Recurrent Rossby waves during Southeast Australian heatwaves and links to quasi-resonant amplification and atmospheric blocks

In the Northern Hemisphere, recurrence of transient Rossby wave packets over periods of days to weeks, termed RRWPs, may repeatedly create similar weather conditions. This recurrence leads to persistent surface anomalies and high-impact weather events. Here, we demonstrate the significance of RRWPs for persistent heatwaves in the Southern Hemisphere (SH). We investigate the relationship between RRWPs, atmospheric blocking, and amplified quasi-stationary Rossby waves with two cases of heatwaves in Southeast Australia (SEA) in 2004 and 2009. This region has seen extraordinary heatwaves in recent years. We also investigate the importance of transient systems such as RRWPs and two other persistent dynamical drivers: atmospheric blocks and quasi-resonant amplification (QRA). We further explore the link between RRWPs, blocks, and QRA in the SH using the ERA-I reanalysis dataset (1979–2018). We find that QRA and RRWPs are strongly associated: 40 % of QRA days feature RRWPs, and QRA events are 13 times more likely to occur with an RRWPs event than without it. Furthermore, days with QRA and RRWPs show high correlations in the composite mean fields of upper-level flows, indicating that both features have a similar hemispheric flow configuration. Blocking frequencies for QRA and RRWP conditions both increase over the south Pacific Ocean but differ substantially over parts of the south Atlantic and Indian Ocean.

Evaluation of convective cloud microphysics in numerical weather predictionmodel with dual-wavelength polarimetric radar observations

<p>The representation of microphysical processes in numerical weather prediction models remains a main source of uncertainty until today. To evaluate the influence of cloud microphysics parameterizations on numerical weather prediction, a convection permitting regional weather model setup has been established using 5 different microphysics schemes of varying complexity (double-moment, spectral bin, particle property prediction (P3)). A polarimetric radar forward operator (CR-SIM) has been applied to simulate radar signals consistent with the simulated particles. The performance of the microphysics schemes is analyzed through a statistical comparison of the simulated radar signals to radar measurements on a dataset of 30 convection days.</p> <p>The observational data basis is provided by two polarimetric research radar systems in the area of Munich, Germany, at C- and Ka-band frequencies and a complementary third polarimetric C-band radar operated by the German Weather Service. By measuring at two different frequencies, the<br />dual-wavelength ratio is derived that facilitates the investigation of the particle size evolution. Polarimetric radars provide in-cloud information about hydrometeor type and asphericity by measuring, e.g., the differential reflectivity ZDR.</p> <p>Within the DFG Priority Programme 2115 PROM, we compare the simulated polarimetric and dual-wavelength radar signals with radar observations of convective clouds. Deviations are found between the schemes and observations in ice and liquid phase, related to the treatment of particle size distributions. Apart from the P3 scheme, simulated reflectivities in the ice phase are too high. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions. Comparison of polarimetric radar signatures reveal issues of all schemes except the spectral bin scheme to correctly represent rain particle size distributions. The polarimetric information is further exploited by applying a hydrometeor classification algorithm to obtain dominant hydrometeor classes. By comparing the simulated and observed distribution of hydrometeors, as well as the frequency, intensity and area of high impact weather situations (e.g., hail or heavy convective precipitation), the influence of cloud microphysics on the ability to correctly predict high impact weather situations is examined.</p>

Identifying community values related to heat: recommendations for forecast and health risk communication

Effective communication of heat risk to public audiences is critical for promoting behavioral changes that reduce susceptibility to heat-related illness. The U.S. National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) provides heat-related information to the public using social media platforms such as Facebook. We applied a novel rhetorical framework to evaluate 5 years (2015–2019) of public responses to heat-related Facebook posts from the NWS office in Phoenix (Arizona) to identify “commonplaces” or community norms, beliefs, and values that may present challenges to the effectiveness of heat risk communication. Phoenix is in one of the hottest regions in North America and is the 10th-largest metropolitan area in the U.S. We found the following two key commonplaces: (1) the normalization of heat and (2) heat as a marker of community identity. These commonplaces imply that local audiences may be resistant to behavioral change, but they can also be harnessed in an effort to promote protective action. We also found that public responses to NWS posts declined over the heat season, further suggesting the normalization of heat and highlighting the need to maintain engagement. This work provides a readily generalizable framework for other messengers of high-impact weather events to improve the effectiveness of their communication with receiver audiences.

Storm Naming in the Eastern Mediterranean: Procedures, Events Review and Impact on the Citizens Risk Perception and Readiness

This paper is devoted to the discussion of the practice of storm naming that has been initiated in January 2017 for the first time in the Eastern Mediterranean Region. Namely the METEO Unit at NOA, taking into consideration that storm naming facilitates meteorologists, researchers, authorities, civil protection officers, the media and citizens to communicate the forecasts of high-impact weather events, started storm naming in January 2017 and has named 35 storms up to September 2021. The criteria of storm naming are discussed, and a synopsis of the events is presented. The monthly distribution shows that 57% of the named storms occurred during the winter period, with January being the month with the highest percentage of occurrence of named storms (28%). The impact of storm naming on citizens risk perception and increased awareness has been also assessed through an internet-based questionnaire that was launched on the fourth year of the storm naming practice in Greece. Overall, results indicate a significant impact of storm naming on the readiness of citizens through the activation of perceptual and cognitive mechanisms.

Innovation management and strategic planning of innovative self-preparedness and self-protection services

Due to climatic change, the number of high impact weather events is globally increasing. The European Commission aims at increasing the preparedness and protection of both citizens and enterprises to such events. The market potential of such services in public administrations is limited. Extending the scope to self-preparedness and self-protection is both promising and challenging. A novel approach for innovation management and strategic planning based on scenario-technique has been developed, applied and validated within four case studies of the European H2020 project ANYWHERE. The new approach combines fundamental domain knowledge, out-of-the-box thinking and agile scenario technique. Targeting both new innovative services as well as add-ons for established service frameworks was identified as key success factor of innovation in the difficult market of self-preparedness and self-protection services for citizens and enterprises.

New Perspectives on Ensemble Sensitivity Analysis with Applications to a Climatology of Severe Convection

Ensemble sensitivity analysis (ESA) is a statistical technique applied within an ensemble to reveal the atmospheric flow features that relate to a chosen aspect of the flow. Given its ease of use (it is simply a linear regression between a chosen function of the forecast variables and the entire atmospheric state earlier or simultaneously in time), ensemble sensitivity has been the focus of several studies over roughly the last ten years. Such studies have primarily tried to understand the relevant dynamics and/or key precursors of high-impact weather events. Other applications of ESA have been more operationally oriented, including observation targeting within data assimilation systems and real-time adjustment techniques that attempt to utilize both sensitivity information and observations to improve forecasts.While ESA has gained popularity, its fundamental properties remain a substantially underutilized basis for realizing the technique’s full scientific potential. For example, the relationship between ensemble sensitivity and the pure dynamics of the system can teach us how to appropriately apply various sensitivity-based applications, and combining sensitivity with other ensemble properties such as spread can distinguish between a fluid dynamics problem and a predictability one. This work aims to present new perspectives on ensemble sensitivity, and clarify its fundamentals, with the hopes of making it a more accessible, attractive, and useful tool in the atmospheric sciences. These new perspectives are applied in part to a short climatology of severe convection forecasts to demonstrate the unique knowledge that can gained through broadened use of ESA.

Reanalyses and a high-resolution model fail to capture the ‘high tail’ of CAPE distributions

Convective available potential energy (CAPE) is of strong interest in climate modeling because of its role in both severe weather and in model construction. Extreme levels of CAPE (> 2000 J/kg) are associated with high-impact weather events, and CAPE is widely used in convective parametrizations to help determine the strength and timing of convection. However, to date few studies have systematically evaluated CAPE biases in models in a climatological context, and none have addressed bias in the high tail of CAPE distributions. This work compares CAPE distributions in ~200,000 summertime proximity soundings from four sources: the observational radiosonde network (IGRA), 0.125 degree reanalyses (ERA-Interim and ERA5), and a 4-km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and the WRF model consistently show too-narrow distributions of CAPE, with the high tail (> 90th percentile) systematically biased low by up to 10% in surface-based CAPE and even more in alternate CAPE definitions. This “missing tail” corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by surface temperature and humidity: reanalyses and the WRF model underpredict observed cases of extreme heat and moisture. These results suggest that reducing inaccuracies in land surface and boundary layer models is critical for accurately reproducing CAPE.

Mediterranean cyclones: Current knowledge and open questions on dynamics, prediction, climatology and impacts

A large number of intense cyclones occur every year in the Mediterranean basin, one of the climate change hotspots. Producing a broad range of severe socio-economic and environmental impacts in such a densely populated region, Mediterranean cyclones call for coordinated and interdisciplinary research efforts. This article aims at supporting these efforts by reviewing the status of knowledge in the broad field of Mediterranean cyclones. First, we focus on the dynamics and atmospheric processes that govern the genesis and development of Mediterranean cyclones. Then, we review the state of the art in forecasting cyclones and relevant high-impact weather. Particular attention is given to Mediterranean cyclone tracks and their physical characteristics in current and future climate. Finally, we focus on the impacts produced by cyclones and we outline the future directions of research that would advance the broader field of Mediterranean cyclones as a whole.

Bias Correction, Anonymization, and Analysis of Smartphone Pressure Observations Using Machine Learning and Multi-Resolution Kriging

With over a billion smartphones capable of measuring atmospheric pressure, a global mesoscale surface pressure network based on smartphone pressure sensors may be possible if key technical issues are solved, including collection technology, privacy and bias correction. To overcome these challenges, a novel framework was developed for the anonymization and bias correction of smartphone pressure observations (SPOs) and was applied to billions of SPOs from The Weather Company (IBM). Bias correction using machine learning reduced the errors of anonymous (ANON) SPOs and uniquely identifiable (UID) SPOs by 43% and 57%, respectively. Applying multi-resolution kriging, gridded analyses of bias-corrected smartphone pressure observations were made for an entire year (2018), using both anonymized (ANON) and non-anonymized (UID) observations. Pressure analyses were also generated using conventional (MADIS) surface pressure networks. Relative to MADIS analyses, ANON and UID smartphone analyses reduced domain-average pressure errors by 21% and 31%. The performance of smartphone and MADIS pressure analyses was evaluated for two high-impact weather events: the landfall of Hurricane Michael and a long-lived mesoscale convective system. For these two events, both anonymized and non-anonymized smartphone pressure analyses better captured the spatial structure and temporal evolution of mesoscale pressure features than the MADIS analyses.