Prediction Skill Research Articles

The role of in situ ocean data assimilation in ECMWF subseasonal forecasts of sea‐surface temperature and mixed‐layer depth over the tropical Pacific ocean

AbstractThe tropical Pacific plays an important role in modulating the global climate through its prevailing sea‐surface temperature spatial structure and dominant climate modes like El Niño–Southern Oscillation (ENSO), the Madden–Julian Oscillation (MJO), and their teleconnections. These modes of variability, including their oceanic anomalies, are considered to provide sources of prediction skill on subseasonal timescales in the Tropics. Therefore, this study aims to examine how assimilating in situ ocean observations influences the initial ocean sea‐surface temperature (SST) and mixed‐layer depth (MLD) and their subseasonal forecasts. We analyze two subseasonal forecast systems generated with the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where the ocean states were initialized using two Observing‐System Experiment (OSE) reanalyses. We find that the SST differences between forecasts with and without ocean data assimilation grow with time, resulting in a reduced cold‐tongue bias when assimilating ocean observations. Two mechanisms related to air–sea coupling are considered to contribute to this growth of SST differences. One is a positive feedback between the zonal SST gradient, pressure gradient, and surface wind. The other is the difference in Ekman suction and mixing at the Equator due to surface wind‐speed differences. While the initial mixed‐layer depth (MLD) can be improved through ocean data assimilation, this improvement is not maintained in the forecasts. Instead, the MLD in both experiments shoals rapidly at the beginning of the forecast. These results emphasize how initialization and model biases influence air–sea interaction and the accuracy of subseasonal forecasts in the tropical Pacific.

SimplETAS: A Benchmark Earthquake Forecasting Model Suitable for Operational Purposes and Seismic Hazard Analysis

Abstract The epidemic-type aftershock sequence (ETAS) model is the most effective mathematical description of the short-term space–time earthquake clustering. However, the use of such a model is sometimes hampered by the difficulty in estimating the high number of its unknown correlated parameters. Moreover, the most recent ETAS formulations introduce the space–time variability of some parameters that makes their estimation even more arduous. Here, we investigate the model in an opposite perspective, looking for the simplest ETAS parameterization that can satisfactorily describes the earthquake clustering in crustal tectonic regions; we named this model simplETAS. We show that simplETAS calibrated with the Italian seismicity of the last decades adequately describes the space–time occurrence of the out-of-sample largest earthquakes in the instrumental and historical catalog, confirming the validity of the assumptions made to build the model. Owing to its simplicity, simplETAS is easily applicable in most regions, and it has some important properties that are worth being remarked. First, simplETAS can be used as a benchmark model to assess the relative predictive skill of more complex earthquake forecasts. Second, it may be used for operational earthquake forecasting purposes in regions with limited earthquake catalogs. Third, it provides a straightforward, flexible, and effective approach to generate synthetic earthquake catalogs of variable length to be implemented in seismic hazard and risk analysis, overcoming all the declustering-related problems and the controversial Poisson assumption.

Sources of the predictability of month‐to‐month variation of precipitation anomalies in East Asia during summer

AbstractDuring boreal summer, the patterns of precipitation anomalies in East Asia have notable month‐to‐month variations, which may induce alternated wet and dry conditions in different regions of East Asia during summer. In this study, based on observation and reanalysis data, the dynamic processes of monthly precipitation anomalies in three sub‐regions of East Asia (South China, the Yangtze River valley and northern–northeastern China) are investigated, which are associated with different combined effects of tropical air–sea interaction and mid‐to‐high latitude Rossby waves during different months. These dynamic processes dominate the leading modes of month‐to‐month variation of precipitation anomalies in East Asia during summer. To understand what dynamic processes are the key sources of the predictability of month‐to‐month variation of precipitation anomalies in East Asia during summer, a method of spatially running pattern correlation coefficient (SR_PCC) is applied to examine the differences in prediction skill of geopotential height anomalies, sea‐level pressure anomalies and sea surface temperature anomalies between the years of well and poorly predicted precipitation anomalies. Based on the results of CFSv2 hindcast and CMIP6‐AMIP experiments, both the air–sea interaction over tropical Indo–Pacific region and the mid‐to‐high latitude Rossby waves over the Eurasian continent are key sources of predictability of month‐to‐month variation of precipitation anomalies in East Asia during boreal summer.

Intelligent learning algorithms integrated with feature engineering for sustainable groundwater salinization modelling: Eastern Province of Saudi Arabia

Groundwater (GW) salinization refers to the natural or human-induced process of GW acquiring a higher level of salt content. While geological factors or proximity to the ocean can naturally cause this phenomenon, it can also be attributed to activities like irrigation, changes in land use, and the discharge of industrial or municipal waste. The following research employs the use of various feature extraction methods coupled with novel chemometrics approaches informed by machine learning (ML) techniques including; Gaussian Process Regression (GPR), Support Vector Regression (SVR), Regression tree (RT) and Robust linear regression (RLR). Based on the feature selection methods, the models were classified into three different combinations. The intelligent learning algorithms equally depict higher PCC values ranging from 0.935 to 1.00 in the training and 0.779 to 0.999 in the validation stage respectively, which depicts a higher relation between the experimental and simulated values. The performance results indicate that GPR-Comb-3 showed the highest performance in both the training and validation stages respectively. It is worth mentioning that even the RLR technique equally depicts exceptional prediction skills in both the training and validation steps. In conclusion, the outcomes of the current research depict the significance of these techniques in evaluating GW salinization.

Evaluating and Improving the Models' Prediction Skills for the Relationship Between the Summer NWPSH in Different Months

AbstractCompared to interannual and interdecadal variations, inadequate attention has been given to intraseasonal changes in the summer northwest Pacific subtropical high (NWPSH). During 1981–2005, the north Indian Ocean (NIO) and NWPSH exhibit significant relationships from June to August, while the influences of the Boreal Summer Intraseasonal Oscillation 1 (BSISO1) in June and August differ from its impacts in July, resulting in a significant correlation between the NWPSH in June and August. The models capture NIO's roles but underestimate BSISO1's influences, leading to an underestimation (overestimation) of the relationship between NWPSH in June and August (adjacent two months). A dynamical‐statistical approach based on the close relationship between June and August NWPSH is proposed to improve models' prediction capabilities. Improved results show the mean correlation coefficients (root mean square errors) between prediction and observation for August precipitation and geopotential height increase by 57% and 21% (decrease by 0.27 mm/day and 1.61 m).

Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation

Abstract. The ability of regional air quality models to skilfully represent pollutant distributions throughout the atmospheric column is important to enabling their skilful prediction at the surface. This provides a requirement for model evaluation at elevated altitudes, though observation datasets available for this purpose are limited. This is particularly true of those offering sampling over extended time periods. To address this requirement and support evaluation of regional air quality models such as the UK Met Offices Air Quality in the Unified Model (AQUM), a long-term, quality-assured dataset of the three-dimensional distribution of key pollutants was collected over the southern United Kingdom from July 2019 to April 2022. Measurements were collected using the Met Office Atmospheric Survey Aircraft (MOASA), a Cessna 421 instrumented for this project to measure gaseous nitrogen dioxide, ozone, sulfur dioxide and fine-mode (PM2.5) aerosol. This paper introduces the MOASA measurement platform, flight strategies and instrumentation and is not intended to be an in-depth diagnostic analysis but rather a comprehensive technical reference for future users of these data. The MOASA air quality dataset includes 63 flight sorties (totalling over 150 h of sampling), the data from which are openly available for use. To illustrate potential uses of these upper-air observations for regional-scale model evaluation, example case studies are presented, which include analyses of the spatial scales of measured pollutant variability, a comparison of airborne to ground-based observations over Greater London and initial work to evaluate performance of the AQUM regional air quality model. These case studies show that, for observations of relative humidity, nitrogen dioxide and particle counts, natural pollutant variability is well observed by the aircraft, whereas SO2 variability is limited by instrument precision. Good agreement is seen between observations aloft and those on the ground, particularly for PM2.5. Analysis of odd oxygen suggests titration of ozone is a dominant chemical process throughout the column for the data analysed, although a slight enhancement of ozone aloft is seen. Finally, a preliminary evaluation of AQUM performance for two case studies suggests a large positive model bias for ozone aloft, coincident with a negative model bias for NO2 aloft. In one case, there is evidence that an underprediction in the modelled boundary layer height contributes to the observed biases at elevated altitudes.

A SST-constructed Ocean Heat Content index in crossing ENSO spring persistence barrier

The El Niño-Southern Oscillation (ENSO) spring persistence barrier (SPB) describes the feature in which the predictive skills of ENSO decrease significantly in the boreal spring. This paper investigates an index constructed using sea surface temperature (SST), namely SSTH, which is based on tropical Pacific Ocean Heat Content (OHC) in crossing ENSO SPB. Inspired by the dynamical relationship between the tropical Pacific OHC and eastern Pacific SST anomalies, SSTH is constructed by SST anomalies (i.e., Niño3.4 index) to represent OHC. We show that this index leads ENSO SST anomalies by about 10 months, making it effective in crossing ENSO SPB. Particularly, among the 50 ENSO events from 1950 to 2022, 27 years were identified to be caused by SSTH signals. Compared with warm water volume (WWV) or the west of WWV (WWVw), this index is more stable and effective after the 21st century because the effective region of subsurface OHC changed dramatically afterward. However, SSTH avoids this problem as it is constructed by SST anomalies alone. Finally, as SST data is reliable before 1980, SSTH is utilized to study the interdecadal lead-lag relationship between subsurface OHC and ENSO SST.

Seasonal Prediction of Arabian Sea Marine Heatwaves

AbstractMarine heatwaves are known to have a detrimental impact on marine ecosystems, yet predicting when and where they will occur remains a challenge. Here, using a large ensemble of initialized predictions from an Earth System Model, we demonstrate skill in predictions of summer marine heatwaves over large marine ecosystems in the Arabian Sea seven months ahead. Retrospective forecasts of summer (June to August) marine heatwaves initialized in the preceding winter (November) outperform predictions based on observed frequencies. These predictions benefit from initialization during winters of medium to strong El Niño conditions, which have an impact on marine heatwave characteristics in the Arabian Sea. Our probabilistic predictions target spatial characteristics of marine heatwaves that are specifically useful for fisheries management, as we demonstrate using an example of Indian oil sardine (Sardinella longiceps).

Model‐Dependent Atlantic Multidecadal Variability Modulations on North Pacific Sea Surface Temperature Variability and Decadal Prediction

AbstractPrevious studies suggested that Atlantic Multidecadal Variability (AMV) modulations on pan‐Pacific sea surface temperature (SST) variability and prediction are model‐dependent. These results were mainly based on SST forcing experiments in which AMV‐related Atlantic SST anomalies were prescribed. However, the AMV itself is also model‐dependent, but its influences on the Pacific remain unclear. Here, we use multi‐model fully coupled experiments from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with observations, to study the model‐dependent AMV trans‐basin effects. We found that AMV strength is a key factor: Stronger (Weaker) model AMV than observations overestimates (underestimates) SST response and decadal prediction skills, mainly in the North Pacific. The reason is that stronger positive phased AMV, for example, leads to higher sea level pressure anomalies over the North Pacific, which lifts sea surface height and deepens thermocline to warm SST. Our study highlights the necessity to improve simulations of AMV strength.

Seasonal Predictability of North American Surface Temperature Arises from SST Propagation over the Western Pacific

Abstract The predictability of temperatures in North America is of great importance for local agriculture and human health. In autumn (SON), the temperature in North America is correlated with ENSO, but its predictive skill is limited. Here, we show that spring (MAM) sea surface temperature (SST) anomalies in the southwest Pacific (SWP) exhibit a higher correlation with autumn temperature in North America (r = 0.73) than ENSO. This cross-seasonal and cross-hemispheric relationship is established via the western tropical Pacific (WTP) region. The spring SWP SST anomalies show a cross-hemispheric propagation embedded in the southerly monsoonal flow through the wind–evaporation–SST feedback, which sustains the progression of SST anomalies toward the WTP in autumn. The AGCM simulations from multimodels show that active convection in the WTP caused by SST warming stimulate a Rossby wave train propagating downward to North America, where it is governed by an anomalous high with increased atmospheric thickness and tropospheric temperature. Consequently, the longwave radiative heating over North America is enhanced, raising the surface air temperature. These results indicate that the SWP SST is a useful predictor of North American temperature 6 months in advance through the cross-hemispheric influence. A model based on the spring SWP SST and preceding winter ENSO (Niño-3.4) shows a high predictive skill for the autumn temperature anomalies in North America. Significance Statement Seasonal predictions of surface temperature are important for agricultural decision-making, disaster prevention, and mitigation. Over the western Pacific, associated with the seasonal evolution of ITCZ, there is a strong northward progression of SST anomaly signals generated in the southwest Pacific. This study finds that this cross-hemispheric SST propagation can significantly impact the autumn surface temperature over central North America through the atmospheric bridge. A statistical model incorporating southwest Pacific SST for spring is constructed to predict the North American autumn surface temperature and exhibits high consistency with observations. This improves our understanding of the interhemispheric interactions at seasonal time scales and the seasonal predictability of the North American climate.

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Identifying predictable states of the climate system allows for enhanced prediction skill on the generally low-skill subseasonal timescale via forecasts with higher confidence and accuracy, known as forecasts of opportunity. This study takes a neural network approach to explore decadal variability of subseasonal predictability, particularly during forecasts of opportunity. Specifically, this work quantifies subseasonal prediction skill provided by the tropics within the Community Earth System Model Version 2 (CESM2) Large Ensemble and assesses how this skill evolves on decadal timescales. Utilizing the networks’ confidence and explainable artificial intelligence, physically meaningful sources of predictability associated with periods of enhanced skill are identified. Using these networks, we find that tropically-driven subseasonal predictability varies on decadal timescales during forecasts of opportunity. Further, we investigate the drivers of the low frequency modulation of the tropical-extratropical teleconnection and discuss the implications. Analysis is extended to ECMWF Reanalysis v5 data, revealing that the relationships learned within the CESM2-Large Ensemble holds in modern reanalysis data. These results indicate that the neural networks are capable of identifying predictable decadal states of the climate system within CESM2 that are useful for making confident, accurate subseasonal precipitation predictions in the real world.

On the estimation of landslide intensity, hazard and density via data-driven models

Maps that attempt to predict landslide occurrences have essentially stayed the same since 1972. In fact, most of the geo-scientific efforts have been dedicated to improve the landslide prediction ability with models that have largely increased their complexity but still have addressed the same binary classification task. In other words, even though the tools have certainly changed and improved in 50 years, the geomorphological community addressed and still mostly addresses landslide prediction via data-driven solutions by estimating whether a given slope is potentially stable or unstable. This concept corresponds to the landslide susceptibility, a paradigm that neglects how many landslides may trigger within a given slope, how large these landslides may be and what proportion of the given slope they may disrupt. The landslide intensity concept summarized how threatening a landslide or a population of landslide in a study area may be. Recently, landslide intensity has been spatially modeled as a function of how many landslides may occur per mapping unit, something, which has later been shown to closely correlate to the planimetric extent of landslides per mapping unit. In this work, we take this observation a step further, as we use the relation between landslide count and planimetric extent to generate maps that predict the aggregated size of landslides per slope, and the proportion of the slope they may affect. Our findings suggest that it may be time for the geoscientific community as a whole, to expand the research efforts beyond the use of susceptibility assessment, in favor of more informative analytical schemes. In fact, our results show that landslide susceptibility can be also reliably estimated (AUC of 0.92 and 0.91 for the goodness-of-fit and prediction skill, respectively) as part of a Log-Gaussian Cox Process model, from which the intensity expressed as count per unit (Pearson correlation coefficient of 0.91 and 0.90 for the goodness-of-fit and prediction skill, respectively) can also be derived and then converted into how large a landslide or several coalescing ones may become, once they trigger and propagate downhill. This chain of landslide intensity, hazard and density may lead to substantially improve decision-making processes related to landslide risk.

A strong high-temperature event in late-spring 2023 in Yunnan province, Southwest China: Characteristics and possible causes

In April–May 2023, Yunnan province of Southwest China experienced a strong high-temperature event with relatively large return period and small occurrence probability, causing adverse impacts on the regional development. The possible causes for the 2023 high-temperature event are mainly involved with an upper-level high-pressure system induced by a Rossby wave train and the precipitation shortage associated with a lower-level anticyclonic circulation over the Bay of Bengal. On the one hand, the atmospheric Rossby wave train is attributed to the decreased snow depth in the North America. On the other hand, the lower-level anticyclone over the Bay of Bengal may be caused by the warming SSTs in the tropical western Indian ocean and in the tropical eastern Pacific via the excited Matsuno-Gill response and the atmospheric signals of Southern Oscillation, respectively. The mechanisms are validated by the pacemaker experiments run with CESM1 climate model. These three factors that cause the 2023 high-temperature event are highly correlated to the surface temperature anomalies in Yunnan. Hence, a physical-based multiple regression model with the leave-five-out cross validation can be constructed to perform a skilful seasonal prediction.

A critical evaluation of decadal solar cycle imprints in the MiKlip historical ensemble simulations

Abstract. Studies concerning solar–terrestrial connections over the last decades claim to have found evidence that the quasi-decadal solar cycle can have an influence on the dynamics in the middle atmosphere in the Northern Hemisphere (NH) during the winter season. It has been argued that feedbacks between the intensity of the UV part of the solar spectrum and low-latitude stratospheric ozone may produce anomalies in meridional temperature gradients which have the potential to alter the zonal-mean flow in middle to high latitudes. Interactions between the zonal wind and planetary waves can lead to a downward propagation of the anomalies, produced in the middle atmosphere, down to the troposphere. More recently, it has been proposed that top-down-initiated decadal solar signals might modulate surface climate and synchronize the North Atlantic Oscillation. A realistic representation of the solar cycle in climate models was suggested to significantly enhance decadal prediction skill. These conclusions have been debated controversial since then due to the lack of realistic decadal prediction model setups and more extensive analysis. In this paper we aim for an objective and improved evaluation of possible solar imprints from the middle atmosphere to the surface and with that from head to toe. Thus, we analyze model output from historical ensemble simulations conducted with the state-of-the-art Max Planck Institute for Meteorology Earth System Model in high-resolution configuration (MPI-ESM-HR). The target of these simulations was to isolate the most crucial model physics to foster basic research on decadal climate prediction and to develop an operational ensemble decadal prediction system within the “Mittelfristige Klimaprognose” (MiKlip) framework. Based on correlations and multiple linear regression analysis we show that the MPI-ESM-HR simulates a realistic, statistically significant and robust shortwave heating rate and temperature response at the tropical stratopause, in good agreement with existing studies. However, the dynamical response to this initial radiative signal in the NH during the boreal winter season is weak. We find a slight strengthening of the polar vortex in midwinter during solar maximum conditions in the ensemble mean, which is consistent with the so-called “top-down” mechanism. The individual ensemble members, however, show a large spread in the dynamical response with opposite signs in response to the solar cycle, which might be a result of the large overall internal variability compensating for rather small solar imprints. We also analyze the possible surface responses to the 11-year solar cycle and review the proposed synchronization between the solar forcing and the North Atlantic Oscillation. We find that the simulated westerly wind anomalies in the lower troposphere, as well as the anomalies in the mean sea level pressure, are most likely independent from the timing of the solar signal in the middle atmosphere and the alleged top-down influences. The pattern rather reflects the decadal internal variability in the troposphere, mimicking positive and negative phases of the Arctic and North Atlantic oscillations throughout the year sporadically, which is then assigned to the solar predictor time series without any plausible physical connection and sound solar contribution. Finally, by applying lead–lag correlations, we find that the proposed synchronization between the solar cycle and the decadal component of the North Atlantic Oscillation might rather be a statistical artifact, affected for example by the internal decadal variability in the ocean, than a plausible physical connection between the UV solar forcing and quasi-decadal variations in the troposphere.

Subseasonal-to-seasonal prediction of arctic sea ice Using a Fully Coupled dynamical ensemble forecast system

With the opening of Arctic passage for maritime transportation under global warming, accurate prediction of Arctic sea ice (ASI) on subseasonal-to-seasonal (S2S) timescales becomes increasingly crucial for the economy and society but challenging. This study examined the S2S hindcast skills of monthly ASI during 1992–2019 using Flexible Global Ocean-Atmosphere-Land System, Finite-Volume version 2 (FGOALS-f2), a global coupled model that includes an interactive dynamical sea ice component. Firstly, the prediction system can accurately capture the seasonal cycle of Arctic sea ice extent (SIE). Still, it slightly overestimates SIE in March–June and significantly underestimates SIE in August–December. Meanwhile, the declining trend of SIE is underestimated in predictions for all target seasons, particularly for spring. Secondly, relatively high skills with an average ACC of 0.44 are found in predicting monthly SIE anomalies (SIEA) after removing the long-term linear trend at a one-to-six-month lead. However, the one-to-six-month lead prediction skills in April drop significantly by 36% when the interdecadal variation is removed. We find that the variation of April SIEA after removing the linear trend is dominated by the Pacific Decadal Oscillation (PDO), which can be better predicted than interannual variability ahead of subseasonal timescales. Unlike the SIE error-based metrics considered in previous studies, the one-to-three-month lead prediction in FGOALS-f2 shows significantly better performance in predicting the spatial distribution of September ASI for extreme years compared to normal years. Finally, the skill for predicting minimum SIEA in FGOALS-f2 is considerably higher at a three-to-six-month lead than anomaly persistence, indicating the advantage of this dynamical prediction system at a longer lead time.

Predictability of Extremely Pluvial Winters Over the Yangtze–Huai River Basin in China

AbstractIn the 2018–2019 winter, an unprecedented pluvial event occurred in the Yangtze‐Huai River Basin (YHRB), which is one of the most developed areas in China. This was a record over the past 40 years. Other notable events during this period were the events of the 2002–2003, 1997–1998, and 1989–1990 winters. This study aims to examine the ability of the Scale Interaction Experiment‐Frontier (SINTEX‐F) to predict these extremely pluvial winters and its predictability source. Extreme precipitation events in the 2018–2019 and 1997–1998 winters were reasonably well predicted 1 month earlier by the ensemble seasonal prediction system from deterministic and probabilistic perspectives. In contrast, the predictions of extremely pluvial winters in 2002–2003 and 1989–1990 were less skillful in some parts of the YHRB. The analysis of co‐variability of inter‐member anomalies suggested that the SST anomalies in the tropical western Indian Ocean (IO) and the tropical Pacific Ocean were responsible for the predictions of the 2018–2019 and 1997–1998 winters through increased low‐level moisture convergences in the YHRB as well as wave trains along the upper westerly jet. Limited performance in predicting the 2002–2003 and 1989–1990 events might be due to less accurate prediction of low‐level circulation around the YHRB associated with the SST anomalies in the tropical eastern IO, western IO, and the Pacific, respectively. Besides, atmospheric internal variability as well as model biases may also limit the deterministic prediction skills.

The effect of initialisation on 20 year multi-decadal climate predictions

This study investigates initialised decadal predictions of 2-m air temperature over lead times of up to 20 years and compares them against uninitialised simulations in the time period 1960–2019. We demonstrate that in the North and South Atlantic, as well as in the Northeast Pacific, the effect of initialisation within the prediction persists for longer than 10 lead years. In these regions, the skill of the initialised decadal predictions does not necessarily regress back to the skill of the uninitialised simulations, which is indeed the case for other regions. We analyse the Atlantic Meridional Overturning Circulation (AMOC) and show that within the first 10 years after initialisation, it drifts towards a state, which is different from both the initial state and the state of the uninitialised simulations. We show that the AMOC stays within this new state for at least another 10 years. We find that in our decadal predictions, the correct determination of future external forcings plays an important role on the global scale, while correct initialisation increases prediction skill on the regional scale.

The NOAA Weather Prediction Center’s Use and Evaluation of Experimental Warn-on-Forecast System Guidance

This study examines use of experimental Warn-on-Forecast System (WoFS) guidance for short-term flash flood prediction at the NOAA Weather Prediction Center’s Meteorological Watch (Metwatch) desk. The WoFS guidance provides storm-scale ensemble forecasts for individual thunderstorms out to six hours and has previously shown great promise in its predictive skill for heavy rainfall events. Its operational utility was examined during 2019 and 2020 in a formal collaboration between Warn-on-Forecast scientists and Metwatch meteorologists. During that time, Metwatch meteorologists integrated real-time WoFS guidance into their Mesoscale Precipitation Discussion forecast processes and provided evaluations via a post-event survey. The survey queried impacts of WoFS guidance on their situational awareness, workload, and confidence, and Metwatch meteorologists also reported subjective assessments of model performance. Survey results highlighted the importance of viewing consistency in WoFS guidance across runs and agreement between WoFS guidance with conceptual models, other numerical weather prediction guidance, and observations. The use of WoFS tended to either maintain or slightly increase Metwatch meteorologists’ workload, while also increasing their confidence (notably for events perceived as better predicted). Of the different forecast attributes evaluated, Metwatch meteorologists reported convective mode as the attribute best predicted by WoFS. Use of WoFS guidance supported Mesoscale Precipitation Discussion decision making, including the placement and spatial extent of the product and the level of specificity provided about the related flash flood threat(s).

Development of early warning system for tsunamis accompanied by collapse of Anak Krakatau volcano, Indonesia

Present tsunami warning systems have been specialized for earthquake-generated tsunamis, but rapidly evaluating the tsunamis caused by volcanic eruptions and/or volcanic sector collapses remains a challenge. In this study, we applied a numerical model to the 2018 Anak Krakatau tsunami event, which was generated by the sector collapse, investigated a tsunami prediction skill by the model, and developed a real-time forecasting method based on a pre-computed database for future tsunamis accompanied by such eruption of Anak Krakatau. The database stores spatiotemporal changes in water surface level and flux, which are simulated under various collapse scenarios, for confined areas in the vicinity of potential source. The areas also cover the locations of observation stations that are virtually placed on uninhabited island surrounding the source area. During an actual volcanic tsunami event, a tsunami is expected to be observed at the observation stations. For real-time tsunami forecasting, the most suitable scenarios to reproduce the observed waveforms are searched quickly in the database. The precomputed results under the identified scenarios are further provided as input for rapid tsunami propagation simulation. Therefore, an effective real-time forecasting can be conducted to densely populated coastal areas located at a considerable distance from the source, such as the coasts of Java and Sumatra. The forecasting performance was examined by applying the method for three hypothetical collapse scenarios assuming different sliding directions. We demonstrated that the tsunamis along the coasts were successfully forecasted. Moreover, we showed that the combination of a pre-computed database and the existence of observation stations near the source area was able to produce appropriate tsunami forecasting for the coastal area even in a volcanic event.

Morphosyntactic predictive processing in adult heritage speakers: effects of cue availability and spoken and written language experience

ABSTRACTWe investigated prediction skills of adult heritage speakers and the role of written and spoken language experience on predictive processing. Using visual world eye-tracking, we focused on predictive use of case-marking cues in verb-medial and verb-final sentences in Turkish with adult Turkish heritage speakers (N = 25) and Turkish monolingual speakers (N = 24). Heritage speakers predicted in verb-medial sentences (when verb-semantic and case-marking cues were available), but not in verb-final sentences (when only case-marking cues were available) while monolinguals predicted in both. Prediction skills of heritage speakers were modulated by their spoken language experience in Turkish and written language experience in both languages. Overall, these results strongly suggest that verb-semantic information is needed to scaffold the use of morphosyntactic cues for prediction in heritage speakers. The findings also support the notion that both spoken and written language experience play an important role in predictive spoken language processing.