Forecast Lead Time Research Articles

Skillful Extended-Range Forecast of Rainfall and Extreme Events in East China Based on Deep Learning

Abstract The extended-range forecast with a lead time of 10–30 days is the gap between weather (<10 days) and climate (>30 days) predictions. Improving the forecast skill of extreme weather events at the extended range is crucial for risk management of disastrous events. In this study, three deep learning (DL) models based on the methods of convolutional neural networks and gate recurrent units are constructed to predict the rainfall anomalies and associated extreme events in East China at lead times of 1–6 pentads. All DL models show skillful prediction of the temporal variation of rainfall anomalies (in terms of temporal correlation coefficient skill) over most regions in East China beyond 4 pentads, outperforming the dynamical models from the China Meteorological Administration (CMA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The spatial distribution of the rainfall anomalies is also better predicted by the DL models than the dynamical models; and the DL models show higher pattern correlation coefficients than the dynamical models at lead times of 3–6 pentads. The higher skill of DL models in predicting the rainfall anomalies will help to improve the accuracy of extreme-event predictions. The Heidke skill scores of the extreme rainfall event forecast performed by the DL models are also superior to those of the dynamical models at a lead time beyond about 4 pentads. Heat map analysis for the DL models shows that the predictability sources are mainly the large-scale factors modulating the East Asian monsoon rainfall. Significance Statement Improving the forecast skill for extreme weather events at the extended range (10–30 days in advance), particularly over populated regions such as East China, is crucial for risk management. This study aims to develop skillful models of the rainfall anomalies and associated extreme heavy rainfall events using deep learning techniques. The models constructed here benefit from the capability of deep learning to identify the predictability sources of rainfall variability, and outperform the current operational models, including the ECMWF and the CMA models, at forecast lead times beyond 3–4 pentads. These results reveal the promising application prospect of deep learning techniques in the extended-range forecast.

A Methodological Framework for Economic Evaluation of Operational Response to Vector-Borne Diseases Based on Early Warning Systems.

Despite significant advances in improving the predictive models for vector-borne diseases, only a few countries have integrated an early warning system (EWS) with predictive and response capabilities into their disease surveillance systems. The limited understanding of forecast performance and uncertainties by decision-makers is one of the primary factors that precludes its operationalization in preparedness and response planning. Further, predictive models exhibit a decrease in forecast skill with longer lead times, a trade-off between forecast accuracy and timeliness and effectiveness of action. This study presents a methodological framework to evaluate the economic value of EWS-triggered responses from the health system perspective. Assuming an operational EWS in place, the framework makes explicit the trade-offs between forecast accuracy, timeliness of action, effectiveness of response, and costs, and uses the net benefit analysis, which measures the benefits of taking action minus the associated costs. Uncertainty in disease forecasts and other parameters is accounted for through probabilistic sensitivity analysis. The output is the probability distribution of the net benefit estimates at given forecast lead times. A non-negative net benefit and the probability of yielding such are considered a general signal that the EWS-triggered response at a given lead time is economically viable. In summary, the proposed framework translates uncertainties associated with disease forecasts and other parameters into decision uncertainty by quantifying the economic risk associated with operational response to vector-borne disease events of potential importance predicted by an EWS. The goal is to facilitate a more informed and transparent public health decision-making under uncertainty.

Contrasting El Niño–La Niña Predictability and Prediction Skill in 2-Year Reforecasts of the Twentieth Century

Abstract Despite the growing demand for long-range ENSO predictions beyond 1 year, quantifying the skill at these lead times remains limited. This is partly due to inadequate long records of seasonal reforecasts that make skill estimates of irregular ENSO events quite challenging. Here, we investigate ENSO predictability and the dependency of prediction skill on the ENSO cycle using 110 years of 24-month-long 10-member ensemble reforecasts from ECMWF’s coupled model (SEAS5-20C) initialized on 1 November and 1 May during 1901–2010. Results show that Niño-3.4 SST can be skillfully predicted up to ∼18 lead months when initialized on 1 November, but skill drops at ∼12 lead months for May starts that encounter the boreal spring predictability barrier in year 2. The skill beyond the first year is highly conditioned to the phase of ENSO: Forecasts initialized at peak El Niño are more skillful in year 2 than those initialized at peak La Niña, with the transition to La Niña being more predictable than to El Niño. This asymmetry is related to the subsurface initial conditions in the western equatorial Pacific: peak El Niño states evolving into La Niña are associated with strong upper-ocean heat discharge of the western Pacific, the memory of which stays beyond 1 year. In contrast, the western Pacific recharged state associated with La Niña is usually weaker and shorter-lived, being a weaker preconditioner for subsequent El Niño, the year after. High prediction skill of ENSO events beyond 1 year provides motivation for extending the lead time of operational seasonal forecasts up to 2 years.

An Analysis of Prediction Skill of Heat Waves Over India Using TIGGE Ensemble Forecasts

AbstractHeat wave has become a great concern for India in the recent years due to its disastrous impact on various sectors including health. Thus, accurate forecasts of heat wave events well in advance are required for preparing adequate mitigation strategies. The present study assesses the prediction skill of numerical weather prediction models in The Observing system Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) experiments for predicting heat waves over India up to 7 days in advance. The models considered for this analysis are; the European Centre for Medium‐Range Weather Forecasts (ECMWF), the UK Met Office (UKMO) and National Centre for Environmental Prediction (NCEP). The model forecast verifications have been carried out for the hot weather season (April–June) over India for the period of 2008–2013. Fourteen heat wave events were identified during the study period using gridded daily maximum temperature (Tmax). The study reveals that the spatial distribution of maximum Temperature is well predicted by the TIGGE models for a forecast lead time of 1–7 days. The analysis suggested that the magnitude of heat wave events, even with a 7 days lead time, can be correctly predicted by more than 80% of ensemble members in all the TIGGE models. The prediction skill of the maximum temperatures over heat wave prone area during heat wave events is higher for ECMWF, then UKMO and NCEP models. The forecast verification analysis thus indicates that the TIGGE models are able to provide early warnings of heat waves with at least 5 days lead time.

Wave forecast in the Atlantic Ocean using a double-stage ConvLSTM network

Accurate forecasting of ocean waves is of great importance to the safety of marine transportation. Despite wave forecasts having been improved, the current level of prediction skill is still far from satisfactory. Here, the authors propose a new physically informed deep learning model, named Double-stage ConvLSTM (D-ConvLSTM), to improve wave forecasts in the Atlantic Ocean. The waves in the next three consecutive days are predicted by feeding the deep learning model with the observed wave conditions in the preceding two days and the simultaneous ECMWF Reanalysis v5 (ERA5) wind forcing during the forecast period. The prediction skill of the d-ConvLSTM model was compared with that of two other forecasting methods—namely, the wave persistence forecast and the original ConvLSTM model. The results showed an increasing prediction error with the forecast lead time when the forecasts were evaluated using ERA5 reanalysis data. The d-ConvLSTM model outperformed the other two models in terms of wave prediction accuracy, with a root-mean-square error of lower than 0.4 m and an anomaly correlation coefficient skill of ∼0.80 at lead times of up to three days. In addition, a similar prediction was generated when the wind forcing was replaced by the IFS forecasted wind, suggesting that the d-ConvLSTM model is comparable to the Wave Model of European Centre for Medium-Range Weather Forecasts (ECMWF-WAM), but more economical and time-saving.摘要海浪预报对海上运输安全至关重要. 本研究提出了一种涵盖物理信息的深度学习模型Double-stage ConvLSTM (D-ConvLSTM) 以改进大西洋的海浪预报. 将D-ConvLSTM模型与海浪持续性预测和原始ConvLSTM模型的预测技巧进行对比. 结果表明, 预测误差随着预测时长的增加而增加. D-ConvLSTM模型在预测准确度方面优于前二者, 且第三天预测的均方根误差低于0.4 m, 距平相关系数约在0.8. 此外, 当使用IFS预测风替代再分析风时, 能够产生相似的预测效果. 这表明D-ConvLSTM模型的预测能力能够与ECMWF-WAM模式相当, 且更节省计算资源和时间.

Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System

Abstract. Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorologically relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks to months is an area of active research and development. NASA's Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1–3-month lead times in the HMA region, including a portion of the Indian subcontinent, during the retrospective forecast period, 1981–2016. To assess forecast skill, we evaluate 2 m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to the similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from an anomaly correlation of Ranom=0.18 for precipitation to Ranom=0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are evaluated against independent observations; results for the 1-month forecast skill range from Ranom=0.13 for snow water equivalent to Ranom=0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system's ability to forecast HMA hydrometeorology.

Filter Likelihood as an Observation-Based Verification Metric in Ensemble Forecasting

In numerical weather prediction (NWP), ensemble forecasting aims to quantify the flow-dependent forecast uncertainty. The focus here is on observation-based verification of the reliability of ensemble forecasting systems. In particular, at short forecast lead times, forecast errors tend to be relatively small compared to observation errors and hence it is very important that the verification metric also accounts for observational uncertainties. This paper studies the so-called <em>filter likelihood score</em> which is deep-rooted in Bayesian estimation theory and fits naturally to the filtering setup of NWP. The filter likelihood score considers observation errors, ensemble mean skill, and ensemble spread in one metric. Importantly, it can be made multivariate and effortlessly expanded to simultaneous verification against all observation types through the observation operators contained in the parental data assimilation scheme. Here observations from the global radiosonde network and satellites (AMSU-A channel 5) are included in the verification of OpenIFS-based ensemble forecasts using different types of initial state perturbations. Our results show that the filter likelihood score is sensitive to the ensemble prediction system quality and compares consistently with other verification metrics such as the relationships between ensemble spread and ensemble mean forecast error, and Dawid-Sebastiani score. Our conclusion is that the filter likelihood score provides a very well-behaving verification metric, that can be made truly multivariate by including covariances, for ensemble prediction systems with a strong foundation in estimation theory.

Scale-Dependent Verification of the OU MAP Convection Allowing Ensemble Initialized with Multi-Scale and Large-Scale Perturbations during the 2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiment

Given the large range of resolvable space and time scales in large-domain convection-allowing for ensemble forecasts, there is a need to better understand optimal initial-condition perturbation strategies to sample the forecast uncertainty across these space and time scales. This study investigates two initial-condition perturbation strategies for CONUS-domain ensemble forecasts that extend into the two-day forecast lead time using traditional and object-based verification methods. Initial conditions are perturbed either by downscaling perturbations from a coarser resolution ensemble (i.e., LARGE) or by adopting the analysis perturbations from a convective-scale, EnKF system (i.e., MULTI). It was found that MULTI had more ensemble spread than LARGE across all scales initially, while LARGE’s perturbation energy surpassed that of MULTI after 3 h and continued to maintain a surplus over MULTI for the rest of the 36h forecast period. Impacts on forecast bias were mixed, depending on the forecast lead time and forecast threshold. However, MULTI was found to be significantly more skillful than LARGE at early forecast hours for the meso-gamma and meso-beta scales (1–9h), which is a result of a larger and better-sampled ensemble spread at these scales. Despite having a smaller ensemble spread, MULTI was also significantly more skillful than LARGE on the meso-alpha scale during the 20–24h period due to a better spread-skill relation. MULTI’s performance on the meso-alpha scale was slightly worse than LARGE’s performance during the 6–12h period, as LARGE’s ensemble spread surpassed that of MULTI. The advantages of each method for different forecast aspects suggest that the optimal perturbation strategy may require a combination of both the MULTI and LARGE techniques for perturbing initial conditions in a large-domain, convection-allowing ensemble.

Towards a pan-European coastal flood awareness system: Skill of extreme sea-level forecasts from the Copernicus Marine Service

European coasts are regularly exposed to severe storms that trigger extreme water-level conditions, leading to coastal flooding and erosion. Early Warning Systems (EWS) are important tools for the increased preparedness and response against coastal flood events, hence greatly reducing associated risks. With this objective, a proof-of-concept for a European Coastal Flood Awareness System (ECFAS) was developed in the framework of the H2020 ECFAS project, which capitalizes on the Copernicus products. In this context, this manuscript evaluates for the first time the capability of the current Copernicus Marine operational ocean models to forecast extreme coastal water levels and hence to feed coastal flood awareness applications at European scale. A methodology is developed to focus the assessment on storm-driven extreme sea level events (EEs) from tide-gauge records. For the detected EEs, the event peak representation is validated, and the impact of forecast lead time is evaluated. Results show satisfactory performance but a general underprediction of peak magnitudes of 10% for water levels and 18% for surges across the detected EEs. In average, the models are capable of independently flagging 76% of the observed EEs. Forecasts show limited lead time impact up to a 4-day lead time, demonstrating the suitability of the systems for early warning applications. Finally, by separating the surge and tidal contributions to the extremes, the potential sources of the prediction misfits are discussed and consequent recommendations for the evolution of the Copernicus Marine Service forecasting models towards coastal flooding applications are provided.

Monthly extended ocean predictions based on a convolutional neural network via the transfer learning method

Sea surface temperature anomalies (SSTAs) and sea surface height anomalies (SSHAs) are indispensable parts of scientific research, such as mesoscale eddy, current, ocean-atmosphere interaction and so on. Nowadays, extended-range predictions of ocean dynamics, especially in SSTA and SSHA, can provide daily prediction services in the range of 30 days, which bridges the gap between synoptic-scale weather forecasts and monthly average scale climate predictions. However, the forecast efficiency of extended range remains problematic. With the development of ocean reanalysis and satellite remote sensing products, large amounts datasets provide an unprecedented opportunity to use big data for the extended range prediction of ocean dynamics. In this study, a hybrid model, combing convolutional neural network (CNN) model with transfer learning (TL), was established to predict SSTA and SSHA at monthly scales, which makes full use of these data resources that arise from delayed gridding reanalysis products and real-time satellite remote sensing observations. The proposed model, where both ocean and atmosphere reanalysis datasets serve as the pretraining dataset and the satellite remote sensing observations are employed for fine-tuning based on the transfer learning (TL) method, can effectively capture the evolving spatial characteristics of SSTAs and SSHAs with low prediction errors over the 30 days range. When the forecast lead time is 30 days, the root means square errors for the SSTAs and SSHAs model results are 0.32°C and 0.027 m in the South China Sea, respectively, indicating that this model has not only satisfactory prediction performance but also offers great potential for practical operational applications in improving the skill of extended-range predictions.

HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic

Abstract. We propose a new deep-learning architecture HIDRA2 for sea level and storm tide modeling, which is extremely fast to train and apply and outperforms both our previous network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. The architecture of HIDRA2 employs novel atmospheric, tidal and sea surface height (SSH) feature encoders as well as a novel feature fusion and SSH regression block. HIDRA2 was trained on surface wind and pressure fields from a single member of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric ensemble and on Koper tide gauge observations. An extensive ablation study was performed to estimate the individual importance of input encoders and data streams. Compared to HIDRA1, the overall mean absolute forecast error is reduced by 13 %, while in storm events it is lower by an even larger margin of 25 %. Consistent superior performance over HIDRA1 as well as over general circulation models is observed in both tails of the sea level distribution: low tail forecasting is relevant for marine traffic scheduling to ports of the northern Adriatic, while high tail accuracy helps coastal flood response. Power spectrum analysis indicates that HIDRA2 most accurately represents the energy density peak centered on the ground state sea surface eigenmode (seiche) and comes a close second to SCHISM in the energy band of the first excited eigenmode. To assign model errors to specific frequency bands covering diurnal and semi-diurnal tides and the two lowest basin seiches, spectral decomposition of sea levels during several historic storms is performed. HIDRA2 accurately predicts amplitudes and temporal phases of the Adriatic basin seiches, which is an important forecasting benefit due to the high sensitivity of the Adriatic storm tide level to the temporal lag between peak tide and peak seiche.

Self-optimization of training dataset improves forecasting of cyanobacterial bloom by machine learning

Data-driven model (DDM) prediction of aquatic ecological responses, such as cyanobacterial harmful algal blooms (CyanoHABs), is critically influenced by the choice of training dataset. However, a systematic method to choose the optimal training dataset considering data history has not yet been developed. Providing a comprehensive procedure with self-based optimal training dataset-selecting algorithm would self-improve the DDM performance. In this study, a novel algorithm was developed to self-generate possible training dataset candidates from the available input and output variable data and self-choose the optimal training dataset that maximizes CyanoHAB forecasting performance. Nine years of meteorological and water quality data (input) and CyanoHAB data (output) from a site on the Nakdong River, South Korea, were acquired and pretreated via an automated process. An artificial neural network (ANN) was chosen from among the DDM candidates by first-cut training and validation using the entire collected dataset. Optimal training datasets for the ANN were self-selected from among the possible self-generated training datasets by systematically simulating the performance in response to 46 periods and 40 sizes (number of data elements) of the generated training datasets. The best-performing models were screened to identify the candidate models. The best performance corresponded to 6–7 years of training data (∼18 % lower error) for forecasting 1–28 d ahead (1–28 d of forecasting lead time (FLT)). After the hyperparameters of the screened model candidates were fine-tuned, the best-performing model (7 years of data with 14 d FLT) was self-determined by comparing the forecasts with unseen CyanoHAB events. The self-determined model could reasonably predict CyanoHABs occurring in Korean waters (cyanobacteria cells/mL ≥ 1000). Thus, our proposed method of self-optimizing the training dataset effectively improved the predictive accuracy and operational efficiency of the DDM prediction of CyanoHAB.

ПОЯВИ ЛЬОДОВИХ ЯВИЩ НА ВОДОСХОВИЩАХ ДНІПРОВСЬКОГО КАСКАДУ ТА МОЖЛИВОСТІ ЇХ ДОВГОСТРОКОВОГО ПРОГНОЗУВАННЯ

Long-term forecasting of the appearance dates of ice phenomena on the Dnipro Cascade reservoirs, especially with the forecast lead time at least 1 month, has important practical and scientific significance. So, it is on the basis of this characteristic of the ice regime that the terms and depths of autumn operation of each of the six Dnipro reservoirs are determined. Along with this, there are no methods for long-term forecasting of the appearance date of ice phenomena on the Dnipro Cascade reservoirs. Obviously, this is due to the fact that the problem of developing reliable long-term forecasts of the ice regime of water bodies is one of the most difficult in hydrometeorology, which has not yet received a fully satisfactory solution. The objective of this of the research is to find connections between quantitative indicators of atmospheric processes in the North Atlantic and the appearance dates of ice phenomena on the Dnipro Cascade reservoirs, and to evaluate the possibilities of using the obtained results for operational long-term forecasting. In long-term forecasting of ice regime is used the statistical, correlational, regression analysis as well as the atmospheric indicators as predictors. Therefore, the minimum atmospheric pressure in the center of the Icelandic minimum, the North Atlantic Oscillation (NAO) pattern and the Scandinavian Oscillation (SCAND) pattern were used in the research. These indicators characterize the atmospheric processes of the North Atlantic. Materials of the Department of Meteorological Forecasts of the Ukrainian Hydrometeorological Center (Kyiv) and the National Oceanic &amp; Atmospheric Administration USA (archival synoptic maps, the values of NAO and SCAND patterns) were used as initial data. The obtained results show that such indicators of the atmospheric processes of the North Atlantic as the minimum atmospheric pressure in the center of the Icelandic minimum, NAO and SCAND patterns undoubtedly have a fairly significant influence on the formation of the appearance dates of ice phenomena on the Dnipro Cascade reservoirs, but such an influence is not predominant and such indicators can only be partially used as predictors for long-term forecasting. Therefore, to obtain more acceptable results, it is necessary to expand the search for predictors of atmospheric processes both in space and time.

A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic Supercell

AbstractEnsemble‐based convective‐scale radar data assimilation commonly suffers from an underdispersive background ensemble. This study introduces a multivariate additive‐inflation method to address such deficiency. The multivariate additive inflation (AI) approach generates coherent random perturbations drawn from a newly constructed convective‐scale static background error covariance matrix for all state variables including hydrometeors and vertical velocity. This method is compared with a previously proposed univariate AI approach, which perturbs each variable individually without cross‐variable coherency. Comparisons are performed on the analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell. Within assimilation cycles, the multivariate approach is more efficient in increasing reflectivity spread and thus has a reduced spinup time than the univariate approach; the additional inclusion of hydrometeors and vertical velocity results in more background spread for both reflectivity and radial velocity. Significant differences among AI experiments also exist in the subsequent forecasts and are more pronounced for the forecasts initialized from the earlier assimilation cycles. The multivariate approach yields better forecasts of low‐level rotation, reflectivity distributions, and storm maintenance for most lead times. The additional inclusion of hydrometeor and vertical velocity in the multivariate method is beneficial in forecasts. Conversely, the additional inclusion of hydrometeor and vertical velocity in the univariate method poses negative impacts for the majority of forecast lead times.

Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?

Abstract The development of machine learning (ML) techniques enables data-driven parameterizations, which have been investigated in many recent studies. Some investigations suggest that a priori-trained ML models exhibit satisfying accuracy during training but poor performance when coupled to dynamical cores and tested. Here we use the evolution of the barotropic vorticity equation (BVE) with periodically reinforced shear instability as a prototype problem to develop and evaluate a model-consistent training strategy, which employs a numerical solver supporting automatic differentiation and includes the solver in the loss function for training ML-based subgrid-scale (SGS) turbulence models. This approach enables the interaction between the dynamical core and the ML-based parameterization during the model training phase. The BVE model was run at low, high, and ultrahigh (truth) resolutions. Our training dataset contains only a short period of coarsened high-resolution simulations. However, given initial conditions long after the training dataset time, the trained SGS model can still significantly increase the effective lead time of the BVE model running at the low resolution by up to 50% relative to the BVE simulation without an SGS model. We also tested using a covariance matrix to normalize the loss function and found it can notably boost the performance of the ML parameterization. The SGS model’s performance is further improved by conducting transfer learning using a limited number of discontinuous observations, increasing the forecast lead-time improvement to 73%. This study demonstrates a potential pathway to using machine learning to enhance the prediction skills of our climate and weather models. Significance Statement Numerical weather prediction is performed at limited resolution for computational feasibility, and the schemes to estimate unresolved processes are called parameterization. We propose a strategy to develop better deep learning–based parameterization in which an automatic differentiable numerical solver is employed as the dynamic core and interacts with the parameterization scheme during its training. Such a numerical solver enables consistent deep learning, because the parameterization is trained with a direct target of making the numerical model (dynamic core and parameterization together) forecast match observations as much as possible. We demonstrate the feasibility and effectiveness of such a strategy using a surrogate model and advocate that such machine learning–enabled numerical models provide a promising pathway to developing next-generation weather forecast and climate models.

Geomagnetic storm forecasting from solar coronal holes

ABSTRACT Coronal holes (CHs) are the source of high-speed streams (HSSs) in the solar wind, whose interaction with the slow solar wind creates corotating interaction regions (CIRs) in the heliosphere. Whenever the CIRs hit the Earth, they can cause geomagnetic storms. We develop a method to predict the strength of CIR/HSS-driven geomagnetic storms directly from solar observations using the CH areas and associated magnetic field polarity. First, we build a data set comprising the properties of CHs on the Sun, the associated HSSs, CIRs, and orientation of the interplanetary magnetic field (IMF) at L1, and the strength of the associated geomagnetic storms by the geomagnetic indices Dst and Kp. Then, we predict the Dst and Kp indices using a Gaussian process model, which accounts for the annual variation of the orientation of Earth’s magnetic field axis. We demonstrate that the polarity of the IMF at L1 associated with CIRs is preserved in around 83 per cent of cases when compared to the polarity of their CH sources. Testing our model over the period 2010–2020, we obtained a correlation coefficient between the predicted and observed Dst index of R = 0.63/0.73, and Kp index of R = 0.65/0.67, for HSSs having a polarity towards/away from the Sun. These findings demonstrate the possibility of predicting CIR/HSS-driven geomagnetic storms directly from solar observations and extending the forecasting lead time up to several days, which is relevant for enhancing space weather predictions.

Strategic bidding for a hydro-wind-photovoltaic hybrid system considering the profit beyond forecast time

Hydro-wind-photovoltaic hybrid systems gain profit by bidding in the forecast lead-time. However, the literature focuses on bidding strategy to maximize current profits, while the future utilities (beyond forecast time) for hybrid systems are generally neglected. To address the research gap, the study proposed an integrated bidding strategy for a hydro-wind-photovoltaic hybrid system with a trade-off between current profits and future utilities considered. Steps are as follows: (1) ensemble forecasts are used within the forecast lead-time, within historical data input as stochastic scenarios beyond forecast time, (2) functional forms between the profit beyond forecast time and the reservoir carryover storage, expected price, inflow, wind and photovoltaic power are fitted, and (3) a two-layer nested bidding model is to maximize the total-profit. With China's Sichuan province as the market, the Jinping-I hybrid system was selected as the case study. Results show that: (1) profits beyond forecast time can be quantified as a linear function with R-squared for 10-day above 0.6, and (2) the optimal bidding strategy raises the profit by 0.45% while reduces water-consumption by 10.3% compared with conventional strategy. The framework is effective in gaining more profit with lower risk for hybrid systems.

Quantifying the evolution of ensemble water temperature forecasts as a function of weather forecast lead-time: case study on the Nechako River watershed

Producing and improving hydrological and hydrodynamic forecasts while accounting for uncertainty through a probabilistic approach is useful in various applications, such as for water temperature forecasting. To produce such ensembles, probabilistic meteorological forecasts can be fed into hydrological and water temperature models for different lead-times. This study aims to gauge the impact of the meteorological forecast quality on the accuracy and reliability of water temperature forecast ensembles generated through the HEC-RAS process-based hydrothermal model. The Nechako River, a managed river system in British Columbia, Canada, is used in this case study. The thermal forecasts were generated and evaluated from 2017 to 2020. The tested hypothesis is that improvements in the meteorological forecasts would result in reducing the uncertainty and improving the accuracy of the water temperature forecast ensembles at various lead-times. The results of this study show that the thermal forecasts were indeed improved in terms of their sharpness and their individual accuracy, but no significant impacts were noted over the accuracy of the ensembles. Reliability was also investigated, and it was revealed that water temperature forecast ensembles were initially under-dispersed over the Nechako River, and that this issue was exacerbated when the HEC-RAS model was forced with better quality ensemble weather forecasts. The presence of lakes along the river and the meteorological forecast’s reliability are considered and discussed as causes for this issue. Overall, it was concluded that a reduction of the meteorological inputs’ uncertainty did not improve the uncertainty representation of water temperature forecasts for this system.

Enhancing Tidal Wave Predictions for the Estuary of the Nakdong River Using a Fixed-Lag Smoother

The prediction of tidal waves is essential for improving not only our understanding of the hydrological cycle at the boundary between the land and ocean but also energy production in coastal areas. As tidal waves are affected by various factors, such as astronomical, meteorological, and hydrological effects, the prediction of tidal waves in estuaries remains uncertain. In this study, we present a novel method that can be used to improve short-term tidal wave prediction using a fixed-lag smoother based on sequential data assimilation (DA). The proposed method was implemented for tidal wave predictions of the estuary of the Nakdong River. As a result, the prediction accuracy was improved by 63.9% through DA and calibration using regression. Although the accuracy of the DA diminished with the increasing forecast lead times, the 1 h lead forecast based on DA still showed a 44.4% improvement compared to the open loop without DA. Moreover, the optimal conditions for the fixed-lag smoother were analyzed in terms of the order of the smoothing function and the length of the assimilation window and forecast leads time. It was suggested that the optimal DA configuration could be obtained with the 8th-order polynomial as the smoothing function using past and future DA assimilation windows assimilated 6 h or longer.

Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China

The 2-m temperature is one of the important meteorological elements, and improving the accuracy of medium- and long-term forecasts of the 2-m temperature is important. The similarity forecasting method is widely used as a calibration technique in the statistical postprocessing of numerical weather prediction (NWP). In this study, the analogue ensemble averaging method is used to correct the deterministic forecast of the 2-m temperature with a forecast lead time from 180 h to 348 h using the CMA-GEPS model. The bias, mean absolute error (MAE), and root mean square error (RMSE) are used as the evaluation metrics. In comparison with NWP, the systematic error of the model for 2-m temperature is effectively reduced during each forecast period when using the analogue ensemble averaging method. In addition, the differences in forecast errors between regions are reduced, and the accuracy of 2-m temperature forecasts over complex terrain, especially in Southwest China, Northwest China, and North China, is improved using this method. In the future, there is certainly potential to apply the analogue ensemble averaging method to the bias correction of medium- and long-term forecasts of more meteorological elements.