Operational Weather Forecasting Research Articles

Lifetime Performance of the Operational Hurricane Weather Research and Forecasting Model (HWRF) for North Atlantic Tropical Cyclones

Abstract The Hurricane Weather Research and Forecasting Model (HWRF) was the flagship hurricane model at NOAA’s National Centers for Environmental Prediction for 16 years and a state-of-the-art tool for tropical cyclone (TC) intensity prediction at the National Weather Service and across the globe. HWRF was a joint development between NOAA research and operations, specifically the Environmental Modeling Center and the Atlantic Oceanographic and Meteorological Laboratory. Significant support also came from the National Hurricane Center, Developmental Testbed Center, University Corporation for Atmospheric Research, universities, cooperative institutes, and the TC community. In the North Atlantic basin, where most improvement efforts focused, HWRF intensity forecast errors decreased by 45%–50% at many lead times between 2007 and 2022. These large improvements resulted from increases in horizontal and vertical resolution, as well as advances in model physics and data assimilation. HWRF intensity forecasts performed particularly well over the Gulf of Mexico in recent years, providing useful guidance for a large number of impactful landfalling hurricanes. Such advances were made possible not only by significant gains in computing but also through substantial investment from the Hurricane Forecast Improvement Program.

Heatwave attribution based on reliable operational weather forecasts

The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2–50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10–50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.

Validation of cloud mask product from multi-channel satellite INSAT-3DR

The Indian National Satellite-3DR (INSAT-3DR) provides half-hourly multispectral images over the Indian region from a geostationary position. It observes diurnal variations highly relevant for variety of application areas, including weather forecasting. This work assesses the performance of the cloud mask product of INSAT-3DR new algorithm developed by Indian Space Research Organization (ISRO) by comparison with Moderate Resolution Imaging Spectrometer radiometer (MODIS) cloud mask data for the months from July 2019 to July 2020. The cloud detection algorithm employs nine different tests, considering solar illumination, satellite angle and surface type conditions to get pixel-resolution cloud mask. Our validation results indicate that the probability of detection (POD) of cloudy (clear) sky is 86 % (82 %) with 83 % hit rate for INSAT-3DR cloud mask, using MODIS cloud mask as a reference. When the INSAT-3DR cloud mask algorithm is also implemented on similar channels of MODIS, the POD of cloudy (clear) sky is 76 % (74 %) with 85 % hit rate. Our findings indicate the reliability of the INSAT-3DR cloud mask retrieved product for research and other applications. This is one of the important parameter of satellite derived products, which plays major role in daily operational weather forecasting.

A dynamic approach to three-dimensional radiative transfer in subkilometer-scale numerical weather prediction models: the dynamic TenStream solver v1.0

Abstract. The increasing resolution of numerical weather prediction models makes inter-column three-dimensional (3D) radiative transport more and more important. However, 3D radiative-transfer solvers are still computationally expensive, largely preventing their use in operational weather forecasting. To address this issue, Jakub and Mayer (2015) developed the TenStream solver. It extends the well-established two-stream method to three dimensions by using 10 instead of 2 streams to describe the transport of radiative energy through Earth's atmosphere. Building upon this method, this paper presents the dynamic TenStream solver, which provides a further acceleration of the original TenStream model. Compared to traditional solvers, this speedup is achieved by utilizing two main concepts. First, radiation is not calculated from scratch every time the model is called. Instead, a time-stepping scheme is introduced to update the radiation field, based on the result from the previous radiation time step. Secondly, the model is based on incomplete solves, with just the first few steps of an iterative scheme towards convergence performed every time it is called. Essentially, the model thereby just uses the ingoing fluxes of a grid box to update its outgoing fluxes. Combined, these two approaches move radiative transfer much closer to the way advection is handled in the dynamical core of a numerical weather prediction (NWP) model, as both use previously calculated results to update their variables and thereby just require access to the neighboring values of an individual grid box, facilitating model parallelization. To demonstrate the feasibility of this new solver, we apply it to a precomputed shallow-cumulus-cloud time series and test its performance in terms of both speed and accuracy. In terms of speed, the dynamic TenStream solver is shown to be about 3 times slower than a traditional 1D δ-Eddington approximation but noticeably faster than currently available 3D solvers. To evaluate the accuracy of the dynamic TenStream solver, we compare its results as well as calculations carried out using a 1D δ-Eddington approximation and the original TenStream solver, to benchmark calculations performed with the 3D Monte Carlo solver MYSTIC. We demonstrate that at the grid box level, dynamic TenStream is able to calculate heating rates and net irradiances at domain boundaries that are very close to those obtained by the original TenStream solver, thus offering a much better representation of the MYSTIC benchmark than the 1D δ-Eddington results. By calling the dynamic TenStream solver less frequently than the δ-Eddington approximation, we furthermore show that our new solver produces significantly better results than a 1D δ-Eddington approximation carried out with a similar computational demand. At these lower calling frequencies, however, the incomplete solves in the dynamic TenStream solver also lead to a buildup of bias with time, which becomes larger the lower the calling frequency is.

Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán

There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare short to medium-range forecasts of Storm Ciarán, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of the warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciarán.

Implications of WRF model resolutions on resolving rainfall variability with topography over East Africa

There is an increasing need to improve the accuracy of extreme weather forecasts for life-saving applications and in support of various socioeconomic sectors in East Africa, a region with remarkable mesoscale systems due to its complex topography defined by sharp gradients in elevation, inland water bodies, and landuse conversions. This study sought to investigate the impacts of the Weather Research and Forecasting (WRF) model spatial resolution on resolving rainfall variability with topography utilizing nested domains at 12 and 2.4 km resolutions. The model was driven by the National Centers for Environmental Prediction (NCEP)-Global Data Assimilation System (GDAS) Global Forecast System (GFS) final (FNL) reanalysis to simulate the weather patterns over East Africa from 3rd April 2018 to 30th April 2018, which were evaluated against several freely available gridded weather datasets alongside rainfall data from the Kenya Meteorological Department (KMD) stations. The reference datasets and the model outputs revealed that the highlands had more rainfall events and higher maximum daily rainfall intensity compared to the surrounding lowlands, attributed to orographic lifting enhancing convection. Rainfall was inversely proportional to altitude from 500 m to 1,100 m above sea level (ASL) for both coarse and fine resolutions. The convection-permitting setup was superior in three aspects: resolving the inverse altitude-rainfall relationship for altitudes beyond 3000 m ASL, simulating heavy rainfall events over the lowlands, and resolution of the diurnal cycle of low-level wind. Although the coarse resolution setup reasonably simulated rainfall over large mountains, only the convection-permitting configuration could accurately resolve rainfall variability over contrasting topographical features. The study notes that high-resolution modeling systems and topography-sensitive bias correction techniques are critical for improving the quality of operational weather forecasts in East Africa.

Challenges of Operational Weather Forecast Verification and Evaluation

Abstract Operational agencies face significant challenges related to the verification and evaluation of weather forecasts. These challenges were investigated in a series of online workshops and polls engaging operational personnel from six countries. Five key themes emerged: inadequate verification approaches for both existing and emerging products; incomplete and uncertain observations; difficulties in accurately capturing users’ real-world experiences using simplified metrics; poor communication and understanding of forecasts and complex verification information; and institutional factors such as limited resources, evolving meteorologist roles, and concerns over reputational damage. We identify nearly 50 operationally relevant scientific questions and suggest calls to action. Addressing these needs includes designing forecast systems with verification as a central consideration, enhancing the availability of observations, and developing and adopting community software systems. Additionally, we propose the establishment of an international community comprising environmental and social science researchers, statisticians, verification practitioners, and users to provide sustained support for this collective endeavor.

The Influence of Time-Varying Sea Ice Concentration on Antarctic and Southern Ocean Numerical Weather Prediction

Abstract Although operational weather forecasting centers are increasingly using global coupled atmosphere–ocean–ice models to replace atmosphere-only models for short- and medium-range (10 day) weather forecasting, the influence of sea ice on such forecasting has yet to be fully quantified, especially in the Southern Ocean. To address this gap, a polar-specific version of the Weather Research and Forecasting Model is implemented with a circumpolar Antarctic domain to investigate the impact of daily updates of sea ice concentration on short- and medium- range weather forecasting. A statistically significant improvement in near-surface atmospheric temperature and humidity is shown from +24 to +192 h when updating the daily sea ice concentration in the model. The forecast skill improvements for 2-m temperature and dewpoint temperature are enhanced from June to September, which is the period of late sea ice advance. Regionally, model improvement is shown to occur in most sea ice regions, although the improvement is strongest in the Ross Sea and Weddell Sea sectors. The surface heat budget also shows remarkable improvement in outgoing radiative heat fluxes and both sensible and latent heat fluxes. This idealized research demonstrates the nonnegligible effect of including more accurate time-varying sea ice concentration in numerical weather forecasting. Significance Statement The purpose of this study is to understand how a more realistic Antarctic sea ice field may influence the skill of short- and medium-range weather forecasts. Many operational atmospheric numerical weather prediction (NWP) models use a static forecast field through the time frame of the model’s forecast—often 3–10 days. In this study, we updated the sea ice concentration field daily and compared the forecast outcomes with those from model runs using a static sea ice concentration field. We found the forecast skill of near-surface temperature and humidity show the most significant improvements in our idealized experiments. This indicates the importance of incorporating improved dynamic sea ice representation in Antarctic short- to medium-range operational weather forecasting.

Evaluation of Daily Temperature Extremes in the ECMWF Operational Weather Forecasts and ERA5 Reanalysis

In weather forecasting and climate monitoring, daily maximum and minimum air temperatures (TMAX and TMIN) are fundamental for operational and research purposes, from early warning of extreme events to climate change studies. This study provides an integrated evaluation of TMAX and TMIN from two European Centre for Medium-range Weather Forecasts (ECMWF) products: ERA5 reanalysis (1980–2019) and operational weather forecasts (2017–2021). Both products are evaluated using in situ observations from the Global Historical Climatology Network (GHCN). While the analyses span globally, emphasis is given to four key regions: Europe, East and West United States, and Australia. Results reveal a general underestimation of TMAX and overestimation of TMIN in both operational forecasts and ERA5, highlighting the limitation of the ECMWF model in estimating the amplitude of the diurnal cycle of air temperature. ERA5′s accuracy has improved over the past decade, due to enhanced constrain of land–atmosphere analysis streaming from more and higher-quality satellite data. Furthermore, ERA5 outperforms one-day-ahead weather forecasts, indicating that non-real-time dependent studies should rely on ERA5 instead of real-time operational forecasts. This study underscores the importance of ongoing research in model and data assimilation, considering the relevance of daily temperature extremes forecasting and reanalysis for operational meteorology and climate monitoring.

FY-3E Satellite Plasma Analyzer

The FY-3E satellite plasma analyzer marks China’s first detection of the characteristics, occurrence, and development of the typical plasma environment in the dawn–dusk orbit space. It provides data source support for operational space weather alerts and forecasts, helps ensure the in-orbit safety of the satellite, and accumulates space environment detection data for space environment modeling and space physics research. This paper gives a detailed introduction to the detection technology adopted by the FY-3E satellite plasma analyzer. We calibrated its performance through a calibration experiment and then analyzed and compared it with similar instruments in China. It is indicated that the instrument is capable of measuring an ion energy spectrum of 24 eV~32 keV and an electron energy spectrum of 23.7 eV~31.6 keV, its field of view reaches 180° × 90°, and the inversed measurement range of spacecraft absolute potential is better than −30 kV~+30 kV. All these contribute to a notably improved technology for plasma and satellite potential detection of China’s LEO satellites.

A modified vertical eddy diffusivity parameterization in the HWRF model based on large eddy simulations and its impact on the prediction of two landfalling hurricanes

Vertical eddy diffusivity (VED) in the planetary boundary layer (PBL) has a significant impact on forecasts of tropical cyclone (TC) structure and intensity. VED uncertainties in PBL parameterizations can be partly attributed to the model’s inability to represent roll vortices (RV). In this study, RV effects on turbulent fluxes derived from a large eddy simulation (LES) by Li et al. (Geophys. Res. Lett., 2021, 48, e2020GL090703) are added to the VED parameterization of the PBL scheme within the operational Hurricane Weather Research and Forecasting (HWRF) model. RV contribution to VED is parameterized through a coefficient and varies with the RV intensity and velocity scale. A modification over land has also been implemented. This modified VED parameterization is compared with the original wind-speed-dependent VED scheme in HWRF. Retrospective HWRF forecasts of Hurricanes Florence (2018) and Laura (2020) are analyzed to evaluate the impacts of the modified VED scheme on landfalling hurricane forecasts. Results show that the modified PBL scheme with the RV effect leads to an improvement in 10-m maximum wind speed forecasts of 14%–31%, with a neutral to positive improvement for track forecasts. Improved wind structure and precipitation forecasts against observations are also noted with the modified PBL scheme. Further diagnoses indicate that the revised PBL scheme enhances moist entropy in the boundary layer over land, leading to improved TC intensity prediction compared to the original scheme.

2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S)

During the 2022 hurricane season, real-time forecasts were conducted using an experimental version of the Hurricane Analysis and Forecast System (HAFS). The version of HAFS detailed in this paper (HAFSV0.3S, hereafter HAFS-S) featured the moving nest recently developed at NOAA AOML, and also model physics upgrades: TC-specific modifications to the planetary boundary layer (PBL) scheme and introduction of the Thompson microphysics scheme. The real-time forecasts covered a large dataset of cases across the North Atlantic and eastern North Pacific 2022 hurricane seasons, providing an opportunity to evaluate this version of HAFS ahead of planned operational implementation of a similar version in 2023. The track forecast results show that HAFS-S outperformed the 2022 version of the operational HWRF model in the Atlantic, and was the best of several regional hurricane models in the eastern North Pacific for track. The intensity results were more mixed, with a dropoff in skill at Days 4–5 in the Atlantic but increased skill in the eastern North Pacific. HAFS-S also showed some larger errors than the long-time operational Hurricane Weather Research and Forecasting (HWRF) model in the radius of 34-knot wind, but other radii metrics are improved. Detailed analysis of Hurricane Ian in the Atlantic highlights both the strengths of HAFS and opportunities for further development and improvement.

The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models

Abstract. Atmospheric fronts are a widely used conceptual model in meteorology, most encountered as two-dimensional (2-D) front lines on surface analysis charts. The three-dimensional (3-D) dynamical structure of fronts has been studied in the literature by means of “standard” 2-D maps and cross-sections and is commonly sketched in 3-D illustrations of idealized weather systems in atmospheric science textbooks. However, only recently has the feasibility of the objective detection and visual analysis of 3-D frontal structures and their dynamics within numerical weather prediction (NWP) data been proposed, and such approaches are not yet widely known in the atmospheric science community. In this article, we investigate the benefit of objective 3-D front detection for case studies of extra-tropical cyclones and for comparison of frontal structures between different NWP models. We build on a recent gradient-based detection approach, combined with modern 3-D interactive visual analysis techniques, and adapt it to handle data from state-of-the-art NWP models including those run at convection-permitting kilometre-scale resolution. The parameters of the detection method (including data smoothing and threshold parameters) are evaluated to yield physically meaningful structures. We illustrate the benefit of the method by presenting two case studies of frontal dynamics within mid-latitude cyclones. Examples include joint interactive visual analysis of 3-D fronts and warm conveyor belt (WCB) trajectories, as well as identification of the 3-D frontal structures characterizing the different stages of a Shapiro–Keyser cyclogenesis event. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment the surface charts by providing additional pertinent information in the vertical dimension. A second application illustrates the relation between convection and 3-D cold-front structure by comparing data from simulations with parameterized and explicit convection. Finally, we consider “secondary fronts” that commonly appear in UK Met Office surface analysis charts. Examination of a case study shows that for this event the secondary front is not a temperature-dominated but a humidity-dominated feature. We argue that the presented approach has great potential to be beneficial for more complex studies of atmospheric dynamics and for operational weather forecasting.

A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region

Surface air temperature is a comprehensive function of aerosols in the atmosphere and various weather factors. However, there is no real-time aerosol concentration feedback in most operational numerical weather prediction (NWP) models. This raises a scientific question of how abnormal changes in air pollutants in a short period of time will affect the temperature prediction skill of NWP models. Thus, the study was carried out to investigate the possible influence of air pollution on the temperature forecast skill based on the operational NWP model over the Beijing–Tianjin–Hebei (BTH) region during January–February 2020. The results show that the average concentrations of PM2.5, SO2, NO2 and CO over the BTH region in February were smaller than those in January by 38.5%, 35.1%, 48.0% and 33.1%, respectively. Simultaneously, the forecast skills for surface temperature in February from both regional (RMAPS, Rapid-refresh Multi-scale Analysis and Prediction System) and global (ECMWF, European Centre for Medium-Range Weather Forecasts) operational NWP models improved markedly compared with that in January. In both models, the underestimation of maximum temperature and the overestimation of minimum temperature in most cities over the BTH region in February were significantly reduced. With the 24 h (24 h) forecast lead time, the RMSE (root mean square error) of BTH daily mean, maximum and minimum temperature prediction in February based on RMAPS were 17.3%, 9.8% and 21.6% lower than that in January, respectively. These are generally consistent with the other statistical indices such as deviation and regression coefficient. As the forecast lead time extended to 48 h and 72 h forecast, the phenomena still existed and were also evident in the ECMWF model. The improvement of temperature forecast skill of NWP models may be attributed to the unexpected dramatical reduction of air pollutants. Less aerosols during the daytime allow more solar radiation reaching the surface and cause a warming in the near-surface temperature, while less aerosols during the nighttime favor the outgoing long-wave radiation and then lead to a cooling near the ground.

Verification of a global weather forecasting system for decision-making in farming over Africa

In the framework of AfriCultuReS project, operational deterministic weather forecasts provide valuable information on the expected weather conditions over the African continent as a part of federation of services within the project. In this study, we investigate the performance of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) over pilot regions in Africa by utilizing available surface observations, satellite and reanalysis data. The verification period covers two consecutive years (June 2018–May 2020). In addition, we assess the ability of the model to provide skillful forecasts through three high-impact precipitation events that occurred during this period. The results show that the model presents both positive and negative biases with respect to its predicted near surface air temperature, underestimates the near surface relative humidity and the mean sea-level pressure, while overestimates the wind speed at 10 m. The neighborhood-based statistical verification of the 24-h accumulated precipitation reveals that the model forecasts the precipitation events more accurately as the verification area is increasing but at higher precipitation thresholds its performance deteriorates. Different variability, errors and correlation between simulated and observed precipitation exist in each forecast lead day and region. A range of model behavior and forecast skill is found with respect to the examined three precipitation events. Skillful forecasts up to four days ahead were provided in the cases of the Tropical Cyclone IDAI and the flash flooding events in northern Tunisia, while the lowest performance was found in the region of the West African Monsoon.

Deep neural networks of solar flare forecasting for complex active regions

Solar flare forecasting is one of major components of operational space weather forecasting. Complex active regions (ARs) are the main source producing major flares, but only a few studies are carried out to establish flare forecasting models for these ARs. In this study, four deep learning models, called Complex Active Region Flare Forecasting Model (CARFFM)-1, −2, −3, and −4, are established. They take AR longitudinal magnetic fields, AR vector magnetic fields, AR longitudinal magnetic fields and the total unsigned magnetic flux in the neutral line region, AR vector magnetic fields and the total unsigned magnetic flux in the neutral region as input, respectively. These four models can predict the production of M-class or above flares in the complex ARs for the next 48 h. Through comparing the performance of the models, CARFFM-4 has the best forecasting ability, which has the most abundant input information. It is suggested that more valuable and rich input can improve the model performance.

Assimilation of Coyote Small Uncrewed Aircraft System Observations in Hurricane Maria (2017) Using Operational HWRF

Abstract This study presents an initial demonstration of assimilating small uncrewed aircraft system (sUAS) data into an operational model with a goal to ultimately improve tropical cyclone (TC) analyses and forecasts. The observations, obtained using the Coyote sUAS in Hurricane Maria (2017), were assimilated into the operational Hurricane Weather Research and Forecast (HWRF) system as they could be in operations. Results suggest that the Coyote data can benefit HWRF forecasts. A single-cycle case study produced the best results when the Coyote observations were assimilated at greater horizontal resolution with more relaxed quality control (QC) than comparable flight-level high-density observations currently used in operations. The case study results guided experiments that cycled HWRF for a roughly 4-day period that covered all Coyote flights into Maria. The cycled experiment that assimilated the most data improved initial inner-core structure in the analyses and better agreed with other aircraft observations. The average errors in track and intensity decreased in the subsequent forecasts. Intensity forecasts were too weak when no Coyote data were assimilated, and assimilating the Coyote data made the forecasts stronger. Results also suggest that a symmetric distribution of Coyote data around the TC center is necessary to maximize its benefits in the current configuration of operational HWRF. Although the sample size was limited, these experiments provide insight for potential operational use of data from newer sUAS platforms in future TC applications. Significance Statement This study represents the first time that observations from a small uncrewed aircraft system (sUAS) have been assimilated into an operational numerical model. Including these data was shown to have potential for improving forecasts of tropical cyclone track and intensity. The data were obtained using the Coyote sUAS, but these results are expected to be applicable to newer platforms that will be operational soon.

SIR‐HUXt—A Particle Filter Data Assimilation Scheme for CME Time‐Elongation Profiles

AbstractWe present SIR‐HUXt, the integration of a sequential importance resampling data assimilation scheme with the HUXt solar wind model. SIR‐HUXt assimilates the time‐elongation profiles of Coronal Mass Ejection (CME) fronts in the low heliosphere, like those extracted from heliospheric imager (HI) data. Observing System Simulation Experiments are used to explore SIR‐HUXt's performance for a simple synthetic CME scenario of an Earth directed CME in a uniform solar wind, where the CME is initialized with the average CME speed and width. These experiments are performed for a range of observer locations, from 20° to 90° behind Earth, spanning the L5 point where ESA's Vigil mission will return HI data for operational space weather forecasting. For this idealized scenario, SIR‐HUXt performs well at constraining the CME speed, and has some success at constraining the CME longitude while the CME width is largely unconstrained by SIR‐HUXt. Rank‐histograms suggest the SIR‐HUXt ensembles are well calibrated, with no indications of bias or under/over dispersion. Improved constraints on the initial CME speed lead to improvements in the CME transit time and arrival speed. For an L5 observer, SIR‐HUXt reduced the transit time and arrival speed uncertainties by 69% and 63%. Therefore, SIR‐HUXt could improve the real‐world representivity of HUXt simulations and reduce the uncertainty of CME arrival time forecasts. The idealized scenario studied here likely enhances SIR‐HUXt's performance relative to the challenge of simulating real‐world CMEs and solar wind conditions. Future work should validate SIR‐HUXt with case studies of real CMEs in structured solar wind.

A case study of the July 2021 Henan extreme rainfall event: From weather forecast to climate risks

During 17–22 July 2021 a rainfall event in Henan Province of China has delivered over 820 mm precipitation around Zhengzhou with hourly intensity exceeded 201 mm. This paper presents a systematic case study of its driving processes, predictability, and future climate risks. The key aspects of this event are 1) geographically stationary and 2) temporally persistent. A sustained low-level moist easterly jetstream resulting from between typhoon In-Fa and a northward displaced subtropical high and orography in the region appear to have played a major role in the event. The event was, overall, well predicted in global operational 5-day weather forecast, though the details may not be very accurate. From a climate perspective, the large-scale low-level circulation pattern was like the August 1975 catastrophic floods in the same region, but opposite to the 2020 summer anomalous extreme Meiyu situation. Both weather patterns were rare during the past 47 years, together accounting for less than 10% of low-level daily weather patterns. The orographic features around Henan make it vulnerable for floods. A risk assessment based two sets of ensemble climate model simulations suggest the probability of such events occurring in the future will increase under the IPCC RCP8.5 scenario.

The Impact of Dropsonde Data on the Performance of the NCEP Global Forecast System during the 2020 Atmospheric Rivers Observing Campaign. Part II: Dynamic Variables and Humidity

Abstract The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical weather forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the National Centers for Environmental Prediction (NCEP) Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble–variational (4DEnVar) data assimilation system. The control run (CTRL) used all of the routinely assimilated data and included data from 628 ARR dropsondes, whereas the denial run (DENY) excluded the dropsonde data. Results from 17 intensive observing periods (IOPs) indicate a mixed impact for mean sea level pressure and geopotential height over the Pacific–North American (PNA) region in CTRL compared to DENY. The overall local impact over the U.S. West Coast and Gulf of Alaska for the 17 IOPs is neutral (−0.45%) for integrated vapor transport (IVT), but positive for wind and moisture profiles (0.5%–1.0%), with a spectrum of statistically significant positive and negative impacts for various IOPs. The positive dropsonde data impact on precipitation forecasts over U.S. West Coast domains appears driven, in part, by improved low-level moisture and wind fields at short-forecast lead times. Indeed, data gaps, especially for accurate and unbiased moisture profiles and wind fields, can be at least partially mitigated to improve U.S. West Coast precipitation forecasts.