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In November 2021, the Royal Meteorological Society Data Assimilation (DA) Special Interest Group and the University of Reading hosted a virtual meeting on the topic of DA for convection‐permitting numerical weather prediction. The goal of the meeting was to discuss recent developments and review the challenges including methodological developments and progress in making the best use of observations. The meeting took place over two half days on the 10 and 12 November, and consisted of six talks and a panel discussion. The scientific presentations highlighted some recent work from Europe and the USA on convection‐permitting DA including novel developments in the assimilation of observations such as cloud‐affected satellite radiances in visible channels, ground‐based profiling networks, aircraft data, and radar reflectivity data, as well as methodological advancements in background and observation error covariance modelling and progress in operational systems. The panel discussion focused on key future challenges including the handling of multiscales (synoptic‐, meso‐, and convective‐scales), ensemble design, the specification of background and observation error covariances, and better use of observations. These will be critical issues to address in order to improve short‐range forecasts and nowcasts of hazardous weather. We report on a meeting on progress and challenges in convection‐permitting data assimilation. Novel developments include the assimilation of novel observations such as cloud‐affected satellite radiances, ground‐based profiling networks and aircraft data, as well as methodological advancements in error covariance modelling, and progress in operational systems. Key future challenges include the handling of multiple scales, the design of ensembles and observation networks: these will be critical issues for short‐range forecasts and nowcasts of hazardous weather.

As part of NOAA’s Warn-on-Forecast (WoF) initiative, a multiscale ensemble-based assimilation and prediction system is developed using the WRF-ARW model and DART assimilation software. To evaluate the capabilities of the system, retrospective short-range probabilistic storm-scale (convection allowing) ensemble analyses and forecasts are produced for the 27 April 2011 Alabama severe weather outbreak. Results indicate that the storm-scale ensembles are able to analyze the observed storms with strong low-level rotation at approximately the correct locations and to retain the supercell structures during the 0–1-h forecasts with reasonable accuracy. The system predicts the low-level mesocyclones of significant isolated tornadic supercells that align well with the locations of radar-derived rotation. For cases with multiple interacting storms in close proximity, the system tends to produce more variability in mesocyclone forecasts from one initialization time to the next until the observations show the dominance of one of the cells. The short-range ensemble probabilistic forecasts obtained from this continuous 5-min storm-scale 6-h-long update system demonstrate the potential of a frequently updated, high-resolution NWP system that could be used to extend severe weather warning lead times. This study also demonstrates the challenges associated with developing a WoF-type system. The results motivate future work to reduce model errors associated with storm motion and spurious cells, and to design storm-scale ensembles that better represent typical 1-h forecast errors.

The conventional Weather Surveillance Radar-1988 Doppler (WSR-88D) scans a given weather phenomenon in approximately 5 min, and past results suggest that it takes 30–60 min to establish a storm into a model assimilating these data using an ensemble Kalman filter (EnKF) data assimilation technique. Severe-weather events, however, can develop and evolve very rapidly. Therefore, assimilating observations for a 30–60-min period prior to the availability of accurate analyses may not be feasible in an operational setting. A shorter assimilation period also is desired if forecasts are produced to increase the warning lead time. With the advent of the emerging phased-array radar (PAR) technology, it is now possible to scan the same weather phenomenon in less than 1 min. Therefore, it is of interest to see if the faster scanning rate of PAR can yield improvements in storm-scale analyses and forecasts from assimilating over a shorter period of time. Observing system simulation experiments are conducted to evaluate the ability to quickly initialize a storm into a numerical model using PAR data in place of WSR-88D data. Synthetic PAR and WSR-88D observations of a splitting supercell storm are created from a storm-scale model run using a realistic volume-averaging technique in native radar coordinates. These synthetic reflectivity and radial velocity observations are assimilated into the same storm-scale model over a 15-min period using an EnKF data assimilation technique followed by a 50-min ensemble forecast. Results indicate that assimilating PAR observations at 1-min intervals over a short 15-min period yields significantly better analyses and ensemble forecasts than those produced using WSR-88D observations. Additional experiments are conducted in which the adaptive scanning capability of PAR is utilized for thunderstorms that are either very close to or far away from the radar location. Results show that the adaptive scanning capability improves the analyses and forecasts when compared with the nonadaptive PAR data. These results highlight the potential for flexible rapid-scanning PAR observations to help to quickly and accurately initialize storms into numerical models yielding improved storm-scale analyses and very short range forecasts.

This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.

As part of NOAA＇s ＂Warn-On-Forecast＂ initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88 Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

Workshop on Current and Future Uses of Unmanned Aircraft Systems (UASs) for Improved Forecasts/Warnings and Scientific Studies What: Sixty-three participants including graduate students, postdoctoral fellows, and senior researchers working in the atmospheric sciences at U.S. and international universities, private companies, and government laboratories met to discuss scientific applications of UASs. [...]observational requirements for model applications (i.e., how many, where, and when) need to be better established, and may be pursuable through coordinated sampling campaigns. [...]the fourth session (additional atmospheric applications of UAS and other scientific discussions) included discussion and presentations on the potential for UAS to contribute to our understanding of cloud properties, aerosols, surface and radiative fluxes, stress terms, complex flows, albedo, surface heterogeneity, vegetation indices, photogrammetry, site surveys, and ocean properties. Currently, there is no gold standard for the often challenging comparisons that need to be made between data from UAS-mounted instrumentation and data from other sensors (e.g., towers, radiometers, lidar, radiosondes). [...]the biggest need for making progress on establishing confidence in UAS data is a reference platform or set of reference instruments that could be robustly tested.

Supercell thunderstorms can produce a wide spectrum of vortical structures, ranging from midlevel mesocyclones to small-scale suction vortices within tornadoes. A less documented class of vortices are horizontally-oriented vortex tubes near and/or wrapping about tornadoes, that are observed either visually or in high-resolution Doppler radar data. In this study, an idealized numerical simulation of a tornadic supercell at 100 m grid spacing is used to analyze the three-dimensional (3D) structure and kinematics of horizontal vortices (HVs) that interact with a simulated tornado. Visualizations based on direct volume rendering aided by visual observations of HVs in a real tornado reveal the existence of a complex distribution of 3D vortex tubes surrounding the tornadic flow throughout the simulation. A distinct class of HVs originates in two key regions at the surface: around the base of the tornado and in the rear-flank downdraft (RFD) outflow and are believed to have been generated via surface friction in regions of strong horizontal near-surface wind. HVs around the tornado are produced in the tornado outer circulation and rise abruptly in its periphery, assuming a variety of complex shapes, while HVs to the south-southeast of the tornado, within the RFD outflow, ascend gradually in the updraft.

The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3- μ m channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. Qualitative and object-based verification of severe weather and the near-storm environment are used to assess the impact of assimilating all-sky radiances compared to the current model configuration. We focus our study through the entire WoFS analysis and forecasting cycle (1900–0600 UTC, daily) so that the impacts throughout the evolution of convection from initiation to large upscale growth can be assessed. Overall, assimilating satellite data improves forecasts relative to radar-only assimilation experiments. The retrieval method with clear-sky radiances performs best overall, but assimilating all-sky radiances does have very positive impacts in certain conditions. In particular, all-sky radiance assimilation improved convective initiation forecast of severe storms in several instances. This work represents an initial attempt at assimilating all-sky radiances into the WoFS and additional research is ongoing to further improve forecast skill.

Numerical experiments over the past years indicate that incorporating environmental variability is crucial for successful very short-range convective-scale forecasts. To explore the impact of model physics on the creation of environmental variability and its uncertainty, combined mesoscale-convective scale data assimilation experiments are conducted for a tornadic supercell storm. Two 36-member WRF-ARW model-based mesoscale EAKF experiments are conducted to provide background environments using either fixed or multiple physics schemes across the ensemble members. Two 36-member convective-scale ensembles are initialized using background fields from either fixed physics or multiple physics mesoscale ensemble analyses. Radar observations from four operational WSR-88Ds are assimilated into convective-scale ensembles using ARPS model-based 3DVAR system and ensemble forecasts are launched. Results show that the ensemble with background fields from multiple physics ensemble provides more realistic forecasts of significant tornado parameter, dryline structure, and near surface variables than ensemble from fixed physics background fields. The probabilities of strong low-level updraft helicity from multiple physics ensemble correlate better with observed tornado and rotation tracks than probabilities from fixed physics ensemble. This suggests that incorporating physics diversity across the ensemble can be important to successful probabilistic convective-scale forecast of supercell thunderstorms, which is the main goal of NOAA’s Warn-on-Forecast initiative.

Landfalling tropical cyclones (TCs) are among the greatest natural threats to life and property in the United States, since they can produce multiple hazards associated with convective storms over a wide region. Of these hazards, tornadoes within TC rainbands pose a particularly difficult forecast problem owing to their rapid evolution and their frequent occurrence coincident with additional hazards, such as flash flooding and damaging winds. During the 2017 Atlantic hurricane season, Hurricanes Harvey and Irma impacted the continental United States, causing significant loss of life and billions of dollars in property damage. Application of the Warn-on-Forecast (WoF) concept of short-term, probabilistic guidance of convective hazards (Stensrud et al. 2009, 2013), including the potential for tornadoes within TCs, offers the ability to provide forecasters with valuable tools for prioritizing the relative risk from multiple convective threats and effectively communicating them to the public.