An Experimental 1-km Warn-on-Forecast System for Hazardous Weather Events

This study compares real-time forecasts produced by the Warn-on-Forecast System (WoFS) and a hybrid ensemble and variational data assimilation and prediction system (WoF-Hybrid) for 31 events during 2021. Object-based verification is used to quantify and compare strengths and weaknesses of WoFS ensemble forecasts with 3-km horizontal grid spacing and WoF-Hybrid deterministic forecasts with 1.5-km horizontal grid spacing. The goal of such comparison is to provide evidence as to whether WoF-Hybrid has performance characteristics that complement or improve upon those of WoFS. Results indicate that both systems provide similar accuracy for timing and placement of thunderstorm objects identified using simulated reflectivity. WoF-Hybrid provides more accurate forecasts of updraft helicity tracks. Differences in forecast quality are case dependent; the largest difference in accuracy favoring WoF-Hybrid occurs in eight cases identified as “high-impact” by the quantity of National Weather Service Local Storm Reports, while WoFS performance is favored at short lead times for 10 “moderate-” and 13 “low-impact” events. WoF-Hybrid reflectivity objects are closer in size and location to observed objects. However, a higher thunderstorm overprediction bias is identified in WoF-Hybrid, particularly early in the forecast. Two severe weather events are selected for detailed investigation. In the case of 26 May, both systems had similar skill; however, for 10 December, WoF-Hybrid forecasts significantly outperformed WoFS forecasts. These results show improved performance for WoF-Hybrid over WoFS under certain regimes that warrants further investigation. To understand reasons for these differences will help incorporate higher-resolution modeling into Warn-on-Forecast systems. Significance Statement The NOAA Warn-on-Forecast (WoF) project uses advanced data assimilation for rapidly updating numerical weather prediction systems to provide forecasts of individual thunderstorms. Forecasts show promise for enabling greater warning lead time for some storms. The flagship Warn-on-Forecast System (WoFS) is a 36-member analysis and 18-member forecast system at 3-km grid spacing. The project also produced a single member system that employs variational analysis and produces a deterministic forecast at 1.5-km grid spacing (WoF-Hybrid). This study seeks to evaluate and compare the performance of WoFS and WoF-Hybrid for 31 severe weather events that occurred during 2021. Results found that WoF-Hybrid predicts storm rotation particularly well compared to WoFS, and several other strengths and limitations of both systems are identified. This research may help us understand the complementary nature of two systems and improve our ability to provide more reliable 0–6-h forecasts in the future.

The Warn-on-Forecast System (WoFS) is a regional, rapidly updating, ensemble data assimilation and prediction system designed to provide short-term probabilistic guidance of severe and hazardous weather, including individual thunderstorms. As with most convection-allowing modeling systems, WoFS occasionally produces forecasts of thunderstorms with storm motion biases, which can be caused by multiple sources of error within the data assimilation and forecast system. The storm motion biases lead to storm displacement errors during the forecasts resulting in increasingly worse forecasts. In this study, we investigate storm displacement errors in WoFS forecasts from cases in 2020–23 using an object-based technique in a novel way to define and match WoFS and Multi-Radar Multi-Sensor (MRMS) reflectivity objects. The storm displacement mean absolute errors and location biases are grouped together by various attributes, including year, lead time, ensemble member, MRMS relative storm age, 850–300-hPa mean wind, and MRMS object mean intensity. Results from this investigation reveal storm displacement errors in WoFS forecasts generally have an eastward bias, grow the fastest within the first hour after forecast initialization, and are the smallest 1–3 h after a thunderstorm has been assimilated. By understanding and characterizing the storm displacement errors, WoFS developers will be able to focus attention on possible error sources and preventative measures to further improve WoFS, and NWS forecasters will be able to mentally account for the storm displacements errors when issuing forecast and warning products. Significance Statement The Warn-on-Forecast System (WoFS) is designed to provide probabilistic guidance of severe and hazardous weather, including individual thunderstorms. While WoFS has been proven to be a successful forecast system, one issue that gets subjectively highlighted by users is the displacement errors in forecast storm locations, especially at later lead times. This study investigates these storm displacement errors in WoFS by exploring different attributes associated with the observed and forecast storms. In general, WoFS forecast storms tend to propagate too fast, resulting in eastward displacements. WoFS developers and NWS forecasters can use this knowledge to improve WoFS and operational forecasts, respectively.

Ensemble-based data assimilation (EDA) has been used for tropical cyclone (TC) analysis and prediction with some success. However, the TC position spread determines the structure of the TC-related background error covariance and affects the performance of EDA. With an idealized experiment and a real TC case study, it is demonstrated that observations in the core region cannot be optimally assimilated when the TC position spread is large. To minimize the negative impact from large position uncertainty, a TC-centered EDA approach is implemented in the Weather Research and Forecasting (WRF) Model–local ensemble transform Kalman filter (WRF-LETKF) assimilation system. The impact of TC-centered EDA on TC analysis and prediction of Typhoon Fanapi (2010) is evaluated. Using WRF Model nested grids with 4-km grid spacing in the innermost domain, the focus is on EDA using dropsonde data from the Impact of Typhoons on the Ocean in the Pacific field campaign. The results show that the TC structure in the background mean state is improved and that unrealistically large ensemble spread can be alleviated. The characteristic horizontal scale in the background error covariance is smaller and narrower compared to those derived from the conventional EDA approach. Storm-scale corrections are improved using dropsonde data, which is more favorable for TC development. The analysis using the TC-centered EDA is in better agreement with independent observations. The improved analysis ameliorates model shock and improves the track forecast during the first 12 h and landfall at 72 h. The impact on intensity prediction is mixed with a better minimum sea level pressure and overestimated peak winds.

The initial condition accuracy is a major concern for tropical cyclone (TC) numerical forecast. The ensemble-based data assimilation techniques have shown great promise to initialize TC forecast. In addition to initial condition uncertainty, representing model errors (e.g. physics deficiencies) is another important issue in an ensemble forecasting system. To improve TC prediction from both deterministic and probabilistic standpoints, a Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) using an ensemble-based data assimilation scheme and a multi-physics approach based on Weather Research and Forecasting (WRF) model, has been developed in Shanghai Typhoon Institute and running realtime since 2015. Performance of TEDAPS in the prediction of track, intensity and associated disaster has been evaluated for the Western North Pacific TCs in the years of 2015–2018, and compared against the NCEP GEFS. TEDAPS produces markedly better intensity forecast by effectively reducing the weak biases and therefore the degree of underdispersion compared to GEFS. The errors of TEDAPS track forecasts are comparative with (slightly worse than) those of GEFS at longer (shorter) forecast leads. TEDAPS ensemble-mean exhibits advantage over deterministic forecast in track forecasts at long lead times, whereas this superiority is limited to typhoon or weaker TCs in intensity forecasts due to systematical underestimation. Four case-studies for three landfalling cyclones and one recurving cyclone demonstrate the capacities of TEDAPS in predicting some challenging TCs, as well as in capturing the forecast uncertainty and the potential threat from TC-associated hazards.

The vision of the National Oceanic and Atmospheric Administration's (NOAA) Warn‐on‐Forecast (WoF) research and development project is to provide very short‐term probabilistic model guidance products that will aid the ability of National Weather Service forecasters to issue probabilistic warnings of severe convective hazards with higher accuracy and longer lead times. The experimental Warn‐on‐Forecast System (WoFS), which is under development, is a frequently cycled, regional, convective‐scale, on‐demand, ensemble data assimilation and prediction system. The system assimilates thunderstorm observations to forecast the life cycle and associated hazards of individual convective storms. Most of the initial research effort, since the beginning of the WoF project in 2009, has been focused on tornadic events, which constitute one of the most violent and difficult to predict weather threats. However, the system emerging from this project has significant potential to help with prediction of other hazards as well: in particular, flash‐flood‐producing, convectively driven intense rainfall events. This study focuses on the application of WoFS in 0–6‐hr probabilistic rainfall forecasts of several flash‐flood‐producing heavy rainfall events during the spring and summer of 2015 and 2016. Results indicate that WoFS successfully initializes the convective storms in the ensemble. Visual inspections and probabilistic verification metrics show that WoFS predicts the intense rainfall that often leads to flash flooding at the correct location with higher accuracy in areal coverage and amount during the 0–3 hr forecast period than during the later 3–6 hr period. Overall results indicate that the frequently updated 0–6‐hr ensemble forecast from the WoFS has the potential to highlight areas where intense rainfall can result in flash flooding and increase near‐term situational awareness of flash flood threats.

Abstract. Data assimilation (DA) provides initial states of model runs by combining observational information and models. Ensemble-based DA methods that depend on the ensemble run of a model have been widely used. In response to the development of seamless prediction based on coupled models or even Earth system models, coupled DA is now in the mainstream of DA development. In this paper, we focus on the technical challenges in developing a coupled ensemble DA system, especially how to conveniently achieve efficient interaction between the ensemble of the coupled model and the DA methods. We first propose a new DA framework, DAFCC1 (Data Assimilation Framework based on C-Coupler2.0, version 1), for weakly coupled ensemble DA, which enables users to conveniently integrate a DA method into a model as a procedure that can be directly called by the model ensemble. DAFCC1 automatically and efficiently handles data exchanges between the model ensemble members and the DA method without global communications and does not require users to develop extra code for implementing the data exchange functionality. Based on DAFCC1, we then develop an example weakly coupled ensemble DA system by combining an ensemble DA system and a regional atmosphere–ocean–wave coupled model. This example DA system and our evaluations demonstrate the correctness of DAFCC1 in developing a weakly coupled ensemble DA system and the effectiveness in accelerating an offline DA system that uses disk files as the interfaces for the data exchange functionality.

Marine ecosystems are a vital component of the global carbon cycle. Our understanding of the cycle within the ocean relies on a combination of numerical models and satellite observations, which are combined through data assimilation (DA) methods. Here we developed a global ensemble DA system for marine ecosystem prediction using the NEMO-MEDUSA coupled ocean-biogeochemistry model and the Parallel Data Assimilation Framework. Unlike deterministic DA systems, the ensemble approach provides flow-dependent uncertainty estimates, improving the reliability of global marine ecosystem forecasts.We applied this ensemble system to investigate the assimilation of a novel phytoplankton carbon product derived from satellite ocean colour observations. Compared to the widely used phytoplankton chlorophyll product, the phytoplankton carbon product demonstrated improved global error statistics and facilitated significant adjustments in unobserved components of the marine ecosystems, including ocean carbon fluxes. Our findings also reveal a discrepancy in the ratio of phytoplankton constituents between observations and model forecasts, highlighting the potential benefits of assimilating different ocean color products to enhance marine ecosystem prediction beyond typical error metrics. These results show the advantage of novel ocean colour products for marine ecosystem modeling and understanding.

Major Tropical Cyclone (TC) events cause extensive damage in coastal regions of the western North Atlantic Basin. The short instrumental record leaves significant gaps in understanding long‐term trends in TC recurrence and intensity, creating uncertainty about future storm trends. Analysis of an ∼520‐year core record from Harvey Lake, located >80 km from the Atlantic coast in southwestern New Brunswick, Canada was carried out using: (a) end‐member mixing analysis (EMMA) of lake sediment grain size data to identify storm‐linked sedimentological processes; and (2) ITRAX X‐ray fluorescence (XRF) derived element/ratios (Fe, Ti, Ca/Sr, Zr/Rb, K/Rb, and Br + Cl/Al) associated with precipitation, weathering, catchment runoff, and air masses. Three derived end members were correlated to heavy rainfall events (EM01), spring freshet (EM02), and TCs (EM03). CONISS analysis of the EMMA and XRF core data resulted in recognition of four unique climatic zones distinguished by distinct distributions of TC and rainfall/weathering/runoff/and air masses. Numerous, major (EM01) rainfall events and (EM03) TC events characterized the basal core record during the early Little Ice Age (LIAa; Zone 1) phase, terminating at ∼1645. A near cessation of heavy rainfall and TC events differentiated the subsequent colder LIAb (∼1645–1825; Zone 2) and subsequent Little Ice Age Transition (∼1825–1895; Zone 3). A resurgence of major rainfall and TC events occurred during recovery from the LIA starting in ∼1895 (Zone 4). EMMA provides a robust tool for recognition of TC and major rainfall events, and greatly expands the potential for paleo‐storm activity research well inland from coastal regions.

A real-time GSI-based and ensemble-based data assimilation (DA) and forecast system was implemented at the University of Oklahoma during the 2015 Plains Elevated Convection at Night (PECAN) experiment. Extensive experiments on the configuration of the cycled DA and on both the DA and forecast physics ensembles were conducted using retrospective cases to optimize the system design for nocturnal convection. The impacts of radar DA between 1200 and 1300 UTC, as well as the frequency and number of DA cycles and the DA physics configuration, extend through the following night. Ten-minute cycling of radar DA leads to more skillful forecasts than both more and less frequent cycling. The Thompson microphysics scheme for DA better analyzes the effects of morning convection on environmental moisture than WSM6, which improves the convection forecast the following night. A multi-PBL configuration during DA leads to less skillful short-term forecasts than even a relatively poorly performing single-PBL scheme. Deterministic and ensemble forecast physics configurations are also evaluated. Thompson microphysics and the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL provide the most skillful nocturnal precipitation forecasts. A well thought out multiphysics configuration is shown to provide advantages over evenly distributing three of the best-performing microphysics and PBL schemes or a fixed MYNN/Thompson ensemble. This is shown using objective and subjective verification of precipitation and nonprecipitation variables, including convective initiation. Predictions of the low-level jet are sensitive to the PBL scheme, with the best scheme being variable and time dependent. These results guided the implementation and verification of a real-time ensemble DA and forecast system for PECAN.

Global Bayesian optimization (GBO) is a derivative-free optimization method that is used widely in the tech-industry to optimize objective functions that are expensive to evaluate, numerically or otherwise. We discuss the use of GBO in ensemble data assimilation (DA), where the goal is to update the state of a numerical model in view of noisy observations. Specifically, we consider three tasks: (i) the estimation of model parameters; (ii) the tuning of localization and inflation in ensemble DA; (iii) doing both, i.e. estimating model parameters while simultaneously tuning the localization and inflation of the ensemble DA. For all three tasks, the GBO works ‘offline’, i.e. a set of ‘training’ observations are used within GBO to determine appropriate model or localization/inflation parameters, which are subsequently deployed within an ensemble DA system. Because of the offline nature of the technique, GBO can easily be combined with existing DA systems and it can effectively decouple (nearly) linear/Gaussian aspects of a problem from highly nonlinear/non-Gaussian ones. We illustrate the use of GBO in simple numerical experiments with the classical Lorenz problems. Our main goals are to introduce GBO in the context of ensemble DA and to spark an interest in GBO and its uses for streamlining important tasks in ensemble DA.

Abstract. Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve state estimates and consequent forecasts. We interface the deterministic ensemble Kalman filter (DEnKF) to the Lagrangian neXt generation Sea Ice Model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble–DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble–DA. As a solution, we perform the DEnKF analysis on a fixed and regular reference mesh, on which model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea ice observations on the model's forecast skills of the Arctic sea ice by comparing satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvements of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. For most of the winter, the correlation between SIT and SIC is weaker, which results in little cross-inference between the two variables in the assimilation step. Overall, the ensemble–DA system based on the stand-alone sea ice model demonstrates the feasibility of winter Arctic sea ice prediction with good computational efficiency. These results open the path toward operational implementation and the extension to multi-year assimilation.

This study cross-validates the radar reflectivity Z, the rainfall drop size distribution parameter (median volume diameter, Do ) and the rainfall rate R estimated from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR), a combined PR and TRMM Microwave Imager (TMI) algorithm (COM) and a C-band dual-polarised ground-radar (GR) for TRMM overpasses during the passage of tropical cyclone (TC) and non-TC events over Darwin, Australia. Two overpass events during the passage of TC Carlos and eleven non-TC overpass events are used in this study and the GR is taken as the reference. It is shown that the correspondence is dependent on the precipitation type whereby events with more (less) stratiform rainfall usually have a positive (negative) bias in the reflectivity and the rainfall rate whereas in the Do the bias is generally positive but small (large). The COM reflectivity estimates are similar to the PR but it has a smaller bias in the Do for most of the greater stratiform events. This suggests that combining the TMI with the PR adjusts the Do towards the "correct" direction if the GR is taken as the reference. Moreover, the association between the TRMM estimates and the GR for the two TC events, which are highly stratiform in nature, is similar to that observed for the highly stratiform non-TC events (there is no significant difference) but it differs largely from that observed for the majority of the highly convective non-TC events.

The Warn-on-Forecast (WoF) program aims to deploy real-time, convection-allowing, ensemble data assimilation and prediction systems to improve short-term forecasts of tornadoes, flooding, lightning, damaging wind, and large hail. Until convection-resolving (horizontal grid spacing Δx < 100 m) systems become available, however, resolution errors will limit the accuracy of ensemble model output. Improved understanding of grid spacing dependence of simulated convection is therefore needed to properly calibrate and interpret ensemble output, and to optimize trade-offs between model resolution and other computationally constrained parameters like ensemble size and forecast lead time. Toward this end, the authors examine grid spacing sensitivities of simulated supercells over Δx of 333 m–4 km. Storm environment and physics parameterization are varied among the simulations. The results suggest that 4-km grid spacing is too coarse to reliably simulate supercells, occasionally leading to premature storm demise, whereas 3-km simulations more often capture operationally important features, including low-level rotation tracks. Further decreasing Δx to 1 km enables useful forecasts of rapid changes in low-level rotation intensity, though significant errors remain (e.g., in timing). Grid spacing dependencies vary substantially among the experiments, suggesting that accurate calibration of ensemble output requires better understanding of how storm characteristics, environment, and parameterization schemes modulate grid spacing sensitivity. Much of the sensitivity arises from poorly resolving small-scale processes that impact larger (well resolved) scales. Repeating some of the 333-m simulations with coarsened initial conditions reveals that supercell forecasts can substantially benefit from reduced grid spacing even when limited observational density precludes finescale initialization.

The implementation of a regional ensemble data assimilation and forecasting system requires the specification of appropriate perturbations of lateral boundary conditions (LBCs), in order to simulate associated errors. The sensitivity of analysis and 6-h forecast ensemble spread to these perturbations is studied here formally and experimentally by comparing three different LBC configurations for the ensemble data assimilation system of the ALADIN-France limited-area model (LAM). While perturbed initial LBCs are provided by the perturbed LAM analyses in each ensemble, the three ensemble configurations differ with respect to LBCs used at 3- and 6-h forecast ranges, which respectively correspond to: (1) perturbed LBCs provided by the operational global ensemble data assimilation system (GLBC), which is considered as a reference configuration; (2) unperturbed LBCs (ULBC) obtained from the global deterministic model; (3) perturbed LBCs obtained by adding random draws of an error covariance model (PLBC) to the global deterministic system. A formal analysis of error and perturbation equations is first carried out, in order to provide an insight of the relative effects of observation perturbations and of LBC perturbations at different ranges, in the various ensemble configurations. Horizontal variations of time-averaged ensemble spread are then examined for 6-h forecasts. Despite the use of perturbed initial LBCs, the regional ensemble ULBC is underdispersive not only near the lateral boundaries, but also in approximately one-third of the inner area, due to advection during the data assimilation cycle. This artefact is avoided in PLBC through the additional use of non-zero LBC perturbations at 3- and 6-h ranges, and the sensitivity to the amplitude scaling of the covariance model is illustrated for this configuration. Some aspects of the temporal variation of ensemble spread and associated sensitivities to LBC perturbations are also studied. These results confirm the importance of LBC perturbations for regional ensemble data assimilation. They also indicate that perturbing initial LBC is not sufficient to obtain realistic ensemble spread, whereas this can be achieved approximately by using random covariance draws for simulating LBC errors during the forecast and associated data assimilation cycling.

Three ensemble‐based analysis‐error representation techniques are applied and compared with the aim of simulating background errors of the mesoscale limited‐area model (LAM) Aire Limitée Adaptation Dynamique Développement International (ALADIN) and thus computing appropriate mesoscale static background‐error covariances for its three‐dimensional variational (3DVAR) data assimilation system. Real data assimilation experiments are performed for the simulation of the background errors, where uncertainties of the initial conditions and lateral boundary conditions are represented, while model errors are not accounted for. The three analysis error‐simulation techniques compared are as follows: (i) the downscaling of the analysis error represented by the stochastic ensemble data assimilation (EDA) system of a global model (DSC EDA); (ii) the representation of the analysis error by a LAM stochastic ensemble data assimilation (LAM EDA) system; and (iii) the representation of the analysis error by a LAM ensemble transform Kalman filter (LAM ETKF) system. Spectral diagnostics have been developed in the study, allowing an examination of the simulated background errors at different spatial scales. It is shown that LAM EDA provides more realistic simulated background errors than DSC EDA, particularly at mesoscales. This is also partly the case for LAM ETKF at mesoscales (although to a lesser extent than for LAM EDA). The error variance is underestimated by LAM ETKF, which suggests that the applied inflation technique could be improved. Analysis and forecast impact studies using background‐error covariance matrices based on the DSC EDA and LAM EDA error samples confirm the superiority of the latter simulation technique.