our forecasting system, FLARE (Forecasting Lake And Reservoir Ecosystems), uses a 1-D coupled hydrodynamic-biogeochemical process model, which we assessed relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over null models across varying environmental conditions. We found that FLARE water temperature forecasts were always more skillful than FLARE oxygen forecasts. Specifically, temperature forecasts outperformed both null models up to 11 days into the future, as compared to only two days for oxygen. Across different years, we observed variable forecast skill, with performance generally decreasing with depth for both variables. Overall, all temperature forecasts and surface oxygen, but not deep oxygen, forecasts were more skillful than at least one null model >80 % of the forecasted period, indicating that our process-based model was able to reproduce the dynamics of these two variables with greater reliability than the null models. However, process-based oxygen forecasts from deeper waters were less skillful than both null models during a majority of the forecasted period, which suggests that deep-water oxygen dynamics are dominated by autocorrelation and seasonal change, which are inherently captured by the null forecasts. Our results highlight that forecast performance varies among lake water quality metrics and that process-based forecasts can provide important information in conjunction with null models in varying environmental conditions. Altogether, these process-based forecasts can be used to develop quantitative tools which inform our understanding of future ecosystem change.

The southwest Indian Ocean (SWIO) recently experienced its most active, costliest and deadliest cyclone season on record (2018–2019). The anticipation and forecasting of natural hazards, such as tropical cyclones, are crucial to preparing for their impacts, but it is important to understand how well forecasting systems can predict them. Despite the vulnerability of the SWIO to tropical cyclones, comparatively little research has focused on this region, including understanding the ability of numerical weather prediction systems to predict cyclones and their impacts in southeast Africa. In this study, we evaluate ensemble probabilistic and high‐resolution deterministic forecasts of tropical cyclones in the SWIO from 2010 to 2020, using two state‐of‐the‐art global forecasting systems: one from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the other from the U.K. Met Office. We evaluate predictions of the track, assessing the location of the centre of each storm and its speed of movement, as well as its intensity, looking at maximum wind speeds and minimum central pressure, and discuss how the forecasts have evolved over the 10‐year period. Overall, ECMWF typically provides more accurate forecasts, but both systems tend to underestimate translation speed and intensity. We also investigate the impact of the Madden‐Julian Oscillation (MJO) on tropical cyclones and their forecasts. The MJO impacts where and when tropical cyclones form, their tracks and intensities, which in turn impacts forecast skill. These results are intended to provide an increased understanding of the ability of global forecasting systems to predict tropical cyclones in the SWIO, for the purpose of decision making and anticipatory action. [ABSTRACT FROM AUTHOR]

Seasonal forecasts are commonly issued in the form of anomalies, which are departures from the average over a specified multiyear reference period (climatology). The model climatology is estimated as the average of the retrospective forecasts over the hindcast period. However, different operational centers that provide seasonal ensemble predictions use different hindcast periods based on their model climatology. Additionally, the hindcast periods of recently developed and upgraded newer models have shifted in the recent years. In this paper, we discuss the recent challenges faced by APCC multi‐model ensemble (MME) operations, especially changes in the hindcast period for individual models. Based on the results of various experiments for MME prediction, we propose changing the hindcast period, which is the most appropriate solution for APCC operation. This makes the newly developed models join the MME and increases the total number of participating models, which facilitates the skill improvement of the MME prediction. Plain Language Summary: In seasonal forecasting, it is well known that the MME, which combines different single‐model predictions from various operational and research centers, is a more effective way to improve forecast skill. Since 2005, the APCC has provided the MME seasonal forecasts, and the models participating in the APCC MME operations have been continuously changing. In particular, as the hindcast periods of newly developed models shift to the latest, they cannot participate in operational MME forecasts because of climatological discrepancies. However, over time, as the number of new models expected to provide skillful forecasts gradually increases, the APCC faces the challenge of continuously reducing the number of participating models or changing the hindcast period to more recent years. Considering various aspects such as the number of participating models, skills, and climatology period, we selected the most appropriate method for APCC operation. Thus, the MME prediction skill has improved over most of the globe and seasons because of the increase in the number of participating models, particularly the inclusion of newer models. Key Points: APCC, which combines all the information from different ensemble prediction systems, recently faced challenges in hindcast period issuesThe proposed solution leads to an increase in the number of models contributing to MME prediction, particularly recently developed modelsIt shows improved skills for both temperature and precipitation predictions over most of the globe and seasons [ABSTRACT FROM AUTHOR]

Abstract The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe. The year‐to‐year contributions to multi‐model seasonal forecast skill in the Copernicus C3S ensemble of seven prediction systems are assessed for the wintertime NAO and EA, and well‐forecast and poorly‐forecast years are identified. Years with high NAO predictability are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Well‐forecast EA winters also generally occurred when there was substantial tropical forcing, although the relationship was less robust than for the NAO. These results support previous findings of the impacts of tropical forcing on the North Atlantic and show this is important from a multi‐model seasonal forecasting perspective.

Fire danger poses a pressing threat to ecosystems and societies worldwide. Adequate preparation and forewarning can help reduce these threats, but these rely on accurate prediction of extreme fire danger. With the knowledge that climatic conditions contribute heavily to overall fire danger, this study evaluates the skill with which episodes of extreme fire danger in Australia can be predicted from the activity of large-scale climate driver patterns. An extremal dependence index for extreme events is used to depict the historical predictive skill of the Australian Bureau of Meteorology's subseasonal climate prediction system in replicating known relationships between the probability of top-decile fire danger and climate driver states at a lead time of 2–3 weeks. Results demonstrate that the El Niño Southern Oscillation, Southern Annular Mode, persistent modes of atmospheric blocking, Indian Ocean Dipole and Madden-Julian Oscillation are all key for contributing to predictability of fire danger forecasts in different regions during critical fire danger periods. Northwest Australia is found to be particularly predictable, with the highest mean index differences (>0.50) when certain climate drivers are active, compared with the climatological index mean. This integrated approach offers a valuable resource for decision-making in fire-prone regions, providing greater confidence to users relying on fire danger outlooks for key management decisions, such as those involved in the sectors of national park and forest estate management, agriculture, emergency services, health and energy. Furthermore, the results highlight strengths and weaknesses in both the Australian Fire Danger Rating System and the operational climate model, contributing additional information for improving and refining future iterations of these systems. [ABSTRACT FROM AUTHOR]

Abstract The southwest Indian Ocean (SWIO) recently experienced its most active, costliest and deadliest cyclone season on record (2018–2019). The anticipation and forecasting of natural hazards, such as tropical cyclones, are crucial to preparing for their impacts, but it is important to understand how well forecasting systems can predict them. Despite the vulnerability of the SWIO to tropical cyclones, comparatively little research has focused on this region, including understanding the ability of numerical weather prediction systems to predict cyclones and their impacts in southeast Africa. In this study, we evaluate ensemble probabilistic and high‐resolution deterministic forecasts of tropical cyclones in the SWIO from 2010 to 2020, using two state‐of‐the‐art global forecasting systems: one from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the other from the U.K. Met Office. We evaluate predictions of the track, assessing the location of the centre of each storm and its speed of movement, as well as its intensity, looking at maximum wind speeds and minimum central pressure, and discuss how the forecasts have evolved over the 10‐year period. Overall, ECMWF typically provides more accurate forecasts, but both systems tend to underestimate translation speed and intensity. We also investigate the impact of the Madden‐Julian Oscillation (MJO) on tropical cyclones and their forecasts. The MJO impacts where and when tropical cyclones form, their tracks and intensities, which in turn impacts forecast skill. These results are intended to provide an increased understanding of the ability of global forecasting systems to predict tropical cyclones in the SWIO, for the purpose of decision making and anticipatory action.

Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT’s ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.

For this paper, a new global atmospheric model (Global-to-Regional Integrated forecast SysTem; GRIST) with improved sub-grid scale orographic parameterization was verified and assessed, with an emphasis on the precipitation caused by typhoons. Four typical typhoon cases were selected for the verification of the model. The results indicate that, compared to the control experiments, the sensitivity experiments consistently simulated the trends in the three-hour cumulative precipitation changes and the high-value regions of total precipitation better. However, the improved experiments only had an ameliorating effect on the cumulative precipitation modelling biases for Typhoon LEKIMA and Typhoon HAGUPIT, not all of them. Precipitation bias is smaller on flat land than that on mountainous land, but the precipitation bias on windward/leeward slopes depends on the typhoon case. Precipitation modelling accuracy varies considerably between flat and mountainous terrain but very little between windward and leeward slopes. The precipitation simulation is poor for all terrains, with large precipitation thresholds in three typhoon cases, but for Typhoon HOTA, after improving the terrain, the model has the ability to forecast the heavy rainfall scenarios of the mountainous terrain. [ABSTRACT FROM AUTHOR]

Seasonal streamflow forecasts are becoming widely used to improve water reservoir operations, especially in areas where climate teleconnections enable predictability on medium and long lead times. Most existing studies have focused on the assimilation of forecasts into operational decision models, an approach that typically banks on predeveloped forecasts to optimize water release decisions. However, this approach may overlook the potential synergies that stand in co-developing forecast and decision-making models. In other words, the opportunities that lie in coupling both forecast and operational decision models have not yet been explored. Here, we address this gap and contribute a novel approach building on the Evolutionary Multi-Objective Direct Policy Search algorithm to design forecast and decision-making models together. The proposed approach is benchmarked against operating policies not informed by any forecast, as well as by forecast-informed policies relying on predeveloped forecasts (data-driven and perfect). Numerical experiments are conducted on the Angat-Umiray water resources system, Philippines, which is operated primarily for ensuring municipal water supply to Metro Manila and irrigation supply to a large agricultural district. Our results show that the integrated design of forecast models and control policies provides a performance gain with respect to policies informed by predesigned forecasts. This result is particularly interesting because the skill of the integrated forecast models is lower than that of the predeveloped ones, thus suggesting that more accurate forecasts do not necessarily produce better water system operations. Overall, our analysis represents a step towards a deeper integration of streamflow forecast and reservoir operation models. [ABSTRACT FROM AUTHOR]

Understanding and predicting ecological dynamics in the presence of noise remains a substantial and important challenge. This is particularly true in light of the poor quality of much ecological data and the imprecision of many ecological models. As a first approach to this problem, we focus here on a simple system expressed as a discrete time model with 2-cycle behavior, reflecting alternating high and low population sizes. Such dynamics naturally arise in ecological systems with overcompensatory density dependence. We ask how the amount of detail included in the population estimates affects the ability to forecast the likelihood of changes in the phase of oscillation, meaning whether high populations occur in odd or in even years. We adjust the level of detail by converting continuous population levels to simple, coarse-grained descriptions using two-state and four-state models. We also consider a cubic noisy over-compensatory model with three parameters. The focus on phase changes is what distinguishes the question we are asking and the methods we use from more standard time series approaches. Obviously, adding observation states improves the ability to forecast phase shifts. In particular, the four-state model and cubic model outperform the two-state model because they include a transition state, through which the dynamics typically pass during a phase change. Nonetheless, at high noise levels the improvement in forecast skill is relatively modest. Additionally, the frequency of phase changes depends strongly on the noise level, and is much less affected by the parameter determining amplitude in the population model, so phase shift frequencies could possibly be used to infer noise levels. [ABSTRACT FROM AUTHOR]

Owing to probabilistic uncertainties associated with seasonal forecasts, especially over areas such as southern Africa where forecast skill is limited, non-climatologists and users of such forecasts frequently prefer them to be presented or distributed in terms of the likelihood (expressed as a probability) of certain categories occurring or thresholds being exceeded. Probabilistic forecast verification is needed to verify such forecasts. Whilst the resulting verification statistics can provide clear insights into forecast attributes, they are often difficult to understand, which might hinder forecast uptake and use. This problem can be addressed by issuing forecasts with some understandable evidence of skill, with the purpose of reflecting how similar forecasts may have performed in the past. In this paper, we present a range of different probabilistic forecast verification scores, and determine if these statistics can be readily compared to more commonly known and understood ‘ordinary’ correlations between forecasts and their associated observations – assuming that ordinary correlations are more intuitively understood and informative to seasonal forecast users. Of the range of scores considered, the relative operating characteristics (ROC) was found to be the most intrinsically similar to correlation. [ABSTRACT FROM AUTHOR]

Intuitively, one would expect a more skillful forecast if predicting weather averaged over one week instead of the weather averaged over one day, and similarly for different spatial averaging areas. However, there are few systematic studies of averaging and forecast skill with modern forecasts, and it is therefore not clear how much improvement in forecast performance is produced via averaging. Here we present a direct investigation of averaging effects, based on data from operational numerical weather forecasts. Data is analyzed for precipitation and surface temperature, for lead times of roughly 1 to 7 days, and for time- and space-averaging diameters of 1 to 7 days and 100 to 4500 km, respectively. For different geographic locations, the effects of time- or space-averaging can be different, and while no clear geographical pattern is seen for precipitation, a clear spatial pattern is seen for temperature. For temperature, in general, time averaging is most effective near coastlines, also effective over land, and least effective over oceans. Based on all locations globally, time averaging was less effective than one might expect. To help understand why time averaging may sometimes be minimally effective, a stochastic model is analyzed as a synthetic weather time series, and analytical formulas are presented for the decorrelation time. In effect, while time averaging creates a time series that is visually smoother, it does not necessarily cause a substantial increase in the predictability of the time series.

This study aimed to systematically and globally evaluate the monthly precipitation forecasts of Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System ver. 2 (JMA/MRI-CPS2), a dynamical seasonal climate forecast (Dyn-SCF) system operated by the Japan Meteorological Agency, by comparing its forecasts with those of a statistical SCF (St-SCF) system using climate indices. We developed a new global St-SCF system using 17 climate indices and compared the monthly precipitation of this system with those of JMA/MRI-CPS2. Consequently, the skill of JMA/MRI-CPS2 was determined to be globally higher than that of the St-SCF for zero-month lead forecasts. Contrarily, for forecasts made with a lead time of 1 month or longer, the deterministic skill of JMA/MRI-CPS2 was comparable to that of the St-SCF, and the probabilistic skill of JMA/MRI-CPS2 remained slightly higher. In addition to evaluating the skill of JMA/MRI-CPS2, we identified several regions and seasons, for which JMA/MRI-CPS2 exhibited a low forecast skill, compared with the St-SCF. This indicated that JMA/MRI-CPS2 cannot sufficiently reproduce certain dynamics. In conclusion, comparing Dyn-SCFs with St-SCFs can elucidate the potential regions and seasons to improve the forecast skill of Dyn-SCFs. [ABSTRACT FROM AUTHOR]

This study assesses the potential influence of global navigation satellite system (GNSS) radio occultation (RO) data assimilation on the forecast skill of tropical cyclone formation over the western North Pacific in September–October 2019 through a regional model. Data from the Constellation Observing System for Meteorology, Ionosphere, and Climate mission II are applied. The forecast skill considers the hits and misses for nine developing cases and the false alarms and correct negatives for 23 non‐developing cases. Forecasts assimilating GNSS RO data reduce the false alarm ratio by 20% and increase the accuracy rate by 19%, compared to forecasts without GNSS RO data. Assimilation of GNSS RO data increases mid‐level moisture around the disturbance centers at the initial time of the forecasts. It also increases low‐level vorticity for developing cases but decreases vorticity throughout most of the troposphere for non‐developing cases. These lead to improved forecast performance for tropical cyclone formation. Plain Language Summary: Tropical cyclone formation is very sensitive to local atmospheric conditions such as moisture, temperature, and winds. Getting these conditions correct is therefore critical for accurate forecasts of tropical cyclone formation. Satellite data are commonly used to characterize atmospheric conditions over the remote oceans where tropical cyclones tend to form. New satellite data, specifically from the radio occultation of global navigation satellite systems, provide fine scale information on moisture, temperature, and pressure. This new information suffers less from contamination by clouds and precipitation than the traditional microwave and infrared satellite observations, and may further improve the representation of atmospheric conditions. In addition to cases of tropical cyclone formation, this study pays particular attention to the quality of forecasts of disturbances that did not develop into tropical cyclones—something that is often overlooked when assessing the quality of tropical cyclone formation forecasts. We find that incorporating radio occultation data into the regional model can improve the local environmental conditions for tropical cyclone formation forecasts. This leads to fewer false alarms and higher hit rates. These improvements may provide critical guidance for tropical cyclone formation forecasters. Key Points: Potential forecast skill of tropical cyclone formation is analyzed for nine developing and 23 non‐developing cases in September–October 2019Assimilation of radio occultation data reduces the false alarm ratio by 20% and increases the accuracy rate by 19%The vorticity improved by radio occultation data is likely a key contributing factor to the improved tropical cyclone formation forecasts [ABSTRACT FROM AUTHOR]

Fire danger poses a pressing threat to ecosystems and societies worldwide. Adequate preparation and forewarning can help reduce these threats, but these rely on accurate prediction of extreme fire danger. With the knowledge that climatic conditions contribute heavily to overall fire danger, this study evaluates the skill with which episodes of extreme fire danger in Australia can be predicted from the activity of large-scale climate driver patterns. An extremal dependence index for extreme events is used to depict the historical predictive skill of the Australian Bureau of Meteorology’s subseasonal climate prediction system in replicating known relationships between the probability of top-decile fire danger and climate driver states at a lead time of 2–3 weeks. Results demonstrate that the El Niño Southern Oscillation, Southern Annular Mode, persistent modes of atmospheric blocking, Indian Ocean Dipole and Madden-Julian Oscillation are all key for contributing to predictability of fire danger forecasts in different regions during critical fire danger periods. Northwest Australia is found to be particularly predictable, with the highest mean index differences (>0.50) when certain climate drivers are active, compared with the climatological index mean. This integrated approach offers a valuable resource for decision-making in fire-prone regions, providing greater confidence to users relying on fire danger outlooks for key management decisions, such as those involved in the sectors of national park and forest estate management, agriculture, emergency services, health and energy. Furthermore, the results highlight strengths and weaknesses in both the Australian Fire Danger Rating System and the operational climate model, contributing additional information for improving and refining future iterations of these systems.

Seasonal forecasts, which look several months into the future, are currently underutilized in active decision-making, particularly for agricultural and natural resource management. This underutilization can be attributed to the absence of forecasts for decision-relevant variables at the required spatiotemporal resolution and at the time when the decisions are made and a perception of poor skill by decision-makers. Addressing these constraints, we quantified the skill of seasonal forecasts in informing two agricultural decisions with differing decision timeframes and influencer variables: (a) whether to apply fertilizer in fall or wait until spring based on expected winter temperatures, and (b) drought response, such as whether to lease water based on expectations of drought. We also looked into how early the forecast can be provided without significant degradation in skill. Currently, drought response decisions are typically formulated in April, utilizing drought forecasts issued in the same month, while fall fertilization decisions are generally made between August and September. There is growing interest among stakeholders in the availability of earlier forecasts to inform these critical choices. We utilized the North American multi-model ensemble (NMME) hindcasts for the time period 1982–2020 over the Pacific Northwest US (PNW) to obtain meteorological variables. Runoff was estimated via simulations of the coupled crop-hydrology VIC-CropSyst model. The skill assessment with the Heidke Skill Score (HSS) yielded promising outcomes in both decisions for the entire PNW region. Notably, NMME’s positive skill (median HSS of 30%) in predicting warmer winters identifies years when fertilizer application should be avoided to prevent fertilizer loss through mineralization (and associated costs). Similarly, there is skill in forecasting drought conditions in most irrigated watersheds for up to two months in advance of April, the current decision time. In conclusion, our findings affirm that contrary to the perception of low skill and resulting underutilization, current seasonal forecasts hold the potential to inform at least some key agricultural decisions.

Rainfall forecasting is a high-priority research problem due to the complex interplay of multiple factors. Despite extensive studies, a systematic quantitative review of recent developments in rainfall forecasting is lacking in the literature. This study conducted a systematic quantitative review of statistical, numerical weather prediction (NWP) and machine learning (ML) techniques for rainfall forecasting. The review adopted the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique for screening keywords and abstracts, leading to 110 qualified papers from multiple databases. The impact of rainfall threshold, meteorological parameters, topography, algorithm techniques, geographic location, the horizontal resolution of the model, and lead time on rainfall forecast was examined. The review shows the importance of precipitable water vapor (PWV) along with other meteorological parameters for accurate nowcasting in coastal and mountainous regions. An increase in rainfall forecast uncertainty with an increase in the lead time makes the NWP model less popular for the short-term forecast. The pre-processing techniques increased the accuracy of ML techniques by considering extreme values and detecting the irregularly distributed multi-scale features of rainfall in space and time. Future research can focus on hybrid models with improved accuracy for nowcasting. The output from the hybrid model serves as input for the decision support system required for urban flood risk management. HIGHLIGHTS The PRISMA method is applied for a systematic quantitative review.; The Global Navigation Satellite System-derived precipitable water vapor (PWV) is found to be capable of analyzing the real-time profile of water vapor content.; Forecast can be improved by considering additional meteorological parameters along with the PWV.; A longer lead time in the NWP model enhances the forecast uncertainty.; There is a significant improvement in the forecast by machine learning models after pre-processing.;

Abstract This study assesses the potential influence of global navigation satellite system (GNSS) radio occultation (RO) data assimilation on the forecast skill of tropical cyclone formation over the western North Pacific in September–October 2019 through a regional model. Data from the Constellation Observing System for Meteorology, Ionosphere, and Climate mission II are applied. The forecast skill considers the hits and misses for nine developing cases and the false alarms and correct negatives for 23 non‐developing cases. Forecasts assimilating GNSS RO data reduce the false alarm ratio by 20% and increase the accuracy rate by 19%, compared to forecasts without GNSS RO data. Assimilation of GNSS RO data increases mid‐level moisture around the disturbance centers at the initial time of the forecasts. It also increases low‐level vorticity for developing cases but decreases vorticity throughout most of the troposphere for non‐developing cases. These lead to improved forecast performance for tropical cyclone formation.

North American Multi‐Model Ensemble (NMME) precipitation forecast skill over Iran is evaluated using Taylor diagrams and ranked probability skill scores (RPSS) as determined over a 29‐year test period (1991–2019). The forecast skill for both monthly (October through June for lead‐times of 0.5–3.5 months) and seasonal (October–December [OND], January–March [JFM], and April–June [AMJ] for lead‐times of 1.5–3.5 months) timescales is evaluated using six NMME models as well as multi‐model ensemble means (MMM). The latest versions of these models for forecasting Iran's precipitation have not been evaluated thus far. The Global Precipitation Climatology Center (GPCC) version 2020 dataset is used to verify the models. Among individual NMME models, Geophysical Fluid Dynamics Laboratory‐Seamless System for Prediction and Earth System Research (GFDL‐SPEAR) has generally the highest forecast skill. Both Taylor diagrams and RPSS of most of the models have indicated that the highest forecast skill is found for the month of November such that the Pearson correlation for both SPEAR and MMM is statistically significant for all lead‐times. For both monthly and seasonal timescales, the temporal Pearson correlation (TPC) between the observed and forecasts of the MMM is higher than the TPC of the individual models. The spatial Pearson correlation (SPC) and normalized centred root mean square error (NCRMSE) of the SPEAR is close to MMM, but the normalized standard deviation (NSD) of the SPEAR is closer to one compared to the MMM for months from November to March and two seasons (OND and JFM seasons). The MMM precipitation forecasts are underestimated over the northern regions and Zagros mountains for JFM and OND seasons for both 1.5‐ and 2.5‐month lead‐times. The degree to which the forecast skill of MMM is dependent on the El Niño–Southern Oscillation (ENSO) connections with precipitation over Iran is examined. Significant Spearman correlations between simultaneous observed Niño3.4 index and Iran precipitation are found for OND, but not for JFM and AMJ. The MMM reproduces the observed ENSO teleconnections to the tropical Pacific in OND, consistent with forecast skill in that season. However, the MMM also produces forecast skill in JFM and AMJ when the ENSO influence is marginal, showing that ENSO is not the only source of skill in the models. [ABSTRACT FROM AUTHOR]

Forecasts of Indian summer monsoon rainfall (ISMR: June to September, JJAS) are issued prior to the onset of rainy season. Thus, an assessment of both potential and actual forecast skills for Indian summer monsoon rainfall should be based on a longer lead time. Based upon the European Center for Medium Range Weather Forecasts (ECMWF) fifth-generation seasonal forecast system (SEAS5), two lead times are considered: one with an April initial condition (IC) and the other with a May IC from 1981 through 2019 (39 years). Our results show that SEAS5 successfully represents the spatial patterns and variations in the mean JJAS precipitation in the ISMR region compared with the observed rainfall patterns. However, there seem to be significant discrepancies in the simulation of mean precipitation, particularly over topographical regions. SEAS5 is capable of reproducing the observed annual precipitation cycle in India. Moreover, the model is able to better predict the realistic ISMR teleconnections with El Niño-Southern Oscillation and the Indian Ocean Dipole at May ICs. The resulting forecasts across the region are characterized by moderate significant potential and actual skill in both leads, and it decreases as lead time increases. The predictability of SEAS5 is directly related to its ability to correctly predict the forcing of the tropical sea surface temperature and its teleconnections. In spite of this, both lead forecasts have a significant number of unpredicted events and false alarms. This study highlights model discrepancies, shows poor performance in predicting ISMR, and highlights the need for further research on this crucial issue of social relevance. [ABSTRACT FROM AUTHOR]

Intuitively, one would expect a more skillful forecast if predicting weather averaged over one week instead of the weather averaged over one day, and similarly for different spatial averaging areas. However, there are few systematic studies of averaging and forecast skill with modern forecasts, and it is therefore not clear how much improvement in forecast performance is produced via averaging. Here we present a direct investigation of averaging effects, based on data from operational numerical weather forecasts. Data is analyzed for precipitation and surface temperature, for lead times of roughly 1 to 7 days, and for time- and space-averaging diameters of 1 to 7 days and 100 to 4500 km, respectively. For different geographic locations, the effects of time- or space-averaging can be different, and while no clear geographical pattern is seen for precipitation, a clear spatial pattern is seen for temperature. For temperature, in general, time averaging is most effective near coastlines, also effective over land, and least effective over oceans. Based on all locations globally, time averaging was less effective than one might expect. To help understand why time averaging may sometimes be minimally effective, a stochastic model is analyzed as a synthetic weather time series, and analytical formulas are presented for the decorrelation time. In effect, while time averaging creates a time series that is visually smoother, it does not necessarily cause a substantial increase in the predictability of the time series. [ABSTRACT FROM AUTHOR]

Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based forecasts which predict multiple ecosystem variables. To explore how forecast performance varies for water temperature and dissolved oxygen, two freshwater variables important for lake ecosystem functioning, we produced probabilistic forecasts at multiple depths over two open-water seasons in Lake Sunapee, NH, USA. Our forecasting system, FLARE (Forecasting Lake And Reservoir Ecosystems), uses a 1-D coupled hydrodynamic-biogeochemical process model, which we assessed relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over null models across varying environmental conditions. We found that FLARE water temperature forecasts were always more skillful than FLARE oxygen forecasts. Specifically, temperature forecasts outperformed both null models up to 11 days into the future, as compared to only two days for oxygen. Across different years, we observed variable forecast skill, with performance generally decreasing with depth for both variables. Overall, all temperature forecasts and surface oxygen, but not deep oxygen, forecasts were more skillful than at least one null model >80 % of the forecasted period, indicating that our process-based model was able to reproduce the dynamics of these two variables with greater reliability than the null models. However, process-based oxygen forecasts from deeper waters were less skillful than both null models during a majority of the forecasted period, which suggests that deep-water oxygen dynamics are dominated by autocorrelation and seasonal change, which are inherently captured by the null forecasts. Our results highlight that forecast performance varies among lake water quality metrics and that process-based forecasts can provide important information in conjunction with null models in varying environmental conditions. Altogether, these process-based forecasts can be used to develop quantitative tools which inform our understanding of future ecosystem change. • Ecological forecasts are rarely assessed across multiple ecological variables. • Forecast skill, relative to a null, allows comparison across variables, space, time. • Lake temperature forecasts were more skilful than oxygen but varied with depth. • Process-based forecasts frequently improved performance above null models. [ABSTRACT FROM AUTHOR]

For this paper, a new global atmospheric model (Global-to-Regional Integrated forecast SysTem; GRIST) with improved sub-grid scale orographic parameterization was verified and assessed, with an emphasis on the precipitation caused by typhoons. Four typical typhoon cases were selected for the verification of the model. The results indicate that, compared to the control experiments, the sensitivity experiments consistently simulated the trends in the three-hour cumulative precipitation changes and the high-value regions of total precipitation better. However, the improved experiments only had an ameliorating effect on the cumulative precipitation modelling biases for Typhoon LEKIMA and Typhoon HAGUPIT, not all of them. Precipitation bias is smaller on flat land than that on mountainous land, but the precipitation bias on windward/leeward slopes depends on the typhoon case. Precipitation modelling accuracy varies considerably between flat and mountainous terrain but very little between windward and leeward slopes. The precipitation simulation is poor for all terrains, with large precipitation thresholds in three typhoon cases, but for Typhoon HOTA, after improving the terrain, the model has the ability to forecast the heavy rainfall scenarios of the mountainous terrain.

Although species distribution models (SDMs) are commonly used to hindcast fine‐scale population metrics, there remains a paucity of information about how well these models predict future responses to climate. Many conventional SDMs rely on spatially‐explicit but time‐invariant conditions to quantify species distributions and densities. We compared these status quo 'static' models with more climate‐informed 'dynamic' SDMs to assess whether the addition of time‐varying processes would improve hindcast performance and/or forecast skill. Here, we present two groundfish case studies from the Bering Sea – a high latitude system that has recently undergone considerable warming. We relied on conventional statistics (R2, % deviance explained, UBRE or GCV) to evaluate hindcast performance for presence–absence, numerical abundance and biomass of arrowtooth flounder Atheresthes stomias and walleye pollock Gadus chalcogrammus. We then used retrospective skill testing to evaluate near‐term forecast skill. Retrospective skill testing enables direct comparisons between forecasts and observations through a process of fitting and forecasting nested submodels within a given time series. We found that the inclusion of time‐varying covariates improved hindcasts. However, dynamic models either did not improve or decreased forecast skill relative to static SDMs. This is likely a result of rapidly changing temperatures within the ecosystem, which required models to predict species responses to environmental conditions that were outside the range of observed values. Until additional model development allows for fully dynamic predictions, static model forecasts (or persistence forecasts from dynamic models) may serve as reliable placeholders, especially when anomalous conditions are anticipated. Nonetheless, our findings demonstrate support for the use of retrospective skill testing rather than selecting forecast models a priori based on their ability to quantify species–habitat associations in the past. [ABSTRACT FROM AUTHOR]

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A multimodel superensemble technique was applied to improve the accuracy of a model predicting the subseasonal surface air temperatures in East Asia. Superensemble forecast data based on seven subseasonal-to-seasonal (S2S) forecast models were produced for the initial times from May to August in 2016–2020 and forecast days in weeks 1–4. Superensemble techniques based on the root-mean-square error ensemble mean (SUPR) and the linear regression coefficient ensemble mean (SUPL) were evaluated and compared with the forecast bias-removed ensemble mean (ENS) data. The magnitudes of the forecasting root-mean-square errors (RMSE) of the SUPR and SUPL methods were 4.2% and 13.8% lower, respectively, than that of the ENS method, and the most significant effect was in 2020. The RMSE improvements of SUPR and SUPL were high when the initial date was in May, June, and August, and the forecast date was within weeks 1–4. Applying multimodel superensemble methods effectively reduced the forecasting errors of the S2S models. If weight coefficients based on the characteristics of the models are appropriately applied, the forecasting performance can be significantly improved. [ABSTRACT FROM AUTHOR]

Here we present a first look at the Gross Primary Production (GPP) forecast skill levels achievable with a state‐of‐the‐art subseasonal‐to‐seasonal (S2S) forecast system. Using NASA's retrospective S2S ensemble forecast in conjunction with a terrestrial biosphere model, and using an independent, remote sensing‐based data set for validation, we demonstrate an ability to accurately forecast spring‐summer carbon uptake at multi‐month leads. Averaged across mid‐ and high latitudes of the Northern Hemisphere land, the GPP forecast initialized on January 1 produces statistically significant skill through summer. The skill achieved, however, is spatially variable, with some regions appearing to extract skill from accurate forecasts of snowpack removal and others extracting skill from the initialization of carbon and nitrogen states. Our results reveal some heretofore unexplored facets of climate predictability and provide a look at what might be possible with future S2S forecast systems that are fully integrated with biogeochemical cycles. Plain Language Summary: Seasonal forecasts of meteorology can support a variety of applications that can help manage precious natural resources. As an integral part of the Earth system, the land ecosystem plays an important role in seasonal forecasts; however, our ability to predict variations in the land's carbon cycle, which is tied strongly to the land's water and energy cycles, is not well understood. This study is a first look at the ability of a state‐of‐the‐art seasonal forecast system to predict year‐to‐year variations in the land's carbon uptake. We demonstrate that the system can accurately forecast spring‐summer carbon uptake several months ahead of time across many mid‐ and high latitude land areas within the Northern Hemisphere. This skill appears to be related both to accurate forecasts of snow removal timing and to the proper specification of land carbon and nitrogen conditions at the beginning of the forecasts. The consideration of carbon and nitrogen dynamics will undoubtedly be standard someday in seasonal forecast systems; this study provide a glimpse at what we might expect from such enhancements. Key Points: We demonstrate skillful forecasts of land carbon uptake in mid‐ to high latitudes at multi‐month leads (winter into summer)During spring, Gross Primary Production (GPP) forecast skill in some regions appears to stem from skillful forecasts of snow removal dateIn certain other regions, initialization of the carbon and nitrogen reservoirs appears to contribute to GPP forecast skill [ABSTRACT FROM AUTHOR]

Quantitative precipitation forecast (QPF) obtained from a numerical weather prediction model is used for streamflow forecasting. This study systematically evaluates the performance of a deterministic QPF obtained from a national agency, the National Centre for Medium Range Weather Forecasting (NCMRWF), compared with that obtained from three international agencies. For observation/reference datasets, we used satellite, raingauge, and satellite-gauge merged data products. The forecast skill is evaluated for 177 sub-basins in Ganga, Narmada, Mahanadi, Tapti, and Godavari river basins. Our results indicate that the forecast accuracy of NCMRWF is closely comparable with that of QPF obtained from a European agency. We conclude that selecting accurate observation/reference data is critical for forecast performance evaluation. We determine the preferred forecast and reference datasets in the selected river basins. Our results suggest that for estimating the parameters of a post-processor, a comparable dataset can be used for which data are available for a longer duration. [ABSTRACT FROM AUTHOR]

Using daily precipitation model data produced by the second version of Climate Forecasting System of NCEP (CFSv2),the ensemble prediction method is used to carry out regional summer precipitation forecast. Three methods,i.e.,the root mean square error(RMSE) and accuracy(ACCU) of the onset and the ending time of Meiyu,the RMSE of the length of Meiyu,and anomaly sign consistency rate (Pc) of the average rainfall amount of Meiyu,are used to evaluate the forecast ability of the model to the Meiyu characteristics in Hubei province. The results show that the onset of Meiyu forecast is 13 days in advance,the ACCU is above 0.5,and the RMSE is less than 3 days. The ending of Meiyu forecast is 14 days in advance,the ACCU is above 0.5,and the RMSE is less than 3 days. The length of Meiyu period forecast is 14 days in advance,and the RMSE is less than 5 days. The average rainfall amount of Meiyu forecast is 14 days in advance,and the Pc is above 0.5. In general,the aging time of Meiyu characteristics forecast is about 14 days. The CFSv2 model shows certain forecast skill for regional summer precipitation during the Meiyu extended-range period.

Based on the convection-allowing scale high resolution (3 km) ensemble prediction system which has been operationally implemented in meteorological services of Chongqing and includes initial perturbation, model physical process perturbation and lateral boundary perturbation in it, the perturbation to model static topographic height field is realized by adopting different topographic interpolation schemes and smoothing schemes to generate static topographic height field of each ensemble members to express the uncertainty of the topographic conversions in the numerical model to some extent. Using these perturbations, we have performed a month consecutive ensemble prediction tests and the comparative analysis with respect to the ensemble forecast results without model topography perturbation. The results show that (1) after adding the model topography perturbation scheme into the ensemble prediction system, the topographic height spread among ensemble members has a good correspondence with the real topographic height gradient, and both spatial distribution characteristics are very similar. The topographic height spread in the plain area is small, while that in the plateau area or mountainous area with complicated terrain is large. (2) The addition of model topography perturbation scheme increases the ensemble different total energy (DTE) whose increase ratio in the low-level is larger than that in the mid- and upper-level. The enhancement of DTE in the short forecast lead time (12 h) is the most significant. It gradually decreased with the forecast lead time becoming longer. Large enhancement centers of DTE mainly appear in the areas with complicated topography and the great topographic height spread among the ensemble members. (3) In general, the addition of model topography perturbation scheme can improve the probability forecast skill and ensemble mean forecast skill of 24 h accumulated rain to some extent. It can optimize the ensemble distribution of all the elements in upper air, and 2 m air temperature, 10 m zonal wind, 10 m meridional wind and other elements near the ground level to a certain extent without the negative effect in the ensemble mean forecast error and ensemble spread.

Changing ocean conditions due to anthropogenic climate change, particularly the increasing severity and frequency of extreme events, are a growing concern for a range of marine sectors. Here we explore the global trends in marine heatwaves (MHWs), specifically onset and decline rates, two metrics which describe how quickly a MHW will emerge or disappear from a location. These rates determine the reaction window—the start of a MHW event to peak MHW temperatures—and the coping window—time from peak temperatures to the end of an event—two important time periods relevant to a marine decision-maker. We show that MHW onset and decline rates are fastest in dynamic ocean regions and that overall, the global trend in onset rate is greater than the global trend in decline rate. We map ocean regions where these rates are changing together with forecast skill from a seasonal dynamical model (ACCESS-S). This analysis highlights areas where the length of the preparation window for impending MHWs is increased by using forecasts, and areas where marine decision-makers should be prepared for rapid responses based on realtime observations as MHWs evolve. In regions such as south Africa and Kerguelen, northwest Atlantic, northwest Pacific, southwest South Atlantic and off Australian east coast where rapid median onset and decline rates are observed, there is also a positive trend in onset and decline rates i.e., MHWs are developing and declining more rapidly. This will be a concern for many decision-makers operating in these regions.

Probabilistic near‐term forecasting facilitates evaluation of model predictions against observations and is of pressing need in ecology to inform environmental decision‐making and effect societal change. Despite this imperative, many ecologists are unfamiliar with the widely used tools for evaluating probabilistic forecasts developed in other fields. We address this gap by reviewing the literature on probabilistic forecast evaluation from diverse fields including climatology, economics, and epidemiology. We present established practices for selecting evaluation data (end‐sample hold out), graphical forecast evaluation (times‐series plots with uncertainty, probability integral transform plots), quantitative evaluation using scoring rules (log, quadratic, spherical, and ranked probability scores), and comparing scores across models (skill score, Diebold–Mariano test). We cover common approaches, highlight mathematical concepts to follow, and note decision points to allow application of general principles to specific forecasting endeavors. We illustrate these approaches with an application to a long‐term rodent population time series currently used for ecological forecasting and discuss how ecology can continue to learn from and drive the cross‐disciplinary field of forecasting science. [ABSTRACT FROM AUTHOR]

The leading modes of intraseasonal surface air temperature (SAT) over mid-high-latitude Eurasia (MHLE) in boreal winter are captured with the observational 10–60-day SAT anomaly during 1979–2016 by an empirical orthogonal function (EOF) analysis. Each cycle of the southeastward-propagating intraseasonal oscillation (ISO) of SAT is divided into eight phases formed by the first two principal components (PC1 and PC2). Reforecast data from five operational models in the Sub-seasonal to Seasonal (S2S) Project are utilized to assess the prediction skill of the intraseasonal SAT over the MHLE based on the PC index. It is revealed that the upper limit of the useful forecast skill ranges from ~ 10 to ~ 20 days and the models display a better score for target strong cases, although they underestimate the amplitude of the intraseasonal signal. Generally, a higher prediction skill appears in target phase 3, while a lower score exists in target phase 6. The phase-dependent feature of prediction skill is found to be associated with the phase-dependence of amplitude bias and phase angle error, indicating the importance of skillfully predicting ISO amplitude and propagation speed. On the basis of PC index, the ECMWF model can skillfully predict the evolution of an ISO event up to ~ 20 days in advance. Further assessment of potential predictability reveals a 5-day skill gap for ECMWF model, which needs to be overcome by reducing forecast errors for the current prediction scheme. In a nutshell, the real-time PC index can be an efficient method to capture and predict the ISO signal over MHLE, providing possibilities for improving sub-seasonal forecast skill of wintertime intraseasonal SAT anomaly. Meanwhile, more efforts are still needed for the improvements of dynamical prediction systems, to facilitate more accurate and longer-lead predictions of intraseasonal SAT signal over MHLE during boreal winter. [ABSTRACT FROM AUTHOR]

Prediction for Indian summer monsoon rainfall (ISMR) is generated by integrating model from initial conditions (ICs) of weather at some time prior to season. We examine the factors responsible for the widely reported highest ISMR forecast skill for February ICs in climate forecast system (CFSv2) model. Skill for February ICs is highest only based on correlation between observed and predicted year-to-year variation of ISMR, whereas other skill scores indicate highest skill for late-April/early-May ICs having shorter yet useful forecast lead-time. Higher correlation for February ICs arises from correct forecasting of 1983 ISMR excess, which is however due to wrong forecast of La Niña and correlation drops to lower value than that for late-April/early-May ICs if 1983 is excluded. Forecast skill for sea-surface temperature variation over equatorial central Pacific (ENSO) in Boreal summer is lowest for February ICs indicating role of dynamical drift induced by long forecast lead-time. Model deficiencies such as oversensitivity of ISMR to ENSO and unrealistic ENSO influence on variation of convection over equatorial Indian Ocean (EQUINOO) lead to errors in ISMR forecasting. In CFSv2, ISMR is mostly decided by ENSO whereas in observation it is influenced by ENSO and EQUINOO independently. [ABSTRACT FROM AUTHOR]

Coastal high water level events are increasing in frequency and severity as global sea‐levels rise, and are exposing coastlines to risks of flooding. Yet, operational seasonal forecasts of sea‐level anomalies are not made for most coastal regions. Advancements in forecasting climate variability using coupled ocean‐atmosphere global models provide the opportunity to predict the likelihood of future high water events several months in advance. However, the skill of these models to forecast seasonal sea‐level anomalies has not been fully assessed, especially in a multi‐model framework. Here, we construct a 10‐model ensemble of retrospective forecasts with future lead times of up to 11 months. We compare predicted sea levels from bias‐corrected forecasts with 20 years of observations from satellite‐based altimetry and shore‐based tide gauges. Forecast skill, as measured by anomaly correlation, tends to be highest in the tropical and subtropical open oceans, whereas the skill is lower in the higher latitudes and along some continental coasts. For most locations, multi‐model averaging produces forecast skill that is comparable to or better than the best performing individual model. We find that the most skillful predictions typically come from forecast systems with more accurate initializations of sea level, which is generally achieved by assimilating altimetry data. Having relatively higher horizontal resolution in the ocean is also beneficial, as such models seem to better capture dynamical processes necessary for successful forecasts. The multi‐model assessment suggests that skillful seasonal sea‐level forecasts are possible in many, though not all, parts of the global ocean. Plain Language Summary: We assess 10 global climate forecasting systems to predict monthly and seasonal anomalies of local sea levels up to a year into the future. We find that skillful seasonal sea‐level forecasts are possible in many parts of the global ocean. Forecast skill is generally highest in the tropical and subtropical open oceans, whereas the skill is lower in the higher latitudes and along continental coasts. For most locations, multi‐model averaging improves the forecast skill, compared to considering the models individually. Overall, the most skillful predictions are from forecasting systems with more accurate initializations of sea level and higher horizontal resolutions of the ocean. Key Points: Prediction skill of seasonal sea‐level anomalies up to a year in the future is assessed in 10 global climate forecasting systemsSkillful seasonal sea‐level forecasts are found in the tropics, whereas the skill is lower in higher latitudes and along continental coastsThe most skillful predictions are from models with more accurate initializations of sea level and higher resolutions of the ocean [ABSTRACT FROM AUTHOR]

Although precipitation forecast skill has improved significantly in recent decades, both location and intensity of precipitation forecast based on kilometric-scale numerical weather prediction (NWP) models still contain errors. The importance of large-scale error in NWP has been recognized, but characteristics of large-scale error under different rainfall regimes and the influence of large-scale error on precipitation forecast skill remain unclear. In this regard, we focused on the Yangtze-Huaihe River Basin (YHRB) and used an operational forecast dataset covering the summer seasons of 2018 and 2019 obtained from the Anhui Meteorology Observatory to investigate large-scale error characteristics and their relationship with precipitation errors under two rainfall regimes. Large-scale error characteristics were measured by root-mean-square errors (RMSE), relative RMSE (RRMSE), and large-scale spatial pattern correlation (LSCC). The large-scale RMSEs were highly correlated with the rainfall intensity and area, while the large-scale RRMSE and LSCC can be used to describe precipitation forecast skill for rainfall events under the two rainfall regimes. In general, the precipitation forecast for strongly forced rainfall events performed better than that for weakly forced rainfall events. Under strong forcing conditions, the majority of rainfall events were concentrated in the intervals of smaller precipitation errors with smaller RRMSEs and higher LSCCs, and the precipitation forecast skill was weakly related to the large-scale RRMSE, LSCC and the initial large-scale variance contribution (LVC). Under weak forcing conditions, most of the rainfall events had a larger precipitation error and the frequency of location error was higher than that of intensity error. The low precipitation forecast skill and large forecast uncertainties for weakly forced rainfall events were strongly related to the large RRMSEs and low LSCCs in the geopotential height in the middle-upper troposphere and meridional wind component (V) in the lower troposphere. In particular, a strong negative correlation was found between the initial LVC and the large-scale RRMSE under weak forcing. This suggests that large-scale information could play a more important role in precipitation forecast for weakly forced rainfall events, and could potentially be used to indicate the magnitude of large-scale forecast errors in short-term forecasts under weak forcing conditions. • Precipitation forecast skill is related to large-scale errors measured by RRMSE. • Weakly forced rainfall events are featured with large RRMSEs and low LSCCs. • The initial LVC has an impact on the large-scale forecast RRMSE under weak forcing. [ABSTRACT FROM AUTHOR]

Accurate forecasting of variations in Indian monsoon precipitation and progression on seasonal time scales remains a challenge for prediction centres. We examine prediction skill for the seasonal-mean Indian summer monsoon and its onset in the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system 5 (SEAS5). We analyse summer hindcasts initialised on 1st of May, with 51 ensemble members, for the 36-year period of 1981–2016. We evaluate the hindcasts against the Global Precipitation Climatology Project (GPCP) precipitation observations and the ECMWF reanalysis 5 (ERA5). The model has significant skill at forecasting dynamical features of the large-scale monsoon and local-scale monsoon onset tercile category one month in advance. SEAS5 shows higher skill for monsoon features calculated using large-scale indices compared to those at smaller scales. Our results also highlight possible model deficiencies in forecasting the all India monsoon rainfall. [ABSTRACT FROM AUTHOR]

In this article, we describe an implementation of single‐precision fast spherical harmonic transform (SHT) in the Yin–He global spectral model (YHGSM). The potential of single‐precision arithmetic is explored for accelerating fast SHT. In particular, we assess the impact of using single‐precision fast SHT on the meteorological skill of YHGSM. Compared to double‐precision SHT, single‐precision fast SHT against is accelerated by about 59.83%, whereas the run‐time reduction achieved 25.28% in model integration for TL2047L137. The simulation results indicate that single‐precision fast SHT can improve computational efficiency without a noticeable impact on the forecast skill. [ABSTRACT FROM AUTHOR]

A multimodel superensemble technique was applied to improve the accuracy of a model predicting the subseasonal surface air temperatures in East Asia. Superensemble forecast data based on seven subseasonal-to-seasonal (S2S) forecast models were produced for the initial times from May to August in 2016–2020 and forecast days in weeks 1–4. Superensemble techniques based on the root-mean-square error ensemble mean (SUPR) and the linear regression coefficient ensemble mean (SUPL) were evaluated and compared with the forecast bias-removed ensemble mean (ENS) data. The magnitudes of the forecasting root-mean-square errors (RMSE) of the SUPR and SUPL methods were 4.2% and 13.8% lower, respectively, than that of the ENS method, and the most significant effect was in 2020. The RMSE improvements of SUPR and SUPL were high when the initial date was in May, June, and August, and the forecast date was within weeks 1–4. Applying multimodel superensemble methods effectively reduced the forecasting errors of the S2S models. If weight coefficients based on the characteristics of the models are appropriately applied, the forecasting performance can be significantly improved.

Accurate forecasts of visibility are important to avoid disruption in air and highway traffic caused due to the formation of dense fog. However, accurate forecasting of visibility/fog remains a challenge as the genesis and development of fog is a result of many processes. In view of this, models have been developed in recent years to forecast visibility and the occurrence of fog is measured in terms of visibility. The global Unified Model of the National Centre for Medium Range Weather Forecasting, known as NCUM, provides direct output of visibility. As aviation is severely affected at the Indira Gandhi International Airport, New Delhi, India, a high-resolution model was set up to forecast visibility over the airport. The present study analyses the performance of the coarse-resolution global model and high-resolution model in predicting visibility over Delhi. Visibility is categorized into three ranges - very poor (0-200 m), poor (200-1000 m) and clear conditions beyond 1 km. The accuracy of forecast in different ranges of visibility is determined using different statistical scores. Evaluation of the results shows that the performance of both high and coarse resolution model remains low in poor visibility conditions. Though the high-resolution model performs better than the coarse-resolution model in predicting a drop in visibility, it also has higher number of false alarms. None of the model is able to predict the very poor visibility conditions. The prediction of visibility from the high-resolution model can further be improved by inclusion of real-time aerosol fields in the model. [ABSTRACT FROM AUTHOR]

Abstract Ten-year seasonal climate reforecasts over South America are obtained using the Eta Regional Climate Model at 40 km resolution, driven by the large-scale forcing from the global atmospheric model of the Center for Weather Forecasts and Climate Studies. The objective of this work is to evaluate these regional reforecasts. The dataset is comprised of four-month seasonal forecasts performed on a monthly basis between 2001 and 2010. An ensemble of five members is constructed from five slightly different initial conditions to partially reduce the uncertainty in the seasonal forecasts. The seasonal mean precipitation and 2-meter temperature forecasts are compared with the observations. The comparison shows that, in general, forecasted precipitation is underestimated in the central part of the continent in the austral summer, whereas the forecasted 2 meter temperature is underestimated in most parts of the continent and throughout the year. Skill scores show higher skill in the northern part of the continent and lower skill in the southern part of the continent, but mixed skill signs are seen in the central part of the continent. During the El Niño and La Niña seasons, the forecast skill scores clearly increase. The downscaling of the Eta model seasonal forecasts provides added value over the driver global model forecasts, especially during rainy periods.