Operational Forecasting System Research Articles

Evaluation of the Forecast Performance for Week-2 Winter Surface Air Temperature from the Model for Prediction Across Scales–Atmosphere (MPAS-A)

Abstract The forecast skill for week-2 wintertime surface air temperature (SAT) over the Northern Hemisphere by the Model for Prediction Across Scales–Atmosphere (MPAS-A) is evaluated and compared with operational forecast systems that participate in the Subseasonal to Seasonal Prediction project (S2S). An intercomparison of the MPAS against the China Meteorological Administration (CMA) model and the European Centre for Medium-Range Weather Forecasts (ECMWF) model was performed using 10-yr reforecasts. Comparing the forecast skill for SAT and atmospheric circulation anomalies at a lead of 2 weeks among the three models, the MPAS shows skill lower than the ECMWF model but higher than the CMA model. The gap in skills between the MPAS model and CMA model is not as large as that between the ECMWF model and MPAS model. Additionally, an intercomparison of the MPAS model against 10 S2S models is presented by using real-time forecasts since 2016 stored in the S2S database. The results show that the MPAS model has forecast skill for week-2 to week-4 wintertime SAT comparable to that in most S2S models. The MPAS model tends to be at an intermediate level compared to current operational forecast models.

Crisis Ocean Modelling with a Relocatable Operational Forecasting System and Its Application to the Lakshadweep Sea (Indian Ocean)

This study presents the Relocatable Operational Ocean Model (ReOMo), which can be used as a Crisis Ocean Modelling System in any region of the global ocean that is free from ice. ReOMo can be quickly nested into an existing coarser resolution (parent) model. The core components of ReOMo are the NEMO hydrodynamic model and Rose-Cylc workflow management software. The principal innovative feature of ReOMo is the use of the Nesting with Data Assimilation (NDA) algorithm, which is based on the model-to-model assimilation technique. The NDA utilises the full 3D set of field variables from the parent model rather than just the 2D boundary conditions. Therefore, ReOMo becomes physically aware of observations that have been assimilated and dynamically balanced in the external model. The NDA also reduces the spatial phase shift of ocean features known as the ‘double penalty effect’. In this study, ReOMo was implemented for the Lakshadweep Sea in the Indian Ocean at 1/20°, 1/60°, or 1/120° resolution with and without model-to-model data assimilation. ReOMo is computationally efficient, and it was validated against a number of observational data sets to show good skills with an additional benefit of having better resolution than the parent model.

Calibration of a Bayesian spatio-temporal ETAS model to the June 2000 South Iceland seismic sequence

SUMMARY The reliable forecasting of seismic sequences following a main shock has practical implications because effective post-event response is crucial in earthquake-stricken regions, aftershocks can progressively cause increased damage and compound economic losses. In the South Iceland Seismic Zone (SISZ), one of two large transform zones in Iceland where earthquake hazard is the highest, an intense seismic sequence took place during 17–24 June 2000, starting with a ${M}_{\rm{w}}$ 6.4 main shock on 17 June 2000, followed by another ${M}_{\rm{w}}$ 6.5 main shock four days later and on a different fault. Both earthquakes caused considerable damage and incurred heavy economic losses. They were immediately followed by intense aftershock activity on the causative faults and triggered earthquakes as far as 80 km away along the transform zone. To investigate the feasibility of forecasting the progression of such complex sequences, we calibrated a spatio-temporal epidemic-type aftershock sequence (ETAS) clustering model to the June 2000 seismic sequence in the framework of Bayesian statistics. Short-term seismicity forecasts were carried out for various forecasting intervals and compared with the observations, the first generated a few hours after the first main shock and followed by daily forecasts. The reliability of the early forecasts was seen to depend on the initial model parameters. By using an adaptive parameter inference approach where the posteriors from each preceding forecasting interval served as informative priors for the next, the fast convergence of the parametric values was ensured. As a result, the 16–84 percentile range of the forecasted number of events captured the actual number of observed events in all daily forecasts, and the model exhibited a strong spatial forecasting ability, even only a few hours after the main shock, and over all subsequent daily forecasts. We present the spatio-temporal ETAS parameters for the June 2000 sequence as ideal candidates of prior estimates for future operational earthquake forecasting of other Icelandic aftershock sequences. Past seismic sequences need to be analysed retrospectively to confirm the stability of the parameters of this study, effectively enable the application of the Bayesian ETAS model as an operational earthquake forecasting system for aftershocks in Iceland.

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

One of the well-known challenges of fog forecasting is the high spatio-temporal variability of fog. An ensemble forecast aims to capture this variability by representing the uncertainty in the initial/lateral boundary conditions (ICs/BCs) and model physics. The present study highlights a new operational Ensemble Forecast System (EFS) developed by the Indian Institute of Tropical Meteorology (IITM), Pune, to predict the fog over the Indo-Gangetic Plain (IGP) region using the visibility (Vis) diagnostic algorithm. The EFS framework comprises the WRF model with a 4 km horizontal resolution, initialized by 21 ICs/BCs. The advantages of probabilistic fog forecasting have been demonstrated by comparing control (CNTL) and ensemble-based fog forecasts. The forecast is verified using fog observations from the Indira Gandhi International (IGI) airport during the winter months of 2020–2021 and 2021–2022. The results show that with a probability threshold of 50%, the ensemble forecasts perform better than the CNTL forecasts. The skill scores of EFS are relatively promising, with a Hit Rate of 0.95 and a Critical Success Index of 0.55; additionally, the False Alarm Rate and Missing Rate are low, with values of 0.43 and 0.04, respectively. The EFS could correctly predict more fog events (37 out of 39) compared with the CNTL forecast (31 out of 39) and shows the potential skill. Furthermore, EFS has a substantially reduced error in predicting fog onset and dissipation (mean onset and dissipation error of 1 h each) compared to the CNTL forecasts.

Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application

Satellite observations play important roles in ocean operational forecasting systems, however, the direct assimilation of satellite observations cannot provide sufficient constraints on the model underwater structure. This study adopted the indirect assimilation method. First, we created a 3D temperature and salinity reconstruction model that took into account the advantage of the nonlinear regression of the generalized regression neural network with the fruit fly optimization (abbreviated as FOAGRNN). Compared with the reanalysis product and the WOA13 climatology data, the synthetic T/S (temperature and salinity) profiles had sufficient accuracy and could better describe the characteristics of mesoscale eddies. Then, the synthetic T/S profiles were assimilated into the Regional Ocean Model System (ROMS) using the Incremental Strong constraint 4D Variational (I4D-Var) data assimilation algorithm. The quantitative and qualitative analysis results indicated that compared with the direct assimilation of satellite observations, the root mean square errors (RMSEs) of temperature and salinity were reduced by 26.0% and 23.1% respectively by assimilating the synthetic T/S profiles. Furthermore, this method significantly improved the simulation effect of the model underwater structure, especially in the 300 m to 500 m water layer. Compared with the National Marine Data Center’s real-time analysis data, the machine learning-based assimilation system demonstrated a significant advantage in the simulation of underwater salinity structure, while showing a similar performance in the simulation of underwater temperature structure.

Implementation of Scale-Dependent Background-Error Covariance Localization in the Canadian Global Deterministic Prediction System

Abstract The approach of applying different amounts of horizontal localization to different ranges of background-error covariance horizontal scales as proposed by Buehner and Shlyaeva was recently implemented in the four-dimensional ensemble–variational (4DEnVar) data assimilation scheme of the global deterministic prediction system (GDPS) at Environment and Climate Change Canada operations. To maximize the benefits from this approach to reduce the sampling noise in the ensemble-derived background-error covariances, it was necessary to adopt a new weighting between the climatological and flow-dependent covariances that increases significantly the role of the latter. Thus, in December 2021 the GDPS became the first operational global deterministic medium-range weather forecasting system to rely completely on flow-dependent covariances in the troposphere and the lower stratosphere. The experiments that led to the adoption of these two related changes and their impacts on the forecasts up to 7 days for various regions of the globe during the boreal summer of 2019 and winter of 2020 are presented here. It is also illustrated that relying more on ensemble-derived covariances amplifies the positive impacts on the GDPS when the background ensemble generation strategy is improved.

Fully Dynamic High–Resolution Model for Dispersion of Icelandic Airborne Mineral Dust

Icelandic topsoil sediments, as confirmed by numerous scientific studies, represent the largest and the most important European source of mineral dust. Strong winds, connected with the intensive cyclonic circulation in the North Atlantic, induce intense emissions of mineral dust from local sources all year and carry away these fine aerosol particles for thousands of kilometers. Various impacts of airborne mineral dust particles on local air quality, human health, transportation, climate and marine ecosystems motivated us to design a fully dynamic coupled atmosphere–dust numerical modelling system in order to simulate, predict and quantify the Icelandic mineral dust process including: local measurements and source specification over Iceland. In this study, we used the Dust Regional Atmospheric Model (DREAM) with improved Icelandic high resolution dust source specification and implemented spatially variable particle size distribution, variable snow cover and soil wetness. Three case studies of intense short- and long-range transport were selected to evaluate the model performance. Results demonstrated the model’s capability to forecast major transport features, such as timing, and horizontal and vertical distribution of the processes. This modelling system can be used as an operational forecasting system, but also as a reliable tool for assessing climate and environmental Icelandic dust impacts.

High groundwater levels: Processes, consequences, and management

AbstractIn recent years, the issue of high groundwater levels has caught attention. Unfavorable consequences of high groundwater levels are especially damage to buildings, infrastructure, and the environment. Processes that lead to high groundwater levels are hydrological (heavy or extended rainfall and flood events), or anthropogenic (reduced groundwater extractions, interaction with sewer networks, hydraulic engineering measures, structural interventions in the water balance, and mining activities). Several different map products have been prepared for the information of inhabitants and for planning purposes, and also methods for damage and risk analysis related to high groundwater levels have been developed. Groundwater management measures and structural measures are available to reduce the risk related to high groundwater levels. An operational management system could be combined from existing components, but operational forecasting systems for high groundwater levels are—different to flood forecasting systems—not yet common practice. A better understanding of the processes and the development of integrated approaches for modeling, design, planning, forecasting, and warning, as well as improvement of interdisciplinary collaboration between different organizations, are recommendations for the future.This article is categorized under: Engineering Water > Engineering Water Water and Life > Conservation, Management, and Awareness Science of Water > Hydrological Processes Science of Water > Water Extremes

Short-term Bayesian ETAS spatiotemporal forecasting of the Ölfus 2008 earthquake sequence in Iceland

Strong earthquakes occurring frequently in south Iceland are always characterized by intense aftershock activity. The aftershock sequences cause progressive damages to structures and considerable psychological strain on inhabitants as the threat of upcoming large earthquakes looms. In this study, we apply a Bayesian epidemiological spatiotemporal aftershock sequence (ETAS) clustering model to forecast the aftershock sequence that commenced with the damaging 29 May 2008 Mw6.3 Ölfus earthquake. The forecast is carried out manually but in a framework of a provisional operational earthquake forecasting (OEF) system and based on an informative set of ETAS parameters derived from the strong June 2000 sequence in the region. We show that the short-term forecasts are unreliable at the early stages of the ongoing seismic sequence due to incomplete aftershocks collected to feed the Bayesian updating procedure of the ETAS model parameters, mainly during the first hours after the Ölfus mainshock. In contrast, issuing 24-h long forecasts, starting 6–8 h after the mainshock gives the best forecasting results. We allow the daily forecasts to adapt to the ongoing 2008 sequence using the adaptive prior estimation method, i.e., by assigning an informative prior distribution to ETAS parameters for the subsequent forecasting interval based on the preceding ETAS parameter posterior distributions. The parametric posteriors do not show narrow distributions, thus indicating inconsistent forecasting performance, both in stark contrast to the June 2000 sequence. Regardless, our results provide a quantitative estimate of the reliability of the forecasts which is a prerequisite for evaluating their usefulness to civil protection.

Flood forecasting with machine learning models in an operational framework

Abstract. Google's operational flood forecasting system was developed to provide accurate real-time flood warnings to agencies and the public with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded geographically. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. Machine learning is used for two of the subsystems. Stage forecasting is modeled with the long short-term memory (LSTM) networks and the linear models. Flood inundation is computed with the thresholding and the manifold models, where the former computes inundation extent and the latter computes both inundation extent and depth. The manifold model, presented here for the first time, provides a machine-learning alternative to hydraulic modeling of flood inundation. When evaluated on historical data, all models achieve sufficiently high-performance metrics for operational use. The LSTM showed higher skills than the linear model, while the thresholding and manifold models achieved similar performance metrics for modeling inundation extent. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area close to 470 000 km2, home to more than 350 000 000 people. More than 100 000 000 flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Current and future work on the system includes extending coverage to additional flood-prone locations and improving modeling capabilities and accuracy.

Testing the Feature Alignment Technique (FAT) in an Ensemble-Based Data Assimilation and Forecast System with Multiple-Storm Scenarios

Abstract Storm displacement errors can arise from a number of potential sources of error within a data assimilation (DA) and forecast system. Conversely, storm displacement errors can cause issues for storm-scale, ensemble-based systems using an ensemble Kalman filter (EnKF), such as NSSL’s Warn-on-Forecast System (WoFS). A previous study developed a fully grid-based feature alignment technique (FAT) to mitigate these phase errors and their impacts. However, that study developed and tested the FAT for single-storm cases. This study advances that work by implementing an object-based merging and matching technique into the FAT and tests the updated FAT in more complex scenarios of multiple storms. Ensemble-based experiments are conducted with and without the FAT for each of the scenarios. The experiments’ analyses and forecasts of storm-related fields are then evaluated using subjective and objective methods. Results from these idealized multiple-storm experiments continue to reveal the potential benefits of correcting storm displacement errors. For example, running the FAT even once can mitigate the “spinup” period experienced by the no-FAT experiments. The new results also show that running the FAT prior to every DA cycling step generally leads to more skillful forecasts at the smaller scales, especially in earlier-initialized forecasts. However, repeatedly running the FAT prior to every DA step can eventually lead to deterioration in analyses and forecasts. Potential solutions to this problem include using longer cycling intervals and running the FAT prior to DA less often. Additional ways to improve the FAT along with other results are presented and discussed. Significance Statement The purpose of this work is to explore the impact of correcting storm displacements on analyses and forecasts of storms using an ensemble-based data assimilation and forecast system in an idealized framework. Storm displacement errors are a common problem in current operational and experimental storm-scale forecast systems, so understanding their impact on these systems and providing a method to help mitigate them is important. Results from this study indicate that correcting storm displacement errors with the feature alignment technique can greatly improve analyses and forecasts in multiple-storm scenarios. Future work will focus on exploring the impact of correcting storm displacement errors in a real-data, storm-scale data assimilation and forecast system.

Real-Time Probabilistic Tropical Cyclone Forecasting in the Cloud

Abstract Despite improvements in predicting the track and intensity of tropical cyclones (TCs), these storms with major societal and economic impacts continue to pose challenges for statically provisioned computational resources. The number of active storms varies from day to day, leading to regular bursts of irregular computational loads atop an already busy production schedule for weather prediction centers. The emergence of high-resolution ensemble TC prediction to quantify the uncertainty in track and intensity exacerbates this problem by requiring multiple forecasts run for each storm, each representing a possible outcome. With more than a decade of progress in the literature describing research and real-time numerical weather prediction in the cloud, we set out to evaluate if the commercial cloud environment could cope with the unique demands of TC ensemble forecasts. We describe a demonstration using a high-performance computing environment within the Microsoft Azure cloud to test dynamic resource provisioning to address time-varying resource challenges. We deployed existing operational models, implemented a combination of vendor-provided and open-source tools to orchestrate the cycling production workflows, and developed techniques for automatic error handling to keep production on schedule with minimal operator intervention. Despite challenges, our production pipeline from data ingest, forecast integration, graphics generation, and dissemination via social media was able to produce real-time forecasts of storm track and intensity with product latencies commensurate with existing operational forecasting systems.

Short-Term Convection-Allowing Ensemble Precipitation Forecast Sensitivity to Resolution of Initial Condition Perturbations and Central Initial States

Abstract Nine sets of 36-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were produced over the conterminous United States for a 4-week period. These CAEs had identical configurations except for their initial conditions (ICs), which were constructed to isolate CAE forecast sensitivity to resolution of IC perturbations and central initial states about which IC perturbations were centered. The IC perturbations and central initial states were provided by limited-area ensemble Kalman filter (EnKF) analyses with both 15- and 3-km horizontal grid spacings, as well as from NCEP’s Global Forecast System (GFS) and Global Ensemble Forecast System. Given fixed-resolution IC perturbations, reducing horizontal grid spacing of central initial states improved ∼1–12-h precipitation forecasts. Conversely, for constant-resolution central initial states, reducing horizontal grid spacing of IC perturbations led to comparatively smaller short-term forecast improvements or none at all. Overall, all CAEs initially centered on 3-km EnKF mean analyses produced objectively better ∼1–12-h precipitation forecasts than CAEs initially centered on GFS or 15-km EnKF mean analyses regardless of IC perturbation resolution, strongly suggesting it is more important for central initial states to possess fine-scale structures than IC perturbations for short-term CAE forecasting applications, although fine-scale perturbations could potentially be critical for data assimilation purposes. These findings have important implications for future operational CAE forecast systems and suggest CAE IC development efforts focus on producing the best possible high-resolution deterministic analyses that can serve as central initial states for CAEs. Significance Statement Ensembles of weather model forecasts are composed of different “members” that, when combined, can produce probabilities that specific weather events will occur. Ensemble forecasts begin from specified atmospheric states, called initial conditions. For ensembles where initial conditions differ across members, the initial conditions can be viewed as a set of small perturbations added to a central state provided by a single model field. Our study suggests it is more important to increase horizontal resolution of the central state than resolution of the perturbations when initializing ensemble forecasts with 3-km horizontal grid spacing. These findings suggest a potential for computational savings and a streamlined process for improving high-resolution ensemble initial conditions.

An extraordinary dry season precipitation event in the subtropical Andes: Drivers, impacts and predictability

A major storm impacted the subtropical Andes during 28–31 January 2021 producing 4-days accumulated precipitation up to 100 mm over central-south Chile. These are high accumulations even for winter events but the storm occurred in the middle of the summer when precipitation is virtually absent, conferring it an extraordinary character. Similar storms have occurred only 2–3 times in the past century. The January 2021 event included periods of high rainfall intensity, hail and lighting, causing dozens of landslides and flash floods with the concomitant social impacts and economical losses. Here we examine the meteorological drivers of this storm at multiples scales, its climatological context, the associated surface impacts, and some aspects of its predictability.About a week before the storm development over central Chile, a large-scale perturbation in the central South Pacific set the stage for the formation of a zonal jet aloft and zonal atmospheric river (ZAR) that extended eastward until reaching the west coast of South America. The ZAR landfalled at 39°S and its subsequent northward displacement resulted in copious orographic precipitation over the Andes and adjacent lowlands, concomitant with a relatively warm environment during the first phase of the storm (28–29 January). During the second phase (30–31 January) the ZAR decayed rapidly but left behind significant amount of water vapor and the formation of a cut-off low (COL) in its poleward flank. The COL facilitated both advection of cyclonic vorticity and cold air at mid-levels, setting the environment for deep convection, intense rain showers, significant lightning activity, and hail.An assessment of the quantitative precipitation forecast (QPF) from the operational Global Forecast System (GFS) indicates that the model captured well the 96-h precipitation accumulation (28–31 January) in terms of timing and spatial extent. However, specific zones with the largest accumulations varied as a function of lead time. The more stable precipitation during the ZAR phase was better predicted than the convective precipitation during the COL phase. Proper dissemination of these forecast and recently established infrastructure contributed to ease the impact of this extraordinary event on the general population.

Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System

AbstractModel bias is one of the main obstacles to improved accuracy and reliability in numerical weather prediction conducted with state‐of‐the‐art atmospheric models. To deal with model bias, a modification of the standard four‐dimensional variational (4D‐Var) algorithm, called weak‐constraint 4D‐Var, has been developed where a forcing term is introduced into the model to correct for the bias that accumulates along the model trajectory. This approach reduced the temperature bias in the stratosphere by up to 50% and is implemented in the European Centre for Medium‐Range Weather Forecasts operational forecasting system. Despite different origins and applications, data assimilation (DA) and Deep Learning are both able to learn about the Earth system from observations. In this paper, a deep learning approach for model bias correction is developed using temperature retrievals from radio occultation (RO) measurements. Neural networks (NNs) require a large number of samples to properly capture the relationship between the temperature first‐guess trajectory and the model bias. As running the Integrated Forecasting System (IFS) DA system for extended periods of time with a fixed model version and at realistic resolutions is computationally very expensive, we have chosen to train, the initial NNs are trained using the ERA5 reanalysis before using transfer learning on 1 year of the current IFS model. Preliminary results show that convolutional NNs are adequate to estimate model bias from RO temperature retrievals. The different strengths and weaknesses of both deep learning and weak constraint 4D‐Var are discussed, highlighting the potential for each method to learn model biases effectively and adaptively.

Adding baroclinicity to a global operational model for forecasting total water level: Approach and impact

Operational flood forecast systems are typically based on barotropic models in order to limit computational cost. In this study, a computationally efficient way of adding spatial and temporal variations of water density (henceforth “adding baroclinicity”) to a global barotropic forecast system is described and evaluated. The approach is illustrated using a global high-resolution (1/12°) model with an optimized vertical grid of nine ocean levels. Temperature (T) and salinity (S) for each layer are weakly nudged to forecast fields provided by a coarser resolution (1/4°), data-assimilative ocean model. This adds realistic baroclinic variability with periods exceeding about 15 d to the 1/12° model while allowing the higher frequency variability to evolve freely. The computational cost of running the new model is only 10% higher than the cost of running the two-dimensional barotropic version of the model that forms the basis of the present total water level (TWL) forecast system. The value of adding baroclinicity is demonstrated across the sea level frequency spectrum using hourly observations of TWL made between October 20, 2019 and February 28, 2021 by a global distribution of 211 tide gauges. Large improvements in the tidal residuals (TWL-tide) are shown for periods exceeding 15 d. This is consistent with the known importance of baroclinic processes to sea level variability in this frequency range. For periods between 10 h and 15 d, baroclinicity also improves the predictions of extreme residuals by up to 15 cm, with the largest improvements occurring on narrow shelves. In terms of extremes, adding baroclinicity improves predictions of the three largest positive tidal residuals for 2020 at 89% of the tide gauges with the largest improvements reaching 42 cm. Similar results were found for the three largest negative residuals for each tide gauge record. The importance of modeling baroclinically-modified coastal trapped waves is illustrated for the west coast of North and South America.

Simplified Cross‐Validation in Principal Component Regression (PCR) and PCR‐Like Machine Learning for Water Supply Forecasting

AbstractCross‐validated principal component regression (PCR) is widely used in day‐to‐day operational forecasting systems for seasonal river runoff volume in western North America. Complexities are increasing in both predictor datasets (including climate‐science products) and in predictive models employed instead of linear regression within the PCR framework (including artificial intelligence), potentially complicating cross‐validation for model evaluation. We explored these issues with 300 modeling experiments on two high‐impact and hydroclimatically diverse basins in the western United States, the Truckee River (Sierra Nevada) and Rio Grande headwaters (southern Rockies), using five different PCR and PCR‐like machine learning models. The results suggest out‐of‐sample error is satisfactorily estimated by applying cross‐validation to only the final, supervised learning, step of PCR/PCR‐like procedures. The outcome facilitates streamlined algorithms and potentially reduced computational times for more complex emerging model architectures and datasets; provides reassurance around a possible inability to perform genuinely complete cross‐validation when predictors include certain complex and externally sourced data sources; and may reflect mitigation of overtraining by geophysical process‐informed model development protocols normally used during feature selection in operational water supply forecast (WSF). The results provide practical guidance helping support the design of next‐generation WSF models.

Combined Assimilation of Doppler Wind Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved Hurricane Prediction with the NCEP Regional HWRF System

Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement.

Pre-Seismic Temporal Integrated Anomalies from Multiparametric Remote Sensing Data

Pre-seismic anomalies have the potential to indicate imminent strong earthquakes in the short to medium terms. However, an improved understanding of the statistical significance between anomalies and earthquakes is required to develop operational forecasting systems. We developed a temporal integrated anomaly (TIA) method to obtain the temporal trends of multiparametric anomalies derived from the Atmospheric Infrared Sounder (AIRS) product before earthquakes. A total of 169 global earthquakes that occurred from 2006 to 2020 and had magnitudes of ≥7.0 and focal depths of ≤70 km were used to test this new method in a retrospective manner. In addition, 169 synthetic earthquakes were randomly generated to demonstrate the suppression capacity of the TIA method for false alarms. We identified four different TIA trends according to the temporal characteristics of positive and negative TIAs. Long-term correlation analyses show that the recognition ability was 12.4–28.4% higher for true earthquakes than for synthetic earthquakes (i.e., higher than that of a random guess). Incorporating 2–5 kinds of TIAs offered the best chance of recognizing imminent shocks, highlighting the importance of multiparameter anomalies. Although the TIA trend characteristics before the earthquakes were not unique, we identified certain unexplained pre-seismic phenomena within the remote sensing data. The results provide new insight into the relationships between pre-seismic anomalies and earthquakes; moreover, the recognition ability of the proposed approach exceeds that of random guessing.

Climate Change Fosters Competing Effects of Dynamics and Thermodynamics in Seasonal Predictability of Arctic Sea Ice

Abstract The fast decline of Arctic sea ice necessitates a stronger focus on understanding the Arctic sea ice predictability and developing advanced forecast methods for all seasons and for pan-Arctic and regional scales. In this study, the operational forecasting system combining an advanced eddy-permitting ocean–sea ice ensemble reanalysis ORAS5 and state-of-the-art seasonal model-based forecasting system SEAS5 is used to investigate effects of sea ice dynamics and thermodynamics on seasonal (growth-to-melt) Arctic sea ice predictability in 1993–2020. We demonstrate that thermodynamics (growth/melt) dominates the seasonal evolution of mean sea ice thickness at pan-Arctic and regional scales. The thermodynamics also dominates the seasonal predictability of sea ice thickness at pan-Arctic scale; however, at regional scales, the predictability is dominated by dynamics (advection), although the contribution from ice growth/melt remains perceptible. We show competing influences of sea ice dynamics and thermodynamics on the temporal change of ice thickness predictability from 1993–2006 to 2007–20. Over these decades, there was increasing predictability due to growth/melt, attributed to increased winter ocean heat flux in both Eurasian and Amerasian basins, and decreasing predictability due to advection. Our results demonstrate an increasing impact of advection on seasonal sea ice predictability as the region of interest becomes smaller, implying that correct modeling of sea ice drift is crucial for developing reliable regional sea ice predictions. This study delivers important information about sea ice predictability in the “new Arctic” conditions. It increases awareness regarding sea ice state and implementation of sea ice forecasts for various scientific and practical needs that depend on accurate seasonal sea ice forecasts.