Meteorology, Volume 1, Issue 4 (December 2022) – 11 articles

Assimilation of cloud properties in the convective scale ensemble data assimilation system is one of the prime topics of research in recent years. Satellites can retrieve cloud properties that are important sources of information of the cloud and atmospheric state. The Advance Baseline Imager (ABI) aboard the GOES-16 geostationary satellite brings an opportunity for retrieving high spatiotemporal resolution cloud properties, including cloud water path over continental United States. This study investigates the potential impacts of assimilating adaptively thinned GOES-16 cloud water path (CWP) observations that are assimilated by the ensemble-based Warn-on-Forecast System and the impact on subsequent weather forecasts. In this study, for CWP assimilation, multiple algorithms have been developed and tested using the adaptive-based thinning method. Three severe weather events are considered that occurred on 19 July 2019, 7 May and 21 June 2020. The superobbing procedure used for CWP data smoothed from 5 to 15 km or more depending on thinning algorithm. The overall performance of adaptively thinned CWP assimilation in the Warn-on-Forecast system is assessed using an object-based verification method. On average, more than 60% of the data was reduced and therefore not used in the assimilation system. Results suggest that assimilating less than 40% of CWP superobbing data into the Warn-on-Forecast system is of similar forecast quality to those obtained from assimilating all available CWP observations. The results of this study can be used on the benefits of cloud assimilation to improve numerical simulation. Full article

In the Mediterranean and occasionally in the Black Sea, low-pressure systems with the character of both mid-latitude and tropical cyclones can form. These hybrid storms are called subtropical storms, subtropical depressions, medistorms/medicanes, or tropical-like cyclones (TLC). A strong low-pressure system given the name Falchion developed in northern part of the Black Sea during 11–20 August 2021. This storm was blamed for damage and more than 30 casualties in the nations bordering the region. At peak intensity, this storm was a as strong as a tropical depression. Falchion developed and moved northeast, reaching peak intensity before becoming nearly stationary. The NCEP reanalyses and satellite data obtained from Eumetsat’s geostationary satellite, Meteosat-8, were used to examine the character of the storm. This study demonstrates that the movement of Falchion was impeded by a blocking event that occurred over central Asia during much of August 2021. The storm did share characteristics with tropical systems, but a comparison of Falchion to tropical depressions and subtropical storms in the North and South Atlantic demonstrated that this storm was more consistent with these types of storms when examining the storm and the proximal environment. This included an examination of integrated water vapor (IVT) plumes, and the plume associated with Falchion did rise to the character of an atmospheric river in spite of the smaller scale. Full article

This study used homogenised mean, maximum, and minimum daily temperatures from 12 stations located in Brno, Czechia, during the 2011–2020 period to analyse heat waves (HW) and their impact on the canopy urban heat island (UHI). HWs were recognized as at least three consecutive days with T_x ≥ 30 °C and urban–rural and intra-urban differences in their measures were analysed. To express the HWs contribution to UHI, we calculated the UHI intensities (UHII) separately during and outside of HWs to determine the heat magnitude (HM). Our results show that all HW measures are significantly higher in urban areas. UHII is mostly positive, on average 0.65 °C; however, day-time UHII is clearly greater (1.93 °C). Furthermore, day-time UHII is amplified during HWs, since HM is on average almost 0.5 °C and in LCZ 2 it is even 0.9 °C. Land use parameters correlate well with UHII and HM at night, but not during the day, indicating that other factors can affect the air temperature extremity. Considering a long-term context, the air temperature extremity has been significantly increasing recently in the region, together with a higher frequency of circulation types that favour the occurrence of HWs, and the last decade mainly contributed to this increase. Full article

Populations around the world have already experienced the increasing severity of extreme weather causing disaster displacement. Anthropogenic climate change can intensify these impacts. Extreme event attribution studies center around the question of whether impactful extreme events could have occurred in a pre-industrial climate. Here, we argue that the next step for attribution science is to focus on those most vulnerable populations to future extremes and impacts from climate change. Up until now, the vulnerability dimension has not been systematically addressed in attribution studies, yet it would add urgently needed context, given the vast differences in adaptive capacity. We propose three integrative points to cascade disaster displacement linked to anthropogenic climate change. Full article

In this study, a pattern detection method is applied on the RCP4.5 and RCP8.5 simulation outputs of seven GCMs—disseminated by the Coupled Model Intercomparison Project Phase 5 (CMIP5)—to determine whether atmospheric teleconnection patterns detected in the ERA-20C reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) will be observable in the future projections of the CMIP5 GCMs. The pattern detection technique—which combines the negative extrema method and receiver operating characteristic (ROC) curve analysis—is used on the geopotential height field at the 500 hPa pressure level in wintertime, in the Northern Hemisphere. It was found that teleconnections obtained from the ERA-20C reanalysis dataset for the period of 1976–2005 remain observable in the majority of the GCM outputs under the RCP4.5 and RCP8.5 scenarios for the periods of 2006–2035, 2021–2050, and 2071–2100. The results imply that atmospheric internal variability is the major factor that controls the teleconnections rather than the impact of radiative forcing. Full article

Since the first climate models in the 1970s, algorithms and computer speeds have increased by a factor of ≈10¹⁷ allowing the simulation of more and more processes at finer and finer resolutions. Yet, the spread of the members of the multi-model ensemble (MME) of the Climate Model Intercomparison Project (CMIP) used in last year’s 6th IPCC Assessment Report was larger than ever: model uncertainty, in the sense of MME uncertainty, has increased. Even if the holy grail is still kilometric scale models, bigger may not be better. Why model structures that live for ≈15 min only to average them over factors of several hundred thousand in order to produce decadal climate projections? In this commentary, I argue that alongside the development of “seamless” (unique) weather-climate models that chase ever smaller—and mostly irrelevant—details, the community should seriously invest in the development of stochastic macroweather models. Such models exploit the statistical laws that are obeyed at scales longer than the lifetimes of planetary scale structures, beyond the deterministic prediction limit (≈10 days). I argue that the conventional General Circulation Models and these new macroweather models are complementary in the same way that statistical mechanics and continuum mechanics are equally valid with the method of choice determined by the application. Candidates for stochastic macroweather models are now emerging, those based on the Fractional Energy Balance Equation (FEBE) are particularly promising. The FEBE is an update and generalization of the classical Budyko–Sellers energy balance models, it respects the symmetries of scaling and energy conservation and it already allows for both state-of-the-art monthly and seasonal, interannual temperature forecasts and multidecadal projections. I demonstrate this with 21st century FEBE climate projections for global mean temperatures. Overall, the projections agree with the CMIP5 and CMIP6 multi-model ensembles and the FEBE parametric uncertainty is about half of the MME structural uncertainty. Without the FEBE, uncertainties are so large that climate policies (mitigation) are largely decoupled from climate consequences (warming) allowing policy makers too much “wiggle room”. The lower FEBE uncertainties will help overcome the current “uncertainty crisis”. Both model types are complementary, a fact demonstrated by showing that CMIP global mean temperatures can be accurately projected using such stochastic macroweather models (validating both approaches). Unsurprisingly, they can therefore be combined to produce an optimum hybrid model in which the two model types are used as copredictors: when combined, the various uncertainties are reduced even further. Full article

At multi-kilometer grid scales, numerical weather prediction models represent surface-based convective eddies as a completely sub-grid one-dimensional vertical mixing and transport process. At tens of meters grid scales, large-eddy simulation models, explicitly resolve all the primary three-dimensional eddies associated with boundary-layer transport from the surface and entrainment at the top. Between these scales, at hundreds of meters grid size, is a so-called grey zone in which the primary transport is neither entirely sub-grid nor resolved, where explicit large-eddy models and sub-grid boundary-layer parameterization models fail in different ways that are outlined in this review article. This article also reviews various approaches that have been taken to span this gap in the proper representation of eddy transports in the sub-kilometer grid range using scale-aware approaches. Introduction of moisture with condensation in the eddies expands this problem to that of handling shallow convection, but similarities between dry and cloud-topped convective boundary layers can lead to some unified views of the processes that need to be represented in convective boundary-layers which will be briefly addressed here. Full article

Singapore is a tiny city-state located in maritime Southeast Asia. Weather systems such as localized thunderstorms, squalls, and monsoon surges bring extreme rainfall to Singapore, influencing the day-to-day conduct of stakeholders in many sectors. Numerical weather prediction models can provide forecast guidance, but existing global models struggle to capture the development and evolution of the small-scale and transient weather systems impacting the region. To address this, Singapore has collaborated with international partners and developed regional numerical weather prediction systems. Steady progress has been made, bringing added value to stakeholders. In recent years, complex earth system and ultra high-resolution urban models have also been developed to meet increasingly diverse stakeholder needs. However, further advancement of weather prediction for Singapore is often hindered by existing challenges, such as the lack of data, limited understanding of underlying processes, and geographical complexities. These may be viewed as opportunities, but are not trivial to address. There are also other opportunities that have remained relatively unexplored over Singapore and the region, such as the integration of earth system models, uncertainty estimation and machine learning methods. These are perhaps key research directions that Singapore should embark on to continue ensuring value for stakeholders. Full article

Numerical models of the atmosphere are based on the best theory available. Understandably, the theoretical assessment of errors induced by the use of such models is confounding. Without clear theoretical guidance, the experimental separation of the model-induced part of the total forecast error is also challenging. In this study, the forecast error and ensemble perturbation variances were decomposed. Smaller- and larger-scale components, separated as a function of the lead time, were independent. They were associated with features with completely vs. only partially lost skill, respectively. For their phenomenological description, the larger-scale variance was further decomposed orthogonally into positional and structural components. An analysis of the various components revealed that chaotically amplifying initial perturbation and error predominantly led to positional differences in forecasts, while structural differences were interpreted as an indicator of the model-induced error. Model-induced errors were found to be relatively small. These results confirmed earlier assumptions and limited empirical evidence that numerical models of the atmosphere may be near perfect on the scales they well resolve. Full article

A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models. Full article

This study characterizes the spatiotemporal solar radiation and air temperature patterns and their dependence on the general atmospheric circulation characterized by the North Atlantic Oscillation (NAO) Index in Germany from 1991 to 2015. Germany was selected as the study area because it can be subdivided into three climatologically different regions: the North German lowlands are under the maritime influence of the North and Baltic Seas. Several low mountain ranges dominate Germany’s center. In the south, the highest low mountain ranges and the Alps govern solar radiation and air temperature differently. Solar radiation and air temperature patterns were studied in the context of the NAO index using daily values from satellite and ground measurements. The most significant long-term solar radiation increase was observed in spring, mainly due to seasonal changes in cloud cover. Air temperature shows a noticeable increase in spring and autumn. Solar radiation and air temperature were significantly correlated in spring and autumn, with correlation coefficient values up to 0.93. In addition, a significant dependence of solar radiation and air temperature on the NAO index was revealed, with correlation coefficient values greater than 0.66. The results obtained are important not only for studies on the climate of the study area but also for photovoltaic system operators to design their systems. They need to be massively expanded to support Germany’s climate neutrality ambitions until 2045. Full article